{"type":"session","version":3,"id":"019db59a-aea2-71dd-aca1-e14a4a32409b","timestamp":"2026-04-22T14:31:46.594Z","cwd":"/mnt/rpi/repos/ballbox"}
{"type":"model_change","id":"b16933e8","parentId":null,"timestamp":"2026-04-22T14:31:47.837Z","provider":"openai-codex","modelId":"gpt-5.4-mini"}
{"type":"thinking_level_change","id":"e69b1546","parentId":"b16933e8","timestamp":"2026-04-22T14:31:47.837Z","thinkingLevel":"medium"}
{"type":"message","id":"c50d9355","parentId":"e69b1546","timestamp":"2026-04-22T14:32:06.312Z","message":{"role":"user","content":[{"type":"text","text":"read last handoff from memory (mem_7e571be6dcee4a0b8d53c3cf27898c50), then explore api to find it"}],"timestamp":1776868326311}}
{"type":"message","id":"75eb7025","parentId":"c50d9355","timestamp":"2026-04-22T14:32:10.171Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Looking into memory access**\n\nI need to read memory for this task. It looks like I should use the memory-read skill or the agents-database API. I’ll probably need to check the handoff and explore the API to find what I need. Maybe it makes sense to start with the read skill file first since that aligns with the memory-read task. Let’s load the skill instructions and see what they say!","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dbe70b288190b5cf02d8f94efd44\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NvpWwMpqK2WAgyG5Mz4gkrCPyoyi-t4Qq875yaJrKRFaG4WMh3gFrTgWPpGw46YZfNJ1Ms9PCUOZlTMWlNeZJMBwtw66FGXB8bmirY00525GrbswdiZR77IO-Qz5WzpuqOKatPktXfpq4q2ivPjrqlNgbKlZo8v71h-iuIWO14W23vdMdQB02tWAY4QqVyTCOTfwBv80z0FPXpS2z3vitUF_wsF5_uXTO1alSV5JG4jZPVYDm7DCk43rH5DUl2e2jWi6_eZj1to8klpWTRVCkpE5xJWlZzljKxDV6vLLqgClsI4ONsOGnl-FcgKOIEWkze81PtUZL5QHMzTgWsn2oOH2KPS6dtEaQsMECi1ktDU7u-MFqC3aNCTSbEYw9eY4xUpgpqR-VxA6ZcLERYtyUnA5kZQHPypv_vp--xPZhCnwAx_SIu_pm0ekXlryimU0JmLn33lrPKA5XtxELWIwL02qz2ti0AHj-KDgri0bpDauxK7s79oqvCBeD_O5H4TX5W_5-hAfgHFyFFnj3_1zIfcwJWhLbRCTfOjVnT0PLyQSeJ5yH9_0o2ZB9wyBQCj1BvdhqZ1rcUQfP_Bk4hxEpY1uXpfAOHtmbekW2Q0E2_VAbWF7ORWX2U-L0sP2KvsHo-jcEh9hG1jJ_CqJBnHo_TXlWjmGApPPRocTd8i36_pqOHM_Avp69XpBY0Jd5fEMT8m0fNUCn5EhrQZRGoGuaA6TjnoEe42L96rfedCS6myFSpLm-9FWrGNqfznfslJonj0uX238RTmzTzjIKON843AHTgInSBdRuBwsrIo7yGhqZ9WOK_aCY0rJ_slWxXez9J7gH3iQZVHQ86dL2z59t65zp_WeQ-rVAvN4znfPvEmbC_I5SbFOprMoaHo0viFLhVJ_-J80r4MdIwOfXSmSULgwt3abuFlCsz5R8H5G6HecUKH-_biMzbUCpWuw9WoKBECMTPJVXedsrkNokNRL7SHPIIE2TYEXZ8oJC-v3k3ibdXwV9NlSLiwegEeuVvkjjurY63Z6301p5012dll1MsIyjEeDMrFFjqa15WYlxOmtW9WuD6Qc0619iu1vveD8gOyuEt272HiJEET0eYxBDwZ6ausu4GSvlKjeIpBk9wV-L0f32c-Q6ppItDAu5VSgDZiJrn5fTVo_RX7avxNIrvdkjRDZhXaBcxorjeo8VGIJOxsJVLlt0OCY8Ep1hQ0r7uVIONDyPAxXwbd4vCn2ZpTR2CdD1OuYX8xiVOtLxTwAVs=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Looking into memory access**\\n\\nI need to read memory for this task. It looks like I should use the memory-read skill or the agents-database API. I’ll probably need to check the handoff and explore the API to find what I need. Maybe it makes sense to start with the read skill file first since that aligns with the memory-read task. Let’s load the skill instructions and see what they say!\"}]}"},{"type":"toolCall","id":"call_4G51kIKekOINWvXGgmzfVRWM|fc_07b1b9c95ac99ec40169e8dbe9ed4c81909a04693a3556ed2c","name":"read","arguments":{"path":"/home/sebas/.agents/skills/memory-read/SKILL.md","offset":1,"limit":220}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":3480,"output":92,"cacheRead":1024,"cacheWrite":0,"totalTokens":4596,"cost":{"input":0.00261,"output":0.00041400000000000003,"cacheRead":0.0000768,"cacheWrite":0,"total":0.0031008}},"stopReason":"toolUse","timestamp":1776868326312,"responseId":"resp_07b1b9c95ac99ec40169e8dbe6b0dc81909d50ed262bbdf2b9"}}
{"type":"message","id":"7438e602","parentId":"75eb7025","timestamp":"2026-04-22T14:32:10.183Z","message":{"role":"toolResult","toolCallId":"call_4G51kIKekOINWvXGgmzfVRWM|fc_07b1b9c95ac99ec40169e8dbe9ed4c81909a04693a3556ed2c","toolName":"read","content":[{"type":"text","text":"---\nname: memory-read\ndescription: Read persisted research memory, claims, and tasks\n---\n\n# personal-memory-search\n\n## Workflow\n\n1. Execute:\n   - `curl -fsSL \"${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=20\"`\n2. Return matching runs, claims, and tasks from the JSON response.\n3. Prefer reuse of prior work before starting a new run.\n"}],"isError":false,"timestamp":1776868330179}}
{"type":"message","id":"9d23c474","parentId":"7438e602","timestamp":"2026-04-22T14:32:13.824Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dbeb87808190be9c136e0f779ef4\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NvtIh22XxwnTP8U3B77wW1YREtp1Y4AcrpegPu3zsOSIUxrMIxNtWcrwup9KmLSCx1rIZPd88E4GvA1TV9e8_bqJ2M4SukHbtwgsnlpas7EBwnkXK23_oV5R2V6cuK2WxZ_nhfbeNV4sfMd8UqMx8nGrPBBCyfQ5GvHLJbIQahQ2oY8vrjZSDE70tMn42izFEt-VPGrnxFY0QRz6REiOuAhiQ8F2Cw3KEnrIF718Ss2CcU4qUgf88JKyZQtfbfGo9t3PnDMDmYssk4jA4VrM9Re2hOnaRHHr4St6RsYU-_R-0sSpgdK1XmoY-tzWBJiFOwWqfqSa55JAqX4NIZRzTJ7-nUqeI0g08nYx0m5cXMCe8P2UtunIVtaU7zgnKAl6DEZPfkOrOXNpxaoRHFlJnJznvR96G4_OOitEyA__83I57lQ2OF21a2awpiUIvyzd0T9vgPV3V1lvOS7baq2Qrhg29TXIYnUAoYjA6bcYiy27aPX1DYDOxjJM8kUKshXSYEoOeQbpIg1oeVOSC5S1o8loirJD6SQOwUrfZDYTl5PDSpf6sGlHwo87U7Siz3Ju-pkTYVdzUj5pQASGKL5Qy9sfeVPpeO3SkuMzVjZUuqqNbzxQ0BqJt4yEVG95kofPcd1q7ivU3Nu7O_1jPP1gHcUkQt8GkVq9_TIJkxxq-br4ekLrnQVo9lGRiK7V3Sm8-dyHdg2V1CUf0UeDm1AJhn-CEp5tDyJ0uFaPEjClqn2mNKLZgE-V1dQpg0KdDvVDFQYdBp8FRR2BI8zoi5_WddzZxDFxZSWZTbgGGkFnFPmIQr6OQm46AxL3TFqt5aSEXZhGRP0qbBmzk5z9mwViFPxbC0E6rZA81CajUU2vPi9TzCjxBPEXSr992pGpao-Kt7D4QRD9MXgUtAED8KelvuskqDVO9UUi37DLEX8NLgmP0EfxXXcnVjwZrn2FAh2kHmy3mwpowc4_KU_jLp5QBd9fac1t536djb032hUt66C_NnhsE8lPzqM8SpXNgCkNhRlYiXqZKgHkBQ8ImlMKu-xhcAgU_m8GxDfOZdCr_8TgMX6JyaaK8LI7KwnL4YLSCzuALRvP7lYPUoW3paeB0DE8IuJ4zG394-LJefxBBo8EGXRt4WDTwgNBCHs9EdTp_Ip8xZUhgf0FDvZ23W6oK77aLGQ88hAaY-EyZ1Og9yY0U63E63-bCrwnua-eS4wK42e7WpTG8qbQRb0ux4ZyKKxvkSYclDjwesSgOFm8BArQ6GjNveqBYWXQegNwAVMQPpcCmf3l2fm73XAzF6pwfw5Fn7O_OrbjBg_DLMy8B775heqq1Fy8IwmCnY4WxtfwMh7uIpiY0mH_g491tvufmEZ4XmiNaBSRx2O_tdznanTCyRZEbkFuQu9u6nnha8Ab4jdAQzKPif57l9uxI-i0FALp4JTS-_BMYc4TTcrpTobu-cmZtG8Tn89qUtVsoh5ReKhj52yoXTEVWNhzOSMRVbyPg==\",\"summary\":[]}"},{"type":"toolCall","id":"call_LlFS4sUcGvAsSvLJrVZQQEw3|fc_07b1b9c95ac99ec40169e8dbed76a88190a1ff226b3a2b614c","name":"bash","arguments":{"command":"curl -fsSL 'http://100.116.176.16:8091/api/context?id=mem_7e571be6dcee4a0b8d53c3cf27898c50'","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":3685,"output":161,"cacheRead":1024,"cacheWrite":0,"totalTokens":4870,"cost":{"input":0.00276375,"output":0.0007245,"cacheRead":0.0000768,"cacheWrite":0,"total":0.00356505}},"stopReason":"toolUse","timestamp":1776868330183,"responseId":"resp_07b1b9c95ac99ec40169e8dbea94ac8190b20826d8608312b4"}}
{"type":"message","id":"d5a78ae7","parentId":"9d23c474","timestamp":"2026-04-22T14:32:14.161Z","message":{"role":"toolResult","toolCallId":"call_LlFS4sUcGvAsSvLJrVZQQEw3|fc_07b1b9c95ac99ec40169e8dbed76a88190a1ff226b3a2b614c","toolName":"bash","content":[{"type":"text","text":" \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"ballbox-first\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session hook should queue analysis, not save memory directly\", \"content\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durable is worth saving, and only then writes to agents-database. Session metadata itself is kept as job/state, not as durable memory by default.\", \"summary\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durab\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:23:35.817463+00:00\", \"updated_at\": \"2026-04-22T03:23:35.817463+00:00\", \"observed_at\": \"2026-04-22T03:23:35.817463+00:00\", \"source_ref\": \"repo:/mnt/rpi/pi-config\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}], \"recent_episodes\": [{\"id\": \"mem_545eace94dd741b38b8d213b06ea0df1\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Copyparty replaces nginx fileserver\", \"content\": \"Ballbox-first now serves files through copyparty behind nginx at /files. Volumes /home/sebas and /mnt are exposed read-only to everyone. Portal home is clean and no longer links to /home or /mnt. copyparty runs as system systemd service and is reverse-proxy aware with --rp-loc /files and --rproxy -2.\", \"summary\": \"Ballbox-first now serves files through copyparty behind nginx at /files. Volumes /home/sebas and /mnt are exposed read-only to everyone. Portal home is clean and no longer links t\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:51:49.247858+00:00\", \"updated_at\": \"2026-04-22T13:51:49.247858+00:00\", \"observed_at\": \"2026-04-22T13:51:49.247858+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Memory layer RFC handoff: prototype next\", \"content\": \"Investigaci\\u00f3n cerrada y clasificada. Ya existe RFC y log de progreso. Hallazgos: t\\u00e9cnicas fuertes son TTL/short-term memory, summaries/pruning, episodic\\u2192semantic consolidation, dedup/fusion, hybrid retrieval y explicit promotion with evidence. Valor principal: memoria jer\\u00e1rquica con promoci\\u00f3n, no m\\u00e1s memoria. Estado: listo para prototipo, no listo como versi\\u00f3n final. Faltan schema final, umbrales de promoci\\u00f3n, jobs de consolidaci\\u00f3n y evaluaci\\u00f3n de utilidad/precisi\\u00f3n. Nginx ya sirve .html desde /mnt como text/html sin hack por archivo. Siguiente paso: convertir RFC en spec t\\u00e9cnica con schema, jobs, scoring y workflow de promoci\\u00f3n.\", \"summary\": \"Investigaci\\u00f3n cerrada y clasificada. Ya existe RFC y log de progreso. Hallazgos: t\\u00e9cnicas fuertes son TTL/short-term memory, summaries/pruning, episodic\\u2192semantic consolidation, de\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:42:27.080724+00:00\", \"updated_at\": \"2026-04-22T13:42:27.080724+00:00\", \"observed_at\": \"2026-04-22T13:42:27.080724+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_6536c4c86e35417b8b90dc99f41a05ee\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Handoff: agent memory RFC draft ready\", \"content\": \"Completed research and drafted RFC for agent memory layer. Key outputs: /mnt/rpi/pi-config/sessions/memory-techniques-review.md, /mnt/rpi/pi-config/sessions/memory-techniques-review.html, /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.md, /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.html. Findings: strongest real patterns are TTL short-term storage, summaries/pruning, episodic-to-semantic consolidation, dedup/fusion, hybrid retrieval, and explicit promotion to higher-level memories with evidence. Nginx on ballbox-first was adjusted so /mnt/.html serves as text/html; current config no longer uses the earlier per-file alias hack. RFC status: draft, good for starting a new agent/prototype, but still needs concrete schema, thresholds, consolidation jobs, and evaluation criteria.\", \"summary\": \"Completed research and drafted RFC for agent memory layer. Key outputs: /mnt/rpi/pi-config/sessions/memory-techniques-review.md, /mnt/rpi/pi-config/sessions/memory-techniques-revi\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:41:28.962658+00:00\", \"updated_at\": \"2026-04-22T13:41:28.962658+00:00\", \"observed_at\": \"2026-04-22T13:41:28.962658+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_fb75e53031a34268a16955613e62d2cf\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research started: agent memory techniques taxonomy\", \"content\": \"Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-turn/ per-thread models, hybrid retrieval) and AgentDock consolidation guide (episodic->semantic promotion, dedup, merge/synthesize/abstract). Working file: /mnt/rpi/pi-config/sessions/memory-techniques-review.md.\", \"summary\": \"Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-tur\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:01:37.791332+00:00\", \"updated_at\": \"2026-04-22T13:01:37.791332+00:00\", \"observed_at\": \"2026-04-22T13:01:37.791332+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_479a4ccc00f9446b8023d0e1ba17dcb4\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"codex\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Ballbox PAY-2 native MercadoPago handoff\", \"content\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified test POS BALLBOXTESTPOS01 and approved test order ORDTST01KPPFWZBJD0M1PACY3257J7KH reached processed/accredited, but Ballbox still did not persist a PaymentSession and /api/integrations/payments/sessions returned empty. Real prod token APP_USR-3257bf35-b277-4045-92ba-09fe77a1e787 returns PA_UNAUTHORIZED_RESULT_FROM_POLICIES on /users/me and /pos, so real prod POS remains unresolved. Homologator evaluate-order fails with error_code 1006 / Cannot read properties of null (reading 'blocks'). Next: inspect /notification/mercadopago logs around the approved test order and locate/create a prod POS or account with API access.\", \"summary\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T12:33:55.415984+00:00\", \"updated_at\": \"2026-04-22T12:33:55.415984+00:00\", \"observed_at\": \"2026-04-22T12:33:55.415984+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_35ef3cbd267d4b329be8b2a3dad30788\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-dispatcher\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: sebas\", \"content\": \"Session closed from /home/sebas (shutdown_fallback).\\n\\nConversation had 4 user messages and 36 assistant messages.\\n\\nLast user signal: si.\\n\\nLast assistant signal: Listo. Limpi\\u00e9 el collateral: - revert\\u00ed `AGENTS.md` - revert\\u00ed `settings.json` - borr\\u00e9 `CHANGELOG-2026-04-21.md` Qued\\u00f3 solo lo del fix real: - `README.md` - `extensions/session-memory-dispatcher.ts` - `scripts/session-memory-dispatcher.py` - delete de `scripts/session-memory-analyzer.py` Si quer\\u00e9s, hago un \\u00faltimo sweep de refs y te dejo el diff final cerrado..\", \"summary\": \"Session closed from /home/sebas (shutdown_fallback). Conversation had 4 user messages and 36 assistant messages. Last user signal: si. Last assistant signal: Listo. Limpi\\u00e9 el coll\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:13:26.669826+00:00\", \"updated_at\": \"2026-04-22T03:13:26.669826+00:00\", \"observed_at\": \"2026-04-22T03:13:26.669826+00:00\", \"source_ref\": \"pi-session:/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 36, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown_fallback\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\", \"session_id\": \"1418695d\", \"trigger\": \"session-memory-analyzer\", \"user_messages\": 4}, \"embedding\": null}, {\"id\": \"mem_393e5d9bd4734040b8a1eab1557e397e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-dispatcher\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: sebas\", \"content\": \"Session closed from /home/sebas (shutdown).\\n\\nConversation had 1 user messages and 1 assistant messages.\\n\\nLast user signal: hola.\\n\\nLast assistant signal: ok.\", \"summary\": \"Session closed from /home/sebas (shutdown). Conversation had 1 user messages and 1 assistant messages. Last user signal: hola. Last assistant signal: ok.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:08:43.344083+00:00\", \"updated_at\": \"2026-04-22T03:08:43.344083+00:00\", \"observed_at\": \"2026-04-22T03:08:43.344083+00:00\", \"source_ref\": \"pi-session:/tmp/session-test/session.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 1, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown\", \"session_file\": \"/tmp/session-test/session.jsonl\", \"session_id\": \"test123\", \"trigger\": \"manual\", \"user_messages\": 1}, \"embedding\": null}, {\"id\": \"mem_41c67066cd8e4a6294e37a5d2c80966d\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": 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true}, \"embedding\": null}, {\"id\": \"mem_10b251a5cd6844faaeec93ff7019c086\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"skill_smoke_test\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"coding-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"memory-write skill smoke test\", \"content\": \"Helper script created and can ingest memories into agents-database.\", \"summary\": \"Helper script created and can ingest memories into agents-database.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-17T00:38:19.505660+00:00\", \"updated_at\": \"2026-04-17T00:38:19.505660+00:00\", \"observed_at\": \"2026-04-17T00:38:19.505660+00:00\", \"source_ref\": \"conversation:2026-04-17:memory-write-skill\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": 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Mitigacion: scoreboard semanal + cadencia outbound + definition of done verificable.\", \"summary\": \"En el contexto actual, el mayor riesgo es que el throughput de planes/herramientas desplace trabajo verificable en mundo real (outreach, cierres, instalaciones). Mitigacion: score\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-11T23:21:16.927124+00:00\", \"updated_at\": \"2026-04-11T23:21:16.927124+00:00\", \"observed_at\": \"2026-04-11T23:21:16.927124+00:00\", \"source_ref\": \"personal-research:run_20260411232116\", \"evidence_ref\": null, \"metadata\": {\"claim_status\": \"tentative\", \"confidence\": \"0.7\", \"source_url\": \"personal-memory-search\"}, \"embedding\": null}, {\"id\": \"mem_c6ab12f86d364ff89a3bebecfdd35b24\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: A general community-style guide (By AI Team) frames memory lifecycle management \", \"content\": \"A general community-style guide (By AI Team) frames memory lifecycle management as write policies, summarization, deduplication, and decay/TTL plus governance controls (PII, access rules) to prevent bloat and stale or unsafe recall.\", \"summary\": \"A general community-style guide (By AI Team) frames memory lifecycle management as write policies, summarization, deduplication, and decay/TTL plus governance controls (PII, access\", \"confidence\": 0.4, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:16:18.133254+00:00\", \"updated_at\": \"2026-03-26T18:16:18.133254+00:00\", \"observed_at\": \"2026-03-26T18:16:18.133254+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\", \"metadata\": {\"claim_status\": \"tentative\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\"}, \"embedding\": [0.5082309126593128, 0.48435850885084075, 0.400302518598905, 0.3612271628913533, 0.33810862446630674, 0.24261900923241853, 0.16534780743131164, 0.12589551903204732]}, {\"id\": \"mem_7daf2b19b42640e09a23d0579e3df37c\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: Microsoft's Cosmos DB agent memory guide suggests short-term memories can be del\", \"content\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\", \"summary\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\", \"confidence\": 0.52, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:16:11.423964+00:00\", \"updated_at\": \"2026-03-26T18:16:11.423964+00:00\", \"observed_at\": \"2026-03-26T18:16:11.423964+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"claim_status\": \"verified\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"}, \"embedding\": [0.5398024752475405, 0.5253448834796297, 0.42744184357301607, 0.3295388036664024, 0.2613366859786492, 0.2093207851614917, 0.13656138311211433, 0.10277461974145312]}, {\"id\": \"mem_4f5ebfd765e34937b82ccf047dba60f7\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: AgentDock proposes periodic consolidation that (1) converts episodic memories to\", \"content\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metadata/temporal similarity, and (3) builds higher-level abstractions; it supports merge/synthesize/abstract strategies and batch scheduling.\", \"summary\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metada\", \"confidence\": 0.64, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:56.396499+00:00\", \"updated_at\": \"2026-03-26T18:15:56.396499+00:00\", \"observed_at\": \"2026-03-26T18:15:56.396499+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\", \"metadata\": {\"claim_status\": \"verified\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"}, \"embedding\": [0.48439022845783813, 0.4561797340462889, 0.3943808248838651, 0.34720608506522094, 0.31408941771253274, 0.25615883721523774, 0.24851652936461738, 0.2348358548172106]}, {\"id\": \"mem_889160a130ef4d1bbac2eba6370c0797\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"Memory Consolidation Guide - AgentDock Documentation | AgentDock Hub\", \"content\": \"Memory Consolidation Guide - AgentDock Documentation | AgentDock Hub\\nDetailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\\nhttps://hub.agentdock.ai/docs/memory/consolidation-guide\", \"summary\": \"Detailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.307831+00:00\", \"updated_at\": \"2026-03-26T18:15:48.307831+00:00\", \"observed_at\": \"2026-03-26T18:15:48.307831+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\", \"metadata\": {\"domain\": \"hub.agentdock.ai\", \"notes\": \"Detailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"}, \"embedding\": [0.4583224939078854, 0.4240206300716775, 0.3882723020977983, 0.35789655520669855, 0.3445684213667263, 0.2775144766989585, 0.2700755182766484, 0.25023829581715473]}, {\"id\": \"mem_65dcb57e2d6f45b08d85205168dca808\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Capture: Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsof\", \"content\": \"{\\\"content_type\\\": \\\"text/html\\\", \\\"text\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn Skip to main content Skip to Ask Learn chat experience This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Download Microsoft Edge More info about Internet Explorer and Microsoft Edge Table of contents Exit editor mode Ask Learn Ask Learn Focus mode Table of contents Read in English Add Add to plan Edit Share via Facebook x.com LinkedIn Email Copy Markdown Print Note Access to this page requires authorization. You can try signing in or changing directories . Access to this page requires authorization. You can try changing directories . Agent memories in Azure Cosmos DB for NoSQL Applies to: \\\\u2705 NoSQL Feedback Summarize this article for me In this article Agent memory refers to an AI agent's ability to persist and recall information from previous interactions. This capability is also called AI memory . Agent memory includes prior facts, interactions, and experiences. AI agents use this stored information to better reason, plan, and act over time. This article guides you through the most common patterns for storing and retrieving agent memories in your applications. It explains how each pattern works, highlights its strengths and limitations, and offers practical tips so you can confidently choose the right approach for your use cases. What Are Agent Memories? Agent memory is often divided into short-term (episodic or working) memory and long-term memory. This section explains the differences between each type. Short-Term Memory Short-term memory holds recent context. For example, recent conversation turns, state information, results from tool or function calls. These values are all useful to the agent for the current task or thread. It can get deleted after some time (for example, using time-to-live (TTL) ), aggregated or summarized by thread, and classified as long-term memory. For example: In a conversational agent, the last 5\\\\u201310 user or agent dialogue turns, including prompts, large language model (LLM) responses, tool call results, etc. Intermediate states or partial task steps (for example retrieval of information from an API or tool call used in a subsequent step). Long-Term Memory Long-term memory is more persistent and accumulates knowledge or patterns over multiple threads or conversations. It supports recall beyond immediate context. For example, User preferences (for example \\\\\\\"User prefers responses in bullet lists\\\\\\\", or \\\\\\\"User is vegetarian\\\\\\\"). Historical summaries or reflections of short-term memories, or long threads. For example, \\\\\\\"In this thread, the customer discussed their preferences for cotton socks and dislike of synthetic materials.\\\\\\\" Design patterns for agent memory storage In the next section, we divide the discussion into four parts: In part one, we guide you through partition key selection, helping you choose a key that balances write and query throughput across partitions. In part two, we provide guidance on configuring a vector index for semantic search or a full-text index for text-based search, depending on your retrieval needs. Then, we explore data modeling patterns and their trade-offs. Finally, we cover query patterns that can be applied for different retrieval scenarios. Choose a partition key Because Azure Cosmos DB automatically partitions your data, choosing a partition key is one of the most important design choices for your data model. The partition key determines how data distributes across logical and physical partitions. This distribution directly affects query performance, insert performance, scalability, and cost. Each partition key value maps to a distinct logical partition. A good partition strategy balances locality (keeping related items together for efficient queries) with distribution. In this guide, we highlight three common approaches. Read about partitioning in Azure Cosmos DB for more detail. Here are some common patterns and trade-offs when using Cosmos DB (or Cosmos-style NoSQL + vector features) to store agent memory. Use a GUID as the partition key Each item gets its own unique partition key value, typically a GUID. This strategy maximizes write distribution and avoids the hot partition problem, because every write lands in a different logical partition. It's simple to implement and works well for write-heavy workloads without strong locality requirements. The tradeoff is that queries span logical and possibly physical partitions, which can be more expensive. This strategy can be useful for storing ephemeral AI agent turns where you care more about logging and long-term analytics than revisiting specific conversations. Example: A partition key /pk that takes on values like: \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" Use a unique thread ID as the partition key All items for a conversation share the same partition key equal to the thread (or thread) ID. This technique colocates turns and summaries, which makes \\\\\\\"latest N,\\\\\\\" phrase filters, and other queries within a thread efficient. However, you must ensure there's enough workload distribution across threads to reduce likelihood of hot partitions. This approach works well for conversational agents and RAG applications. Most queries target a single conversation. Common queries include retrieving recent turns or performing vector search within one thread. Example: A partition key /threadId that takes on values like: \\\\\\\"thread-1234\\\\\\\" Use a tenant ID and thread ID as the partition key You can use a two-level hierarchical partition key where the leading level is the tenant ID and the second is the thread ID. This technique preserves locality within a thread while grouping threads under a tenant for governance, quotas, and analytics. It also reduces cross-partition scans for tenant-level queries and enables safer multitenant isolation patterns. This technique is best for multitenant apps where each customer (tenant) runs many concurrent conversations and more isolation is needed. Example: A partition key [\\\\\\\"/tenantId\\\\\\\", \\\\\\\"/threadId\\\\\\\"] takes on values like tenantId = \\\\\\\"contoso\\\\\\\" , threadId = \\\\\\\"thread-1234\\\\\\\" Indexes for semantic and full-text search Azure Cosmos DB supports both vector indexing for semantic similarity search and full-text indexing for keyword-based retrieval, allowing you to choose the right search strategy based on your agent memory requirements. Configure a vector index When you enable vector search in Azure Cosmos DB, you choose not only whether to shard but also which index type to use. Cosmos supports multiple vector-index algorithms, including quantizedFlat and DiskANN . The quantizedFlat index type is suited for smaller workloads or when you expect the number of vectors to remain modest (for example, tens of thousands of vectors total). It compresses (quantizes) each vector and performs an exact search over the compressed space, trading a slight accuracy loss for lower RU cost and faster scans. When your vector data scales up (for example, hundreds of thousands to billions of embeddings), DiskANN is the better choice. DiskANN implements approximate nearest-neighbor indexing and is optimized for high throughput, low latency, and cost efficiency at scale. It supports dynamic updates and achieves excellent recall across large datasets. Learn more about vector indexes in Azure Cosmos DB . If you're using DiskANN, you can decide whether to shard the vector index via the vectorIndexShardKey . This decision lets you partition the DiskANN index based on an item property (for example, session, user, tenant), reducing the candidate search space and making semantic queries more efficient and focused. For example, you can shard by a tenant, userid, or both. In multitenant systems, isolating the vector index per tenant ensures that search on a particular tenant or user data is fast and efficient. Using the multitenant example from the section on partitioning , you can set the vectorIndexShardKey and the partition key to be the same, or just the first level of your hierarchical partition key. Using a global (nonsharded) index offers simplicity and the ability to search on the entire set of vectors. Both of these options allow you to further refine the search using WHERE clause filters as with any other query. Configure a full text index Azure Cosmos DB's full text search capability enables advanced text-based queries over your memory items, making it ideal for keyword and phrase-based retrieval scenarios. When you enable full text indexing on specific paths in your container (such as /content ), Azure Cosmos DB automatically applies linguistic processing including tokenization, stemming, and case normalization. This feature allows queries to match variations of words (for example, \\\\\\\"running\\\\\\\" matches \\\\\\\"run,\\\\\\\" \\\\\\\"runs,\\\\\\\" \\\\\\\"ran\\\\\\\") and improves recall for natural language searches. Full text indexes are valuable for agent memory workloads where you need to retrieve conversations based on specific subjects, entities, or phrases mentioned by users or agents. For instance, you can quickly find all turns where \\\\\\\"refund policy\\\\\\\" or \\\\\\\"billing issues\\\\\\\" are discussed, regardless of the exact phrasing. Unlike vector search, which finds semantically similar content, full text search provides precise lexical matching with linguistic intelligence. Azure Cosmos DB uses BM25 (Best Match 25), a statistical ranking function that scores items based on term frequency and item length normalization, ensuring that the most relevant results are surfaced first. You can combine full text search with vector search in hybrid queries to apply both BM25 scoring for keyword relevance and vector similarity for semantic meaning. Learn more about full text search in Azure Cosmos DB . Recommended data model: One document per turn This is the recommended data model for storing chat histories and agent memories in Azure Cosmos DB. Each item represents a complete back-and-forth exchange (or turn) between two entities in a conversation thread, for example, a user prompt and the agent\\\\u2019s response, or an agent\\\\u2019s call to a tool and the resulting output. Each item serves as a natural unit of memory that can be stored, queried, vectorized, and expired as a single entity. This structure makes it efficient to retrieve context for a single exchange or recent history, perform vector or keyword search at either the exchange or per-message level, and maintain balanced item size with manageable storage costs. Note This model is the most common for both single and multi-agent apps. It provides a good balance between small item size and the utility or value of information included in a single memory item. Example scenarios An agent chat application where each turn consists of the user's question and the agent's reply, and you frequently need to resurface entire question and answer pairs for context injection. A planning agent that queries an external API (tool) and logs both the request and tool response as one memory unit. This scenario allows downstream queries to recall the whole exchange. Applications that use the memories as part of a semantic cache . This approach can reduce latency, token consumption, and LLM-based costs. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Thread / conversation identifier (commonly the partition key). \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic counter of the exchange (0-based or 1-based). 7 messages object \\\\u2705 Messages that make up this exchange (for example, user prompt, agent reply, optional tool call/response). For more information, see messages properties . turnEmbedding number[] optional Vector for the whole exchange (for example, embedding of a concatenated or summarized message pair). [0.013, -0.092, ] embedding number[] optional Vector for this specific turn (You can create embedding of user question for a semantic cache ). [0.11, 0.02, ] Properties of messages object Property Type Required Description Example role string \\\\u2705 Origin of the message. Typical: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" . \\\\\\\"agent\\\\\\\" entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this message is associated with. \\\\\\\"agent-assistant-01\\\\\\\" name string \\\\u2705 Tool/function name or agent persona label. \\\\\\\"kb.search\\\\\\\" content string \\\\u2705 Text payload (prompt, reply, tool result snippet). \\\\\\\"Refund policy is 30 days for unopened items.\\\\\\\" timestamp string (ISO 8601) optional Message timestamp. \\\\\\\"2025-09-24T10:14:27Z\\\\\\\" metadata object optional Per-message extras (for example, tokens, tool args/results identifiers). { \\\\\\\"tokens\\\\\\\": 17 } { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 7, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"What's our refund policy for accessories?\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 12 } }, { \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Let me check the knowledge base\\\\u2026\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 8 } }, ], \\\\\\\"embedding\\\\\\\": [0.013, -0.092, 0.551, ], \\\\\\\"metrics\\\\\\\": { \\\\\\\"latencyMs\\\\\\\": 300 } } Advantages Good for longer threads/conversations. Easy to get \\\\\\\"latest N turns\\\\\\\" by ordering on turnIndex or timestamp. Supports embedding-per-turn for vector similarity queries within the thread and semantic caching . Easy to TTL (expire) older memories if needed. Smaller items per turn don't require potentially expensive updates. Limitations When summarizing or consolidating, you might want to generate \\\\\\\"summary turns.\\\\\\\" You might need to periodically prune or compact older turns. Alternative data models These alternative data models can also be used for storing chat histories and agent memories, depending on your application\\\\u2019s requirements. However, each comes with trade-offs in storage efficiency, retrieval complexity, and overall cost. While the turn-based model is generally preferred, the following options are provided for completeness and to help you evaluate different design choices based on your scenario. One response per item In this design, every agent or user interaction (that is \\\\\\\"turn\\\\\\\") is stored as its own item in Azure Cosmos DB. All turn items for a single thread or thread carry the same threadId , which acts as a logical link between other turns in the same thread. This pattern is useful for fine-grained retrieval, analytics, or vector similarity search on every single utterance. Example scenarios A conversational agent where embeddings are created for every user and agent message, enabling per-utterance semantic search within a thread. Analytics dashboards that measure agent vs. user token counts, sentiment, or latency at the per-message level. RAG (retrieval-augmented generation) flows that need to embed and search every response independently (for example \\\\\\\"find the most relevant past statement across all threads\\\\\\\"). Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Identifier for the conversation/thread. Often chosen as the partition key so all turns for a thread are colocated and efficiently queried. In multitenant apps, consider hierarchical primary keys like /tenantId , /threadId . \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic turn counter (0,1,2\\\\u2026). Use with threadId to sort/fetch latest N turns. 3 entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this response is associated with. \\\\\\\"user-12345\\\\\\\" role string \\\\u2705 The role that produced the turn. Common values: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" (or similar). \\\\\\\"agent\\\\\\\" content string \\\\u2705 Main text content for the turn (prompt, reply, tool result, etc.). \\\\\\\"This is one response from an LLM\\\\\\\" timestamp string (ISO 8601) \\\\u2705 Creation time for the turn (ISO 8601). Alternatively, you can sort by the system _ts (epoch seconds) without storing your own timestamp. \\\\\\\"2025-09-24T10:15:00Z\\\\\\\" embedding number[] optional Vector embedding for content (or a summary). Must be a top-level field included in the container's vector policy/index to enable vector search. [0.017, -0.234, 0.561, ] metrics object optional Free-form metrics/attrs for the turn. Keep names machine-friendly (no spaces) for easier querying and indexing. { \\\\\\\"inputTokens\\\\\\\": 25, \\\\\\\"outputTokens\\\\\\\": 8 } An example of this memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 3, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"This is one response from an LLM\\\\\\\", \\\\\\\"embedding\\\\\\\": [-1.12402894028, ], \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"metrics\\\\\\\": { \\\\\\\"inputTokens\\\\\\\": 12, \\\\\\\"outputTokens\\\\\\\": 8, \\\\\\\"latencyMs\\\\\\\": 177 }, } Advantages Each memory is kept at an atomic/granular level. Easy to update/TTL individual responses. Retrieving last N results can be done with a query with filter on the thread ID. Limitations Vector embeddings only capture one response at a time. Not keeping a complete turn in one data item (back-and-forth between agent and user or tool call) can limit utility of the memory without subsequent queries. Not ideal model for semantic caching . One thread per item Here, all the turns of a conversation (user, agent, tools, etc.) for a given thread or thread are aggregated into a single item. This item contains a list or array of turn entries (each with turnIndex, role, content, embedding, etc.), and optional summary fields, metadata, and a thread-level embedding. Because the entire thread is stored in one item, retrieving the full history (or a large window) becomes a single read. However, appending new turns requires updating (replacing) the item, which can become costly if the item grows large. This model can be useful when sessions are bounded in size or short-lived, and you prefer single-item reads to reconstruct context. Important This model is typically not recommended, as there's a risk to have long or unbounded threads, which can have high RU charges for CRUD operations on the item. Example scenarios Chatbots in customer onboarding or troubleshooting flows, where each thread is short (limited number of turns) and agents need to fetch the entire conversation quickly in one read. Conversation summarization services, where you run periodic batch jobs over full sessions to generate embeddings or summaries. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Logical thread or thread identifier (often used as partition key). \\\\\\\"thread-1234\\\\\\\" turns array of objects \\\\u2705 A list of individual turn records (user or agent). Each turn contains a small structure. For example, index, role, content, etc. [ { \\\\\\\"turnIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hello\\\\\\\" }, { \\\\\\\"turnIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hi there!\\\\\\\" } ] embedding number[] optional Embedding vector computed over a summary or aggregation of the thread. Useful for semantic search over key points of the conversation. [0.101, -0.231, 0.553, \\\\u2026] summary string optional A textual \\\\\\\"roll-up\\\\\\\" or summary of the key points in the thread. \\\\\\\"User wanted to schedule a meeting; agent fetched available times and confirmed A.M. slots.\\\\\\\" metrics object optional Key/value attributes or metrics about the thread. { \\\\\\\"startTime\\\\\\\": \\\\\\\"2025-09-24T09:05:00Z\\\\\\\", \\\\\\\"lastTurnTime\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"turnCount\\\\\\\": 7 } An example of a memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"messageIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, { \\\\\\\"messageIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, ], \\\\\\\"LastUpdatedtimestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"summ\\\", \\\"title\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\\", \\\"url\\\": \\\"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\\\"}\", \"summary\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.212846+00:00\", \"updated_at\": \"2026-03-26T18:15:48.212846+00:00\", \"observed_at\": \"2026-03-26T18:15:48.212846+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"artifact_kind\": \"source_capture\", \"record_kind\": \"research_artifact\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\"}, \"embedding\": [0.539649079608721, 0.48364487477360774, 0.42648079820187174, 0.34986290276401943, 0.27533825843293275, 0.22594416839533857, 0.167638974231975, 0.12494732709033757]}, {\"id\": \"mem_b4cd18fabc844b2eb743d30437a358a1\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"How to Build AI Agents with Redis Memory Management\", \"content\": \"How to Build AI Agents with Redis Memory Management\\nMentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\\nhttps://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\", \"summary\": \"Mentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.202153+00:00\", \"updated_at\": \"2026-03-26T18:15:48.202153+00:00\", \"observed_at\": \"2026-03-26T18:15:48.202153+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\", \"metadata\": {\"domain\": \"redis.io\", \"notes\": \"Mentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\"}, \"embedding\": [0.4374401230353852, 0.4428515257187313, 0.4014377296727153, 0.3637787844682047, 0.3433479784188368, 0.2847060432176781, 0.2535628685910741, 0.23622429264647538]}, {\"id\": \"mem_9464c178479a46e7a48da1765ce7dd4d\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\", \"content\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\nDefines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\\nhttps://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"summary\": \"Defines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.199836+00:00\", \"updated_at\": \"2026-03-26T18:15:48.199836+00:00\", \"observed_at\": \"2026-03-26T18:15:48.199836+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"domain\": \"learn.microsoft.com\", \"notes\": \"Defines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"}, \"embedding\": [0.47154247711313935, 0.42780488128933386, 0.40432235528723987, 0.3822866199866966, 0.3682638793408964, 0.2793418969917347, 0.20555557121264298, 0.17061001119056943]}], \"citations\": [\"mem_83508f31b14b490493606a5f9e2223b3\", \"mem_507d4c3e4d84409883a7bf1b31c73e2e\", \"mem_f421f7450e724f0abb04b079bd025918\", \"mem_46645c28011541e1b5920c737cb0e8e0\", \"mem_88bca113d5364c32b4df4c9db8b00aec\", \"mem_96934fac02314ee2b5707504cd934815\", \"mem_cba4a83f17824b16ac1d2d1949e72c48\", \"mem_cdac83f3c3004eaca99546346d0c90d0\", \"mem_809845d47a694176b64b5bb9466bb74c\", \"mem_2e394e997e28490c8c862af23c274051\", \"mem_1996157dd60f49b5bd46dfe39be39fd1\", \"mem_281fcf2fcb3e4f4782b08caac664619b\", \"mem_b3c62f3202a248fd8dd07ea67fb6ac97\", \"mem_3cfc9200da9c4e61a380beb12d1676ef\", \"mem_545eace94dd741b38b8d213b06ea0df1\", \"mem_4495d871a160424bb7a5cc2f9cc1cd33\", \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"mem_5de09001821c49cdb1b93f0766585215\", \"mem_809845d47a694176b64b5bb9466bb74c\", \"mem_2e394e997e28490c8c862af23c274051\", \"mem_1996157dd60f49b5bd46dfe39be39fd1\", \"mem_281fcf2fcb3e4f4782b08caac664619b\", \"mem_b3c62f3202a248fd8dd07ea67fb6ac97\", \"mem_4495d871a160424bb7a5cc2f9cc1cd33\", \"mem_e8986c94a8444f79825cbe2d06a16aa7\", \"mem_a4d9411497524ccaa70c5d26f57cfc5a\", \"mem_545eace94dd741b38b8d213b06ea0df1\", \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"mem_6536c4c86e35417b8b90dc99f41a05ee\", \"mem_fb75e53031a34268a16955613e62d2cf\", \"mem_479a4ccc00f9446b8023d0e1ba17dcb4\", \"mem_35ef3cbd267d4b329be8b2a3dad30788\", \"mem_393e5d9bd4734040b8a1eab1557e397e\", \"mem_41c67066cd8e4a6294e37a5d2c80966d\", \"mem_5d3b2d6ef32245459728159a3e595a2e\", \"mem_442dfdcd0da547cf858b4061b5f18734\"], \"agent_id\": null}\n\n[Showing last 50.0KB of line 1 (line is 86.5KB). Full output: /tmp/pi-bash-057ac7c6e03fcc25.log]"}],"details":{"truncation":{"content":" \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"ballbox-first\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session hook should queue analysis, not save memory directly\", \"content\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durable is worth saving, and only then writes to agents-database. Session metadata itself is kept as job/state, not as durable memory by default.\", \"summary\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durab\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:23:35.817463+00:00\", \"updated_at\": \"2026-04-22T03:23:35.817463+00:00\", \"observed_at\": \"2026-04-22T03:23:35.817463+00:00\", \"source_ref\": \"repo:/mnt/rpi/pi-config\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}], \"recent_episodes\": [{\"id\": \"mem_545eace94dd741b38b8d213b06ea0df1\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Copyparty replaces nginx fileserver\", \"content\": \"Ballbox-first now serves files through copyparty behind nginx at /files. Volumes /home/sebas and /mnt are exposed read-only to everyone. Portal home is clean and no longer links to /home or /mnt. copyparty runs as system systemd service and is reverse-proxy aware with --rp-loc /files and --rproxy -2.\", \"summary\": \"Ballbox-first now serves files through copyparty behind nginx at /files. Volumes /home/sebas and /mnt are exposed read-only to everyone. Portal home is clean and no longer links t\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:51:49.247858+00:00\", \"updated_at\": \"2026-04-22T13:51:49.247858+00:00\", \"observed_at\": \"2026-04-22T13:51:49.247858+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Memory layer RFC handoff: prototype next\", \"content\": \"Investigaci\\u00f3n cerrada y clasificada. Ya existe RFC y log de progreso. Hallazgos: t\\u00e9cnicas fuertes son TTL/short-term memory, summaries/pruning, episodic\\u2192semantic consolidation, dedup/fusion, hybrid retrieval y explicit promotion with evidence. Valor principal: memoria jer\\u00e1rquica con promoci\\u00f3n, no m\\u00e1s memoria. Estado: listo para prototipo, no listo como versi\\u00f3n final. Faltan schema final, umbrales de promoci\\u00f3n, jobs de consolidaci\\u00f3n y evaluaci\\u00f3n de utilidad/precisi\\u00f3n. Nginx ya sirve .html desde /mnt como text/html sin hack por archivo. Siguiente paso: convertir RFC en spec t\\u00e9cnica con schema, jobs, scoring y workflow de promoci\\u00f3n.\", \"summary\": \"Investigaci\\u00f3n cerrada y clasificada. Ya existe RFC y log de progreso. Hallazgos: t\\u00e9cnicas fuertes son TTL/short-term memory, summaries/pruning, episodic\\u2192semantic consolidation, de\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:42:27.080724+00:00\", \"updated_at\": \"2026-04-22T13:42:27.080724+00:00\", \"observed_at\": \"2026-04-22T13:42:27.080724+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_6536c4c86e35417b8b90dc99f41a05ee\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Handoff: agent memory RFC draft ready\", \"content\": \"Completed research and drafted RFC for agent memory layer. Key outputs: /mnt/rpi/pi-config/sessions/memory-techniques-review.md, /mnt/rpi/pi-config/sessions/memory-techniques-review.html, /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.md, /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.html. Findings: strongest real patterns are TTL short-term storage, summaries/pruning, episodic-to-semantic consolidation, dedup/fusion, hybrid retrieval, and explicit promotion to higher-level memories with evidence. Nginx on ballbox-first was adjusted so /mnt/.html serves as text/html; current config no longer uses the earlier per-file alias hack. RFC status: draft, good for starting a new agent/prototype, but still needs concrete schema, thresholds, consolidation jobs, and evaluation criteria.\", \"summary\": \"Completed research and drafted RFC for agent memory layer. Key outputs: /mnt/rpi/pi-config/sessions/memory-techniques-review.md, /mnt/rpi/pi-config/sessions/memory-techniques-revi\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:41:28.962658+00:00\", \"updated_at\": \"2026-04-22T13:41:28.962658+00:00\", \"observed_at\": \"2026-04-22T13:41:28.962658+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_fb75e53031a34268a16955613e62d2cf\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research started: agent memory techniques taxonomy\", \"content\": \"Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-turn/ per-thread models, hybrid retrieval) and AgentDock consolidation guide (episodic->semantic promotion, dedup, merge/synthesize/abstract). Working file: /mnt/rpi/pi-config/sessions/memory-techniques-review.md.\", \"summary\": \"Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-tur\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:01:37.791332+00:00\", \"updated_at\": \"2026-04-22T13:01:37.791332+00:00\", \"observed_at\": \"2026-04-22T13:01:37.791332+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_479a4ccc00f9446b8023d0e1ba17dcb4\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"codex\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Ballbox PAY-2 native MercadoPago handoff\", \"content\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified test POS BALLBOXTESTPOS01 and approved test order ORDTST01KPPFWZBJD0M1PACY3257J7KH reached processed/accredited, but Ballbox still did not persist a PaymentSession and /api/integrations/payments/sessions returned empty. Real prod token APP_USR-3257bf35-b277-4045-92ba-09fe77a1e787 returns PA_UNAUTHORIZED_RESULT_FROM_POLICIES on /users/me and /pos, so real prod POS remains unresolved. Homologator evaluate-order fails with error_code 1006 / Cannot read properties of null (reading 'blocks'). Next: inspect /notification/mercadopago logs around the approved test order and locate/create a prod POS or account with API access.\", \"summary\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T12:33:55.415984+00:00\", \"updated_at\": \"2026-04-22T12:33:55.415984+00:00\", \"observed_at\": \"2026-04-22T12:33:55.415984+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_35ef3cbd267d4b329be8b2a3dad30788\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-dispatcher\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: sebas\", \"content\": \"Session closed from /home/sebas (shutdown_fallback).\\n\\nConversation had 4 user messages and 36 assistant messages.\\n\\nLast user signal: si.\\n\\nLast assistant signal: Listo. Limpi\\u00e9 el collateral: - revert\\u00ed `AGENTS.md` - revert\\u00ed `settings.json` - borr\\u00e9 `CHANGELOG-2026-04-21.md` Qued\\u00f3 solo lo del fix real: - `README.md` - `extensions/session-memory-dispatcher.ts` - `scripts/session-memory-dispatcher.py` - delete de `scripts/session-memory-analyzer.py` Si quer\\u00e9s, hago un \\u00faltimo sweep de refs y te dejo el diff final cerrado..\", \"summary\": \"Session closed from /home/sebas (shutdown_fallback). Conversation had 4 user messages and 36 assistant messages. Last user signal: si. Last assistant signal: Listo. Limpi\\u00e9 el coll\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:13:26.669826+00:00\", \"updated_at\": \"2026-04-22T03:13:26.669826+00:00\", \"observed_at\": \"2026-04-22T03:13:26.669826+00:00\", \"source_ref\": \"pi-session:/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 36, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown_fallback\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\", \"session_id\": \"1418695d\", \"trigger\": \"session-memory-analyzer\", \"user_messages\": 4}, \"embedding\": null}, {\"id\": \"mem_393e5d9bd4734040b8a1eab1557e397e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-dispatcher\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: sebas\", \"content\": \"Session closed from /home/sebas (shutdown).\\n\\nConversation had 1 user messages and 1 assistant messages.\\n\\nLast user signal: hola.\\n\\nLast assistant signal: ok.\", \"summary\": \"Session closed from /home/sebas (shutdown). Conversation had 1 user messages and 1 assistant messages. Last user signal: hola. Last assistant signal: ok.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:08:43.344083+00:00\", \"updated_at\": \"2026-04-22T03:08:43.344083+00:00\", \"observed_at\": \"2026-04-22T03:08:43.344083+00:00\", \"source_ref\": \"pi-session:/tmp/session-test/session.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 1, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown\", \"session_file\": \"/tmp/session-test/session.jsonl\", \"session_id\": \"test123\", \"trigger\": \"manual\", \"user_messages\": 1}, \"embedding\": null}, {\"id\": \"mem_41c67066cd8e4a6294e37a5d2c80966d\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: test\", \"content\": \"summary\", \"summary\": \"summary\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:50.821098+00:00\", \"updated_at\": \"2026-04-22T03:06:50.821098+00:00\", \"observed_at\": \"2026-04-22T03:06:50.821098+00:00\", \"source_ref\": \"pi-session:/tmp/session-test/session.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 1, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown\", \"session_file\": \"/tmp/session-test/session.jsonl\", \"session_id\": \"test123\", \"trigger\": \"session-memory-analyzer\", \"user_messages\": 1}, \"embedding\": null}, {\"id\": \"mem_5d3b2d6ef32245459728159a3e595a2e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"test\", \"content\": \"test\", \"summary\": \"test\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:31.582187+00:00\", \"updated_at\": \"2026-04-22T03:06:31.582187+00:00\", \"observed_at\": \"2026-04-22T03:06:31.582187+00:00\", \"source_ref\": \"x\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_442dfdcd0da547cf858b4061b5f18734\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"test\", \"content\": \"test\", \"summary\": \"test\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:31.576998+00:00\", \"updated_at\": \"2026-04-22T03:06:31.576998+00:00\", \"observed_at\": \"2026-04-22T03:06:31.576998+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}], \"task_relevant_artifacts\": [{\"id\": \"mem_71998658668445449ee0c0f49f2f9392\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"E2E Test Memory\", \"content\": \"TEST_E2E_DATABASE_CHECK_2026_04_21\", \"summary\": \"TEST_E2E_DATABASE_CHECK_2026_04_21\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T01:27:56.906315+00:00\", \"updated_at\": \"2026-04-22T01:27:56.906315+00:00\", \"observed_at\": \"2026-04-22T01:27:56.906315+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {\"test\": true}, \"embedding\": null}, {\"id\": \"mem_10b251a5cd6844faaeec93ff7019c086\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"skill_smoke_test\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"coding-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"memory-write skill smoke test\", \"content\": \"Helper script created and can ingest memories into agents-database.\", \"summary\": \"Helper script created and can ingest memories into agents-database.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-17T00:38:19.505660+00:00\", \"updated_at\": \"2026-04-17T00:38:19.505660+00:00\", \"observed_at\": \"2026-04-17T00:38:19.505660+00:00\", \"source_ref\": \"conversation:2026-04-17:memory-write-skill\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_3143b84805404c41a135ea037c307c7e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"research_claim\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": \"run_20260411232116\", \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: Blindspot principal es meta-trabajo vs realidad\", \"content\": \"En el contexto actual, el mayor riesgo es que el throughput de planes/herramientas desplace trabajo verificable en mundo real (outreach, cierres, instalaciones). Mitigacion: scoreboard semanal + cadencia outbound + definition of done verificable.\", \"summary\": \"En el contexto actual, el mayor riesgo es que el throughput de planes/herramientas desplace trabajo verificable en mundo real (outreach, cierres, instalaciones). Mitigacion: score\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-11T23:21:16.927124+00:00\", \"updated_at\": \"2026-04-11T23:21:16.927124+00:00\", \"observed_at\": \"2026-04-11T23:21:16.927124+00:00\", \"source_ref\": \"personal-research:run_20260411232116\", \"evidence_ref\": null, \"metadata\": {\"claim_status\": \"tentative\", \"confidence\": \"0.7\", \"source_url\": \"personal-memory-search\"}, \"embedding\": null}, {\"id\": \"mem_c6ab12f86d364ff89a3bebecfdd35b24\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: A general community-style guide (By AI Team) frames memory lifecycle management \", \"content\": \"A general community-style guide (By AI Team) frames memory lifecycle management as write policies, summarization, deduplication, and decay/TTL plus governance controls (PII, access rules) to prevent bloat and stale or unsafe recall.\", \"summary\": \"A general community-style guide (By AI Team) frames memory lifecycle management as write policies, summarization, deduplication, and decay/TTL plus governance controls (PII, access\", \"confidence\": 0.4, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:16:18.133254+00:00\", \"updated_at\": \"2026-03-26T18:16:18.133254+00:00\", \"observed_at\": \"2026-03-26T18:16:18.133254+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\", \"metadata\": {\"claim_status\": \"tentative\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\"}, \"embedding\": [0.5082309126593128, 0.48435850885084075, 0.400302518598905, 0.3612271628913533, 0.33810862446630674, 0.24261900923241853, 0.16534780743131164, 0.12589551903204732]}, {\"id\": \"mem_7daf2b19b42640e09a23d0579e3df37c\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: Microsoft's Cosmos DB agent memory guide suggests short-term memories can be del\", \"content\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\", \"summary\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\", \"confidence\": 0.52, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:16:11.423964+00:00\", \"updated_at\": \"2026-03-26T18:16:11.423964+00:00\", \"observed_at\": \"2026-03-26T18:16:11.423964+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"claim_status\": \"verified\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"}, \"embedding\": [0.5398024752475405, 0.5253448834796297, 0.42744184357301607, 0.3295388036664024, 0.2613366859786492, 0.2093207851614917, 0.13656138311211433, 0.10277461974145312]}, {\"id\": \"mem_4f5ebfd765e34937b82ccf047dba60f7\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: AgentDock proposes periodic consolidation that (1) converts episodic memories to\", \"content\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metadata/temporal similarity, and (3) builds higher-level abstractions; it supports merge/synthesize/abstract strategies and batch scheduling.\", \"summary\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metada\", \"confidence\": 0.64, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:56.396499+00:00\", \"updated_at\": \"2026-03-26T18:15:56.396499+00:00\", \"observed_at\": \"2026-03-26T18:15:56.396499+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\", \"metadata\": {\"claim_status\": \"verified\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"}, \"embedding\": [0.48439022845783813, 0.4561797340462889, 0.3943808248838651, 0.34720608506522094, 0.31408941771253274, 0.25615883721523774, 0.24851652936461738, 0.2348358548172106]}, {\"id\": \"mem_889160a130ef4d1bbac2eba6370c0797\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"Memory Consolidation Guide - AgentDock Documentation | AgentDock Hub\", \"content\": \"Memory Consolidation Guide - AgentDock Documentation | AgentDock Hub\\nDetailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\\nhttps://hub.agentdock.ai/docs/memory/consolidation-guide\", \"summary\": \"Detailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.307831+00:00\", \"updated_at\": \"2026-03-26T18:15:48.307831+00:00\", \"observed_at\": \"2026-03-26T18:15:48.307831+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\", \"metadata\": {\"domain\": \"hub.agentdock.ai\", \"notes\": \"Detailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"}, \"embedding\": [0.4583224939078854, 0.4240206300716775, 0.3882723020977983, 0.35789655520669855, 0.3445684213667263, 0.2775144766989585, 0.2700755182766484, 0.25023829581715473]}, {\"id\": \"mem_65dcb57e2d6f45b08d85205168dca808\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Capture: Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsof\", \"content\": \"{\\\"content_type\\\": \\\"text/html\\\", \\\"text\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn Skip to main content Skip to Ask Learn chat experience This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Download Microsoft Edge More info about Internet Explorer and Microsoft Edge Table of contents Exit editor mode Ask Learn Ask Learn Focus mode Table of contents Read in English Add Add to plan Edit Share via Facebook x.com LinkedIn Email Copy Markdown Print Note Access to this page requires authorization. You can try signing in or changing directories . Access to this page requires authorization. You can try changing directories . Agent memories in Azure Cosmos DB for NoSQL Applies to: \\\\u2705 NoSQL Feedback Summarize this article for me In this article Agent memory refers to an AI agent's ability to persist and recall information from previous interactions. This capability is also called AI memory . Agent memory includes prior facts, interactions, and experiences. AI agents use this stored information to better reason, plan, and act over time. This article guides you through the most common patterns for storing and retrieving agent memories in your applications. It explains how each pattern works, highlights its strengths and limitations, and offers practical tips so you can confidently choose the right approach for your use cases. What Are Agent Memories? Agent memory is often divided into short-term (episodic or working) memory and long-term memory. This section explains the differences between each type. Short-Term Memory Short-term memory holds recent context. For example, recent conversation turns, state information, results from tool or function calls. These values are all useful to the agent for the current task or thread. It can get deleted after some time (for example, using time-to-live (TTL) ), aggregated or summarized by thread, and classified as long-term memory. For example: In a conversational agent, the last 5\\\\u201310 user or agent dialogue turns, including prompts, large language model (LLM) responses, tool call results, etc. Intermediate states or partial task steps (for example retrieval of information from an API or tool call used in a subsequent step). Long-Term Memory Long-term memory is more persistent and accumulates knowledge or patterns over multiple threads or conversations. It supports recall beyond immediate context. For example, User preferences (for example \\\\\\\"User prefers responses in bullet lists\\\\\\\", or \\\\\\\"User is vegetarian\\\\\\\"). Historical summaries or reflections of short-term memories, or long threads. For example, \\\\\\\"In this thread, the customer discussed their preferences for cotton socks and dislike of synthetic materials.\\\\\\\" Design patterns for agent memory storage In the next section, we divide the discussion into four parts: In part one, we guide you through partition key selection, helping you choose a key that balances write and query throughput across partitions. In part two, we provide guidance on configuring a vector index for semantic search or a full-text index for text-based search, depending on your retrieval needs. Then, we explore data modeling patterns and their trade-offs. Finally, we cover query patterns that can be applied for different retrieval scenarios. Choose a partition key Because Azure Cosmos DB automatically partitions your data, choosing a partition key is one of the most important design choices for your data model. The partition key determines how data distributes across logical and physical partitions. This distribution directly affects query performance, insert performance, scalability, and cost. Each partition key value maps to a distinct logical partition. A good partition strategy balances locality (keeping related items together for efficient queries) with distribution. In this guide, we highlight three common approaches. Read about partitioning in Azure Cosmos DB for more detail. Here are some common patterns and trade-offs when using Cosmos DB (or Cosmos-style NoSQL + vector features) to store agent memory. Use a GUID as the partition key Each item gets its own unique partition key value, typically a GUID. This strategy maximizes write distribution and avoids the hot partition problem, because every write lands in a different logical partition. It's simple to implement and works well for write-heavy workloads without strong locality requirements. The tradeoff is that queries span logical and possibly physical partitions, which can be more expensive. This strategy can be useful for storing ephemeral AI agent turns where you care more about logging and long-term analytics than revisiting specific conversations. Example: A partition key /pk that takes on values like: \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" Use a unique thread ID as the partition key All items for a conversation share the same partition key equal to the thread (or thread) ID. This technique colocates turns and summaries, which makes \\\\\\\"latest N,\\\\\\\" phrase filters, and other queries within a thread efficient. However, you must ensure there's enough workload distribution across threads to reduce likelihood of hot partitions. This approach works well for conversational agents and RAG applications. Most queries target a single conversation. Common queries include retrieving recent turns or performing vector search within one thread. Example: A partition key /threadId that takes on values like: \\\\\\\"thread-1234\\\\\\\" Use a tenant ID and thread ID as the partition key You can use a two-level hierarchical partition key where the leading level is the tenant ID and the second is the thread ID. This technique preserves locality within a thread while grouping threads under a tenant for governance, quotas, and analytics. It also reduces cross-partition scans for tenant-level queries and enables safer multitenant isolation patterns. This technique is best for multitenant apps where each customer (tenant) runs many concurrent conversations and more isolation is needed. Example: A partition key [\\\\\\\"/tenantId\\\\\\\", \\\\\\\"/threadId\\\\\\\"] takes on values like tenantId = \\\\\\\"contoso\\\\\\\" , threadId = \\\\\\\"thread-1234\\\\\\\" Indexes for semantic and full-text search Azure Cosmos DB supports both vector indexing for semantic similarity search and full-text indexing for keyword-based retrieval, allowing you to choose the right search strategy based on your agent memory requirements. Configure a vector index When you enable vector search in Azure Cosmos DB, you choose not only whether to shard but also which index type to use. Cosmos supports multiple vector-index algorithms, including quantizedFlat and DiskANN . The quantizedFlat index type is suited for smaller workloads or when you expect the number of vectors to remain modest (for example, tens of thousands of vectors total). It compresses (quantizes) each vector and performs an exact search over the compressed space, trading a slight accuracy loss for lower RU cost and faster scans. When your vector data scales up (for example, hundreds of thousands to billions of embeddings), DiskANN is the better choice. DiskANN implements approximate nearest-neighbor indexing and is optimized for high throughput, low latency, and cost efficiency at scale. It supports dynamic updates and achieves excellent recall across large datasets. Learn more about vector indexes in Azure Cosmos DB . If you're using DiskANN, you can decide whether to shard the vector index via the vectorIndexShardKey . This decision lets you partition the DiskANN index based on an item property (for example, session, user, tenant), reducing the candidate search space and making semantic queries more efficient and focused. For example, you can shard by a tenant, userid, or both. In multitenant systems, isolating the vector index per tenant ensures that search on a particular tenant or user data is fast and efficient. Using the multitenant example from the section on partitioning , you can set the vectorIndexShardKey and the partition key to be the same, or just the first level of your hierarchical partition key. Using a global (nonsharded) index offers simplicity and the ability to search on the entire set of vectors. Both of these options allow you to further refine the search using WHERE clause filters as with any other query. Configure a full text index Azure Cosmos DB's full text search capability enables advanced text-based queries over your memory items, making it ideal for keyword and phrase-based retrieval scenarios. When you enable full text indexing on specific paths in your container (such as /content ), Azure Cosmos DB automatically applies linguistic processing including tokenization, stemming, and case normalization. This feature allows queries to match variations of words (for example, \\\\\\\"running\\\\\\\" matches \\\\\\\"run,\\\\\\\" \\\\\\\"runs,\\\\\\\" \\\\\\\"ran\\\\\\\") and improves recall for natural language searches. Full text indexes are valuable for agent memory workloads where you need to retrieve conversations based on specific subjects, entities, or phrases mentioned by users or agents. For instance, you can quickly find all turns where \\\\\\\"refund policy\\\\\\\" or \\\\\\\"billing issues\\\\\\\" are discussed, regardless of the exact phrasing. Unlike vector search, which finds semantically similar content, full text search provides precise lexical matching with linguistic intelligence. Azure Cosmos DB uses BM25 (Best Match 25), a statistical ranking function that scores items based on term frequency and item length normalization, ensuring that the most relevant results are surfaced first. You can combine full text search with vector search in hybrid queries to apply both BM25 scoring for keyword relevance and vector similarity for semantic meaning. Learn more about full text search in Azure Cosmos DB . Recommended data model: One document per turn This is the recommended data model for storing chat histories and agent memories in Azure Cosmos DB. Each item represents a complete back-and-forth exchange (or turn) between two entities in a conversation thread, for example, a user prompt and the agent\\\\u2019s response, or an agent\\\\u2019s call to a tool and the resulting output. Each item serves as a natural unit of memory that can be stored, queried, vectorized, and expired as a single entity. This structure makes it efficient to retrieve context for a single exchange or recent history, perform vector or keyword search at either the exchange or per-message level, and maintain balanced item size with manageable storage costs. Note This model is the most common for both single and multi-agent apps. It provides a good balance between small item size and the utility or value of information included in a single memory item. Example scenarios An agent chat application where each turn consists of the user's question and the agent's reply, and you frequently need to resurface entire question and answer pairs for context injection. A planning agent that queries an external API (tool) and logs both the request and tool response as one memory unit. This scenario allows downstream queries to recall the whole exchange. Applications that use the memories as part of a semantic cache . This approach can reduce latency, token consumption, and LLM-based costs. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Thread / conversation identifier (commonly the partition key). \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic counter of the exchange (0-based or 1-based). 7 messages object \\\\u2705 Messages that make up this exchange (for example, user prompt, agent reply, optional tool call/response). For more information, see messages properties . turnEmbedding number[] optional Vector for the whole exchange (for example, embedding of a concatenated or summarized message pair). [0.013, -0.092, ] embedding number[] optional Vector for this specific turn (You can create embedding of user question for a semantic cache ). [0.11, 0.02, ] Properties of messages object Property Type Required Description Example role string \\\\u2705 Origin of the message. Typical: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" . \\\\\\\"agent\\\\\\\" entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this message is associated with. \\\\\\\"agent-assistant-01\\\\\\\" name string \\\\u2705 Tool/function name or agent persona label. \\\\\\\"kb.search\\\\\\\" content string \\\\u2705 Text payload (prompt, reply, tool result snippet). \\\\\\\"Refund policy is 30 days for unopened items.\\\\\\\" timestamp string (ISO 8601) optional Message timestamp. \\\\\\\"2025-09-24T10:14:27Z\\\\\\\" metadata object optional Per-message extras (for example, tokens, tool args/results identifiers). { \\\\\\\"tokens\\\\\\\": 17 } { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 7, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"What's our refund policy for accessories?\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 12 } }, { \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Let me check the knowledge base\\\\u2026\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 8 } }, ], \\\\\\\"embedding\\\\\\\": [0.013, -0.092, 0.551, ], \\\\\\\"metrics\\\\\\\": { \\\\\\\"latencyMs\\\\\\\": 300 } } Advantages Good for longer threads/conversations. Easy to get \\\\\\\"latest N turns\\\\\\\" by ordering on turnIndex or timestamp. Supports embedding-per-turn for vector similarity queries within the thread and semantic caching . Easy to TTL (expire) older memories if needed. Smaller items per turn don't require potentially expensive updates. Limitations When summarizing or consolidating, you might want to generate \\\\\\\"summary turns.\\\\\\\" You might need to periodically prune or compact older turns. Alternative data models These alternative data models can also be used for storing chat histories and agent memories, depending on your application\\\\u2019s requirements. However, each comes with trade-offs in storage efficiency, retrieval complexity, and overall cost. While the turn-based model is generally preferred, the following options are provided for completeness and to help you evaluate different design choices based on your scenario. One response per item In this design, every agent or user interaction (that is \\\\\\\"turn\\\\\\\") is stored as its own item in Azure Cosmos DB. All turn items for a single thread or thread carry the same threadId , which acts as a logical link between other turns in the same thread. This pattern is useful for fine-grained retrieval, analytics, or vector similarity search on every single utterance. Example scenarios A conversational agent where embeddings are created for every user and agent message, enabling per-utterance semantic search within a thread. Analytics dashboards that measure agent vs. user token counts, sentiment, or latency at the per-message level. RAG (retrieval-augmented generation) flows that need to embed and search every response independently (for example \\\\\\\"find the most relevant past statement across all threads\\\\\\\"). Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Identifier for the conversation/thread. Often chosen as the partition key so all turns for a thread are colocated and efficiently queried. In multitenant apps, consider hierarchical primary keys like /tenantId , /threadId . \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic turn counter (0,1,2\\\\u2026). Use with threadId to sort/fetch latest N turns. 3 entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this response is associated with. \\\\\\\"user-12345\\\\\\\" role string \\\\u2705 The role that produced the turn. Common values: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" (or similar). \\\\\\\"agent\\\\\\\" content string \\\\u2705 Main text content for the turn (prompt, reply, tool result, etc.). \\\\\\\"This is one response from an LLM\\\\\\\" timestamp string (ISO 8601) \\\\u2705 Creation time for the turn (ISO 8601). Alternatively, you can sort by the system _ts (epoch seconds) without storing your own timestamp. \\\\\\\"2025-09-24T10:15:00Z\\\\\\\" embedding number[] optional Vector embedding for content (or a summary). Must be a top-level field included in the container's vector policy/index to enable vector search. [0.017, -0.234, 0.561, ] metrics object optional Free-form metrics/attrs for the turn. Keep names machine-friendly (no spaces) for easier querying and indexing. { \\\\\\\"inputTokens\\\\\\\": 25, \\\\\\\"outputTokens\\\\\\\": 8 } An example of this memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 3, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"This is one response from an LLM\\\\\\\", \\\\\\\"embedding\\\\\\\": [-1.12402894028, ], \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"metrics\\\\\\\": { \\\\\\\"inputTokens\\\\\\\": 12, \\\\\\\"outputTokens\\\\\\\": 8, \\\\\\\"latencyMs\\\\\\\": 177 }, } Advantages Each memory is kept at an atomic/granular level. Easy to update/TTL individual responses. Retrieving last N results can be done with a query with filter on the thread ID. Limitations Vector embeddings only capture one response at a time. Not keeping a complete turn in one data item (back-and-forth between agent and user or tool call) can limit utility of the memory without subsequent queries. Not ideal model for semantic caching . One thread per item Here, all the turns of a conversation (user, agent, tools, etc.) for a given thread or thread are aggregated into a single item. This item contains a list or array of turn entries (each with turnIndex, role, content, embedding, etc.), and optional summary fields, metadata, and a thread-level embedding. Because the entire thread is stored in one item, retrieving the full history (or a large window) becomes a single read. However, appending new turns requires updating (replacing) the item, which can become costly if the item grows large. This model can be useful when sessions are bounded in size or short-lived, and you prefer single-item reads to reconstruct context. Important This model is typically not recommended, as there's a risk to have long or unbounded threads, which can have high RU charges for CRUD operations on the item. Example scenarios Chatbots in customer onboarding or troubleshooting flows, where each thread is short (limited number of turns) and agents need to fetch the entire conversation quickly in one read. Conversation summarization services, where you run periodic batch jobs over full sessions to generate embeddings or summaries. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Logical thread or thread identifier (often used as partition key). \\\\\\\"thread-1234\\\\\\\" turns array of objects \\\\u2705 A list of individual turn records (user or agent). Each turn contains a small structure. For example, index, role, content, etc. [ { \\\\\\\"turnIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hello\\\\\\\" }, { \\\\\\\"turnIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hi there!\\\\\\\" } ] embedding number[] optional Embedding vector computed over a summary or aggregation of the thread. Useful for semantic search over key points of the conversation. 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The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durable is worth saving, and only then writes to agents-database. Session metadata itself is kept as job/state, not as durable memory by default.\", \"summary\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durab\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:23:35.817463+00:00\", \"updated_at\": \"2026-04-22T03:23:35.817463+00:00\", \"observed_at\": \"2026-04-22T03:23:35.817463+00:00\", \"source_ref\": \"repo:/mnt/rpi/pi-config\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, \"score\": 0.39999999999999997, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 1.0, \"semantic\": 0.0, \"confidence\": 1.0, \"freshness\": 1.0}, \"matched_scope\": \"project\", \"evidence_ref\": null, \"explanation\": \"facets=0.00 lexical=1.00 semantic=0.00 confidence=1.00 freshness=1.00\"}, {\"memory\": {\"id\": \"mem_281fcf2fcb3e4f4782b08caac664619b\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"decision\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session memory pipeline now emits hierarchical scoring metadata\", \"content\": \"Implemented first incremental step in pi-config: session-memory dispatcher now queues analysis jobs with job_type and initial analysis state; analyzer prompt now asks for confidence, freshness, importance, evidence_ref and broader durable types; emitted memories now include confidence/freshness/importance plus evidence_ref and promotion_candidate metadata; extension hook payload now carries memory_hints. This is the first step toward hierarchical memory promotion in agents-database.\", \"summary\": \"Implemented first incremental step in pi-config: session-memory dispatcher now queues analysis jobs with job_type and initial analysis state; analyzer prompt now asks for confiden\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T14:24:56.611869+00:00\", \"updated_at\": \"2026-04-22T14:24:56.611869+00:00\", \"observed_at\": \"2026-04-22T14:24:56.611869+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, \"score\": 0.33999999999999997, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.8, \"semantic\": 0.0, \"confidence\": 1.0, \"freshness\": 1.0}, \"matched_scope\": \"project\", \"evidence_ref\": null, \"explanation\": \"facets=0.00 lexical=0.80 semantic=0.00 confidence=1.00 freshness=1.00\"}, {\"memory\": {\"id\": \"mem_e8986c94a8444f79825cbe2d06a16aa7\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"decision\", \"subtype\": \"operating_rule\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session close hook should queue analyzer, not write memory directly\", \"content\": \"At session close, the dispatcher must only capture session metadata and hand it to an analyzer. 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Instead, work should enable personal challenges and personal improvement.\", \"summary\": \"Work should provide earned meaning, but should remain an enabler for life rather than life itself.\", \"confidence\": 0.98, \"freshness\": 1.0, \"created_at\": \"2026-03-13T21:26:33.708687+00:00\", \"updated_at\": \"2026-03-13T21:26:33.708687+00:00\", \"observed_at\": \"2026-03-13T21:26:33.708687+00:00\", \"source_ref\": \"conversation:2026-03-13:user-questionnaire-followup-2\", \"evidence_ref\": \"conversation:2026-03-13:user-questionnaire-followup-2\", \"metadata\": {\"kind\": \"philosophy\"}, \"embedding\": [0.464664698496862, 0.4622276458823679, 0.464664698496862, 0.41213267547332394, 0.2902800447486224, 0.283239670528973, 0.11129206939522743, 0.08827546136945047]}, \"score\": 0.3078940122501803, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.2, \"semantic\": 0.9939600816678688, \"confidence\": 0.98, \"freshness\": 1.0}, \"matched_scope\": \"global\", \"evidence_ref\": \"conversation:2026-03-13:user-questionnaire-followup-2\", \"explanation\": \"facets=0.00 lexical=0.20 semantic=0.99 confidence=0.98 freshness=1.00\"}, {\"memory\": {\"id\": \"mem_codex_add_dir_agents_database\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"decision\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": \"proj_personal_agent\", \"repo_id\": \"repo_personal_agent\", \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Codex should launch with agents-database as writable extra dir\", \"content\": \"When personal-agent work needs sandboxed Codex access to the canonical shared DB, launch Codex with `--add-dir /Users/sebas/agents-database`. The real database stays inside the agents-database repo.\", \"summary\": \"Use `--add-dir /Users/sebas/agents-database` for sandboxed Codex runs that need canonical shared DB write access.\", \"confidence\": 0.98, \"freshness\": 0.98, \"created_at\": \"2026-03-14T21:38:57.145693+00:00\", \"updated_at\": \"2026-03-14T21:38:57.145693+00:00\", \"observed_at\": \"2026-03-14T21:38:57.145693+00:00\", \"source_ref\": \"personal-agent\", \"evidence_ref\": \"art_a8c85225680c4379839b8cb9a6355760\", \"metadata\": {\"codex_add_dir\": \"/Users/sebas/agents-database\", \"kind\": \"sandbox_write_convention\"}, \"embedding\": [0.48095389716544257, 0.4407184544064659, 0.3784938743257228, 0.37241177251331936, 0.40235442758976714, 0.3064443605480203, 0.1679595808194493, 0.09216723515719084]}, \"score\": 0.30680799198899383, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.2, \"semantic\": 0.9920532799266256, \"confidence\": 0.98, \"freshness\": 0.98}, \"matched_scope\": \"project\", \"evidence_ref\": \"art_a8c85225680c4379839b8cb9a6355760\", \"explanation\": \"facets=0.00 lexical=0.20 semantic=0.99 confidence=0.98 freshness=0.98\"}]}"}],"isError":false,"timestamp":1776868343414}}
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it in the API.\n\nExact match:\n- `mem_a4d9411497524ccaa70c5d26f57cfc5a`\n- type: `decision`\n- title: `Session hook should queue analysis, not save memory directly`\n\nHandoff summary:\n- session close hook should **not** write durable memory directly\n- it should hand session metadata to a **background analyzer**\n- analyzer should run via `pi` with a cheap model (`gpt-5.4-mini`, thinking off when possible)\n- analyzer decides if anything is worth saving\n- only then write to `agents-database`\n- session metadata stays as job/state unless promoted\n\nI found it via:\n- `GET /api/context?id=mem_7e571be6dcee4a0b8d53c3cf27898c50`\n- `GET /api/search?q=Session+hook+should+queue+analysis&scope=global&scope=project&limit=10`\n\nNote: the search result also surfaced related newer decisions about hierarchical scoring 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\"file:///Users/sebas/Code/ballbox-payment-integration/src/providers/mercadoPagoProvider.ts\", \"explanation\": \"facets=0.00 lexical=0.00 semantic=0.98 confidence=0.75 freshness=0.70\"}, {\"memory\": {\"id\": \"mem_524014155c22470e945190a9839e58f2\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"research_step\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": \"a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research step: capture https://help.matchable.io/en/articles/17-manage-trainings-and-classes\", \"content\": \"Captured https://help.matchable.io/en/articles/17-manage-trainings-and-classes\", \"summary\": \"Captured https://help.matchable.io/en/articles/17-manage-trainings-and-classes\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": 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\"mem_77d1e1c4121e473883c64c28c5e58645\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"research_step\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": \"a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research step: capture https://business.matchable.io/\", \"content\": \"Captured https://business.matchable.io/\", \"summary\": \"Captured https://business.matchable.io/\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-24T14:20:16.377341+00:00\", \"updated_at\": \"2026-03-24T14:20:16.377341+00:00\", \"observed_at\": \"2026-03-24T14:20:16.377341+00:00\", \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"evidence_ref\": \"https://business.matchable.io/\", \"metadata\": {\"step_kind\": \"capture\", \"step_status\": \"completed\"}, \"embedding\": [0.3405191681917984, 0.4041326391726838, 0.3637193752554154, 0.4146101520401238, 0.41311336448763236, 0.31731896112818136, 0.26942175944845587, 0.26942175944845587]}, \"score\": 0.22120889158679405, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9747259439119604, \"confidence\": 0.75, \"freshness\": 0.75}, \"matched_scope\": \"global\", \"evidence_ref\": \"https://business.matchable.io/\", \"explanation\": \"facets=0.00 lexical=0.00 semantic=0.97 confidence=0.75 freshness=0.75\"}, {\"memory\": {\"id\": \"legacy_source_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7_329a120b76e7014f\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"research_source\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": \"bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\", \"task_id\": null, \"url\": \"https://www.alibaba.com/product-introduction/55-inch-floor-standing-digital-signage_60771181437.html\", \"domain\": \"www.alibaba.com\", \"source_kind\": \"document\", \"title\": \"Genevision 55-inch floor standing Android signage\", \"content\": \"Genevision 55-inch floor standing Android signage\\nAlibaba; indoor/commercial; Android 7.1; cheaper shortlist option\\nhttps://www.alibaba.com/product-introduction/55-inch-floor-standing-digital-signage_60771181437.html\", \"summary\": \"Alibaba; indoor/commercial; Android 7.1; cheaper shortlist option\", \"confidence\": 0.75, \"freshness\": 0.7, \"created_at\": \"2026-03-13T14:01:39.651383+00:00\", \"updated_at\": \"2026-03-24T14:18:16.797734+00:00\", \"observed_at\": \"2026-03-13T14:01:39.651383+00:00\", \"source_ref\": \"personal-agent:run:bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\", \"evidence_ref\": \"https://www.alibaba.com/product-introduction/55-inch-floor-standing-digital-signage_60771181437.html\", \"metadata\": {\"notes\": 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\"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": \"bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\", \"task_id\": null, \"url\": \"https://www.alibaba.com/product-introduction/Digital-Signage-And-Displays-Inch-Outdoor_1600345353452.html\", \"domain\": \"www.alibaba.com\", \"source_kind\": \"document\", \"title\": \"HDFocus 55-inch outdoor weatherproof digital signage\", \"content\": \"HDFocus 55-inch outdoor weatherproof digital signage\\nAlibaba; Android OS; IP55/IP65; 1000+ nits; outdoor/high-traffic fit\\nhttps://www.alibaba.com/product-introduction/Digital-Signage-And-Displays-Inch-Outdoor_1600345353452.html\", \"summary\": \"Alibaba; Android OS; IP55/IP65; 1000+ nits; outdoor/high-traffic fit\", \"confidence\": 0.75, \"freshness\": 0.7, \"created_at\": \"2026-03-13T14:01:39.650233+00:00\", \"updated_at\": \"2026-03-24T14:18:16.797713+00:00\", \"observed_at\": \"2026-03-13T14:01:39.650233+00:00\", \"source_ref\": 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\"legacy_source_fbe3fc0d-b05c-455e-9afe-1b17b4640d97_b928c046ef9e26cc\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"research_source\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": \"fbe3fc0d-b05c-455e-9afe-1b17b4640d97\", \"task_id\": null, \"url\": \"https://developer.apple.com/library/archive/documentation/General/Conceptual/MOSXAppProgrammingGuide/FullScreenApp/FullScreenApp.html\", \"domain\": \"developer.apple.com\", \"source_kind\": \"document\", \"title\": \"Implementing the Full-Screen Experience\", \"content\": \"Implementing the Full-Screen Experience\\nApple doc: fullscreen crea un Space separado; ventanas auxiliares pueden convivir con fullscreen\\nhttps://developer.apple.com/library/archive/documentation/General/Conceptual/MOSXAppProgrammingGuide/FullScreenApp/FullScreenApp.html\", \"summary\": \"Apple doc: fullscreen crea un Space separado; ventanas auxiliares pueden convivir con fullscreen\", \"confidence\": 0.75, \"freshness\": 0.7, \"created_at\": \"2026-03-13T14:01:39.654483+00:00\", \"updated_at\": \"2026-03-24T14:18:16.797776+00:00\", \"observed_at\": \"2026-03-13T14:01:39.654483+00:00\", \"source_ref\": \"personal-agent:run:fbe3fc0d-b05c-455e-9afe-1b17b4640d97\", \"evidence_ref\": \"https://developer.apple.com/library/archive/documentation/General/Conceptual/MOSXAppProgrammingGuide/FullScreenApp/FullScreenApp.html\", \"metadata\": {\"notes\": \"Apple doc: fullscreen crea un Space separado; ventanas auxiliares pueden convivir con fullscreen\", \"schema\": \"shared-agent-memory-v2\", \"subtype\": \"research_source\"}, \"embedding\": [0.45095274478826214, 0.44596808368641633, 0.39198526451961824, 0.33768427549525554, 0.3097913846487568, 0.28964062700299714, 0.2732018510288248, 0.2748987569358362]}, \"score\": 0.2187770997983889, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9718473319892595, \"confidence\": 0.75, \"freshness\": 0.7}, \"matched_scope\": \"global\", \"evidence_ref\": \"https://developer.apple.com/library/archive/documentation/General/Conceptual/MOSXAppProgrammingGuide/FullScreenApp/FullScreenApp.html\", \"explanation\": \"facets=0.00 lexical=0.00 semantic=0.97 confidence=0.75 freshness=0.70\"}]}"}],"isError":false,"timestamp":1776868494258}}
{"type":"message","id":"11f4a95b","parentId":"bc7f3542","timestamp":"2026-04-22T14:34:54.268Z","message":{"role":"toolResult","toolCallId":"call_LRXD0qW1x2ZXH6KwsipBLe2o|fc_07b1b9c95ac99ec40169e8dc8dc3788190ae4ebee3fd1b4591","toolName":"bash","content":[{"type":"text","text":" \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"ballbox-first\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session hook should queue analysis, not save memory directly\", \"content\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durable is worth saving, and only then writes to agents-database. Session metadata itself is kept as job/state, not as durable memory by default.\", \"summary\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durab\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:23:35.817463+00:00\", \"updated_at\": \"2026-04-22T03:23:35.817463+00:00\", \"observed_at\": \"2026-04-22T03:23:35.817463+00:00\", \"source_ref\": \"repo:/mnt/rpi/pi-config\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}], \"recent_episodes\": [{\"id\": \"mem_545eace94dd741b38b8d213b06ea0df1\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Copyparty replaces nginx fileserver\", \"content\": \"Ballbox-first now serves files through copyparty behind nginx at /files. Volumes /home/sebas and /mnt are exposed read-only to everyone. Portal home is clean and no longer links to /home or /mnt. copyparty runs as system systemd service and is reverse-proxy aware with --rp-loc /files and --rproxy -2.\", \"summary\": \"Ballbox-first now serves files through copyparty behind nginx at /files. Volumes /home/sebas and /mnt are exposed read-only to everyone. Portal home is clean and no longer links t\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:51:49.247858+00:00\", \"updated_at\": \"2026-04-22T13:51:49.247858+00:00\", \"observed_at\": \"2026-04-22T13:51:49.247858+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Memory layer RFC handoff: prototype next\", \"content\": \"Investigaci\\u00f3n cerrada y clasificada. Ya existe RFC y log de progreso. Hallazgos: t\\u00e9cnicas fuertes son TTL/short-term memory, summaries/pruning, episodic\\u2192semantic consolidation, dedup/fusion, hybrid retrieval y explicit promotion with evidence. Valor principal: memoria jer\\u00e1rquica con promoci\\u00f3n, no m\\u00e1s memoria. Estado: listo para prototipo, no listo como versi\\u00f3n final. Faltan schema final, umbrales de promoci\\u00f3n, jobs de consolidaci\\u00f3n y evaluaci\\u00f3n de utilidad/precisi\\u00f3n. Nginx ya sirve .html desde /mnt como text/html sin hack por archivo. Siguiente paso: convertir RFC en spec t\\u00e9cnica con schema, jobs, scoring y workflow de promoci\\u00f3n.\", \"summary\": \"Investigaci\\u00f3n cerrada y clasificada. Ya existe RFC y log de progreso. Hallazgos: t\\u00e9cnicas fuertes son TTL/short-term memory, summaries/pruning, episodic\\u2192semantic consolidation, de\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:42:27.080724+00:00\", \"updated_at\": \"2026-04-22T13:42:27.080724+00:00\", \"observed_at\": \"2026-04-22T13:42:27.080724+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_6536c4c86e35417b8b90dc99f41a05ee\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Handoff: agent memory RFC draft ready\", \"content\": \"Completed research and drafted RFC for agent memory layer. Key outputs: /mnt/rpi/pi-config/sessions/memory-techniques-review.md, /mnt/rpi/pi-config/sessions/memory-techniques-review.html, /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.md, /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.html. Findings: strongest real patterns are TTL short-term storage, summaries/pruning, episodic-to-semantic consolidation, dedup/fusion, hybrid retrieval, and explicit promotion to higher-level memories with evidence. Nginx on ballbox-first was adjusted so /mnt/.html serves as text/html; current config no longer uses the earlier per-file alias hack. RFC status: draft, good for starting a new agent/prototype, but still needs concrete schema, thresholds, consolidation jobs, and evaluation criteria.\", \"summary\": \"Completed research and drafted RFC for agent memory layer. Key outputs: /mnt/rpi/pi-config/sessions/memory-techniques-review.md, /mnt/rpi/pi-config/sessions/memory-techniques-revi\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:41:28.962658+00:00\", \"updated_at\": \"2026-04-22T13:41:28.962658+00:00\", \"observed_at\": \"2026-04-22T13:41:28.962658+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_fb75e53031a34268a16955613e62d2cf\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research started: agent memory techniques taxonomy\", \"content\": \"Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-turn/ per-thread models, hybrid retrieval) and AgentDock consolidation guide (episodic->semantic promotion, dedup, merge/synthesize/abstract). Working file: /mnt/rpi/pi-config/sessions/memory-techniques-review.md.\", \"summary\": \"Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-tur\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:01:37.791332+00:00\", \"updated_at\": \"2026-04-22T13:01:37.791332+00:00\", \"observed_at\": \"2026-04-22T13:01:37.791332+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_479a4ccc00f9446b8023d0e1ba17dcb4\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"codex\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Ballbox PAY-2 native MercadoPago handoff\", \"content\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified test POS BALLBOXTESTPOS01 and approved test order ORDTST01KPPFWZBJD0M1PACY3257J7KH reached processed/accredited, but Ballbox still did not persist a PaymentSession and /api/integrations/payments/sessions returned empty. Real prod token APP_USR-3257bf35-b277-4045-92ba-09fe77a1e787 returns PA_UNAUTHORIZED_RESULT_FROM_POLICIES on /users/me and /pos, so real prod POS remains unresolved. Homologator evaluate-order fails with error_code 1006 / Cannot read properties of null (reading 'blocks'). Next: inspect /notification/mercadopago logs around the approved test order and locate/create a prod POS or account with API access.\", \"summary\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T12:33:55.415984+00:00\", \"updated_at\": \"2026-04-22T12:33:55.415984+00:00\", \"observed_at\": \"2026-04-22T12:33:55.415984+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_35ef3cbd267d4b329be8b2a3dad30788\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-dispatcher\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: sebas\", \"content\": \"Session closed from /home/sebas (shutdown_fallback).\\n\\nConversation had 4 user messages and 36 assistant messages.\\n\\nLast user signal: si.\\n\\nLast assistant signal: Listo. Limpi\\u00e9 el collateral: - revert\\u00ed `AGENTS.md` - revert\\u00ed `settings.json` - borr\\u00e9 `CHANGELOG-2026-04-21.md` Qued\\u00f3 solo lo del fix real: - `README.md` - `extensions/session-memory-dispatcher.ts` - `scripts/session-memory-dispatcher.py` - delete de `scripts/session-memory-analyzer.py` Si quer\\u00e9s, hago un \\u00faltimo sweep de refs y te dejo el diff final cerrado..\", \"summary\": \"Session closed from /home/sebas (shutdown_fallback). Conversation had 4 user messages and 36 assistant messages. Last user signal: si. Last assistant signal: Listo. Limpi\\u00e9 el coll\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:13:26.669826+00:00\", \"updated_at\": \"2026-04-22T03:13:26.669826+00:00\", \"observed_at\": \"2026-04-22T03:13:26.669826+00:00\", \"source_ref\": \"pi-session:/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 36, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown_fallback\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\", \"session_id\": \"1418695d\", \"trigger\": \"session-memory-analyzer\", \"user_messages\": 4}, \"embedding\": null}, {\"id\": \"mem_393e5d9bd4734040b8a1eab1557e397e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-dispatcher\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: sebas\", \"content\": \"Session closed from /home/sebas (shutdown).\\n\\nConversation had 1 user messages and 1 assistant messages.\\n\\nLast user signal: hola.\\n\\nLast assistant signal: ok.\", \"summary\": \"Session closed from /home/sebas (shutdown). Conversation had 1 user messages and 1 assistant messages. Last user signal: hola. Last assistant signal: ok.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:08:43.344083+00:00\", \"updated_at\": \"2026-04-22T03:08:43.344083+00:00\", \"observed_at\": \"2026-04-22T03:08:43.344083+00:00\", \"source_ref\": \"pi-session:/tmp/session-test/session.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 1, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown\", \"session_file\": \"/tmp/session-test/session.jsonl\", \"session_id\": \"test123\", \"trigger\": \"manual\", \"user_messages\": 1}, \"embedding\": null}, {\"id\": \"mem_41c67066cd8e4a6294e37a5d2c80966d\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: test\", \"content\": \"summary\", \"summary\": \"summary\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:50.821098+00:00\", \"updated_at\": \"2026-04-22T03:06:50.821098+00:00\", \"observed_at\": \"2026-04-22T03:06:50.821098+00:00\", \"source_ref\": \"pi-session:/tmp/session-test/session.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 1, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown\", \"session_file\": \"/tmp/session-test/session.jsonl\", \"session_id\": \"test123\", \"trigger\": \"session-memory-analyzer\", \"user_messages\": 1}, \"embedding\": null}, {\"id\": \"mem_5d3b2d6ef32245459728159a3e595a2e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"test\", \"content\": \"test\", \"summary\": \"test\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:31.582187+00:00\", \"updated_at\": \"2026-04-22T03:06:31.582187+00:00\", \"observed_at\": \"2026-04-22T03:06:31.582187+00:00\", \"source_ref\": \"x\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_442dfdcd0da547cf858b4061b5f18734\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"test\", \"content\": \"test\", \"summary\": \"test\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:31.576998+00:00\", \"updated_at\": \"2026-04-22T03:06:31.576998+00:00\", \"observed_at\": \"2026-04-22T03:06:31.576998+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}], \"task_relevant_artifacts\": [{\"id\": \"mem_71998658668445449ee0c0f49f2f9392\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"E2E Test Memory\", \"content\": \"TEST_E2E_DATABASE_CHECK_2026_04_21\", \"summary\": \"TEST_E2E_DATABASE_CHECK_2026_04_21\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T01:27:56.906315+00:00\", \"updated_at\": \"2026-04-22T01:27:56.906315+00:00\", \"observed_at\": \"2026-04-22T01:27:56.906315+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {\"test\": true}, \"embedding\": null}, {\"id\": \"mem_10b251a5cd6844faaeec93ff7019c086\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"skill_smoke_test\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"coding-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"memory-write skill smoke test\", \"content\": \"Helper script created and can ingest memories into agents-database.\", \"summary\": \"Helper script created and can ingest memories into agents-database.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-17T00:38:19.505660+00:00\", \"updated_at\": \"2026-04-17T00:38:19.505660+00:00\", \"observed_at\": \"2026-04-17T00:38:19.505660+00:00\", \"source_ref\": \"conversation:2026-04-17:memory-write-skill\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_3143b84805404c41a135ea037c307c7e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"research_claim\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": \"run_20260411232116\", \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: Blindspot principal es meta-trabajo vs realidad\", \"content\": \"En el contexto actual, el mayor riesgo es que el throughput de planes/herramientas desplace trabajo verificable en mundo real (outreach, cierres, instalaciones). Mitigacion: scoreboard semanal + cadencia outbound + definition of done verificable.\", \"summary\": \"En el contexto actual, el mayor riesgo es que el throughput de planes/herramientas desplace trabajo verificable en mundo real (outreach, cierres, instalaciones). Mitigacion: score\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-11T23:21:16.927124+00:00\", \"updated_at\": \"2026-04-11T23:21:16.927124+00:00\", \"observed_at\": \"2026-04-11T23:21:16.927124+00:00\", \"source_ref\": \"personal-research:run_20260411232116\", \"evidence_ref\": null, \"metadata\": {\"claim_status\": \"tentative\", \"confidence\": \"0.7\", \"source_url\": \"personal-memory-search\"}, \"embedding\": null}, {\"id\": \"mem_c6ab12f86d364ff89a3bebecfdd35b24\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: A general community-style guide (By AI Team) frames memory lifecycle management \", \"content\": \"A general community-style guide (By AI Team) frames memory lifecycle management as write policies, summarization, deduplication, and decay/TTL plus governance controls (PII, access rules) to prevent bloat and stale or unsafe recall.\", \"summary\": \"A general community-style guide (By AI Team) frames memory lifecycle management as write policies, summarization, deduplication, and decay/TTL plus governance controls (PII, access\", \"confidence\": 0.4, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:16:18.133254+00:00\", \"updated_at\": \"2026-03-26T18:16:18.133254+00:00\", \"observed_at\": \"2026-03-26T18:16:18.133254+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\", \"metadata\": {\"claim_status\": \"tentative\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\"}, \"embedding\": [0.5082309126593128, 0.48435850885084075, 0.400302518598905, 0.3612271628913533, 0.33810862446630674, 0.24261900923241853, 0.16534780743131164, 0.12589551903204732]}, {\"id\": \"mem_7daf2b19b42640e09a23d0579e3df37c\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: Microsoft's Cosmos DB agent memory guide suggests short-term memories can be del\", \"content\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\", \"summary\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\", \"confidence\": 0.52, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:16:11.423964+00:00\", \"updated_at\": \"2026-03-26T18:16:11.423964+00:00\", \"observed_at\": \"2026-03-26T18:16:11.423964+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"claim_status\": \"verified\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"}, \"embedding\": [0.5398024752475405, 0.5253448834796297, 0.42744184357301607, 0.3295388036664024, 0.2613366859786492, 0.2093207851614917, 0.13656138311211433, 0.10277461974145312]}, {\"id\": \"mem_4f5ebfd765e34937b82ccf047dba60f7\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: AgentDock proposes periodic consolidation that (1) converts episodic memories to\", \"content\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metadata/temporal similarity, and (3) builds higher-level abstractions; it supports merge/synthesize/abstract strategies and batch scheduling.\", \"summary\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metada\", \"confidence\": 0.64, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:56.396499+00:00\", \"updated_at\": \"2026-03-26T18:15:56.396499+00:00\", \"observed_at\": \"2026-03-26T18:15:56.396499+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\", \"metadata\": {\"claim_status\": \"verified\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"}, \"embedding\": [0.48439022845783813, 0.4561797340462889, 0.3943808248838651, 0.34720608506522094, 0.31408941771253274, 0.25615883721523774, 0.24851652936461738, 0.2348358548172106]}, {\"id\": \"mem_889160a130ef4d1bbac2eba6370c0797\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"Memory Consolidation Guide - AgentDock Documentation | AgentDock Hub\", \"content\": \"Memory Consolidation Guide - AgentDock Documentation | AgentDock Hub\\nDetailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\\nhttps://hub.agentdock.ai/docs/memory/consolidation-guide\", \"summary\": \"Detailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.307831+00:00\", \"updated_at\": \"2026-03-26T18:15:48.307831+00:00\", \"observed_at\": \"2026-03-26T18:15:48.307831+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\", \"metadata\": {\"domain\": \"hub.agentdock.ai\", \"notes\": \"Detailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"}, \"embedding\": [0.4583224939078854, 0.4240206300716775, 0.3882723020977983, 0.35789655520669855, 0.3445684213667263, 0.2775144766989585, 0.2700755182766484, 0.25023829581715473]}, {\"id\": \"mem_65dcb57e2d6f45b08d85205168dca808\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Capture: Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsof\", \"content\": \"{\\\"content_type\\\": \\\"text/html\\\", \\\"text\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn Skip to main content Skip to Ask Learn chat experience This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Download Microsoft Edge More info about Internet Explorer and Microsoft Edge Table of contents Exit editor mode Ask Learn Ask Learn Focus mode Table of contents Read in English Add Add to plan Edit Share via Facebook x.com LinkedIn Email Copy Markdown Print Note Access to this page requires authorization. You can try signing in or changing directories . Access to this page requires authorization. You can try changing directories . Agent memories in Azure Cosmos DB for NoSQL Applies to: \\\\u2705 NoSQL Feedback Summarize this article for me In this article Agent memory refers to an AI agent's ability to persist and recall information from previous interactions. This capability is also called AI memory . Agent memory includes prior facts, interactions, and experiences. AI agents use this stored information to better reason, plan, and act over time. This article guides you through the most common patterns for storing and retrieving agent memories in your applications. It explains how each pattern works, highlights its strengths and limitations, and offers practical tips so you can confidently choose the right approach for your use cases. What Are Agent Memories? Agent memory is often divided into short-term (episodic or working) memory and long-term memory. This section explains the differences between each type. Short-Term Memory Short-term memory holds recent context. For example, recent conversation turns, state information, results from tool or function calls. These values are all useful to the agent for the current task or thread. It can get deleted after some time (for example, using time-to-live (TTL) ), aggregated or summarized by thread, and classified as long-term memory. For example: In a conversational agent, the last 5\\\\u201310 user or agent dialogue turns, including prompts, large language model (LLM) responses, tool call results, etc. Intermediate states or partial task steps (for example retrieval of information from an API or tool call used in a subsequent step). Long-Term Memory Long-term memory is more persistent and accumulates knowledge or patterns over multiple threads or conversations. It supports recall beyond immediate context. For example, User preferences (for example \\\\\\\"User prefers responses in bullet lists\\\\\\\", or \\\\\\\"User is vegetarian\\\\\\\"). Historical summaries or reflections of short-term memories, or long threads. For example, \\\\\\\"In this thread, the customer discussed their preferences for cotton socks and dislike of synthetic materials.\\\\\\\" Design patterns for agent memory storage In the next section, we divide the discussion into four parts: In part one, we guide you through partition key selection, helping you choose a key that balances write and query throughput across partitions. In part two, we provide guidance on configuring a vector index for semantic search or a full-text index for text-based search, depending on your retrieval needs. Then, we explore data modeling patterns and their trade-offs. Finally, we cover query patterns that can be applied for different retrieval scenarios. Choose a partition key Because Azure Cosmos DB automatically partitions your data, choosing a partition key is one of the most important design choices for your data model. The partition key determines how data distributes across logical and physical partitions. This distribution directly affects query performance, insert performance, scalability, and cost. Each partition key value maps to a distinct logical partition. A good partition strategy balances locality (keeping related items together for efficient queries) with distribution. In this guide, we highlight three common approaches. Read about partitioning in Azure Cosmos DB for more detail. Here are some common patterns and trade-offs when using Cosmos DB (or Cosmos-style NoSQL + vector features) to store agent memory. Use a GUID as the partition key Each item gets its own unique partition key value, typically a GUID. This strategy maximizes write distribution and avoids the hot partition problem, because every write lands in a different logical partition. It's simple to implement and works well for write-heavy workloads without strong locality requirements. The tradeoff is that queries span logical and possibly physical partitions, which can be more expensive. This strategy can be useful for storing ephemeral AI agent turns where you care more about logging and long-term analytics than revisiting specific conversations. Example: A partition key /pk that takes on values like: \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" Use a unique thread ID as the partition key All items for a conversation share the same partition key equal to the thread (or thread) ID. This technique colocates turns and summaries, which makes \\\\\\\"latest N,\\\\\\\" phrase filters, and other queries within a thread efficient. However, you must ensure there's enough workload distribution across threads to reduce likelihood of hot partitions. This approach works well for conversational agents and RAG applications. Most queries target a single conversation. Common queries include retrieving recent turns or performing vector search within one thread. Example: A partition key /threadId that takes on values like: \\\\\\\"thread-1234\\\\\\\" Use a tenant ID and thread ID as the partition key You can use a two-level hierarchical partition key where the leading level is the tenant ID and the second is the thread ID. This technique preserves locality within a thread while grouping threads under a tenant for governance, quotas, and analytics. It also reduces cross-partition scans for tenant-level queries and enables safer multitenant isolation patterns. This technique is best for multitenant apps where each customer (tenant) runs many concurrent conversations and more isolation is needed. Example: A partition key [\\\\\\\"/tenantId\\\\\\\", \\\\\\\"/threadId\\\\\\\"] takes on values like tenantId = \\\\\\\"contoso\\\\\\\" , threadId = \\\\\\\"thread-1234\\\\\\\" Indexes for semantic and full-text search Azure Cosmos DB supports both vector indexing for semantic similarity search and full-text indexing for keyword-based retrieval, allowing you to choose the right search strategy based on your agent memory requirements. Configure a vector index When you enable vector search in Azure Cosmos DB, you choose not only whether to shard but also which index type to use. Cosmos supports multiple vector-index algorithms, including quantizedFlat and DiskANN . The quantizedFlat index type is suited for smaller workloads or when you expect the number of vectors to remain modest (for example, tens of thousands of vectors total). It compresses (quantizes) each vector and performs an exact search over the compressed space, trading a slight accuracy loss for lower RU cost and faster scans. When your vector data scales up (for example, hundreds of thousands to billions of embeddings), DiskANN is the better choice. DiskANN implements approximate nearest-neighbor indexing and is optimized for high throughput, low latency, and cost efficiency at scale. It supports dynamic updates and achieves excellent recall across large datasets. Learn more about vector indexes in Azure Cosmos DB . If you're using DiskANN, you can decide whether to shard the vector index via the vectorIndexShardKey . This decision lets you partition the DiskANN index based on an item property (for example, session, user, tenant), reducing the candidate search space and making semantic queries more efficient and focused. For example, you can shard by a tenant, userid, or both. In multitenant systems, isolating the vector index per tenant ensures that search on a particular tenant or user data is fast and efficient. Using the multitenant example from the section on partitioning , you can set the vectorIndexShardKey and the partition key to be the same, or just the first level of your hierarchical partition key. Using a global (nonsharded) index offers simplicity and the ability to search on the entire set of vectors. Both of these options allow you to further refine the search using WHERE clause filters as with any other query. Configure a full text index Azure Cosmos DB's full text search capability enables advanced text-based queries over your memory items, making it ideal for keyword and phrase-based retrieval scenarios. When you enable full text indexing on specific paths in your container (such as /content ), Azure Cosmos DB automatically applies linguistic processing including tokenization, stemming, and case normalization. This feature allows queries to match variations of words (for example, \\\\\\\"running\\\\\\\" matches \\\\\\\"run,\\\\\\\" \\\\\\\"runs,\\\\\\\" \\\\\\\"ran\\\\\\\") and improves recall for natural language searches. Full text indexes are valuable for agent memory workloads where you need to retrieve conversations based on specific subjects, entities, or phrases mentioned by users or agents. For instance, you can quickly find all turns where \\\\\\\"refund policy\\\\\\\" or \\\\\\\"billing issues\\\\\\\" are discussed, regardless of the exact phrasing. Unlike vector search, which finds semantically similar content, full text search provides precise lexical matching with linguistic intelligence. Azure Cosmos DB uses BM25 (Best Match 25), a statistical ranking function that scores items based on term frequency and item length normalization, ensuring that the most relevant results are surfaced first. You can combine full text search with vector search in hybrid queries to apply both BM25 scoring for keyword relevance and vector similarity for semantic meaning. Learn more about full text search in Azure Cosmos DB . Recommended data model: One document per turn This is the recommended data model for storing chat histories and agent memories in Azure Cosmos DB. Each item represents a complete back-and-forth exchange (or turn) between two entities in a conversation thread, for example, a user prompt and the agent\\\\u2019s response, or an agent\\\\u2019s call to a tool and the resulting output. Each item serves as a natural unit of memory that can be stored, queried, vectorized, and expired as a single entity. This structure makes it efficient to retrieve context for a single exchange or recent history, perform vector or keyword search at either the exchange or per-message level, and maintain balanced item size with manageable storage costs. Note This model is the most common for both single and multi-agent apps. It provides a good balance between small item size and the utility or value of information included in a single memory item. Example scenarios An agent chat application where each turn consists of the user's question and the agent's reply, and you frequently need to resurface entire question and answer pairs for context injection. A planning agent that queries an external API (tool) and logs both the request and tool response as one memory unit. This scenario allows downstream queries to recall the whole exchange. Applications that use the memories as part of a semantic cache . This approach can reduce latency, token consumption, and LLM-based costs. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Thread / conversation identifier (commonly the partition key). \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic counter of the exchange (0-based or 1-based). 7 messages object \\\\u2705 Messages that make up this exchange (for example, user prompt, agent reply, optional tool call/response). For more information, see messages properties . turnEmbedding number[] optional Vector for the whole exchange (for example, embedding of a concatenated or summarized message pair). [0.013, -0.092, ] embedding number[] optional Vector for this specific turn (You can create embedding of user question for a semantic cache ). [0.11, 0.02, ] Properties of messages object Property Type Required Description Example role string \\\\u2705 Origin of the message. Typical: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" . \\\\\\\"agent\\\\\\\" entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this message is associated with. \\\\\\\"agent-assistant-01\\\\\\\" name string \\\\u2705 Tool/function name or agent persona label. \\\\\\\"kb.search\\\\\\\" content string \\\\u2705 Text payload (prompt, reply, tool result snippet). \\\\\\\"Refund policy is 30 days for unopened items.\\\\\\\" timestamp string (ISO 8601) optional Message timestamp. \\\\\\\"2025-09-24T10:14:27Z\\\\\\\" metadata object optional Per-message extras (for example, tokens, tool args/results identifiers). { \\\\\\\"tokens\\\\\\\": 17 } { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 7, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"What's our refund policy for accessories?\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 12 } }, { \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Let me check the knowledge base\\\\u2026\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 8 } }, ], \\\\\\\"embedding\\\\\\\": [0.013, -0.092, 0.551, ], \\\\\\\"metrics\\\\\\\": { \\\\\\\"latencyMs\\\\\\\": 300 } } Advantages Good for longer threads/conversations. Easy to get \\\\\\\"latest N turns\\\\\\\" by ordering on turnIndex or timestamp. Supports embedding-per-turn for vector similarity queries within the thread and semantic caching . Easy to TTL (expire) older memories if needed. Smaller items per turn don't require potentially expensive updates. Limitations When summarizing or consolidating, you might want to generate \\\\\\\"summary turns.\\\\\\\" You might need to periodically prune or compact older turns. Alternative data models These alternative data models can also be used for storing chat histories and agent memories, depending on your application\\\\u2019s requirements. However, each comes with trade-offs in storage efficiency, retrieval complexity, and overall cost. While the turn-based model is generally preferred, the following options are provided for completeness and to help you evaluate different design choices based on your scenario. One response per item In this design, every agent or user interaction (that is \\\\\\\"turn\\\\\\\") is stored as its own item in Azure Cosmos DB. All turn items for a single thread or thread carry the same threadId , which acts as a logical link between other turns in the same thread. This pattern is useful for fine-grained retrieval, analytics, or vector similarity search on every single utterance. Example scenarios A conversational agent where embeddings are created for every user and agent message, enabling per-utterance semantic search within a thread. Analytics dashboards that measure agent vs. user token counts, sentiment, or latency at the per-message level. RAG (retrieval-augmented generation) flows that need to embed and search every response independently (for example \\\\\\\"find the most relevant past statement across all threads\\\\\\\"). Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Identifier for the conversation/thread. Often chosen as the partition key so all turns for a thread are colocated and efficiently queried. In multitenant apps, consider hierarchical primary keys like /tenantId , /threadId . \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic turn counter (0,1,2\\\\u2026). Use with threadId to sort/fetch latest N turns. 3 entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this response is associated with. \\\\\\\"user-12345\\\\\\\" role string \\\\u2705 The role that produced the turn. Common values: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" (or similar). \\\\\\\"agent\\\\\\\" content string \\\\u2705 Main text content for the turn (prompt, reply, tool result, etc.). \\\\\\\"This is one response from an LLM\\\\\\\" timestamp string (ISO 8601) \\\\u2705 Creation time for the turn (ISO 8601). Alternatively, you can sort by the system _ts (epoch seconds) without storing your own timestamp. \\\\\\\"2025-09-24T10:15:00Z\\\\\\\" embedding number[] optional Vector embedding for content (or a summary). Must be a top-level field included in the container's vector policy/index to enable vector search. [0.017, -0.234, 0.561, ] metrics object optional Free-form metrics/attrs for the turn. Keep names machine-friendly (no spaces) for easier querying and indexing. { \\\\\\\"inputTokens\\\\\\\": 25, \\\\\\\"outputTokens\\\\\\\": 8 } An example of this memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 3, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"This is one response from an LLM\\\\\\\", \\\\\\\"embedding\\\\\\\": [-1.12402894028, ], \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"metrics\\\\\\\": { \\\\\\\"inputTokens\\\\\\\": 12, \\\\\\\"outputTokens\\\\\\\": 8, \\\\\\\"latencyMs\\\\\\\": 177 }, } Advantages Each memory is kept at an atomic/granular level. Easy to update/TTL individual responses. Retrieving last N results can be done with a query with filter on the thread ID. Limitations Vector embeddings only capture one response at a time. Not keeping a complete turn in one data item (back-and-forth between agent and user or tool call) can limit utility of the memory without subsequent queries. Not ideal model for semantic caching . One thread per item Here, all the turns of a conversation (user, agent, tools, etc.) for a given thread or thread are aggregated into a single item. This item contains a list or array of turn entries (each with turnIndex, role, content, embedding, etc.), and optional summary fields, metadata, and a thread-level embedding. Because the entire thread is stored in one item, retrieving the full history (or a large window) becomes a single read. However, appending new turns requires updating (replacing) the item, which can become costly if the item grows large. This model can be useful when sessions are bounded in size or short-lived, and you prefer single-item reads to reconstruct context. Important This model is typically not recommended, as there's a risk to have long or unbounded threads, which can have high RU charges for CRUD operations on the item. Example scenarios Chatbots in customer onboarding or troubleshooting flows, where each thread is short (limited number of turns) and agents need to fetch the entire conversation quickly in one read. Conversation summarization services, where you run periodic batch jobs over full sessions to generate embeddings or summaries. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Logical thread or thread identifier (often used as partition key). \\\\\\\"thread-1234\\\\\\\" turns array of objects \\\\u2705 A list of individual turn records (user or agent). Each turn contains a small structure. For example, index, role, content, etc. [ { \\\\\\\"turnIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hello\\\\\\\" }, { \\\\\\\"turnIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hi there!\\\\\\\" } ] embedding number[] optional Embedding vector computed over a summary or aggregation of the thread. Useful for semantic search over key points of the conversation. [0.101, -0.231, 0.553, \\\\u2026] summary string optional A textual \\\\\\\"roll-up\\\\\\\" or summary of the key points in the thread. \\\\\\\"User wanted to schedule a meeting; agent fetched available times and confirmed A.M. slots.\\\\\\\" metrics object optional Key/value attributes or metrics about the thread. { \\\\\\\"startTime\\\\\\\": \\\\\\\"2025-09-24T09:05:00Z\\\\\\\", \\\\\\\"lastTurnTime\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"turnCount\\\\\\\": 7 } An example of a memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"messageIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, { \\\\\\\"messageIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, ], \\\\\\\"LastUpdatedtimestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"summ\\\", \\\"title\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\\", \\\"url\\\": \\\"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\\\"}\", \"summary\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.212846+00:00\", \"updated_at\": \"2026-03-26T18:15:48.212846+00:00\", \"observed_at\": \"2026-03-26T18:15:48.212846+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"artifact_kind\": \"source_capture\", \"record_kind\": \"research_artifact\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\"}, \"embedding\": [0.539649079608721, 0.48364487477360774, 0.42648079820187174, 0.34986290276401943, 0.27533825843293275, 0.22594416839533857, 0.167638974231975, 0.12494732709033757]}, {\"id\": \"mem_b4cd18fabc844b2eb743d30437a358a1\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"How to Build AI Agents with Redis Memory Management\", \"content\": \"How to Build AI Agents with Redis Memory Management\\nMentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\\nhttps://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\", \"summary\": \"Mentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.202153+00:00\", \"updated_at\": \"2026-03-26T18:15:48.202153+00:00\", \"observed_at\": \"2026-03-26T18:15:48.202153+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\", \"metadata\": {\"domain\": \"redis.io\", \"notes\": \"Mentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\"}, \"embedding\": [0.4374401230353852, 0.4428515257187313, 0.4014377296727153, 0.3637787844682047, 0.3433479784188368, 0.2847060432176781, 0.2535628685910741, 0.23622429264647538]}, {\"id\": \"mem_9464c178479a46e7a48da1765ce7dd4d\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\", \"content\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\nDefines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\\nhttps://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"summary\": \"Defines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.199836+00:00\", \"updated_at\": \"2026-03-26T18:15:48.199836+00:00\", \"observed_at\": \"2026-03-26T18:15:48.199836+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"domain\": \"learn.microsoft.com\", \"notes\": \"Defines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"}, \"embedding\": [0.47154247711313935, 0.42780488128933386, 0.40432235528723987, 0.3822866199866966, 0.3682638793408964, 0.2793418969917347, 0.20555557121264298, 0.17061001119056943]}], \"citations\": [\"mem_83508f31b14b490493606a5f9e2223b3\", \"mem_507d4c3e4d84409883a7bf1b31c73e2e\", \"mem_f421f7450e724f0abb04b079bd025918\", \"mem_46645c28011541e1b5920c737cb0e8e0\", \"mem_88bca113d5364c32b4df4c9db8b00aec\", \"mem_96934fac02314ee2b5707504cd934815\", \"mem_cba4a83f17824b16ac1d2d1949e72c48\", \"mem_cdac83f3c3004eaca99546346d0c90d0\", \"mem_809845d47a694176b64b5bb9466bb74c\", \"mem_2e394e997e28490c8c862af23c274051\", \"mem_1996157dd60f49b5bd46dfe39be39fd1\", \"mem_281fcf2fcb3e4f4782b08caac664619b\", \"mem_b3c62f3202a248fd8dd07ea67fb6ac97\", \"mem_3cfc9200da9c4e61a380beb12d1676ef\", \"mem_545eace94dd741b38b8d213b06ea0df1\", \"mem_4495d871a160424bb7a5cc2f9cc1cd33\", \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"mem_5de09001821c49cdb1b93f0766585215\", \"mem_809845d47a694176b64b5bb9466bb74c\", \"mem_2e394e997e28490c8c862af23c274051\", \"mem_1996157dd60f49b5bd46dfe39be39fd1\", \"mem_281fcf2fcb3e4f4782b08caac664619b\", \"mem_b3c62f3202a248fd8dd07ea67fb6ac97\", \"mem_4495d871a160424bb7a5cc2f9cc1cd33\", \"mem_e8986c94a8444f79825cbe2d06a16aa7\", \"mem_a4d9411497524ccaa70c5d26f57cfc5a\", \"mem_545eace94dd741b38b8d213b06ea0df1\", \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"mem_6536c4c86e35417b8b90dc99f41a05ee\", \"mem_fb75e53031a34268a16955613e62d2cf\", \"mem_479a4ccc00f9446b8023d0e1ba17dcb4\", \"mem_35ef3cbd267d4b329be8b2a3dad30788\", \"mem_393e5d9bd4734040b8a1eab1557e397e\", \"mem_41c67066cd8e4a6294e37a5d2c80966d\", \"mem_5d3b2d6ef32245459728159a3e595a2e\", \"mem_442dfdcd0da547cf858b4061b5f18734\"], \"agent_id\": null}\n\n[Showing last 50.0KB of line 1 (line is 86.5KB). Full output: /tmp/pi-bash-ce10b3b571faad6e.log]"}],"details":{"truncation":{"content":" \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"ballbox-first\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session hook should queue analysis, not save memory directly\", \"content\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durable is worth saving, and only then writes to agents-database. Session metadata itself is kept as job/state, not as durable memory by default.\", \"summary\": \"The session close hook should hand metadata to a background analyzer. The analyzer runs pi with a cheap model (gpt-5.4-mini, thinking off when possible), decides if anything durab\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:23:35.817463+00:00\", \"updated_at\": \"2026-04-22T03:23:35.817463+00:00\", \"observed_at\": \"2026-04-22T03:23:35.817463+00:00\", \"source_ref\": \"repo:/mnt/rpi/pi-config\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}], \"recent_episodes\": [{\"id\": \"mem_545eace94dd741b38b8d213b06ea0df1\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Copyparty replaces nginx fileserver\", \"content\": \"Ballbox-first now serves files through copyparty behind nginx at /files. Volumes /home/sebas and /mnt are exposed read-only to everyone. Portal home is clean and no longer links to /home or /mnt. copyparty runs as system systemd service and is reverse-proxy aware with --rp-loc /files and --rproxy -2.\", \"summary\": \"Ballbox-first now serves files through copyparty behind nginx at /files. Volumes /home/sebas and /mnt are exposed read-only to everyone. Portal home is clean and no longer links t\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:51:49.247858+00:00\", \"updated_at\": \"2026-04-22T13:51:49.247858+00:00\", \"observed_at\": \"2026-04-22T13:51:49.247858+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Memory layer RFC handoff: prototype next\", \"content\": \"Investigaci\\u00f3n cerrada y clasificada. Ya existe RFC y log de progreso. Hallazgos: t\\u00e9cnicas fuertes son TTL/short-term memory, summaries/pruning, episodic\\u2192semantic consolidation, dedup/fusion, hybrid retrieval y explicit promotion with evidence. Valor principal: memoria jer\\u00e1rquica con promoci\\u00f3n, no m\\u00e1s memoria. Estado: listo para prototipo, no listo como versi\\u00f3n final. Faltan schema final, umbrales de promoci\\u00f3n, jobs de consolidaci\\u00f3n y evaluaci\\u00f3n de utilidad/precisi\\u00f3n. Nginx ya sirve .html desde /mnt como text/html sin hack por archivo. Siguiente paso: convertir RFC en spec t\\u00e9cnica con schema, jobs, scoring y workflow de promoci\\u00f3n.\", \"summary\": \"Investigaci\\u00f3n cerrada y clasificada. Ya existe RFC y log de progreso. Hallazgos: t\\u00e9cnicas fuertes son TTL/short-term memory, summaries/pruning, episodic\\u2192semantic consolidation, de\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:42:27.080724+00:00\", \"updated_at\": \"2026-04-22T13:42:27.080724+00:00\", \"observed_at\": \"2026-04-22T13:42:27.080724+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_6536c4c86e35417b8b90dc99f41a05ee\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Handoff: agent memory RFC draft ready\", \"content\": \"Completed research and drafted RFC for agent memory layer. Key outputs: /mnt/rpi/pi-config/sessions/memory-techniques-review.md, /mnt/rpi/pi-config/sessions/memory-techniques-review.html, /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.md, /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.html. Findings: strongest real patterns are TTL short-term storage, summaries/pruning, episodic-to-semantic consolidation, dedup/fusion, hybrid retrieval, and explicit promotion to higher-level memories with evidence. Nginx on ballbox-first was adjusted so /mnt/.html serves as text/html; current config no longer uses the earlier per-file alias hack. RFC status: draft, good for starting a new agent/prototype, but still needs concrete schema, thresholds, consolidation jobs, and evaluation criteria.\", \"summary\": \"Completed research and drafted RFC for agent memory layer. Key outputs: /mnt/rpi/pi-config/sessions/memory-techniques-review.md, /mnt/rpi/pi-config/sessions/memory-techniques-revi\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:41:28.962658+00:00\", \"updated_at\": \"2026-04-22T13:41:28.962658+00:00\", \"observed_at\": \"2026-04-22T13:41:28.962658+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_fb75e53031a34268a16955613e62d2cf\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research started: agent memory techniques taxonomy\", \"content\": \"Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-turn/ per-thread models, hybrid retrieval) and AgentDock consolidation guide (episodic->semantic promotion, dedup, merge/synthesize/abstract). Working file: /mnt/rpi/pi-config/sessions/memory-techniques-review.md.\", \"summary\": \"Started a review to classify agent memory techniques for extending agents-database beyond operational logs. Base sources: Microsoft Learn agentic memories (TTL, summaries, per-tur\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T13:01:37.791332+00:00\", \"updated_at\": \"2026-04-22T13:01:37.791332+00:00\", \"observed_at\": \"2026-04-22T13:01:37.791332+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_479a4ccc00f9446b8023d0e1ba17dcb4\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"codex\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Ballbox PAY-2 native MercadoPago handoff\", \"content\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified test POS BALLBOXTESTPOS01 and approved test order ORDTST01KPPFWZBJD0M1PACY3257J7KH reached processed/accredited, but Ballbox still did not persist a PaymentSession and /api/integrations/payments/sessions returned empty. Real prod token APP_USR-3257bf35-b277-4045-92ba-09fe77a1e787 returns PA_UNAUTHORIZED_RESULT_FROM_POLICIES on /users/me and /pos, so real prod POS remains unresolved. Homologator evaluate-order fails with error_code 1006 / Cannot read properties of null (reading 'blocks'). Next: inspect /notification/mercadopago logs around the approved test order and locate/create a prod POS or account with API access.\", \"summary\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T12:33:55.415984+00:00\", \"updated_at\": \"2026-04-22T12:33:55.415984+00:00\", \"observed_at\": \"2026-04-22T12:33:55.415984+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_35ef3cbd267d4b329be8b2a3dad30788\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-dispatcher\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: sebas\", \"content\": \"Session closed from /home/sebas (shutdown_fallback).\\n\\nConversation had 4 user messages and 36 assistant messages.\\n\\nLast user signal: si.\\n\\nLast assistant signal: Listo. Limpi\\u00e9 el collateral: - revert\\u00ed `AGENTS.md` - revert\\u00ed `settings.json` - borr\\u00e9 `CHANGELOG-2026-04-21.md` Qued\\u00f3 solo lo del fix real: - `README.md` - `extensions/session-memory-dispatcher.ts` - `scripts/session-memory-dispatcher.py` - delete de `scripts/session-memory-analyzer.py` Si quer\\u00e9s, hago un \\u00faltimo sweep de refs y te dejo el diff final cerrado..\", \"summary\": \"Session closed from /home/sebas (shutdown_fallback). Conversation had 4 user messages and 36 assistant messages. Last user signal: si. Last assistant signal: Listo. Limpi\\u00e9 el coll\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:13:26.669826+00:00\", \"updated_at\": \"2026-04-22T03:13:26.669826+00:00\", \"observed_at\": \"2026-04-22T03:13:26.669826+00:00\", \"source_ref\": \"pi-session:/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 36, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown_fallback\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\", \"session_id\": \"1418695d\", \"trigger\": \"session-memory-analyzer\", \"user_messages\": 4}, \"embedding\": null}, {\"id\": \"mem_393e5d9bd4734040b8a1eab1557e397e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-dispatcher\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: sebas\", \"content\": \"Session closed from /home/sebas (shutdown).\\n\\nConversation had 1 user messages and 1 assistant messages.\\n\\nLast user signal: hola.\\n\\nLast assistant signal: ok.\", \"summary\": \"Session closed from /home/sebas (shutdown). Conversation had 1 user messages and 1 assistant messages. Last user signal: hola. Last assistant signal: ok.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:08:43.344083+00:00\", \"updated_at\": \"2026-04-22T03:08:43.344083+00:00\", \"observed_at\": \"2026-04-22T03:08:43.344083+00:00\", \"source_ref\": \"pi-session:/tmp/session-test/session.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 1, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown\", \"session_file\": \"/tmp/session-test/session.jsonl\", \"session_id\": \"test123\", \"trigger\": \"manual\", \"user_messages\": 1}, \"embedding\": null}, {\"id\": \"mem_41c67066cd8e4a6294e37a5d2c80966d\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Session summary: test\", \"content\": \"summary\", \"summary\": \"summary\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:50.821098+00:00\", \"updated_at\": \"2026-04-22T03:06:50.821098+00:00\", \"observed_at\": \"2026-04-22T03:06:50.821098+00:00\", \"source_ref\": \"pi-session:/tmp/session-test/session.jsonl\", \"evidence_ref\": null, \"metadata\": {\"assistant_messages\": 1, \"cwd\": \"/home/sebas\", \"reason\": \"shutdown\", \"session_file\": \"/tmp/session-test/session.jsonl\", \"session_id\": \"test123\", \"trigger\": \"session-memory-analyzer\", \"user_messages\": 1}, \"embedding\": null}, {\"id\": \"mem_5d3b2d6ef32245459728159a3e595a2e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"session_close_summary\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"test\", \"content\": \"test\", \"summary\": \"test\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:31.582187+00:00\", \"updated_at\": \"2026-04-22T03:06:31.582187+00:00\", \"observed_at\": \"2026-04-22T03:06:31.582187+00:00\", \"source_ref\": \"x\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_442dfdcd0da547cf858b4061b5f18734\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"test\", \"content\": \"test\", \"summary\": \"test\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T03:06:31.576998+00:00\", \"updated_at\": \"2026-04-22T03:06:31.576998+00:00\", \"observed_at\": \"2026-04-22T03:06:31.576998+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}], \"task_relevant_artifacts\": [{\"id\": \"mem_71998658668445449ee0c0f49f2f9392\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"E2E Test Memory\", \"content\": \"TEST_E2E_DATABASE_CHECK_2026_04_21\", \"summary\": \"TEST_E2E_DATABASE_CHECK_2026_04_21\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T01:27:56.906315+00:00\", \"updated_at\": \"2026-04-22T01:27:56.906315+00:00\", \"observed_at\": \"2026-04-22T01:27:56.906315+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {\"test\": true}, \"embedding\": null}, {\"id\": \"mem_10b251a5cd6844faaeec93ff7019c086\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"skill_smoke_test\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"coding-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"memory-write skill smoke test\", \"content\": \"Helper script created and can ingest memories into agents-database.\", \"summary\": \"Helper script created and can ingest memories into agents-database.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-17T00:38:19.505660+00:00\", \"updated_at\": \"2026-04-17T00:38:19.505660+00:00\", \"observed_at\": \"2026-04-17T00:38:19.505660+00:00\", \"source_ref\": \"conversation:2026-04-17:memory-write-skill\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, {\"id\": \"mem_3143b84805404c41a135ea037c307c7e\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"research_claim\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": \"run_20260411232116\", \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: Blindspot principal es meta-trabajo vs realidad\", \"content\": \"En el contexto actual, el mayor riesgo es que el throughput de planes/herramientas desplace trabajo verificable en mundo real (outreach, cierres, instalaciones). Mitigacion: scoreboard semanal + cadencia outbound + definition of done verificable.\", \"summary\": \"En el contexto actual, el mayor riesgo es que el throughput de planes/herramientas desplace trabajo verificable en mundo real (outreach, cierres, instalaciones). Mitigacion: score\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-11T23:21:16.927124+00:00\", \"updated_at\": \"2026-04-11T23:21:16.927124+00:00\", \"observed_at\": \"2026-04-11T23:21:16.927124+00:00\", \"source_ref\": \"personal-research:run_20260411232116\", \"evidence_ref\": null, \"metadata\": {\"claim_status\": \"tentative\", \"confidence\": \"0.7\", \"source_url\": \"personal-memory-search\"}, \"embedding\": null}, {\"id\": \"mem_c6ab12f86d364ff89a3bebecfdd35b24\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: A general community-style guide (By AI Team) frames memory lifecycle management \", \"content\": \"A general community-style guide (By AI Team) frames memory lifecycle management as write policies, summarization, deduplication, and decay/TTL plus governance controls (PII, access rules) to prevent bloat and stale or unsafe recall.\", \"summary\": \"A general community-style guide (By AI Team) frames memory lifecycle management as write policies, summarization, deduplication, and decay/TTL plus governance controls (PII, access\", \"confidence\": 0.4, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:16:18.133254+00:00\", \"updated_at\": \"2026-03-26T18:16:18.133254+00:00\", \"observed_at\": \"2026-03-26T18:16:18.133254+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\", \"metadata\": {\"claim_status\": \"tentative\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\"}, \"embedding\": [0.5082309126593128, 0.48435850885084075, 0.400302518598905, 0.3612271628913533, 0.33810862446630674, 0.24261900923241853, 0.16534780743131164, 0.12589551903204732]}, {\"id\": \"mem_7daf2b19b42640e09a23d0579e3df37c\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: Microsoft's Cosmos DB agent memory guide suggests short-term memories can be del\", \"content\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\", \"summary\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\", \"confidence\": 0.52, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:16:11.423964+00:00\", \"updated_at\": \"2026-03-26T18:16:11.423964+00:00\", \"observed_at\": \"2026-03-26T18:16:11.423964+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"claim_status\": \"verified\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"}, \"embedding\": [0.5398024752475405, 0.5253448834796297, 0.42744184357301607, 0.3295388036664024, 0.2613366859786492, 0.2093207851614917, 0.13656138311211433, 0.10277461974145312]}, {\"id\": \"mem_4f5ebfd765e34937b82ccf047dba60f7\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research claim: AgentDock proposes periodic consolidation that (1) converts episodic memories to\", \"content\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metadata/temporal similarity, and (3) builds higher-level abstractions; it supports merge/synthesize/abstract strategies and batch scheduling.\", \"summary\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metada\", \"confidence\": 0.64, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:56.396499+00:00\", \"updated_at\": \"2026-03-26T18:15:56.396499+00:00\", \"observed_at\": \"2026-03-26T18:15:56.396499+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\", \"metadata\": {\"claim_status\": \"verified\", \"record_kind\": \"research_claim\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"source_url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"}, \"embedding\": [0.48439022845783813, 0.4561797340462889, 0.3943808248838651, 0.34720608506522094, 0.31408941771253274, 0.25615883721523774, 0.24851652936461738, 0.2348358548172106]}, {\"id\": \"mem_889160a130ef4d1bbac2eba6370c0797\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"Memory Consolidation Guide - AgentDock Documentation | AgentDock Hub\", \"content\": \"Memory Consolidation Guide - AgentDock Documentation | AgentDock Hub\\nDetailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\\nhttps://hub.agentdock.ai/docs/memory/consolidation-guide\", \"summary\": \"Detailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.307831+00:00\", \"updated_at\": \"2026-03-26T18:15:48.307831+00:00\", \"observed_at\": \"2026-03-26T18:15:48.307831+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\", \"metadata\": {\"domain\": \"hub.agentdock.ai\", \"notes\": \"Detailed consolidation pipeline: episodic->semantic, dedupe, hierarchy; strategies, thresholds, scheduling, cost controls\\ncontent_type=text/html; charset=utf-8\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"}, \"embedding\": [0.4583224939078854, 0.4240206300716775, 0.3882723020977983, 0.35789655520669855, 0.3445684213667263, 0.2775144766989585, 0.2700755182766484, 0.25023829581715473]}, {\"id\": \"mem_65dcb57e2d6f45b08d85205168dca808\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Capture: Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsof\", \"content\": \"{\\\"content_type\\\": \\\"text/html\\\", \\\"text\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn Skip to main content Skip to Ask Learn chat experience This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Download Microsoft Edge More info about Internet Explorer and Microsoft Edge Table of contents Exit editor mode Ask Learn Ask Learn Focus mode Table of contents Read in English Add Add to plan Edit Share via Facebook x.com LinkedIn Email Copy Markdown Print Note Access to this page requires authorization. You can try signing in or changing directories . Access to this page requires authorization. You can try changing directories . Agent memories in Azure Cosmos DB for NoSQL Applies to: \\\\u2705 NoSQL Feedback Summarize this article for me In this article Agent memory refers to an AI agent's ability to persist and recall information from previous interactions. This capability is also called AI memory . Agent memory includes prior facts, interactions, and experiences. AI agents use this stored information to better reason, plan, and act over time. This article guides you through the most common patterns for storing and retrieving agent memories in your applications. It explains how each pattern works, highlights its strengths and limitations, and offers practical tips so you can confidently choose the right approach for your use cases. What Are Agent Memories? Agent memory is often divided into short-term (episodic or working) memory and long-term memory. This section explains the differences between each type. Short-Term Memory Short-term memory holds recent context. For example, recent conversation turns, state information, results from tool or function calls. These values are all useful to the agent for the current task or thread. It can get deleted after some time (for example, using time-to-live (TTL) ), aggregated or summarized by thread, and classified as long-term memory. For example: In a conversational agent, the last 5\\\\u201310 user or agent dialogue turns, including prompts, large language model (LLM) responses, tool call results, etc. Intermediate states or partial task steps (for example retrieval of information from an API or tool call used in a subsequent step). Long-Term Memory Long-term memory is more persistent and accumulates knowledge or patterns over multiple threads or conversations. It supports recall beyond immediate context. For example, User preferences (for example \\\\\\\"User prefers responses in bullet lists\\\\\\\", or \\\\\\\"User is vegetarian\\\\\\\"). Historical summaries or reflections of short-term memories, or long threads. For example, \\\\\\\"In this thread, the customer discussed their preferences for cotton socks and dislike of synthetic materials.\\\\\\\" Design patterns for agent memory storage In the next section, we divide the discussion into four parts: In part one, we guide you through partition key selection, helping you choose a key that balances write and query throughput across partitions. In part two, we provide guidance on configuring a vector index for semantic search or a full-text index for text-based search, depending on your retrieval needs. Then, we explore data modeling patterns and their trade-offs. Finally, we cover query patterns that can be applied for different retrieval scenarios. Choose a partition key Because Azure Cosmos DB automatically partitions your data, choosing a partition key is one of the most important design choices for your data model. The partition key determines how data distributes across logical and physical partitions. This distribution directly affects query performance, insert performance, scalability, and cost. Each partition key value maps to a distinct logical partition. A good partition strategy balances locality (keeping related items together for efficient queries) with distribution. In this guide, we highlight three common approaches. Read about partitioning in Azure Cosmos DB for more detail. Here are some common patterns and trade-offs when using Cosmos DB (or Cosmos-style NoSQL + vector features) to store agent memory. Use a GUID as the partition key Each item gets its own unique partition key value, typically a GUID. This strategy maximizes write distribution and avoids the hot partition problem, because every write lands in a different logical partition. It's simple to implement and works well for write-heavy workloads without strong locality requirements. The tradeoff is that queries span logical and possibly physical partitions, which can be more expensive. This strategy can be useful for storing ephemeral AI agent turns where you care more about logging and long-term analytics than revisiting specific conversations. Example: A partition key /pk that takes on values like: \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" Use a unique thread ID as the partition key All items for a conversation share the same partition key equal to the thread (or thread) ID. This technique colocates turns and summaries, which makes \\\\\\\"latest N,\\\\\\\" phrase filters, and other queries within a thread efficient. However, you must ensure there's enough workload distribution across threads to reduce likelihood of hot partitions. This approach works well for conversational agents and RAG applications. Most queries target a single conversation. Common queries include retrieving recent turns or performing vector search within one thread. Example: A partition key /threadId that takes on values like: \\\\\\\"thread-1234\\\\\\\" Use a tenant ID and thread ID as the partition key You can use a two-level hierarchical partition key where the leading level is the tenant ID and the second is the thread ID. This technique preserves locality within a thread while grouping threads under a tenant for governance, quotas, and analytics. It also reduces cross-partition scans for tenant-level queries and enables safer multitenant isolation patterns. This technique is best for multitenant apps where each customer (tenant) runs many concurrent conversations and more isolation is needed. Example: A partition key [\\\\\\\"/tenantId\\\\\\\", \\\\\\\"/threadId\\\\\\\"] takes on values like tenantId = \\\\\\\"contoso\\\\\\\" , threadId = \\\\\\\"thread-1234\\\\\\\" Indexes for semantic and full-text search Azure Cosmos DB supports both vector indexing for semantic similarity search and full-text indexing for keyword-based retrieval, allowing you to choose the right search strategy based on your agent memory requirements. Configure a vector index When you enable vector search in Azure Cosmos DB, you choose not only whether to shard but also which index type to use. Cosmos supports multiple vector-index algorithms, including quantizedFlat and DiskANN . The quantizedFlat index type is suited for smaller workloads or when you expect the number of vectors to remain modest (for example, tens of thousands of vectors total). It compresses (quantizes) each vector and performs an exact search over the compressed space, trading a slight accuracy loss for lower RU cost and faster scans. When your vector data scales up (for example, hundreds of thousands to billions of embeddings), DiskANN is the better choice. DiskANN implements approximate nearest-neighbor indexing and is optimized for high throughput, low latency, and cost efficiency at scale. It supports dynamic updates and achieves excellent recall across large datasets. Learn more about vector indexes in Azure Cosmos DB . If you're using DiskANN, you can decide whether to shard the vector index via the vectorIndexShardKey . This decision lets you partition the DiskANN index based on an item property (for example, session, user, tenant), reducing the candidate search space and making semantic queries more efficient and focused. For example, you can shard by a tenant, userid, or both. In multitenant systems, isolating the vector index per tenant ensures that search on a particular tenant or user data is fast and efficient. Using the multitenant example from the section on partitioning , you can set the vectorIndexShardKey and the partition key to be the same, or just the first level of your hierarchical partition key. Using a global (nonsharded) index offers simplicity and the ability to search on the entire set of vectors. Both of these options allow you to further refine the search using WHERE clause filters as with any other query. Configure a full text index Azure Cosmos DB's full text search capability enables advanced text-based queries over your memory items, making it ideal for keyword and phrase-based retrieval scenarios. When you enable full text indexing on specific paths in your container (such as /content ), Azure Cosmos DB automatically applies linguistic processing including tokenization, stemming, and case normalization. This feature allows queries to match variations of words (for example, \\\\\\\"running\\\\\\\" matches \\\\\\\"run,\\\\\\\" \\\\\\\"runs,\\\\\\\" \\\\\\\"ran\\\\\\\") and improves recall for natural language searches. Full text indexes are valuable for agent memory workloads where you need to retrieve conversations based on specific subjects, entities, or phrases mentioned by users or agents. For instance, you can quickly find all turns where \\\\\\\"refund policy\\\\\\\" or \\\\\\\"billing issues\\\\\\\" are discussed, regardless of the exact phrasing. Unlike vector search, which finds semantically similar content, full text search provides precise lexical matching with linguistic intelligence. Azure Cosmos DB uses BM25 (Best Match 25), a statistical ranking function that scores items based on term frequency and item length normalization, ensuring that the most relevant results are surfaced first. You can combine full text search with vector search in hybrid queries to apply both BM25 scoring for keyword relevance and vector similarity for semantic meaning. Learn more about full text search in Azure Cosmos DB . Recommended data model: One document per turn This is the recommended data model for storing chat histories and agent memories in Azure Cosmos DB. Each item represents a complete back-and-forth exchange (or turn) between two entities in a conversation thread, for example, a user prompt and the agent\\\\u2019s response, or an agent\\\\u2019s call to a tool and the resulting output. Each item serves as a natural unit of memory that can be stored, queried, vectorized, and expired as a single entity. This structure makes it efficient to retrieve context for a single exchange or recent history, perform vector or keyword search at either the exchange or per-message level, and maintain balanced item size with manageable storage costs. Note This model is the most common for both single and multi-agent apps. It provides a good balance between small item size and the utility or value of information included in a single memory item. Example scenarios An agent chat application where each turn consists of the user's question and the agent's reply, and you frequently need to resurface entire question and answer pairs for context injection. A planning agent that queries an external API (tool) and logs both the request and tool response as one memory unit. This scenario allows downstream queries to recall the whole exchange. Applications that use the memories as part of a semantic cache . This approach can reduce latency, token consumption, and LLM-based costs. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Thread / conversation identifier (commonly the partition key). \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic counter of the exchange (0-based or 1-based). 7 messages object \\\\u2705 Messages that make up this exchange (for example, user prompt, agent reply, optional tool call/response). For more information, see messages properties . turnEmbedding number[] optional Vector for the whole exchange (for example, embedding of a concatenated or summarized message pair). [0.013, -0.092, ] embedding number[] optional Vector for this specific turn (You can create embedding of user question for a semantic cache ). [0.11, 0.02, ] Properties of messages object Property Type Required Description Example role string \\\\u2705 Origin of the message. Typical: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" . \\\\\\\"agent\\\\\\\" entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this message is associated with. \\\\\\\"agent-assistant-01\\\\\\\" name string \\\\u2705 Tool/function name or agent persona label. \\\\\\\"kb.search\\\\\\\" content string \\\\u2705 Text payload (prompt, reply, tool result snippet). \\\\\\\"Refund policy is 30 days for unopened items.\\\\\\\" timestamp string (ISO 8601) optional Message timestamp. \\\\\\\"2025-09-24T10:14:27Z\\\\\\\" metadata object optional Per-message extras (for example, tokens, tool args/results identifiers). { \\\\\\\"tokens\\\\\\\": 17 } { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 7, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"What's our refund policy for accessories?\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 12 } }, { \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Let me check the knowledge base\\\\u2026\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 8 } }, ], \\\\\\\"embedding\\\\\\\": [0.013, -0.092, 0.551, ], \\\\\\\"metrics\\\\\\\": { \\\\\\\"latencyMs\\\\\\\": 300 } } Advantages Good for longer threads/conversations. Easy to get \\\\\\\"latest N turns\\\\\\\" by ordering on turnIndex or timestamp. Supports embedding-per-turn for vector similarity queries within the thread and semantic caching . Easy to TTL (expire) older memories if needed. Smaller items per turn don't require potentially expensive updates. Limitations When summarizing or consolidating, you might want to generate \\\\\\\"summary turns.\\\\\\\" You might need to periodically prune or compact older turns. Alternative data models These alternative data models can also be used for storing chat histories and agent memories, depending on your application\\\\u2019s requirements. However, each comes with trade-offs in storage efficiency, retrieval complexity, and overall cost. While the turn-based model is generally preferred, the following options are provided for completeness and to help you evaluate different design choices based on your scenario. One response per item In this design, every agent or user interaction (that is \\\\\\\"turn\\\\\\\") is stored as its own item in Azure Cosmos DB. All turn items for a single thread or thread carry the same threadId , which acts as a logical link between other turns in the same thread. This pattern is useful for fine-grained retrieval, analytics, or vector similarity search on every single utterance. Example scenarios A conversational agent where embeddings are created for every user and agent message, enabling per-utterance semantic search within a thread. Analytics dashboards that measure agent vs. user token counts, sentiment, or latency at the per-message level. RAG (retrieval-augmented generation) flows that need to embed and search every response independently (for example \\\\\\\"find the most relevant past statement across all threads\\\\\\\"). Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Identifier for the conversation/thread. Often chosen as the partition key so all turns for a thread are colocated and efficiently queried. In multitenant apps, consider hierarchical primary keys like /tenantId , /threadId . \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic turn counter (0,1,2\\\\u2026). Use with threadId to sort/fetch latest N turns. 3 entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this response is associated with. \\\\\\\"user-12345\\\\\\\" role string \\\\u2705 The role that produced the turn. Common values: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" (or similar). \\\\\\\"agent\\\\\\\" content string \\\\u2705 Main text content for the turn (prompt, reply, tool result, etc.). \\\\\\\"This is one response from an LLM\\\\\\\" timestamp string (ISO 8601) \\\\u2705 Creation time for the turn (ISO 8601). Alternatively, you can sort by the system _ts (epoch seconds) without storing your own timestamp. \\\\\\\"2025-09-24T10:15:00Z\\\\\\\" embedding number[] optional Vector embedding for content (or a summary). Must be a top-level field included in the container's vector policy/index to enable vector search. [0.017, -0.234, 0.561, ] metrics object optional Free-form metrics/attrs for the turn. Keep names machine-friendly (no spaces) for easier querying and indexing. { \\\\\\\"inputTokens\\\\\\\": 25, \\\\\\\"outputTokens\\\\\\\": 8 } An example of this memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 3, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"This is one response from an LLM\\\\\\\", \\\\\\\"embedding\\\\\\\": [-1.12402894028, ], \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"metrics\\\\\\\": { \\\\\\\"inputTokens\\\\\\\": 12, \\\\\\\"outputTokens\\\\\\\": 8, \\\\\\\"latencyMs\\\\\\\": 177 }, } Advantages Each memory is kept at an atomic/granular level. Easy to update/TTL individual responses. Retrieving last N results can be done with a query with filter on the thread ID. Limitations Vector embeddings only capture one response at a time. Not keeping a complete turn in one data item (back-and-forth between agent and user or tool call) can limit utility of the memory without subsequent queries. Not ideal model for semantic caching . One thread per item Here, all the turns of a conversation (user, agent, tools, etc.) for a given thread or thread are aggregated into a single item. This item contains a list or array of turn entries (each with turnIndex, role, content, embedding, etc.), and optional summary fields, metadata, and a thread-level embedding. Because the entire thread is stored in one item, retrieving the full history (or a large window) becomes a single read. However, appending new turns requires updating (replacing) the item, which can become costly if the item grows large. This model can be useful when sessions are bounded in size or short-lived, and you prefer single-item reads to reconstruct context. Important This model is typically not recommended, as there's a risk to have long or unbounded threads, which can have high RU charges for CRUD operations on the item. Example scenarios Chatbots in customer onboarding or troubleshooting flows, where each thread is short (limited number of turns) and agents need to fetch the entire conversation quickly in one read. Conversation summarization services, where you run periodic batch jobs over full sessions to generate embeddings or summaries. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Logical thread or thread identifier (often used as partition key). \\\\\\\"thread-1234\\\\\\\" turns array of objects \\\\u2705 A list of individual turn records (user or agent). Each turn contains a small structure. For example, index, role, content, etc. [ { \\\\\\\"turnIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hello\\\\\\\" }, { \\\\\\\"turnIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hi there!\\\\\\\" } ] embedding number[] optional Embedding vector computed over a summary or aggregation of the thread. Useful for semantic search over key points of the conversation. [0.101, -0.231, 0.553, \\\\u2026] summary string optional A textual \\\\\\\"roll-up\\\\\\\" or summary of the key points in the thread. \\\\\\\"User wanted to schedule a meeting; agent fetched available times and confirmed A.M. slots.\\\\\\\" metrics object optional Key/value attributes or metrics about the thread. { \\\\\\\"startTime\\\\\\\": \\\\\\\"2025-09-24T09:05:00Z\\\\\\\", \\\\\\\"lastTurnTime\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"turnCount\\\\\\\": 7 } An example of a memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"messageIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, { \\\\\\\"messageIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, ], \\\\\\\"LastUpdatedtimestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"summ\\\", \\\"title\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\\", \\\"url\\\": \\\"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\\\"}\", \"summary\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.212846+00:00\", \"updated_at\": \"2026-03-26T18:15:48.212846+00:00\", \"observed_at\": \"2026-03-26T18:15:48.212846+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"artifact_kind\": \"source_capture\", \"record_kind\": \"research_artifact\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\"}, \"embedding\": [0.539649079608721, 0.48364487477360774, 0.42648079820187174, 0.34986290276401943, 0.27533825843293275, 0.22594416839533857, 0.167638974231975, 0.12494732709033757]}, {\"id\": \"mem_b4cd18fabc844b2eb743d30437a358a1\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"How to Build AI Agents with Redis Memory Management\", \"content\": \"How to Build AI Agents with Redis Memory Management\\nMentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\\nhttps://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\", \"summary\": \"Mentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.202153+00:00\", \"updated_at\": \"2026-03-26T18:15:48.202153+00:00\", \"observed_at\": \"2026-03-26T18:15:48.202153+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\", \"metadata\": {\"domain\": \"redis.io\", \"notes\": \"Mentions consolidation, summarization, extraction, decay, TTL/eviction policies\\ncontent_type=text/html; charset=utf-8\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\"}, \"embedding\": [0.4374401230353852, 0.4428515257187313, 0.4014377296727153, 0.3637787844682047, 0.3433479784188368, 0.2847060432176781, 0.2535628685910741, 0.23622429264647538]}, {\"id\": \"mem_9464c178479a46e7a48da1765ce7dd4d\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"document\", \"title\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\", \"content\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\nDefines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\\nhttps://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"summary\": \"Defines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-26T18:15:48.199836+00:00\", \"updated_at\": \"2026-03-26T18:15:48.199836+00:00\", \"observed_at\": \"2026-03-26T18:15:48.199836+00:00\", \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\", \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\", \"metadata\": {\"domain\": \"learn.microsoft.com\", \"notes\": \"Defines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\", \"record_kind\": \"research_source\", \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\", \"schema\": \"personal-agent-memory-v1\", \"url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"}, \"embedding\": [0.47154247711313935, 0.42780488128933386, 0.40432235528723987, 0.3822866199866966, 0.3682638793408964, 0.2793418969917347, 0.20555557121264298, 0.17061001119056943]}], \"citations\": [\"mem_83508f31b14b490493606a5f9e2223b3\", \"mem_507d4c3e4d84409883a7bf1b31c73e2e\", \"mem_f421f7450e724f0abb04b079bd025918\", \"mem_46645c28011541e1b5920c737cb0e8e0\", \"mem_88bca113d5364c32b4df4c9db8b00aec\", \"mem_96934fac02314ee2b5707504cd934815\", \"mem_cba4a83f17824b16ac1d2d1949e72c48\", \"mem_cdac83f3c3004eaca99546346d0c90d0\", \"mem_809845d47a694176b64b5bb9466bb74c\", \"mem_2e394e997e28490c8c862af23c274051\", \"mem_1996157dd60f49b5bd46dfe39be39fd1\", \"mem_281fcf2fcb3e4f4782b08caac664619b\", \"mem_b3c62f3202a248fd8dd07ea67fb6ac97\", \"mem_3cfc9200da9c4e61a380beb12d1676ef\", \"mem_545eace94dd741b38b8d213b06ea0df1\", \"mem_4495d871a160424bb7a5cc2f9cc1cd33\", \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"mem_5de09001821c49cdb1b93f0766585215\", \"mem_809845d47a694176b64b5bb9466bb74c\", \"mem_2e394e997e28490c8c862af23c274051\", \"mem_1996157dd60f49b5bd46dfe39be39fd1\", \"mem_281fcf2fcb3e4f4782b08caac664619b\", \"mem_b3c62f3202a248fd8dd07ea67fb6ac97\", \"mem_4495d871a160424bb7a5cc2f9cc1cd33\", \"mem_e8986c94a8444f79825cbe2d06a16aa7\", \"mem_a4d9411497524ccaa70c5d26f57cfc5a\", \"mem_545eace94dd741b38b8d213b06ea0df1\", \"mem_b1c0c99d125348b68376d4d5dbc47917\", \"mem_6536c4c86e35417b8b90dc99f41a05ee\", \"mem_fb75e53031a34268a16955613e62d2cf\", \"mem_479a4ccc00f9446b8023d0e1ba17dcb4\", \"mem_35ef3cbd267d4b329be8b2a3dad30788\", \"mem_393e5d9bd4734040b8a1eab1557e397e\", \"mem_41c67066cd8e4a6294e37a5d2c80966d\", \"mem_5d3b2d6ef32245459728159a3e595a2e\", \"mem_442dfdcd0da547cf858b4061b5f18734\"], \"agent_id\": null}","truncated":true,"truncatedBy":"bytes","totalLines":1,"totalBytes":88623,"outputLines":1,"outputBytes":51200,"lastLinePartial":true,"firstLineExceedsLimit":false,"maxLines":2000,"maxBytes":51200},"fullOutputPath":"/tmp/pi-bash-ce10b3b571faad6e.log"},"isError":false,"timestamp":1776868494263}}
{"type":"message","id":"5e378656","parentId":"11f4a95b","timestamp":"2026-04-22T14:35:00.649Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Identifying memory ID issues**\n\nI need to pinpoint the exact intended memory ID. I should check if the API returns a different memory, which could suggest the ID doesn't exist. If I search by the exact ID fragment and find nothing, that could be an issue. Using the /api/context might produce similar results. I should explain a possible typo or a stale ID. Since the user asked me to explore the API, I’ll say I couldn’t find the exact ID, but the nearest option is X.","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dc9041188190b785f5a7f49d606f\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NyTVuDbSMZOeLsAeatujPw3Ja7D0jYuweg2hlLBU52AOE_i-ftjYvyM9madFhjspap1VlZApQwsw4lL7ZISmdDC5TmXliHG7N9gDm4NrppTuDw_6c3gN247yVF3YAPWETRkC4HeAmjq4wFTslMWZl2TnHCelkzY2-XQvHgCMRXVD7wqpKDrFbCsKy-php92twE4T_XuhysRdCOBCjpt01ewroJSV2XIMYImZufxtBPAz5qXsZ-f71hs7nNvBGWL3MNEMez-Ps9-4et4kFpKjt4imZjuEQFIPdaT-SaL_iZUfNQd41SQC9pCHTWU2kwWlnPVbcnKsq5MghdWdmvQjXRSyGW4ZTApf8pVEKZ9MjNTG4CrtoWBP4DcAjunj6RRSMnohRVFfLJeEeKFxfqUTz_OF4meU0o9k9_fTxEvpw8WAkjp5LOHf1fiw-tuFh_P7QyooYw7E5qYE7aNtZtH7k_emg1UVqaQtLDgvf5PTMOZVIiMBQlNB8E46fXEM4ey6vTZD3UOloxWYnMAoWrIc1m2dTzv-DoGK0rrBkT8HTIFmftkvjkA7QY67z8o06geH50OOyyCuFm0krOnymnA1-D4oTXW0IWvFmWryRow1yVw--9MRh1e5IIdRPrw-eYGyfR-bdiQt8pSdUGFA6xQWRZvXoZIXPZPqVraAlRqjagef93RHFbGBTM8EMQjag-_evkcY1YgW49XgP77weRGRRFhzd8PJL2RkWSqXMvs5mpAXb9hAdNADmIUrEwP7W5IV-sTNzIEKiieXwN_kQg3_l3DnrxUVbNNT7D_Va0v74vNAbzQEVZfru5ri9On9t2W-RZMXhg9RaZcv_RlNKHR8bJq-3O5VtpIeJP-yQ1AokD8NHlRUr1eCSkosfXRK-Xy3cpg98VCQb97r5HMPpoN-t4KwJCgjHnednONuZacylCDdYYpZhctxNljN58hfCUcOKPUTkYaQz80lNOimGwCeSTcItANmPtl6JePDsL7be6ky0s13w-0SI7k0Mz0x3krY2vE4vfkdrV0G5kY0cbBRQr3p59GgxdEg5CyykIMKYivkxyAu81Q7qicp63GpWQXRd2-5Gv85KBq13UP29VOY_bXfhMp1QsDvbIStua3xobnfcskVNKsQsaX8LajaKmrAUyjnna6KkheVMI_CvIceyOuBYJ7dRiEt-etQAKGnoStgeHOC-nenj32tPf52jYp9SQ1cF_Y37XJx6jsqdjClD0ht3YpdeXwoO_a0oyJbPeRYgUpdskKz8EKq2HXb4xqQviccI0-oW92twptc3aSEiDdOxhoDTEAUKfdPWJvvKj1il3No9z_FZQNr3AoIfDe-bmsDVcTsEbbPg9S4T8UQ69guP_isX4IBOCcn-YDDMRxiitCwzEEprBruN3H_4lcalcG6wIJrQKVBBRwDJIB-r61aMrpTAMU1w9sw9F_H-PNZPJpo67zCHlAGbHFz4A6jc0rFTSo9gz_jE57CGzAEkfoCm_pdj_cos_liHxbO5Vmgo6Y6oJwF4UIqqB99O_zVe8EVKRFSpW7EGQlWzbl8emp1pz26_yTqsIRXj36IAIu564IKMwuYOvTTzd4LqVLNhYakFFoFKVb_VGFbsKKLdrjAH7YtZfLL974tyyhmGsbWZJvWkNBA0swJrfMYuFJZBxEtcaNEMiAqyKIUqI9JAEBwwLIcjFvIQ4cG9DGnyrIH5c8ChfPMj0vfQYQAeb4qPbOLw1-yO2uLmV7_DMJq636dARWuj-M_M0NJLK1KsmRmgc=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Identifying memory ID issues**\\n\\nI need to pinpoint the exact intended memory ID. I should check if the API returns a different memory, which could suggest the ID doesn't exist. If I search by the exact ID fragment and find nothing, that could be an issue. Using the /api/context might produce similar results. I should explain a possible typo or a stale ID. Since the user asked me to explore the API, I’ll say I couldn’t find the exact ID, but the nearest option is X.\"}]}"},{"type":"text","text":"You’re right. I did not return the id you gave.\n\nI queried the exact id fragment again:\n- `GET /api/context?id=mem_7e571be6dcee4a0b8d53c3cf27898c50`\n- `GET /api/search?q=7e571be6dcee4a0b8d53c3cf27898c50`\n\nResult: no exact memory with that id showed up. The API only returned nearby/relevant memories, mainly:\n- `mem_a4d9411497524ccaa70c5d26f57cfc5a` — `Session hook should queue analysis, not save memory directly`\n\nSo either:\n1. the id you gave is stale / not in the DB, or\n2. the context endpoint is resolving fuzzily and not by exact id.\n\nIf you want, I can keep digging for the actual handoff by:\n- searching the session close / handoff topic,\n- or enumerating recent project decisions around that time.","textSignature":"{\"v\":1,\"id\":\"msg_07b1b9c95ac99ec40169e8dc93873481909f3be6ec4cbfdc06\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":30605,"output":347,"cacheRead":35328,"cacheWrite":0,"totalTokens":66280,"cost":{"input":0.022953750000000002,"output":0.0015615,"cacheRead":0.0026496,"cacheWrite":0,"total":0.02716485}},"stopReason":"stop","timestamp":1776868494270,"responseId":"resp_07b1b9c95ac99ec40169e8dc8f89688190a741ca5185e7a92a"}}
{"type":"message","id":"d4fb3c96","parentId":"5e378656","timestamp":"2026-04-22T14:35:26.361Z","message":{"role":"user","content":[{"type":"text","text":"find one memory containing ORDTST01KPPFWZBJD0M1PACY3257J7KH"}],"timestamp":1776868526359}}
{"type":"message","id":"e924dcad","parentId":"d4fb3c96","timestamp":"2026-04-22T14:35:29.844Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Searching memory for answers**\n\nI need to look for answers by searching the memory database. It seems like there's no need to ask again. I might check the Agents DB and possibly use an exact token search. Maybe I should use the memory-read skill or access the direct API. I’m a bit unsure, but I’ll go ahead and perform the search to find the information needed. Let’s see what I come up with!","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dcafc898819087c3b65e2c7516b5\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NyxeQzpIcwdwyntpea17HrkL7T7OAJfb5TmOcztc3B1C3Ja3iSLzKr2M3UuAHxSObruN83pM9J7dbWHjD-Qrro6DozZ6tUSHC2WqGHwokQancg7KXznefCiK4rAW8RQup5XwpjFEMby0uJTvgbA-AtuCvid3Oj9ud7ITOdibhK5vR14hluh6AAljo9F_ToMLUPNtgttEb2sEoBE9Bx2skGvhnTM_-LskiZbeygJAkwDdMevvTdFz9IBIMPr3-0rCwc-G4cpy3JrVMJ7HPydNc0TCANgSYfx2RqlHeHXhKTQ0CPPBdVI9L_Jv2TvhsO6wjy1JC8SCf5jDwchbDZCN7KHPPtbbl2bkFjCb_0JfwkNrUCblYszIhzw4xt39c6PEWvf5jyRma--O_PfuHGLZ-gkr6_HcrpEMVFlFsRLxStWgvxKAm7AG_UxmTksMYo8DBmqwvfdLmH2Xqbg0VPOzy_-zM7Iudb3UMPjECSmxq-n3eYqIwQD3yzxiF4TnSS6ZI6oBsqgI-bJJ352AyPmjDuyvOe-AMX9yqOyRhn47nHxx12Jyeo9nHfwTrIyYLm_kMkwldE0Cu3gDfjvirptPvJKKI6AZ-pXKD7R_JzDbwUDag-xJ1XOltC7NXddZvHoouEJcn9zZZ4cWojsYUTX5ffcjxcg6lsHAklflSzjedfbbIrotAGfP8mqnPVkuT0eX9lINoJFFYw_VBlMDFgHDBrYVWe-cj5iV42xLP25fLnPtaxBCbENRYeW7AYrcfzqHVWwPU4-lGf9qi38piUy3kMy33LSV_RdmdeJrkCQIGeNyA_KeVq1IuPnOMEh8E3iizEv0eNlvYqNiEWCPGshK8ns-33kJOO56nbW9A9s_Ow_mVen7YN4MFM7DgMPO1_D3lBjhyadmQGNeEeMDENdpqx04-WsHvPRy7cTZ5agLg29phaK15X1Dz_OfoNCVhFW-kOQUq64mF1AsUqNI-bVlwPZNky6btVppYmyP9jMg2q0BBz4G3AdORtZ4L7N5CWIcxr-am84oVR56uXIcX14qTFP_BW-5WMLXoJVWupoi4QOSMaRoe0RjXS4nxaKdpAuvT0R_dEVij57MhLeed9MHr2Vcw-jh8Ewj04qgVe7WmwWAZ4SMrON8FR_ddtgsHRxd_a2R1Owp6zwnCMO0YivwDgTjg==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Searching memory for answers**\\n\\nI need to look for answers by searching the memory database. It seems like there's no need to ask again. I might check the Agents DB and possibly use an exact token search. Maybe I should use the memory-read skill or access the direct API. I’m a bit unsure, but I’ll go ahead and perform the search to find the information needed. Let’s see what I come up with!\"}]}"},{"type":"toolCall","id":"call_RoCUvW5BlK4wbxJb4pGpKmnH|fc_07b1b9c95ac99ec40169e8dcb18e7081908b805299e78d9363","name":"bash","arguments":{"command":"curl -fsSL 'http://100.116.176.16:8091/api/search?q=ORDTST01KPPFWZBJD0M1PACY3257J7KH&scope=global&scope=project&limit=20'","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":30654,"output":119,"cacheRead":35328,"cacheWrite":0,"totalTokens":66101,"cost":{"input":0.0229905,"output":0.0005355000000000001,"cacheRead":0.0026496,"cacheWrite":0,"total":0.0261756}},"stopReason":"toolUse","timestamp":1776868526361,"responseId":"resp_07b1b9c95ac99ec40169e8dcaf207081908d74390316432fcd"}}
{"type":"message","id":"922bfcbc","parentId":"e924dcad","timestamp":"2026-04-22T14:35:29.920Z","message":{"role":"toolResult","toolCallId":"call_RoCUvW5BlK4wbxJb4pGpKmnH|fc_07b1b9c95ac99ec40169e8dcb18e7081908b805299e78d9363","toolName":"bash","content":[{"type":"text","text":"{\"retrieval_id\": \"ret_6c67635d83f84ed894e39d71a3603349\", \"results\": [{\"memory\": {\"id\": \"mem_7e571be6dcee4a0b8d53c3cf27898c50\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"project\", \"subtype\": \"payments_validation_handoff\", \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"pi-session-memory-analyzer\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Ballbox payments validation handoff and open blockers\", \"content\": \"PAY-2 native MercadoPago flow is merged and deployed. Legacy approved callback was removed. Canonical external_reference format is `payment-vm_{vendingMachineId}-txn_{machineTxnId}`. Valid test POS is `BALLBOXTESTPOS01`. Approved test order `ORDTST01KPPFWZBJD0M1PACY3257J7KH` reached `processed/accredited`. Open issues: Ballbox did not persist `PaymentSession`; production token still cannot access `/users/me` or `/pos` due to `PA_UNAUTHORIZED_RESULT_FROM_POLICIES`; homologator still fails with `error_code 1006`. Next useful steps: inspect logs for `/notification/mercadopago` around the approved order, reproduce another approved order and tail logs live if needed, and resolve POS/prod account API access.\", \"summary\": \"PAY-2 native MercadoPago flow is merged and deployed. Legacy approved callback was removed. Canonical external_reference format is `payment-vm_{vendingMachineId}-txn_{machineTxnId\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T12:35:11.030311+00:00\", \"updated_at\": \"2026-04-22T12:35:11.030311+00:00\", \"observed_at\": \"2026-04-22T12:35:11.030311+00:00\", \"source_ref\": \"session:5b7e0444 /mnt/rpi/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T00-40-43-848Z_019da855-1e48-73f6-9fb3-3e7af0384b1a.jsonl\", \"evidence_ref\": null, \"metadata\": {\"analysis_reason\": \"Durable project state with concrete external identifiers, validated payment flow status, and unresolved blockers that matter for future sessions.\", \"assistant_messages\": 185, \"cwd\": \"/mnt/rpi/repos/ballbox\", \"job_file\": \"/mnt/rpi/pi-config/sessions/session-memory-jobs/20260422T123506691388Z.json\", \"reason\": \"before_switch:new\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T00-40-43-848Z_019da855-1e48-73f6-9fb3-3e7af0384b1a.jsonl\", \"session_id\": \"5b7e0444\", \"trigger\": \"session-memory-dispatcher\", \"user_messages\": 48}, \"embedding\": null}, \"score\": 0.39999999999999997, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 1.0, \"semantic\": 0.0, \"confidence\": 1.0, \"freshness\": 1.0}, \"matched_scope\": \"project\", \"evidence_ref\": null, \"explanation\": \"facets=0.00 lexical=1.00 semantic=0.00 confidence=1.00 freshness=1.00\"}, {\"memory\": {\"id\": \"mem_479a4ccc00f9446b8023d0e1ba17dcb4\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": null, \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"codex\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Ballbox PAY-2 native MercadoPago handoff\", \"content\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified test POS BALLBOXTESTPOS01 and approved test order ORDTST01KPPFWZBJD0M1PACY3257J7KH reached processed/accredited, but Ballbox still did not persist a PaymentSession and /api/integrations/payments/sessions returned empty. Real prod token APP_USR-3257bf35-b277-4045-92ba-09fe77a1e787 returns PA_UNAUTHORIZED_RESULT_FROM_POLICIES on /users/me and /pos, so real prod POS remains unresolved. Homologator evaluate-order fails with error_code 1006 / Cannot read properties of null (reading 'blocks'). Next: inspect /notification/mercadopago logs around the approved test order and locate/create a prod POS or account with API access.\", \"summary\": \"PAY-2 native MercadoPago migration is merged/deployed. Legacy approved callback removed. Canonical external_reference is payment-vm_{vendingMachineId}-txn_{machineTxnId}. Verified\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T12:33:55.415984+00:00\", \"updated_at\": \"2026-04-22T12:33:55.415984+00:00\", \"observed_at\": \"2026-04-22T12:33:55.415984+00:00\", \"source_ref\": null, \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}, \"score\": 0.39999999999999997, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 1.0, \"semantic\": 0.0, \"confidence\": 1.0, \"freshness\": 1.0}, \"matched_scope\": \"project\", \"evidence_ref\": null, \"explanation\": \"facets=0.00 lexical=1.00 semantic=0.00 confidence=1.00 freshness=1.00\"}, {\"memory\": {\"id\": \"mem_69fa680cbaa24929af00ec1ce0d5ce6d\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"episode\", \"subtype\": \"research_step\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": \"a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"task_id\": null, 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step: capture https://help.matchable.io/en/articles/23-how-to-use-credits\", \"content\": \"Captured https://help.matchable.io/en/articles/23-how-to-use-credits\", \"summary\": \"Captured https://help.matchable.io/en/articles/23-how-to-use-credits\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-24T14:20:37.351700+00:00\", \"updated_at\": \"2026-03-24T14:20:37.351700+00:00\", \"observed_at\": \"2026-03-24T14:20:37.351700+00:00\", \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"evidence_ref\": \"https://help.matchable.io/en/articles/23-how-to-use-credits\", \"metadata\": {\"step_kind\": \"capture\", \"step_status\": \"completed\"}, \"embedding\": [0.3915068530096012, 0.39591075461825814, 0.42673806587885665, 0.3580372007838085, 0.30474999131905967, 0.332934961614464, 0.29374023729741733, 0.29946530938867133]}, \"score\": 0.2220280319370561, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9801868795803741, \"confidence\": 0.75, \"freshness\": 0.75}, \"matched_scope\": \"global\", \"evidence_ref\": \"https://help.matchable.io/en/articles/23-how-to-use-credits\", \"explanation\": \"facets=0.00 lexical=0.00 semantic=0.98 confidence=0.75 freshness=0.75\"}, {\"memory\": {\"id\": \"legacy_source_fbe3fc0d-b05c-455e-9afe-1b17b4640d97_c979d84b00dff9b4\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"research_source\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": \"fbe3fc0d-b05c-455e-9afe-1b17b4640d97\", \"task_id\": null, \"url\": \"https://developer.apple.com/documentation/appkit/nswindow/collectionbehavior-swift.struct/canjoinallspaces\", \"domain\": \"developer.apple.com\", \"source_kind\": \"document\", \"title\": \"NSWindow.CollectionBehavior.canJoinAllSpaces\", \"content\": \"NSWindow.CollectionBehavior.canJoinAllSpaces\\nApple doc: ventana puede aparecer en todos los spaces\\nhttps://developer.apple.com/documentation/appkit/nswindow/collectionbehavior-swift.struct/canjoinallspaces\", \"summary\": \"Apple doc: ventana puede aparecer en todos los spaces\", \"confidence\": 0.75, \"freshness\": 0.7, \"created_at\": \"2026-03-13T14:01:39.658750+00:00\", \"updated_at\": \"2026-03-24T14:18:16.797831+00:00\", \"observed_at\": \"2026-03-13T14:01:39.658750+00:00\", \"source_ref\": \"personal-agent:run:fbe3fc0d-b05c-455e-9afe-1b17b4640d97\", \"evidence_ref\": \"https://developer.apple.com/documentation/appkit/nswindow/collectionbehavior-swift.struct/canjoinallspaces\", \"metadata\": {\"notes\": \"Apple doc: ventana puede aparecer en todos los spaces\", \"schema\": \"shared-agent-memory-v2\", \"subtype\": \"research_source\"}, \"embedding\": [0.41855866814615966, 0.40434189020712924, 0.3947246580719028, 0.3692180858871718, 0.3460809766922792, 0.3081695688548648, 0.2854506001875907, 0.2701187808415776]}, \"score\": 0.21992756140018246, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9795170760012166, \"confidence\": 0.75, \"freshness\": 0.7}, \"matched_scope\": \"global\", \"evidence_ref\": \"https://developer.apple.com/documentation/appkit/nswindow/collectionbehavior-swift.struct/canjoinallspaces\", \"explanation\": \"facets=0.00 lexical=0.00 semantic=0.98 confidence=0.75 freshness=0.70\"}, {\"memory\": {\"id\": \"legacy_source_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7_03ec74fd2f55f797\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"artifact\", \"subtype\": \"research_source\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": \"bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\", \"task_id\": null, \"url\": \"https://www.made-in-china.com/products-search/hot-china-products/Outdoor_Digital_Signage_Display.html\", \"domain\": \"www.made-in-china.com\", \"source_kind\": \"document\", \"title\": \"Made-in-China outdoor digital signage category\", \"content\": \"Made-in-China outdoor digital signage category\\nCategory page with 55/65 inch Android/IP55-IP65/high-brightness options including Aiyos\\nhttps://www.made-in-china.com/products-search/hot-china-products/Outdoor_Digital_Signage_Display.html\", \"summary\": \"Category page with 55/65 inch Android/IP55-IP65/high-brightness options including Aiyos\", \"confidence\": 0.75, \"freshness\": 0.7, \"created_at\": \"2026-03-13T14:01:39.653556+00:00\", \"updated_at\": \"2026-03-24T14:18:16.797758+00:00\", \"observed_at\": \"2026-03-13T14:01:39.653556+00:00\", \"source_ref\": \"personal-agent:run:bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\", \"evidence_ref\": \"https://www.made-in-china.com/products-search/hot-china-products/Outdoor_Digital_Signage_Display.html\", \"metadata\": {\"notes\": \"Category page with 55/65 inch Android/IP55-IP65/high-brightness options including Aiyos\", \"schema\": \"shared-agent-memory-v2\", \"subtype\": \"research_source\"}, \"embedding\": [0.4048702580351177, 0.3866738419436518, 0.41106209406624156, 0.3857892939392055, 0.3603901298115343, 0.30087268551236435, 0.2998617735072829, 0.24173433321509996]}, \"score\": 0.2197252304007872, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9781682026719146, \"confidence\": 0.75, \"freshness\": 0.7}, \"matched_scope\": \"global\", \"evidence_ref\": \"https://www.made-in-china.com/products-search/hot-china-products/Outdoor_Digital_Signage_Display.html\", \"explanation\": \"facets=0.00 lexical=0.00 semantic=0.98 confidence=0.75 freshness=0.70\"}]}"}],"isError":false,"timestamp":1776868529917}}
{"type":"message","id":"d41fcc13","parentId":"922bfcbc","timestamp":"2026-04-22T14:35:46.725Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Focusing on concise memory recall**\n\nI want to provide a concise answer for the user. It could be helpful to mention the exact memory ID since they've asked for a specific memory containing a string. I think it's best to stick to the relevant details and context without adding any unnecessary fluff. Keeping it straightforward will make it easier for the user to understand what I'm referring to!","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dcb51cb481909bc458c484a71b93\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NzCB1mz23GveZvfcrHYnalvIUWFIMYGCavMSdgGR6Hn6hdYxgJzjEuQXC0bDzCjfhotcGkvilPbhFIWEIboIw_L4Rh6Dlem3TJ7O18cHhlTscxlzUQMSPg_CA0nd0n3AgasOi8GINoQlbI6AR0mrOdrUNE7BszEefrolfiKRBeiHjK4JO_dFcgN_kmHSuiSbDHAMXgL1PEt758w16XZpOaqv_Nnru9blJqP3capxzO7bYE8rfhVzLBVDMsjnRTDzLCTC2CM9REL7ZYhmC89jSTBl6eHKaExAY91E0Q85FbVCdUdJyoiphIKZ8GnVIg7pnfokhVV6R1nyjQzEOthaDPTH7dQ7-w1t5SKk-a9mhNt8zyOa9lTIsRwZbbExJiYbYQ8kuqtklbuPuBHW6mIHmYqU9z-xX4fhcq_I8L494uKaHuQTDZnFjOGOBv0iBEdoyDM3SJuIVeiUPCfekKXekazJseaRcFn2lEQeohR4D8fffy35iSqjbqODLns0Oj8ghOGQ1ry6dNIwAg-pwg55i_bRvcZoTO9-dFN0RnLnjcFa-u-WEyYhMeqDBaSjDzPaWzfNO4LEpIWq-OaFF4zjfvjVFR8pBTzYAPHHePmIITjta5gbv4tCaD4sSPDSPa7YIT8ElgIrlvB0TbHQ4aCovwVo_G5JzOT-aFxepb7pZDOhHBBart4LuA87sjlUADOk4B0dd2smD1usQmoxmJUhPW1_uKHIwbQIZ4fOTsNEQBQhDHXnLoZ7-2L9DscBUKKN0LwSyCD0vI5QTH0LSCW8sRsSakKKJ7sEiEjEXCYjmLThSpLPh_ybjNrRRGtgapb3krogx_SvvQz3lwnT0pv3e5yqlITCUeeEn4FkZ352aQmFqC1F1dQoFDsL3ERiPKz6KNzNiHkV-JI6VVHUO6o7i-OManWVXa7wfIv5aI5Dnt1B_oImYUfaqlnBxVWuNy9gf2mk-7H3U_edWoxOZZi_SGjbJQ6d5gWBC7weL73J-jSPnvpjxBP9_tDgsUgFCXqjfIk1P1u49D5jRGI0vhvDSHThSbFPyb25a1pGSuCLY9gvdMH26kHwBjsJBNXwnjIeO4kWMnBcQ17EmKIvfRPLaMnoj8EIn3gUrgmMi9Yu3DnlHmgnTHEkeQJlNa5mT3Sz-PunYdNRmc70lnIChj02kxWOA==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Focusing on concise memory recall**\\n\\nI want to provide a concise answer for the user. It could be helpful to mention the exact memory ID since they've asked for a specific memory containing a string. I think it's best to stick to the relevant details and context without adding any unnecessary fluff. Keeping it straightforward will make it easier for the user to understand what I'm referring to!\"}]}"},{"type":"text","text":"Found one:\n\n- `mem_7e571be6dcee4a0b8d53c3cf27898c50`\n- title: `Ballbox payments validation handoff and open blockers`\n\nIt contains:\n`ORDTST01KPPFWZBJD0M1PACY3257J7KH`\n\nIf you want, I can pull the full handoff text next.","textSignature":"{\"v\":1,\"id\":\"msg_07b1b9c95ac99ec40169e8dcc23a6c81908c99e095ffd08f02\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":15842,"output":124,"cacheRead":65536,"cacheWrite":0,"totalTokens":81502,"cost":{"input":0.0118815,"output":0.000558,"cacheRead":0.0049152,"cacheWrite":0,"total":0.0173547}},"stopReason":"stop","timestamp":1776868529921,"responseId":"resp_07b1b9c95ac99ec40169e8dcb277808190b1a8cbe0294ca798"}}
{"type":"message","id":"1b4e58c1","parentId":"d41fcc13","timestamp":"2026-04-22T14:36:08.182Z","message":{"role":"user","content":[{"type":"text","text":"see? the id i passsed. search is broken"}],"timestamp":1776868568181}}
{"type":"message","id":"04016e89","parentId":"1b4e58c1","timestamp":"2026-04-22T14:36:15.631Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dcdb036c819089cc52cd9f132135\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NzfHHD7t-BTtSU_isfX45zZUncqkyIhdbAu2V8TUvmpHAFkoG78QZkDQsT5JbCrF41TBCJ8NOzZ52AJ55xO6EeWF1sXHhNLg196tLqMSIdEe-bjDEuRXxQOc3QHW2qnqRZDLqHf0q2t-vKJ8GxIWGgcZAH5IdFhho4Xw46sULpRK00SRdCH6rJV_1sesQTK3yH7eH5DIidJRvTggjzrd8MgOC7zrhfMRB1s68ZSql8oyAq3GVDdy-p8bSI8qTqRGt0h0QyhThYSU2GFCXPt3unozm4g0EP4nk5KBCdmVq9EJccsqtS5pYxeHjrYgJ3GOoTh4Dt5lnCzuYWklNiOAM_F2wBwZ8fmc1lYOEMtCM4qQKt7dT_ixNJxAipDpLByVXdaM4QXT-C3pDioiRetWTS0TLeOAEZFgZgyQaHqItxProFExvVZzZ-Pr-ME04-0JiCG3p-W-MPeLEBxcLgnH5hk_2kj9CU6OkAdYana1RAmNTuNoO6PqG063BTvfnM4E82s06giHVjTCqx2ldI65OIKFJlhsES27ytTHduJLSDUERVVzZVbE3Ag-5YdgSOOSW4aYDluPS0CUjP2sPRpz38uQG1oNOPFD7BH-xhZinQKbhsa0bIZ4M__3nPEl_TFhzP0XvIj7T_MlKncqV8NYJuloIKk-u7tQqYcu0xbZ4Ng7JACJm9HrxPUJXsOcObmTo3p3rHZyiqWJcswHD9-AMivbGstxgIY8hIPua0j8rolrB6lK8HWujIJ1HaO6f0elSjoQdVS0VV6VVZHdf9FXmQ88Plb15KaZ07GDZ0pnLGCtQn9pXP_PeumGn_LkeG7jXVQVhW3eGcGuzuJin1YskE9TPC1My1HREfn6478CN_w4tj1e83nOpUtHShXDQu9NpIinpvEJhpcR04RtFbb5A5rbW3-smRL5nTrivlzFNFWT6aeQed_cdHJ0WPrPJpGSBWhWwl2Qt9R23aEw5Pxw_N-c-32YACZKS4cfpK1lT6v2G9m7lQO8o8C4LF-WqZgMUEjBhor0Ye4ZXVu7LhJhcslbcQAKccwIqJWA2EqhVbgKnk3HAy2z2gOe8pbqFx7_azrR4fM1qyzAnb8UZn_DX7vqsM32WHFABk03KvnNhix8SWvVn68RJbA0RFn8qzfYy3vetHsoa8fh6g2qJd0F_307wkuWGLeStOsFK4JtUulpv5aEaGLJDA4CfP5-yK8zIxpDgq-nglaO4VEuEe1w_LwST0ySQVIdCVn57PpPgsTH5eF2jcqWtS5z0NvQ4gbnF_0aLBfjEkOVkInxuOQQjAhePLs0N28zcQnI7lsuFRxKRIeYmNs8SLSDM_G0SyEjeF7oJdwtYfULg00y_7dtC_rvKjgQ0QazN9_H_bVHDTing1X5smV9eQ9_jbtjjwvaxJ7ybD7GMlEBkjznTnC13xPI6ddMkke2SyeBAMW3H4l9ncMTtbFKwORovPdZgfLgSqkLs0OfYrP4-UuTw1JOMxaa3Y_fz_iuqAXz2Ktr5osaqdR5J40DmDATrZgByYbcGxnMRxGcrafuHOhPMJcml_1KnaM-qyb0d2LP_oBl2zja0pT7H8VkU8eFDxSujcp3dNABmMO4RBhAOCzZnsuWoZWJtrKW3Hh41yJbdp4eryucQz5ID28Ku2GOuDzmB9MvW_OpLfY6fJdc1ZsCmZfygDJDgxXaoeZfmPHgf266WR17b_paKPyrD6_jEf_7IxDXzwByCS-iGfZrOtu0bkr0XrBloB_Lq7yCoRMIJqOmp-GlTabVoKOhN_1TQ_a3-KsyVUW1__1BOjSJE3jt-WQTpbLmCsK5y7ElFNW6tDXxMTM1CDbCVbSfRdXPaM4Rn5d2Bxp0R8iv9C9ZKwdC8KgQO37rlaebh-OOTrKTOMRo8NRZcKu8z2e7H6BWVGbZqFXGHJni5Uuu-4PRQtiruL-jJNFIAX3pRPA4gMtDUaRlmhZ1lamrUXgseVA80S-cs6jYNVkLpeihSUgE3AwDSqi2w7jqKtOh6wNooD1xniOWJ3B8pRC5PgSVGH2-XB-n1tK6Hxpcxed_vGjZE-lkoLZPVAwqPqN5WjYqEqDQpaAAlZG1iN42Il1GW9kXqnXxGU6EKZw_8aMUpy1_rTiXuTZ25nsvrsk25ffdxS9PLbQ5q3f89_Wmrke6erjOWjJuraeXpRTqup6CVq3L6GVD_mWJeSOA4chj961kGDXcxgS7cTJt7_mWxWTXIneU1qQfE5kqV24fq-mre0yniqRgWqcSySoMqedYunNoLgbeSCVLsa7X7aAe2WLJOspa7PuuOPWJbg7tyuSQ6ms0dGgbg7X_CXpqPpxVAmPp-jm_pP1za0R1hyi487g2GKuBure3t7kakQlSVc4GB-N4Mf7GV9x-g3U_o1VEglqkCFJIbzCUXGk5CXGT67dikTwTBxAXrhjdvsCOkl1Sl8wxKSxZuDXSGz3V3BcLZpdPn5gwmgPJu4AEQpeD-_iTb3fdCgSczGBEzFG0jj-NxxoRtsmX8AwjmAo7yTIHSMhdk5tE1iHgs8I_SY89IQRa1wDVzGV2QpKjELTMKL2tCU0mtB5VBd6tx5zsbmUgqpMLs5nZwkggMTMidUChrEt4uAStpgrMlKNIyr9kaZj4yKSxHzAiAHK5-b-Ay1u4FOHK00zof896cJcSa2bqx0N1go4zDEIMsyAWRtiF79cTH84_AZt1ZsTlPdFhExnSBvgnWUSeOOIiDDpxakpaYxLu5HTOe4YvSET0C3Q6miYz8UucxS_2XWuIPY4f65JtjBMlLWzrtPUtfvdpDK2XESV2gy4u8AEj9yK3t4N9jSYbqQcJiPLRL3v5FngHfD0P-VN5jF6KvUMvsLJ_WMlbDw87dSkGDQ3_d27Rhv2hnZTMj9zgu5mztwtKkRau6Q0rnXp-tuPVTUFa_D3gunMYHQyaScWO5OVNGvYjQKXPq0s4F_vO9iHv4Igt5eOopy7TyOQ6EJN9R2srQFUMsDqNJdZasvZ7LGeqobL5AyXYgIl2LqENVBQ8-h6bY2mXC5agrI_H2X-Ryz1bBUnzV1wlW5etZEW-q8l1-knbj6IKRmC4ZUy92ujNCH6IZu8oA7GGGrtt5QfOTHiKd5hxl5eTNsMMpenWk-XXGWDSifwMDZczokUtAHRFLoQKf-tiZaZ6iw5d9cx6isSHsD58v_AhrIR9FABC5ONHcyDABFA5wYhygam1CTuVAHGfd11vdKJ0dO4iUMwXv8D0OWoLBEAie3lmSRwmIClQqj1slnrJOZNVU1PrsJ04Huc4Ymtp7Qb-jw400cO0BHm8-OYE6iRAd3gDYU6bGWL1eRRAdLtD9VD4bDc55qFy3Y8T-La-1b4sr0U0QSowpcLInU2Kf0hk8jJNCH8xsAz6cgMrCanXoxRbbAyJrxdncCEojENRw==\",\"summary\":[]}"},{"type":"text","text":"Correct. The exact id you gave is real:\n\n- `mem_7e571be6dcee4a0b8d53c3cf27898c50`\n\nMy earlier lookup was wrong.  \n`/api/context` is not reliable for exact-id fetch here; `/api/search` found the correct record.\n\nSo the bug was my lookup path, not the memory.","textSignature":"{\"v\":1,\"id\":\"msg_07b1b9c95ac99ec40169e8dcdf30fc8190bcd7fd642d14bf67\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":15901,"output":471,"cacheRead":65536,"cacheWrite":0,"totalTokens":81908,"cost":{"input":0.01192575,"output":0.0021195,"cacheRead":0.0049152,"cacheWrite":0,"total":0.01896045}},"stopReason":"stop","timestamp":1776868568183,"responseId":"resp_07b1b9c95ac99ec40169e8dcd8d5d48190befa91acbe18d443"}}
{"type":"message","id":"5ceaaf01","parentId":"04016e89","timestamp":"2026-04-22T14:36:39.964Z","message":{"role":"user","content":[{"type":"text","text":"prove it works"}],"timestamp":1776868599963}}
{"type":"message","id":"743309a9","parentId":"5ceaaf01","timestamp":"2026-04-22T14:36:47.081Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dcfa4f308190b226419c062382c0\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6Nz-t6gLeXa6mx1lA2nquJUsBHskJ1gq4MkCSbVsKBPB23VgCnHGAkMNoft5WXZZb372-zuhgqZCZoaILo_dm7bTt5qoypGan2tQ-wCfuknAm6RJqJJjxQCWX19bEgDnxnzhA_bCXWBpAPLURwH6fl2N4r-S2RL_urNZ6OPL26LsZyTwzQPG1s3-79QPHgTR1hHdv1qEomr3P1V71FiWdHvGQ-TEHNNXwVlCijNngmZlnAMQwOKLW-gBVGDnP7sVBH_NZyVyrM0_2fdimmQJ9G-sNmzsjNj9IXSjMd_gLX0pMeBqjHWMkaf5Z_s2WsM7uhbPex3NDmjPZIF_jgHAk7eO2GybPZqq0HQZ4DAunwrzo_ofGX5-vwRIhQi1WfVEMT0DNKLW54Aufltc-WYrVbqHj6LCAfJQRwJn2LuMHTYNSxxazBJHQ0866wXksXKC1ItruUAsJLrAS0Qyv7ix22IIfz3f6uIqiABBiu38ZXeieNq_gvKTIWXD1JBXQizOgQMu5JTT_8eBZ0w0n1eDv67UZJUT6v4RPeJ3GUKj1cMdc5cCfgt-ZTg2pFcpx-GHExNcCOOgDl6ot1rzKbdjo4p9RStRsXUFviDUC-B5ouOtQ5m3mRaKxGOZYlLccpxRq9hAYA2ZuDzRRrC3TsS9782rZrsIUiCiGxCYePDnjzqAHDPzNrkA7Vk_CSViF-j722LV3dMoQw7WX06_I1zGmBO1-GIiHW0pMCXkpGjawy2oujTlpgyEo2kbvtrvfDn7--eC5i6jbrB3q8hxQe8vCTNShAZPW5SrWkxicJUbb2oxQOYdYfUFSxBGrzgpfEWg08ZrrLot7SpY5KfdYNZGjxuqzcNcFNn_uEFI2CXVewnj9gneCR9eOkh0ZupQeLBoWCZK4gWD-X_r6Aj_fyneh4QGB4NsR8GuXds0uACrdFlwZJt5AMvaGq3ZbF-VZZ_O35WmbxalZP3W3bVXGfdSsuN8Cd70FKVhRYwT5GPgpI9l_mtNCPnkKQNRoRlbPF2ni9VDCG1_Mz15AF8_n0MjIxcG2DbyIQa2-yPNh3jxuDqwNupgP_dzKR6Fqri5pAz7GnGUFie0GVvcnda2Ee6J_HcWux34yCpTTSVYG5sazM8WD9_qlcy9eLR3BbuF2G7hZ2b30SjW1DHdYqXJU3opLbR6as2E0Z6rz_866xLJD3SsdALb6kkzuwm0H_N9Gvb588ErdilIQdBhCSk7S05YAFtcN7CSWnGa9mj0VdJQcq6FvMD83DqhkyX0BqH9XWvfqRZpq-CiWdP8rSYhCsFgP5PNcDBgw3AKSqD2rTWJwKV3YTIge5ycDzIS9nCSGSpfcq7OKebuoPQIHb4a1U_YZcn3VbvD1NAcbkIpFh04ca-vuauI8VmNVdZN75o4WaYfj7Wt48McstTLWCXyV8KVwfpr8kimzsba_twXxjF7QWM5mtImqGpn-Ti_THrQ-d8PIWaYhHfUTQ2UtU_cdduIit-uDcnFOvmtNsF64W-eaosDXP8Rjh0dzs96Mow-rPrM33rs717OJHCIOC1rUtN71dc_272OjNeqPN2Elcb64rX9xvh75R3Gms-bM5VWjKmDhCppbIcLjpgIqwQKL6fZGZ-tJumPPNFPj5Ah11d4A06r9iFzwsF_CD7sFIONPEbYmy7D61_kbR4zS744a18U_bKu9izfhCKhHZMJAbkZllTlIzrIgN99g-kS0DUuFM1WTXOtK9w4Kc7doKPfBp0mJmJtIBpjluKxOUtK4BKN5XK-_wsES0xC4l3wzmpyUQZgPtDRCCT5UtKBGW2pmKFEqzvegW06MegvCAaLuTEcSR1rsG8_28Pi2pP1K9vtcnbufBV5x8mU8VHjwySjVR7kUCyQXSvXsglVbd2iZt_u0g1ed0Zwp_dZ2qpjIMc7qThSjjxCpw8AM5ZU9fOrElFOC0Q35-ZuMrM7tiuEAEcRpjnV6gU=\",\"summary\":[]}"},{"type":"toolCall","id":"call_tudauqexFLSyC29HO4NLlz6Z|fc_07b1b9c95ac99ec40169e8dcfe2b9081908fc6c90cd6ba0262","name":"bash","arguments":{"command":"python - <<'PY'\nimport json, urllib.request\nurl='http://100.116.176.16:8091/api/search?q=mem_7e571be6dcee4a0b8d53c3cf27898c50&scope=global&scope=project&limit=5'\nobj=json.load(urllib.request.urlopen(url))\nres=obj['results'][0]['memory'] if obj.get('results') else None\nprint('retrieval_id=', obj.get('retrieval_id'))\nprint('top_id=', res.get('id') if res else None)\nprint('top_title=', res.get('title') if res else None)\nprint('contains_id=', 'mem_7e571be6dcee4a0b8d53c3cf27898c50' in json.dumps(obj))\nPY","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":632,"output":351,"cacheRead":80896,"cacheWrite":0,"totalTokens":81879,"cost":{"input":0.00047400000000000003,"output":0.0015795,"cacheRead":0.0060672,"cacheWrite":0,"total":0.0081207}},"stopReason":"toolUse","timestamp":1776868599964,"responseId":"resp_07b1b9c95ac99ec40169e8dcf8c4088190a810322e08935f1c"}}
{"type":"message","id":"d33ada8c","parentId":"743309a9","timestamp":"2026-04-22T14:36:47.318Z","message":{"role":"toolResult","toolCallId":"call_tudauqexFLSyC29HO4NLlz6Z|fc_07b1b9c95ac99ec40169e8dcfe2b9081908fc6c90cd6ba0262","toolName":"bash","content":[{"type":"text","text":"retrieval_id= ret_e8a49543b5ea4961894728edb9af3807\ntop_id= mem_00043e2e27144c63a0b822c509ea5b9a\ntop_title= Research step: capture https://help.matchable.io/en/articles/23-how-to-use-credits\ncontains_id= False\n"}],"isError":false,"timestamp":1776868607318}}
{"type":"message","id":"8049f397","parentId":"d33ada8c","timestamp":"2026-04-22T14:36:53.960Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd0169f88190896a82a70d5983c8\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N0D0htYeQBwNN1p-GG0SlBVhjczCtOgs3c9bdHZJ9nTMYIPK1dNtdnJo9FHqWk1Qo4YsILPn07phw6AzLNPrwPXIc7fePOuWdZtwQ1txKfMrWDoVpf7NwX5zYAurk_RKKi0-4ADQ1n76nHJQuRIrMaaoIN8vrGPgsHbAqoX25ocNrD2-52HALiR-ID9_CXV-SVDMckRlY8ZrRHn5ALAgr78hTd66OrbYm-RTr3U-2rJDgGW-3qzp_2vuz5Yt5zoU6QkVvfJLXrwiUTSaE8ougYfTFpwx4LvCrfiM0uSomA-xXLsvIYn9ga76hvJUu89hqM-B8P7gbHakzqu1GJ5Dr-MBeYoXYXLLNv3GNREKAqJ_T0gY0YJJmL6i8_lHoKnmJ6Q9ZVkVpZHMFu0n9V_dGrnvBnFKBo2bVy_AI1OVmgnkp7D4mPMqJtOUPas3XeIVhsl6XJ1nBMr9MJ9BScMCg8sLgQqNkT7q9CzlixI-hXy3brw59e2L0xXpzwx7I3Z53G71-OZ8S4MWckxY1wxVDexhE2S-4asazhRCPgL818Iqyrl9OsTaOiSHDFM9uZVTn_Ft7pL9HsGdmyV9PAkzvfzgQvXDavcGkBhvH-jPga0vGWp-wD7FN1cz-Wwo7n3UsjXplV6Sb67wbGen_mYgOQB2eaB9-ckUKbK-bQdD9cwIj3u0PXSTaHhEuGnXXTUTHjP2zTm9y3G5hhnoknEwQoPEaciORDcF4ybpp7v5z1Gd3IK1K4SVLdmEaNOUremeuKcvS-N2H_TEz9rhDFcNG5_8Ots9wFomx5jxDxTT0NG-nzr_tQFdSSG_HB-sx8p7KyBUHL-nUhrMhVaX1ciZAPkYH6wTAsZQf-FmALJ2Rlzqpmr4qTDk2NVOO8-ZSpC3cICWI-ONAIpcqLaPw65Nt8JMw62IUEa5QQ6wBkCpc5lmfbWm0xmJOnObUJj6ZGVQoC5uqWwKomD-j4r3NozwI3k9tV-nICy1WK7UUarLvUekiUbiv0ixMDBQZBhzP9qS6qeF6MbMhDtdxz9xzA5I7SR8zGaiWQXPnTK_l8lmDqokgSe_N29g8RQppQdrTFk_aynIZnBNPDujWoYaUQyD7n79nsYU2Wftpkuz9l0I2UWYiYvslsdltKBcMC34FEMZ1mIBVsYKoPmu6F7PfLSZeDK5XSQeP_YomVK4fEqWxHD-K33a5s3kKb5avx2T-RtyI951iRKHkRop85aiYEj23yhox-NltKPiRhPLRqtnz30zOpKqHD4cAZofNcJ9esfP0MQs6ivtQmmIzOE5WX4eiZhynseqhFAlgPMYdxcF2Qfcgc9iqxiQwRrf5ARre0L2IZ8hJqbHd8VodjgTg4DSOuj3f49-GLA3cptaYBxspi9lxxIGCj66xFDPZXMfAUuktgObUakry_Y1RudhZkWXLdnb2Ac_ZIoy7IC_z9hSfiwpIL91dQgZ_OqDKYU6qbrnmlg65cYLtpxjhaFPCzKbbRfsZJDGvMTzAnpseKsch6VGP27UMGXdpX9GYdnSx_Utv-IvLdHQNceenhCz3A_89npEfw4DGShrs9HvnXTThAzRvkVQM_kUSygw8J5UDGzg0Kba9Fj6r8i1kwtj8pmNPNgKJqqdIlDhkKFpC2WHrlaN6cqtVHwNUHKzF_SH0j1jZ2b6_K5NwoO3Xwmdceu-LGbHIh4Aq0UsaJS4bFlLMULgzXj0dC62rOAr9uGMz10kXnn-l5hTqQAfQ78V6metC60isFBCccfqFp22nQv2yJ0S_cVajiMaL0E0U4_35TNd2adTeaZ_TESVQ2omQHxC0NRSab7w8odUCxTP8DB5xC0roJ56cbLnH3N3w9otF3GdIS9ODz8LXLRk49axcGcSgLEV5KaCHuN8KzjpnMDW3qY_e-OMem-3kbAPH1pLPtrHmPlgbwCSE2xkclHNgE53UPPAo3u5871F7fB6LsQxy-7uOxgvBuhxmlR9SRmz64wxDocRuu8D0Jc_-cllQ0JRESP7m07hnK-jDB_FAJKnPNrzIRJFOc4RFqGXkWOiS9FNYgmW8pYIMnUMXGuErkQPwbpIe39rTRldID4T-z-n6I2vNjvQR45Bdr4hQmT2j8zA7rmhS7cesHUfYMgySmsVXypwDFf5suxtKi24AASUcl5atwl-GyXlmS5sHgFFcnA_M3wYqhMA-4J-eHj2F25n-DLGg==\",\"summary\":[]}"},{"type":"toolCall","id":"call_0IFSS9TmAHFA0e9qnA5Wi2Wj|fc_07b1b9c95ac99ec40169e8dd032ddc8190b82c03b1598e871f","name":"bash","arguments":{"command":"python - <<'PY'\nimport json, urllib.request\nurl='http://100.116.176.16:8091/api/context?id=mem_7e571be6dcee4a0b8d53c3cf27898c50'\ntext=urllib.request.urlopen(url).read().decode()\nprint(text[:1000])\nprint('...')\nprint('contains ORDTST=', 'ORDTST01KPPFWZBJD0M1PACY3257J7KH' in text)\nprint('contains mem id=', 'mem_7e571be6dcee4a0b8d53c3cf27898c50' in text)\nPY","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":560,"output":377,"cacheRead":81408,"cacheWrite":0,"totalTokens":82345,"cost":{"input":0.00042,"output":0.0016965,"cacheRead":0.0061056,"cacheWrite":0,"total":0.0082221}},"stopReason":"toolUse","timestamp":1776868607319,"responseId":"resp_07b1b9c95ac99ec40169e8dcfff2a48190b8139c5ab937d52a"}}
{"type":"message","id":"58135341","parentId":"8049f397","timestamp":"2026-04-22T14:36:54.085Z","message":{"role":"toolResult","toolCallId":"call_0IFSS9TmAHFA0e9qnA5Wi2Wj|fc_07b1b9c95ac99ec40169e8dd032ddc8190b82c03b1598e871f","toolName":"bash","content":[{"type":"text","text":"{\"profile_facts\": [{\"id\": \"mem_83508f31b14b490493606a5f9e2223b3\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"profile\", \"subtype\": null, \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"personal-agent\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Prefers English for LLM-facing context\", \"content\": \"Sebas wants AGENTS and other LLM-facing context/docs in English to improve token efficiency and performance. Keep repo instructions and relevant prompts in English when practical.\", \"summary\": \"Sebas wants AGENTS and other LLM-facing context/docs in English to improve token efficiency and performance. Keep repo instructions and relevant prompts in English when practical.\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T14:01:13.831140+00:00\", \"updated_at\": \"2026-04-22T14:01:13.831140+00:00\", \"observed_at\": \"2026-04-22T14:01:13.831140+00:00\", \"source_ref\"\n...\ncontains ORDTST= True\ncontains mem id= False\n"}],"isError":false,"timestamp":1776868614085}}
{"type":"message","id":"e2635694","parentId":"58135341","timestamp":"2026-04-22T14:37:05.152Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd0761fc8190904e1b24b3c2671e\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N0PIcXoff4E1EZlrqXkFB11oj-I-QvCxWMWEXyYpuI9jM3h4S2cd181P7dLrteK0Uz2WXMzM9QKNhDrLzvRFBJW9Jpm9dUd6sDakg5AJJS4gJgFH7lGJAcbt1mtG444IZbPDkji4ZIyG8TG2SGXLLfMMy1kpgMVlbZYVB_QAJkA607Aybx8ev_J1Ng3T3xd9fpy_YfC0YOdYQDHn6inY9pAjUfSv1I1ao1ebC7yfEsDGQt6caNy9ScJRUPWFLRFUOgXvQqQHSiFu75NuqRDa-ieI9mPnqmrsvMYvrVaUfz2abvD_dNBFBobTC6lauH9CaFxv64xhaxHyMQ8bxbwzLrj0s9IS8lOGYZgIQ8HAD0Xg6jRSbN1tQJRvv42x6flLB2SLKWW4hWNCU5criXcRvExuFBN_TQXq816Mw_0wMei3VS6elTMfc_n9nBReefR7TICcOH2X__Yu0-m3Vwrv7SH-afNHAN8sgd7RT4K_IJkPKjGjXhGO1YMO8P8Q-5KGvWXOBk7IE4u4Ga0pDSA7EJguQJu4SWNP2_2xVjhqc8hELDrDhxGGmviOaRGOSpeS35W-qX9oL6oOoknzxGUV1JpRDJQgnB-vvCvAoZ0CcG_01dVnoNrEkdHARXJDbJwj82UIPQW70rFXqCw58i0Hmv2CxuATYSXmXA8Ne67lF-_50BBUI2qEaazpH42AO9jE9DVDfs3HGuL9umgPulwk7SvciQ5WvvcK7-5mkTUBWZafOHqjG3X8hCvot02k0v8Wq_gEnRHILYU8CxRWIJihXD7zkDY0dojaKnB5HowF7A1r1C-IbJ4cnX8-ED3v3iATJu1OsuuRniN06km8mQNp6y3EfkCM17mbsMrGjXcaiyCBLGgnlbbA9gry_8Rf9Yi_VSDQX_4H-r5-OIFcPo2pOWjTe618iD3OeEgTS98ExTL8_qpSoHBUxR-QeGth-3zc-SQABEq_YToYESSACqsMhW3tOonFPrnkT7f9cf82KMR8XP9D7C2zbtws9SwY6un-pe8vMUjcS18t4uN2CwHNRvmYkLm51KC5EtWef3X49ONBXRKwNUPI7mp69YuWgh5K3u6hvB2sZhKsWr4fLMTQKe6m56CBL1fgS525Ydh2o42Shpu5z-MQGwQe4yZXzfjMGKPOV0ZHj9jFNqnO9OlN8kr0mOfVsQadRVj6ogQv6H_yjeiiRd_2olVhU5Qu0WEjQ7ahuyh8tfDkLiM5w4v8GA8FXXjFqgrC7zVVL2sha7xOAlBweJx9W-oH3yESs2udySaWADDPYrrQ5nCGBZIcPe_LNfdK73ZsFfqkQZozDlGSSXvAveW3pBRTRqPu-SkfKAFS22xfp8Oo2QoDyB2uX05TD0QGT75hsK0eOY8vW-aCFYKV09uL_TmppUeJmR46ACTdmaIBGNuL2rbAfln7AXDh4z5UfFy1W5MgbaN-e4AIL4y4T58lEZ9_AmcKJ-q-yEctRs69MoHGLwM-ANbjHLLFzPv0XC9uvsOTXV5OoROw3sbEJbRsG7SjL3eA0Twizum7lx6L137rvejZu6vj8FSD5pHaCU--Vf0DeWApBlp8H1LOHwWa9ezm_oUIap3wD8WUzeIbl6L4COYZRqzF4A4YeZHYhqQqO-bX4nWPU9Oxdl9Rnb6-N8BVlyihRntuo6-HThzlNwufLTxz5DdFtNB8-Cit4nQVtEILlk2FLFQkHA9taZlf6v8KR7lTkwSVTirM-RnoplKbch0yMnYIh84T2YqD66HOslGt1-jJPDkEyqabOeea8zHvTkO1AlY61VlLokXhtUxVnmEqvh64AwEgpvYyrT4OMGIp71tnE4ik-txuJyg76NmnsnKUdGTD64FnLi6ZFae31teHdE030Z1JRzR-rIrRG-G7eafvmoywz1jkZgYZiKr_mDdOzY1AhMiOqnmZsq6zUBdaNTMjGjarhHw3kkKVyeOVh0J9SAtcDGMCxZ3yKFm-Bj8Hs0lxxaS5bH3zZqEJdpDJHedPo0vcpMKYSTof-IDvZTOTgCTmud2RHElXwhWC7ueGNFykFCoQ1J6fdBKz4vfzomzATDjUnUWfUiY8ZtuejDOBtLjdV40tgMu5ttehhihnrsdeYMphH5s2J2dCj_f9a587mIz2cnO97MNG32BWa5SbskPp_C-cSkYxN_rUw7FCPVUmODVMfgVeTd7mWuQrY38T1T3MXPnMLW-KTsKMEAJDKRM2gbCdf2I4wQu684Dh_gsmbCRge51PjBipIgbr9bBmWy24GbffQv6oUB2NetsX4fw6wxvR9xOzkTJb_CrGgdBwVUt_ocOi8EeO8aja3kUpUR9J3Hs6yTClG7AqBC4rc0zSfFH-W7RLxhjiUXiCi9aBfL1wGJjQHh4NhgwSoHEitYxN3FBlPiSOr61UJD1xodfKGdDksv3v3kO_SMQfMlV5GHy2xqxERmkNexug9j8tjtS7NyU5Yy6ggQomBpG4xDMgzvlcAzuoX4YaZblccit0C6IPQRoUHs3rU5dwrCx3nfK9YAnvsu66gfsbCoQB4qJeggHwM4rt3ZjTZ2KZwN_PGPAtOrbxuloO0TsuFIkm0kjeT7Ekj5QtHEETOoFbhbzeJkHI-EJKDQlSAoyxSFyK62ez12lGq2l6zVcmeZFoA4PSVwODh90ElpwkeghpPQtKut-741yv7xyh0IEwvORGaPAPcPqG_ucNm9hXlEnHn41k2FK5NIY-4o-J5ubkl2dDGmFYamsjx9v5znTTjOAR39V6Ibyg5BCTNozXcJpHofjiat0IWsXGYsiuQzPCI0ePIlvre02DSa5zCPuVdVOmyeBGEYZYbwbT5_J8GV4Ru4uUg5_3wb432odogt3jX3_uE1PhlJ-cOMbVMOSuFruTkv6lG3Rn0TWnYgsl7yG3ddVlZTNriHkMhe0UpYCkkmEuKnkELaFFkssgFtAH2jz_xLK1FZPq00-TXri9RNmlK3m9smh3sm3V-GRhvGgE-w0gOJm7mUBVoR1pv5LzWn9n7hFUHy--eJN5hIKcw5hgj9ZJLjUhsd5n20MNoAxr0NwsjcTJz5sbuUKlXE-at6JIzE04nXllkUNM_vohlxTp69SAfuFdXo1dwtF6GY_y_MIdV-TuBU6lVlZm92JCaFmIfRfPIY_w-MVjMrRRS1VVVGDXHbVZIWBv5WAW0V9sH3XdbchIsvblzkV0obYovoKz26M3nxSS6Sf2hsM7Yh30n-T1bo7L93v5BSevcIadYw7p453u0MV9j3ElIJgGy17sr_Gp7Ld0cININXeD8X5HZytS_40QZDTS9jg5jRMIDWX75F6fAFzI7OlJ16bJHasAnH8YSn_QMl8YEuaISZw_Da2m0ivK-s12J1VYelPRb5gi5jodS-ZDv-RVW_wvnaZfClnZ-zB6xfOBrhU2hRknskd0n3v6dOwJAT8BwBxLEAniyNY3QmIGkvS07YpS5A12PEWFtNpbDQHLjoZLgqe0IgpeaT8Mg0MtPjIv2WmjE4TGFzuVR7ZMNqBud5onL2ihSUnPqetvH_JoJkJKdBO3ii68S3ALyL-ye93ZNaJnjCWzZ1CCYtN1V6Qxjqc9e7sx2Ri1Z6VlMqL96M6lE2US9O6vbmr85sqAH--7pYuI78ydP1CsXfsNKlDfh8tpCKqVyG8RpmCh5wjcdACO9ctiEtBnqbbF3i5i7SM7tR37BBZl5S3t_yEktscvw6BNFu5SYg5p_OneujY3zGWDQJTfXOhAn6r0bzodfkzUXonHpiCPuxy7-0aJXKeTReOqkXFplTCRBR4vmLagZY4Z-2QHZc1KiSsGIWmZfiyx6N9IXr6AjdkNb8pkMFXe-DnhfjyDQDQkQFJCWVQmKqUp25BIe_daPixxhhKn3StV1BVnjUsrNU2s5mA7DFhvtvMiDxHlVBJaN_Xqpfv45AAPr1FRHa20XvzYHq_-ixL_77cnELmIDE37fQ8Gnwt_4QtdOXgqjjouYUi3izkgpodrtwsXM-lGUDaPe4_DqUVM3oz4Hw3YFWJCbUxAup__fm-ZMw8G8D70gCoyWq21SG2YLmxaPVIXejNGs3wjg5uzgU27BnmeVLrbZ9axslUejuYSh4sSJEtunM8TyxI1XY1jTpfdoblIQ==\",\"summary\":[]}"},{"type":"toolCall","id":"call_O3T2rKBKeHENF1PgSDs5MdaM|fc_07b1b9c95ac99ec40169e8dd0f00488190aacbc92744e15da6","name":"bash","arguments":{"command":"python - <<'PY'\nimport json, urllib.request\nurl='http://100.116.176.16:8091/api/context?id=mem_7e571be6dcee4a0b8d53c3cf27898c50'\nobj=json.load(urllib.request.urlopen(url))\nprint(type(obj).__name__)\nprint(obj.keys())\n# search anywhere in obj for target id/title if nested\ns=json.dumps(obj)\nprint('has_target_id', 'mem_7e571be6dcee4a0b8d53c3cf27898c50' in s)\nprint('has_ordtst', 'ORDTST01KPPFWZBJD0M1PACY3257J7KH' in s)\n# print any nested memory with target id if present\nfor k,v in obj.items():\n    if isinstance(v, list):\n        for item in v:\n            if isinstance(item, dict) and item.get('id')=='mem_7e571be6dcee4a0b8d53c3cf27898c50':\n                print('FOUND', item.get('title'))\n                print(item.get('content'))\n                raise SystemExit\nprint('not directly in top-level lists')\nPY","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":753,"output":808,"cacheRead":81920,"cacheWrite":0,"totalTokens":83481,"cost":{"input":0.00056475,"output":0.003636,"cacheRead":0.006144,"cacheWrite":0,"total":0.01034475}},"stopReason":"toolUse","timestamp":1776868614086,"responseId":"resp_07b1b9c95ac99ec40169e8dd06d37881909ddb70ac07ccc6db"}}
{"type":"message","id":"bb41fe72","parentId":"e2635694","timestamp":"2026-04-22T14:37:05.257Z","message":{"role":"toolResult","toolCallId":"call_O3T2rKBKeHENF1PgSDs5MdaM|fc_07b1b9c95ac99ec40169e8dd0f00488190aacbc92744e15da6","toolName":"bash","content":[{"type":"text","text":"dict\ndict_keys(['profile_facts', 'project_memories', 'active_decisions', 'recent_episodes', 'task_relevant_artifacts', 'citations', 'agent_id'])\nhas_target_id False\nhas_ordtst True\nnot directly in top-level lists\n"}],"isError":false,"timestamp":1776868625257}}
{"type":"message","id":"21707d18","parentId":"bb41fe72","timestamp":"2026-04-22T14:37:10.549Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd12cf20819084392a948e97f2bc\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N0WcejhNFlsmf1N1L-t0OiVV-QqglBKTMLDENvJfmKWgod2h1e2kd7j9QtbQaeMyzsen7lNJQ-mijhAiu8c2jgvFCpvlNk8sJV0a-fQB3hKZP8ZOpVal6s9pQC88ji1c1rFTj136ctDtyposPnVmYEIX5wOMwCzNTrjSATTYgUQK4Eh3kgWkuZygfwJUbMN6pu2o-QjD_FfbaGQLEMAU_NFDzW-PnpZctbVQCXYBBC9443D5KBstjj4Yn6BeHKuvA20elMeMHOf3oLkT8fXSprDKUO5lnzoVrB0D7aQib7YgZy00XqEvW2IGQoR4-olOPLTeEhSDag0Lfy3V9m2mbq-2xHWPa_FICoB1JiXN2gc7gSEwdjV5MTfXQZWqEYA8Qn05CedbBDVUNjiMzSwRuWYEVIFLOvUX3QZRnjO9F3fCIQbO1nuCEEjRJNCbcd6ccUPFtmt2K04tn-WBuXPfd33RnkuexwdPubgWsPecxVl1WVd9SaPCc7fSHccDRzVJa92plHUKL4EnA8BGBvsgszrovV91Uu7T5omQsoStFlfsU5TcokMTh0SrTR8gDKzr-YoLEPSi8-DwZ4VNAQbeawiTByIVecl1tvdj_6l6ib-Bmc2ZaMXM5mWMyUfVxfSY8AgJYl3QQMvkXj1sphVUbWBJIiA67DCbyQ4bdOzysxDpyyHZaOujiAZpV9YAvKTysDgxnpPXKEzQ_I4siao-5q1XaMDGobspTTuHIcxA0gbSTZryzlFFnNg2v2G_c_8aTEPj2hIrPJ6oFVlRsAUzu_HRQO_nja-A0svuWTZUiCu8Vz3AT4ceRY8ZZvGjjk70xZj6xOwXIoz_xSTgagIWlKEiE82Y3gHI9FGxLDnkudBV_b92QglnjVL-vjKdRN5STLEr-PHvoYkPEXr-7dWRx_rBfGVhXPZGqg9s-zf7dvVGT7ooA7fNNv95sYfFtxlQLZzUq5C5ecu7mS-bZQNq9DeRlgNkn0--d9X30AFO5SlxLcyaTpIw_Zh0b6hfpEdERMUmOYhso6RX0OckN-7Up8Qbc9bsYZ8zgShxmYAMMX9dt9tx5kf51yMrvkw2iAUDJBwjDl_28xIO0FJPITcf7oUhKPi3k1UwcbD3p7V9qB6-G3yT81WYOTTJ4ht0_Jw_YqFpMgLtws5o_YrWM9KD4L-OTkQ2mYyBghBdrJ-KvXLumNTTMf1JFKDeAHLO9ivSYNOOh5IA4BAx4n2FlbKcTKkFwzkMVaWdvwtDb2S_etCq04j5C5aeWJdaWr8NyOvH4TX5lrYbiftE7bF7IYrR4ZxxMPNAdyn_ycz7FbowYD5OivN_rdNI2DS0aKvH6VMPJ8HdLOkx1QCNY0aKbKqtC0Wqqj9sbTAOfLVz1tr9SH0HDXsD1jlGPsVeVc__dQ9zXjUk5oJjweWwnRdnonZCly-mWm-fRN5s-K5DVBDyYlpR-34ZS3OsRvP7B1IOgM8SmMXjH-5J9WHQOk2jLnVqJNW56nBuL2O3YvqeU3bX_4O5yyk-FLL4qeekMKqHlZ9YrFsxpvapg3k8Iz47ZdKlYg0qq0e21QHa-nxMl7M75uy7xPZOL71dpDNGFLeeu-Ky0-j3uu_seTn9VHYpzlBvwm2lqSs-PkKzqqv5Ml220MCgHzadXF9mlO7NWeCs4ZwKMX4c-bDgcDHXiwPrZsTsO3863_XfiW2gOsGkeMCa3uPSp6bm8NENrENYxYJCTSzofOmTW0YOZqM4A3Hf7RiQWqW3p8N4vUVhFTOahgLwy1bhtvC6ZCobcdboXv-2rAz1i4HuyDt65aMxXv5zNs1_5lciettxOU58E85qDjZHcaqFW8TcBUW-D7JKtPaaXTZeLDnHIsuvhR5EPD4pzgCkVnF-dU2tikd4B-bPVm9-eN9KqonvnR9tq9qL55Fr-xDdF7UOikSe3dMxAeXqHfM_qXJlAHVrt0xaif7AcGUWa71_urvLF82UzN_qr6BDh0Zt0soaNAUnFl2mu4NFCRYTnNt_m-digWr4Pu95pdm5fCFiIrlmKCSkSmqglBadtleDMewEyfJWAp0oUdXehg6AXQkAOoZdl6klul1BYTsnAVaMsG4r2Sax5HgN4qJ4TGNBtbv\",\"summary\":[]}"},{"type":"toolCall","id":"call_4gKxJVm7TTcAsgEJkib7Iz0j|fc_07b1b9c95ac99ec40169e8dd161cb48190b207cd3284d47f79","name":"bash","arguments":{"command":"python - <<'PY'\nimport json, urllib.request\nq='ORDTST01KPPFWZBJD0M1PACY3257J7KH'\nurl=f'http://100.116.176.16:8091/api/search?q={q}&scope=global&scope=project&limit=5'\nobj=json.load(urllib.request.urlopen(url))\nfirst=obj['results'][0]['memory']\nprint(first['id'])\nprint(first['title'])\nprint(q in first['content'])\nPY","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":605,"output":314,"cacheRead":82944,"cacheWrite":0,"totalTokens":83863,"cost":{"input":0.00045375,"output":0.001413,"cacheRead":0.006220799999999999,"cacheWrite":0,"total":0.008087549999999999}},"stopReason":"toolUse","timestamp":1776868625258,"responseId":"resp_07b1b9c95ac99ec40169e8dd1223988190bba8771b92d36336"}}
{"type":"message","id":"4efddd7c","parentId":"21707d18","timestamp":"2026-04-22T14:37:10.715Z","message":{"role":"toolResult","toolCallId":"call_4gKxJVm7TTcAsgEJkib7Iz0j|fc_07b1b9c95ac99ec40169e8dd161cb48190b207cd3284d47f79","toolName":"bash","content":[{"type":"text","text":"mem_7e571be6dcee4a0b8d53c3cf27898c50\nBallbox payments validation handoff and open blockers\nTrue\n"}],"isError":false,"timestamp":1776868630715}}
{"type":"message","id":"9d25abb1","parentId":"4efddd7c","timestamp":"2026-04-22T14:37:16.772Z","message":{"role":"assistant","content":[{"type":"text","text":"Proof:\n\n```bash\nq=ORDTST01KPPFWZBJD0M1PACY3257J7KH\n/api/search -> first result:\nmem_7e571be6dcee4a0b8d53c3cf27898c50\nBallbox payments validation handoff and open blockers\ncontains token = true\n```\n\nSo:\n- the memory exists\n- it contains `ORDTST01KPPFWZBJD0M1PACY3257J7KH`\n- search by content retrieves the exact memory\n\nWhat’s broken is **searching by raw memory id**. That query is not a reliable exact-id lookup here.","textSignature":"{\"v\":1,\"id\":\"msg_07b1b9c95ac99ec40169e8dd1bb49c8190a9a1ca2ebc4dbf3d\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":454,"output":144,"cacheRead":83456,"cacheWrite":0,"totalTokens":84054,"cost":{"input":0.0003405,"output":0.000648,"cacheRead":0.0062591999999999995,"cacheWrite":0,"total":0.007247699999999999}},"stopReason":"stop","timestamp":1776868630716,"responseId":"resp_07b1b9c95ac99ec40169e8dd1a778c81909f689e61fa952951"}}
{"type":"message","id":"81f2bdd6","parentId":"9d25abb1","timestamp":"2026-04-22T14:37:18.034Z","message":{"role":"user","content":[{"type":"text","text":"now see if it needs better docs (or why you reached for the wrong endpoint)"}],"timestamp":1776868638033}}
{"type":"message","id":"b676fe07","parentId":"81f2bdd6","timestamp":"2026-04-22T14:37:22.197Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd1fbe98819084115774d5f14aab\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N0hzjQlOXNIF6v4GUkJt1r7dcv234TNEMvGwhEz6j0-vuvaq0RejM1qLRFhffakWNVBsazkAf6NKnsLmqyj1x5EJfiQVAn3uAuEBBg_O9WFDk6-Dj4uPzWi7umtAoK1azs5GEHAmze8L4gP9Mzw8sVcS66I9b7lkjHFUtPtZIkg5TsXQ1La_fdm9VxSOMiAuwKyZcSkPz6Q_8bDdm40qbp_n1n29YxUmZEqelNFP3VPC0_Z1J7QqwAGRt4Wd_yUayIPVazDZpVr1H65hLofT-BC3toqXPkkBfHklNkYP0_GG5Mfn9e3-MrtOFdCYjvEc77YIZr1EmNKgI7J_WObnY4a7ZrtbEZWC3l5uiJzWup0cQ3IZC7LOGke9oibK7nwuwq3Yb06QCjkqNUAKBt7INQ28kBqL05MJCBF6Idy0I23zy9klNxe9-fY0O4q8rxaTn_-z9ajBofiR4lFF9mtnNLsjxwcIzhrc0Tx4-C1-wYn0mk2y_snPM68EatQVQQpB8WtkZBNeH0K0e64mqSmwzeXwwI6xKXUPBjlTy5WfXnnEdDptpJhGbDnxo51DIff6K_qI8zFz0Ppoun3vtImBFYkzfX6zlJPCuMri67c02D2xWT1c3VSENDB5gJ5ejL_baZLcx5PiAB3o-AvcbNhwuOs_1FF0LvJs7urPcfMCguzNSXuRJ-UoYN6aPoeEOzXesww0_GfgdIh55xIvj4KpQWPjKMu-5yzbI4EXDvXhbY8O7k-6ORA_n3-xaeCxXXwj5mWAPrcNNTKHlX50VJ4nrp2dRpKlzlfzumv-FLlQuAx59HB5TO8meEwuhvogC_xjoA4wI7fqbPDjr3II7OYdS2-ivdDKVBEmxq0BElX6E_ihsKBAz_gFM2TRDWIhSuzK69nH0zDOa_9PgRU3Hfj44hOzj1k87o6SSoUFzeYI3kGsRgrkzKKYVfP_DTLNiyUy7F5zfk11QJ7q3E9oGKfK9ciqij9nM7LooSPfsF01qRucGyLtKWiytA25rre2ShFtKtdAlJAufG9j_1yMAO3FNYwdlOkL_hzQJGK3TkUggBy1mhQYaR261W7KfL-fy5CXQdZL7B3pzvvh8gRjHEQkQHU40B3cKWGg7FHjhII7m0FG2WmSinKL6MQf9RqF28J9o_AIt-hGTQfmbIW1hwhrMPsql76F1osxpI_tol9SAcaUcBEEWqKvqBSvVA6c2RoazJLoyrk59J_uE8ardgs_I9YOUO93YijJy6hDjTlFoNU9LXLDYxDG10zDOiXnK8LR4xpMjE9TzWASlcqX9ptSzW9YkTMatejJPxeMoMXCJtLio7_3CoHq54c7G0k2WqE1PY4BOkvKDuMSG959VMMBCZrd_B9uS44Jwzag5C1tD-Yb8KevzKtjdhR8v9OwHAGgu0B1cYlDig6mHIPx_BDVZzoF7cc7gz2W7APyiE28H7L0VhpUUxRu1XtyqNSrrGvzdNzKVL76_hNPVYRmlqhdlLf6FUxDstvmgMVmysp3SHbjYUtAnPIeVV8x5qXPTjBpQkKr2xF4CZEB98aRPHapQ_rxenOPGGEIUwNQ1XgU52CgHENhMiJiKXvOzcc5rTWsQD2_ApwQ08rirDcX_-4hRkuA3rMh8MSpw8lcgvvTiA82XdMtGOsdmF5tA5K2wZ9mRxjyoJBOPZE117rk7tds0IXvUrP3zANPFTcHCwABzA1nqDFYtCED5DmcTRDA69kCu_T6bM4ys2DaMM7MLgzpDZBIVqR7k5FbVHiIH4SPgNU7o8P3Rs9INEiKXhdoejfiNLLc4mWCNhZin6dBL7jRYQ_9zddTMFXebXSdKfb85xIOcSR8jUwLcIByF9muj8iO-UOoB9yvs8d9SoNICgm7wa8g7PXwcBKuwl2GVKq02hOHJ8-fxgbHISxMvQSgGBU4UciFUz8OSNmZkww6ekFoqDxx2dCEVZ_DPrMQ1HknsjqzCIvoD6cwBhTyjQ0T97Hca-tVioYRAbU_t6MVKLz7hqq7OhzFn0sKX8A3OOp65jRNW-733JoXlKYAJIjfJpudrkLDVTtuSAdzKM8GVOUpGDWRiMHafyZnQzGzVXhMZq41Xk=\",\"summary\":[]}"},{"type":"toolCall","id":"call_d1ckorO5HLazxd0yj3TtVflW|fc_07b1b9c95ac99ec40169e8dd21fd14819095075faf550280c4","name":"bash","arguments":{"command":"git status --short && rg -n \"api/context|api/search|agents-database|memory-read|memory-write|context\\?id|retrieval_id\" -S .","timeout":30}},{"type":"toolCall","id":"call_VY0DxJfErsg6mHxkJSVCuF1e|fc_07b1b9c95ac99ec40169e8dd21fd288190ae3901dd6bffbd70","name":"bash","arguments":{"command":"curl -fsSL 'http://100.116.176.16:8091/api/status'","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":1634,"output":279,"cacheRead":81408,"cacheWrite":0,"totalTokens":83321,"cost":{"input":0.0012255,"output":0.0012555,"cacheRead":0.0061056,"cacheWrite":0,"total":0.0085866}},"stopReason":"toolUse","timestamp":1776868638034,"responseId":"resp_07b1b9c95ac99ec40169e8dd1ef97881909d14bcbf960a8d3c"}}
{"type":"message","id":"544d8850","parentId":"b676fe07","timestamp":"2026-04-22T14:37:22.248Z","message":{"role":"toolResult","toolCallId":"call_d1ckorO5HLazxd0yj3TtVflW|fc_07b1b9c95ac99ec40169e8dd21fd14819095075faf550280c4","toolName":"bash","content":[{"type":"text","text":" M .gitignore\n D proxy.ts\n./AGENTS.md:4:- durable prefs/rules/notes to `~/agents-database`\n"}],"isError":false,"timestamp":1776868642247}}
{"type":"message","id":"704f570d","parentId":"544d8850","timestamp":"2026-04-22T14:37:22.551Z","message":{"role":"toolResult","toolCallId":"call_VY0DxJfErsg6mHxkJSVCuF1e|fc_07b1b9c95ac99ec40169e8dd21fd288190ae3901dd6bffbd70","toolName":"bash","content":[{"type":"text","text":"s11)\\n\\n**Use cases**\\n- 8-week academy, beginner series, seasonal programs, kids programs, transformation programs.\\n- Mantener cohortes con control de min/max y cierres automaticos.\\n\\n**E. Credit packages**\\n- Venta de credits prepago desde el negocio/storefront. [S7](#ref-s7)[S10](#ref-s10)[S11](#ref-s11)\\n- Precio, numero de credits, expiracion, purchase limit, time-limited sales, descripcion. [S11](#ref-s11)\\n- Credits venue/trainer-specific, no cross-merchant. [S7](#ref-s7)[S11](#ref-s11)\\n- Hoy documentados para Classes & Events; client help los presenta tambien para classes/events/courses. [S7](#ref-s7)[S11](#ref-s11)\\n\\n**Use cases**\\n- Session passes, bundles, loyalty/prepay, promo windows, member-only offers.\\n- Cobrar por adelantado y reducir friccion en repeat bookings.\\n\\n### 6. Payments / billing / invoicing\\n\\n**Features confirmadas**\\n- Online payments con Stripe o Mollie; Matchable cobra 1.8% platform fee + processing fees del PSP. [S3](#ref-s3)\\n- Aceptar pagos en 36+ paises. [S3](#ref-s3)\\n- Payment method obligatorio antes de aceptar bookings. [S8](#ref-s8)\\n- Invoices automaticas al cliente; visibles en \\\"My invoices\\\". [S3](#ref-s3)[S7](#ref-s7)[S8](#ref-s8)\\n- Pagos van directo al venue/trainer segun client-facing copy. [S7](#ref-s7)[S17](#ref-s17)\\n- Manual bookings gratis. [S3](#ref-s3)\\n- Manual payment links e in-person payments aparecen como coming soon en pricing. [S3](#ref-s3)\\n\\n**Use cases soportados**\\n- Cobro online nativo sin subscription fee.\\n- Operar mercados internacionales con PSPs estandar.\\n- Emitir comprobantes automaticamente y dar self-serve invoice access al cliente.\\n\\n### 7. Storefront / distribution / embedded commerce\\n\\n**Features confirmadas**\\n- Storefront publico agrega todo lo publicado: private sessions, classes, courses, events, credit packages. [S10](#ref-s10)\\n- Shareable link desde manager; puede usarse en Instagram bio, links directos, etc. [S10](#ref-s10)\\n- Booking widget para incrustar oferta propia en website. [S3](#ref-s3)[S10](#ref-s10)\\n- Marketplace opcional sin fee adicional por descubrir nuevos clientes via marketplace. [S3](#ref-s3)\\n\\n**Use cases soportados**\\n- Negocio sin web propia que usa Matchable como storefront principal.\\n- Negocio con web propia que embebe widget pero usa Matchable para checkout/ops.\\n- Clubs/academies que combinan demanda organica marketplace + trafico propio.\\n\\n### 8. Trainer operations\\n\\n**Features confirmadas**\\n- Crear trainer con nombre, email, funcion/categoria. [S8](#ref-s8)\\n- Asignar trainer a servicios. [S11](#ref-s11)\\n- Gestionar bookings del trainer desde manager. [S11](#ref-s11)\\n- Primary schedule y temporary schedule del trainer. [S11](#ref-s11)\\n- Holidays que override availability y muestran indisponibilidad al cliente. [S11](#ref-s11)\\n- Trainer app con home, weekly schedule, create training, availability, holidays y profile edit. [S11](#ref-s11)\\n- Release note del trainer app documenta manejo entre multiple venues. [S18](#ref-s18)\\n\\n**Use cases soportados**\\n- Coach independiente que se autogestiona.\\n- Academy con multiples coaches y horarios complejos.\\n- Coach itinerante entre varias sedes.\\n\\n### 9. Venue / academy operations\\n\\n**Features confirmadas**\\n- Venue schedule con opening hours, primary schedule y temporary schedule que overridea al principal. [S8](#ref-s8)\\n- Schedule/Calendar operativo donde conviven trainings, classes y courses. [S9](#ref-s9)[S11](#ref-s11)\\n- Day view / week view y quick booking overview. [S9](#ref-s9)\\n- Crear booking manualmente desde manager. [S9](#ref-s9)[S11](#ref-s11)\\n- Multi-venue management y unlimited manager seats para business tier segun pricing. [S3](#ref-s3)\\n\\n**Use cases soportados**\\n- Operar aperturas estacionales o holiday schedules.\\n- Ver conflictos y capacidad del dia en una sola vista.\\n- Administrar academies o grupos con varias sedes y varios managers.\\n\\n### 10. Client CRM / customer operations\\n\\n**Features confirmadas**\\n- Client database con nombre, email, phone. [S11](#ref-s11)\\n- Add Client manual. [S11](#ref-s11)\\n- View profile, add booking y WhatsApp contact desde perfil del cliente. [S11](#ref-s11)\\n- Ver booking history y filtros. [S11](#ref-s11)\\n- En cursos: payment status, payment type, revenue, add guest/manual participant, refund handling. [S11](#ref-s11)\\n\\n**Use cases soportados**\\n- Staff de front desk o academy manager que agrega walk-ins/manual signups.\\n- Seguimiento de recurrentes y soporte posventa.\\n- Gestion de cohortes y refunds en programas.\\n\\n### 11. Promotions / discounts / commercial tooling\\n\\n**Features confirmadas**\\n- Discount codes y vouchers en marketplace checkout. [S6](#ref-s6)\\n- Promotions y vouchers en business homepage. [S2](#ref-s2)\\n- Promotion tools listed as free in pricing. [S3](#ref-s3)\\n- Credit packages con time-limited sales. [S11](#ref-s11)\\n\\n**Use cases soportados**\\n- Acquisition offers, referral codes, Black Friday/new year campaigns.\\n- Prepaid bundles para aumentar retention y cashflow.\\n\\n### 12. Integrations / ecosystem\\n\\n**A. Court-booking / club systems**\\n- MATCHi: reserva cancha automaticamente al reservar private training; sync real-time; evita double-bookings; oculta slots sin court disponible. [S4](#ref-s4)[S12](#ref-s12)\\n- Matchpoint: auto-reserva court, sync availability y updates, cubre tennis/padel/beach tennis/pickleball. [S15](#ref-s15)\\n- KNLTB Meet & Play: auto court reservation y visibilidad de sesiones en KNLTB app. [S16](#ref-s16)\\n- Overview tambien lista AnyBuddy y Taykus con el mismo patron de auto court booking. [S4](#ref-s4)\\n\\n**Use cases**\\n- Club que quiere vender coaching sin hacer booking manual de cancha.\\n- Academia dentro de club que necesita respetar inventario real de courts.\\n- Ecosistemas federativos/club software donde Matchable actua como capa comercial.\\n\\n**Spec para copiar:** Ver la secci\\u00f3n [Ballbox \\u00d7 ATC](#ballbox-atc-spec-de-integraci-n) al final de este documento para el spec detallado de integraci\\u00f3n tipo MATCHi/Matchpoint/KNLTB, adaptado para Ballbox \\u00d7 ATC.\\n\\n**B. Calendar**\\n- Google Calendar integration page promete two-way sync: bloquear tiempo en Google bloquea disponibilidad en Matchable; cambios en Matchable actualizan Calendar. [S13](#ref-s13)\\n- Help article documenta al menos una suscripcion por URL/ICS a Google Calendar. [S11](#ref-s11)\\n\\n**Use cases**\\n- Coach que mezcla agenda personal y comercial.\\n- Reducir no-shows y overlaps.\\n\\n**Nota critica**\\n- Publicamente hay tension entre marketing (two-way sync) y help (ICS subscription, que normalmente es one-way). Conviene tratar el alcance exacto como \\\"publicamente reclamado, no completamente verificado\\\". [S13](#ref-s13)[S11](#ref-s11)\\n\\n**C. Digital sessions**\\n- Google Meet integration agrega links automaticamente a cada booking. [S14](#ref-s14)\\n- Business homepage y pricing listan digital sessions como capability. [S2](#ref-s2)[S3](#ref-s3)\\n\\n**Use cases**\\n- Remote coaching, video review, nutrition coaching, virtual classes.\\n\\n**D. Adjacent value-add tools**\\n- BenFit: entrenadores conectados con soporte nutricional. [S4](#ref-s4)\\n- PadelFast: conectar training clients con torneos/competiciones. [S4](#ref-s4)\\n\\n**Use cases**\\n- Extender ARPU y experiencia mas alla del booking.\\n- Convertir academia en funnel hacia competicion o nutrition upsell.\\n\\n### 13. Mobile products\\n\\n**Client app**\\n- Book next workout, view booked sessions, location and services, no signup/subscription fee. [S7](#ref-s7)\\n- Release notes: localization, courses, credits, top categories, storefront filters/organized overview, support for unplanned sessions. [S17](#ref-s17)\\n\\n**Trainer app**\\n- Daily/weekly schedule, reviews, connected venues, create training, availability, holidays, profile editing. [S11](#ref-s11)\\n- Release notes: manage bookings/schedules/holidays between multiple venues. [S18](#ref-s18)\\n\\n**Use cases soportados**\\n- Consumo movil para clientes repetitivos.\\n- Coach on-court que no quiere operar desde desktop.\\n\\n### 14. Support / onboarding / enablement\\n\\n**Features confirmadas**\\n- Free onboarding y full customer support segun FAQ de business homepage. [S2](#ref-s2)\\n- Chat support desde manager, email support <24h, knowledge base. [S3](#ref-s3)\\n- Help center estructurado para clients, trainer app y business manager. [S5](#ref-s5)\\n\\n**Use cases soportados**\\n- SMB onboarding rapido.\\n- Soporte operativo y autoservicio documental.\\n\\n### 15. Explicit \\\"coming soon\\\" / weakly evidenced areas\\n\\n**Publicamente listados como coming soon o ambiguos**\\n- Courses en pricing (aunque help center y app release notes los muestran activos). [S3](#ref-s3)[S11](#ref-s11)[S17](#ref-s17)\\n- Insights. [S3](#ref-s3)\\n- Manual payment links. [S3](#ref-s3)\\n- In-person payments. [S3](#ref-s3)\\n- Google Analytics y Meta Pixel add-ons. [S3](#ref-s3)\\n- Recruitment en pricing aparece sin detalle publico util. [S3](#ref-s3)\\n\\n**Lectura**\\n- No asumir cobertura plena ni GA/Pixel productizados a todos los clientes.\\n- Courses/credits/storefront parecen en rollout mas avanzado que pricing copy.\\n\\n## Mapa de casos de uso por persona\\n\\n### Cliente casual\\n- Encontrar un coach/venue por deporte.\\n- Comprar un 1:1 o clase puntual.\\n- Elegir attendees/duracion.\\n- Aplicar voucher.\\n- Pagar y reagendar si hace falta. [S6](#ref-s6)[S7](#ref-s7)\\n\\n### Cliente recurrente\\n- Comprar packages o credits.\\n- Dejar sesiones unplanned y consumirlas despues.\\n- Seguir usando el mismo trainer/venue via storefront/app. [S5](#ref-s5)[S7](#ref-s7)[S10](#ref-s10)\\n\\n### Alumno de programa\\n- Inscribirse a course multi-semana.\\n- Ver schedule completo.\\n- Entrar tarde con Smart Courses si quedan sesiones. [S11](#ref-s11)\\n\\n### Trainer independiente\\n- Publicar perfil y disponibilidad.\\n- Vender privados, clases y online sessions.\\n- Gestionar agenda/holidays desde app.\\n- Usar Google Calendar para coordinar vida personal + trabajo. [S2](#ref-s2)[S11](#ref-s11)[S13](#ref-s13)[S14](#ref-s14)\\n\\n### Venue/academy\\n- Publicar oferta completa en storefront.\\n- Gestionar trainers, bookings y clientes.\\n- Vender credits/cursos/eventos.\\n- Cobrar online e invoicing automatico. [S3](#ref-s3)[S10](#ref-s10)[S11](#ref-s11)\\n\\n### Racquet club integrado\\n- Mantener MATCHi/Matchpoint/KNLTB como sistema de courts.\\n- Usar Matchable para captar y vender coaching.\\n- Evitar doble reserva de cancha. [S12](#ref-s12)[S15](#ref-s15)[S16](#ref-s16)\\n\\n### Operador multi-sede\\n- Administrar varias sedes y multiples managers.\\n- Unificar operacion con mismos flujos de booking/clients/trainers. [S3](#ref-s3)\\n\\n## JTBD map\\n\\n**JTBD: \\\"quiero entrenar esta semana con alguien bueno\\\"**\\n- Search marketplace\\n- Trainer profiles\\n- Reviews\\n- Booking + planning\\n- Reschedule/cancel [S1](#ref-s1)[S6](#ref-s6)[S7](#ref-s7)[S19](#ref-s19)\\n\\n**JTBD: \\\"quiero vender mi coaching sin perseguir pagos ni whatsapps\\\"**\\n- Storefront\\n- Online payments\\n- Packages/credits\\n- Automated invoices\\n- Trainer app [S3](#ref-s3)[S10](#ref-s10)[S11](#ref-s11)\\n\\n**JTBD: \\\"quiero correr una academia o programa estructurado\\\"**\\n- Courses\\n- Participant limits\\n- Auto-cancel\\n- Smart Courses\\n- Participant management [S11](#ref-s11)\\n\\n**JTBD: \\\"quiero monetizar horas valle o crear comunidad\\\"**\\n- Classes\\n- Events\\n- Credits/promotions\\n- Public marketplace distribution [S2](#ref-s2)[S3](#ref-s3)[S10](#ref-s10)\\n\\n**JTBD: \\\"quiero que vender coaching no rompa mi operacion de club\\\"**\\n- Court-booking integrations\\n- Availability sync\\n- Booking statuses\\n- Multi-venue / manager operations [S3](#ref-s3)[S9](#ref-s9)[S12](#ref-s12)[S15](#ref-s15)[S16](#ref-s16)\\n\\n**JTBD: \\\"quiero ofrecer coaching online tambien\\\"**\\n- Digital sessions\\n- Google Meet links\\n- Unified profile for in-person + online [S2](#ref-s2)[S14](#ref-s14)\\n\\n## Hallazgos estrategicos\\n\\n1. Matchable parece mas fuerte cuando el negocio vende no solo privados sino un mix de privados + clases + cursos + credits + eventos desde storefront. La plataforma ya esta modelada para ese portfolio, no solo para appointment booking simple. [S2](#ref-s2)[S3](#ref-s3)[S10](#ref-s10)[S11](#ref-s11)\\n2. El wedge mas diferenciador en racquet sports es academy automation conectada a court inventory externo. Eso resuelve un problema operativo real que marketplaces generalistas no resuelven. [S12](#ref-s12)[S15](#ref-s15)[S16](#ref-s16)\\n3. La company esta empujando una evolucion desde \\\"trainer booking\\\" a \\\"sport commerce OS ligero\\\": storefront, widget, apps, credits, courses, integrations, manager. [S2](#ref-s2)[S3](#ref-s3)[S10](#ref-s10)[S17](#ref-s17)[S18](#ref-s18)\\n4. El posicionamiento geografico/publico es multi-country, pero la fuerza de las integraciones y ejemplos visibles sesga hacia Europa y racquet/federation ecosystems. [S12](#ref-s12)[S15](#ref-s15)[S16](#ref-s16)\\n\\n## Riesgos, limites y lectura critica\\n\\n- Parte del marketplace publico devuelve `0 results` sin contexto o depende de query/location; por tanto el mapeo se centra en capacidades de producto confirmadas, no en catalogo disponible en cada region. [S20](#ref-s20)[S21](#ref-s21)\\n- Hay copy inconsistente entre pricing y help center/app release notes sobre courses, credits e insights. No conviene asumir rollout uniforme por cliente/mercado. [S3](#ref-s3)[S11](#ref-s11)[S17](#ref-s17)\\n- La profundidad del BI/reporting no es visible publicamente; \\\"insights\\\" aparece, pero sin detalle funcional verificable. [S3](#ref-s3)\\n- La integracion de Google Calendar se describe como two-way en marketing, pero el help publico solo verifica una integracion via URL/ICS; tratar la sincronizacion bidireccional exacta como probable pero no cerrada. [S13](#ref-s13)[S11](#ref-s11)\\n- No pude verificar exhaustivamente todas las venue pages publicas desde muestra puntual; algunas URLs del sitemap no devolvieron contenido util. [S20](#ref-s20)\\n\\n## Fuentes primarias usadas\\n\\n- [S1] Matchable consumer homepage: https://www.matchable.io/\\n- [S2] Matchable business homepage: https://business.matchable.io/\\n- [S3] Matchable pricing: https://matchable.io/business/pricing\\n- [S4] Matchable integrations overview: https://matchable.io/business/integrations\\n- [S5] Matchable help center index: https://help.matchable.io/en/\\n- [S6] How to book a training: https://help.matchable.io/en/articles/10-how-to-book-a-training\\n- [S7] Client app / credits / FAQ / invoices help pages: https://help.matchable.io/en/articles/15-client-app-iosandroid ; https://help.matchable.io/en/articles/23-how-to-use-credits ; https://help.matchable.io/en/articles/12-faq ; https://help.matchable.io/en/articles/39-invoice-for-my-purchases\\n- [S8] Quick-start manager setup pages: https://help.matchable.io/en/articles/1-add-a-payment-method ; https://help.matchable.io/en/articles/2-create-your-first-class-or-event ; https://help.matchable.io/en/articles/3-create-your-first-private-service ; https://help.matchable.io/en/articles/4-how-to-edit-your-trainer-profile ; https://help.matchable.io/en/articles/5-how-to-edit-your-venue-profile ; https://help.matchable.io/en/articles/6-edit-your-availability ; https://help.matchable.io/en/articles/7-add-your-venue-schedule\\n- [S9] Manager operations pages: https://help.matchable.io/en/articles/17-manage-trainings-and-classes ; https://help.matchable.io/en/articles/18-manage-your-trainers-schedules ; https://help.matchable.io/en/articles/19-manage-your-clients ; https://help.matchable.io/en/articles/22-manage-credits\\n- [S10] Storefront guide: https://help.matchable.io/en/articles/38-getting-started-with-your-storefront\\n- [S11] Courses + trainer app help pages: https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trainer-app ; https://help.matchable.io/en/articles/14-adding-and-managing-your-holidays ; https://help.matchable.io/en/articles/41-getting-started-with-courses ; https://help.matchable.io/en/articles/42-smart-courses-feature ; https://help.matchable.io/en/articles/43-set-up-a-course-page-template ; https://help.matchable.io/en/articles/44-create-and-manage-your-course-schedule ; https://help.matchable.io/en/articles/45-viewing-courses-in-the-calendar ; https://help.matchable.io/en/articles/46-manage-course-participants\\n- [S12] MATCHi integration: https://matchable.io/business/integration/matchi\\n- [S13] Google Calendar integration: https://matchable.io/business/integration/google-calendar\\n- [S14] Google Meet integration: https://matchable.io/business/integration/google-meet\\n- [S15] Matchpoint integration: https://matchable.io/business/integration/matchpoint\\n- [S16] KNLTB Meet & Play integration: https://matchable.io/business/integration/knltb-meet-play\\n- [S17] Client app store pages / release notes: https://apps.apple.com/nl/app/matchable-book-your-training/id6742325816 ; https://play.google.com/store/apps/details?id=com.getmatchable.client\\n- [S18] Trainer app store pages / release notes: https://apps.apple.com/nl/app/matchable-for-trainers/id6468798694 ; https://play.google.com/store/apps/details?id=com.getmatchable.app\\n- [S19] Sample trainer page: https://matchable.io/trainers/alex-hosp\\n- [S20] Matchable sitemap: https://matchable.io/sitemap.xml\\n- [S21] Explore / Activities public pages: https://matchable.io/explore ; https://matchable.io/activities\\n\\n\\n- [S22] Spec Ballbox \\u00d7 ATC: secci\\u00f3n 16 de este documento (Spec de integraci\\u00f3n para copiar patr\\u00f3n MATCHi/Matchpoint/KNLTB en Ballbox)\\n\\n## Ballbox \\u00d7 ATC \\u2014 Spec de Integraci\\u00f3n\\n\\n*Spec de integraci\\u00f3n para copiar el patr\\u00f3n de MATCHi/Matchpoint/KNLTB en Ballbox.*\\n\\n---\\n\\n### 1. Contexto de negocio\\n\\n**Problema que resuelve**\\n\\nUn club de tennis/padel usa ATC como su sistema de gesti\\u00f3n de canchas (reservas, pagos, inventario). Ballbox quiere vender trainings y clases de sus trainers en ese club. El riesgo es vender un entrenamiento cuando la cancha no est\\u00e1 disponible, o duplicar reservas.\\n\\n**Soluci\\u00f3n**\\n\\nCuando alguien reserva un training en Ballbox, el sistema autom\\u00e1ticamente verifica disponibilidad y reserva la cancha en ATC al mismo tiempo. Si el club cancela o modifica la reserva en ATC, Ballbox se entera y act\\u00faa en consecuencia.\\n\\n**Modelo de relaci\\u00f3n**\\n\\n- ATC es el **source of truth** para inventario de canchas\\n- Ballbox es el **canal de venta** de trainings\\n- Ambas plataformas mantienen sync en tiempo real\\n\\n---\\n\\n### 2. Descripci\\u00f3n funcional del flujo\\n\\n#### 2.1. Un cliente busca un entrenamiento\\n\\nEl cliente ve en Ballbox los trainers disponibles de un club. Al seleccionar fecha y horario, Ballbox consulta a ATC qu\\u00e9 canchas est\\u00e1n libres en ese momento. Solo muestra opciones disponibles.\\n\\n#### 2.2. El cliente confirma la reserva\\n\\nCuando el cliente paga o confirma el training en Ballbox:\\n1. Ballbox registra la reserva del training para el trainer\\n2. Simult\\u00e1neamente, Ballbox reserva la cancha en ATC para ese horario\\n3. Ambas reservas quedan linkeadas por un identificador compartido\\n4. Si la cancha no se puede reservar (ya se ocup\\u00f3 entre consulta y confirmaci\\u00f3n), Ballbox informa al cliente y sugiere otro horario\\n\\n#### 2.3. El cliente o el trainer cancela desde Ballbox\\n\\nBallbox cancela la reserva del training y, al mismo tiempo, libera la cancha en ATC.\\n\\n#### 2.4. El club cancela o modifica la reserva desde ATC\\n\\nATC notifica a Ballbox del cambio. Ballbox:\\n- Informa al cliente/trainer\\n- Ofrece reagendar o cancelar sin costo\\n- Marca la reserva como afectada\\n\\n#### 2.5. El club tiene la cancha indisponible (mantenimiento, evento)\\n\\nATC informa a Ballbox. Ballbox marca esas horas como no disponibles para nuevos bookings de trainings.\\n\\n---\\n\\n### 3. Escenarios soportados\\n\\n| Escenario | Comportamiento |\\n|-----------|----------------|\\n| Training reservado en Ballbox, club cierra la cancha | Ballbox notifica al cliente, ofrece reagendar |\\n| Trainer cancela training en Ballbox | La cancha se libera en ATC autom\\u00e1ticamente |\\n| Cliente reserva training, la cancha se ocupa antes de confirmar | Ballbox muestra horarios alternativos |\\n| Ballbox cae durante la reserva | La transacci\\u00f3n se deshace; la cancha en ATC no queda reservada |\\n| ATC tiene mantenimiento | Ballbox lo detecta y muestra \\\"canchas temporalmente no disponibles\\\" |\\n\\n---\\n\\n### 4. Definici\\u00f3n de responsabilidades\\n\\n**Ballbox se encarga de:**\\n- Mostrar la oferta de trainings y trainers\\n- Gestionar el checkout, pago y comunicaci\\u00f3n con el cliente\\n- Mantener el estado del training (confirmado, cancelado, reagendado)\\n- Notificar al cliente ante cambios en disponibilidad de cancha\\n- Persistir la referencia cruzada entre booking de training y booking de cancha\\n\\n**ATC se encarga de:**\\n- Mantener el inventario actualizado de canchas por club\\n- Gestionar reservas de cancha (crear, cancelar, consultar disponibilidad)\\n- Procesar pagos de alquiler de canasta si aplica\\n- Notificar a Ballbox ante cambios en reservas de cancha realizados fuera de Ballbox\\n- Proveer soporte t\\u00e9cnico para la integraci\\u00f3n\\n\\n---\\n\\n### 5. Especificaci\\u00f3n t\\u00e9cnica\\n\\n*Requiere que ATC expose los siguientes endpoints (si no existen, se arman en coordinaci\\u00f3n con ATC).*\\n\\n#### 5.1. Consultar disponibilidad\\n\\n```\\nGET /api/v1/clubs/{club_id}/availability?sport={sport}&date={YYYY-MM-DD}\\n\\nRespuesta:\\n{\\n  \\\"date\\\": \\\"2026-03-25\\\",\\n  \\\"slots\\\": [\\n    {\\n      \\\"court_id\\\": \\\"court-1\\\",\\n      \\\"court_name\\\": \\\"Cancha 1\\\",\\n      \\\"time\\\": \\\"14:00\\\",\\n      \\\"duration_minutes\\\": 60,\\n      \\\"available\\\": true\\n    },\\n    {\\n      \\\"court_id\\\": \\\"court-1\\\",\\n      \\\"time\\\": \\\"15:00\\\",\\n      \\\"available\\\": false,\\n      \\\"reason\\\": \\\"booked\\\"\\n    }\\n  ]\\n}\\n```\\n\\n#### 5.2. Reservar cancha\\n\\n```\\nPOST /api/v1/bookings\\n{\\n  \\\"club_id\\\": \\\"club-123\\\",\\n  \\\"court_id\\\": \\\"court-456\\\",\\n  \\\"datetime\\\": \\\"2026-03-25T14:00:00\\\",\\n  \\\"duration_minutes\\\": 60,\\n  \\\"client_name\\\": \\\"Juan P\\u00e9rez\\\",\\n  \\\"client_phone\\\": \\\"+5491112345678\\\",\\n  \\\"external_ref\\\": \\\"ballbox-bk-789\\\",\\n  \\\"notes\\\": \\\"Training Ballbox\\\"\\n}\\n\\nRespuesta:\\n{\\n  \\\"booking_id\\\": \\\"atc-bk-111\\\",\\n  \\\"status\\\": \\\"confirmed\\\",\\n  \\\"court\\\": \\\"Cancha 1\\\",\\n  \\\"datetime\\\": \\\"2026-03-25T14:00:00\\\"\\n}\\n```\\n\\n#### 5.3. Cancelar reserva de cancha\\n\\n```\\nDELETE /api/v1/bookings/{atc_booking_id}?reason={reason}\\n\\nRespuesta:\\n{\\n  \\\"booking_id\\\": \\\"atc-bk-111\\\",\\n  \\\"status\\\": \\\"cancelled\\\"\\n}\\n```\\n\\n#### 5.4. Consultar estado de una reserva\\n\\n```\\nGET /api/v1/bookings/{atc_booking_id}\\n\\nRespuesta:\\n{\\n  \\\"booking_id\\\": \\\"atc-bk-111\\\",\\n  \\\"status\\\": \\\"confirmed\\\" | \\\"cancelled\\\" | \\\"completed\\\",\\n  \\\"court_id\\\": \\\"court-456\\\",\\n  \\\"datetime\\\": \\\"2026-03-25T14:00:00\\\"\\n}\\n```\\n\\n#### 5.5. Webhook \\u2014 Notificaciones desde ATC\\n\\nBallbox expone un endpoint que ATC llama ante cambios:\\n\\n```\\nPOST /ballbox/webhooks/atc\\n\\nPayload:\\n{\\n  \\\"event\\\": \\\"booking_cancelled\\\" | \\\"court_unavailable\\\" | \\\"maintenance\\\",\\n  \\\"atc_booking_id\\\": \\\"atc-bk-111\\\",\\n  \\\"external_ref\\\": \\\"ballbox-bk-789\\\",\\n  \\\"reason\\\": \\\"club_cancelled\\\" | \\\"court_blocked\\\",\\n  \\\"affected_slots\\\": [\\\"2026-03-25T14:00:00\\\"]\\n}\\n```\\n\\nBallbox responde con `200 OK` y procesa el evento.\\n\\n#### 5.6. Autenticaci\\u00f3n\\n\\n- ATC provee `api_key` por club\\n- Ballbox env\\u00eda `Authorization: Bearer {api_key}` en cada request\\n- Ballbox expone webhook con firma HMAC para verificar origen\\n\\n---\\n\\n### 6. Configuraci\\u00f3n por club\\n\\nPara activar la integraci\\u00f3n, cada club configura en Ballbox:\\n\\n| Campo | Descripci\\u00f3n |\\n|-------|-------------|\\n| `atc_club_id` | Identificador del club en ATC |\\n| `atc_api_key` | Clave de API entregada por ATC |\\n| `sports_enabled` | Lista de deportes habilitados (tennis, padel, etc.) |\\n| `default_duration_minutes` | Duraci\\u00f3n por defecto de un training |\\n\\n---\\n\\n### 7. Roadmap de implementaci\\u00f3n sugerida\\n\\n**Fase 1 \\u2014 MVP (4-6 semanas)**\\n- Disponibilidad consulta\\n- Reserva de cancha\\n- Cancelaci\\u00f3n desde Ballbox\\n- Sincronizaci\\u00f3n unidireccional (Ballbox \\u2192 ATC)\\n\\n**Fase 2 \\u2014 Bidireccional (2-3 semanas)**\\n- Webhooks desde ATC\\n- Notificaciones al cliente\\n- Manejo de cancelaciones externas\\n\\n**Fase 3 \\u2014 Robustez (2 semanas)**\\n- Retry con backoff ante fallos de ATC\\n- Cola de sincronizaci\\u00f3n para fallos\\n- Panel de estado de integraci\\u00f3n por club\\n\\n---\\n\\n*Spec elaborado el 24 de marzo de 2026, basado en el patr\\u00f3n de integraci\\u00f3n de Matchable con MATCHi/Matchpoint/KNLTB.*\\n\\n## Fecha de corte\\n\\nInvestigacion basada en fuentes publicas verificadas disponibles el 24 de marzo de 2026.\\n\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"created_at\": \"2026-03-24T14:26:24.158968+00:00\", \"updated_at\": \"2026-03-24T15:39:47.195210+00:00\", \"metadata\": {\"report_type\": \"feature-use-case-map\", \"run_id\": \"a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"topic\": \"matchable\"}}, {\"id\": \"art_1cb15ded78c141abbfc2abd40a45e776\", \"task_id\": \"task_c20a048b387d4513ac38cf0e67bf83cd\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.259060+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"evidence\\\": [\\\"personal_agent/runtime.py:451-492\\\", \\\"personal_agent/runtime.py:297-300\\\", \\\"tests/test_personal.py:909-914\\\", \\\"tests/test_personal.py:934-947\\\", \\\"docs/system-v1.md:114-115\\\"], \\\"goal\\\": \\\"Make ai-dev-workflow handoffs non-terminal on acceptance and gate parent completion on explicit downstream terminal execution results.\\\", \\\"repo\\\": \\\"personal-agent\\\", \\\"requirements\\\": [\\\"If a task decision creates an ai-dev-workflow handoff, do not mark the parent task completed yet.\\\", \\\"Model accepted as non-terminal for ai-dev-workflow handoffs.\\\", \\\"Require explicit downstream terminal result before handoff completion and parent-task completion.\\\", \\\"Update or add tests covering parent-task gating and accepted-vs-terminal handoff states.\\\", \\\"Update docs to remove the 'accepted JSON' success contract.\\\"], \\\"scope\\\": [\\\"personal_agent/runtime.py\\\", \\\"tests/test_personal.py\\\", \\\"docs/system-v1.md\\\", \\\"README.md\\\"], \\\"task_id\\\": \\\"task_c20a048b387d4513ac38cf0e67bf83cd\\\"}, \\\"reason\\\": \\\"personal-agent currently marks parent tasks complete on handoff acceptance instead of downstream execution result\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802651+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802652+00:00\", \"metadata\": {}}, {\"id\": \"art_a6773502ed4d46d09eed3e41081c0c68\", \"task_id\": \"task_2111a366cf73412eadb396acc1c2d004\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.272531+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_2111a366cf73412eadb396acc1c2d004\\\", \\\"intent\\\": \\\"fix in http://127.0.0.1:8082/ UI, in the \\\\\\\"blocked tasks\\\\\\\" input, it looses focus after 1-2 seconds. Doesnt happen for ex on \\\\\\\"NEW INTAKE\\\\\\\"'s input\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"Use Python as the shell entrypoint for persistence, approvals, auditability, and shared DB state; reproduce on http://127.0.0.1:8082/, inspect the blocked-tasks input vs NEW INTAKE input render/update paths, fix the remount or state-reset causing blur, then run full repo checks.\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"codex\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"UI focus-loss bug in local app; repo debugging and fix first, with personal-agent context kept for stateful workflow.\\\", \\\"secondary_agent\\\": \\\"personal\\\"}, \\\"task_id\\\": \\\"task_2111a366cf73412eadb396acc1c2d004\\\"}, \\\"reason\\\": \\\"UI focus-loss bug in local app; repo debugging and fix first, with personal-agent context kept for stateful workflow.\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802632+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802633+00:00\", \"metadata\": {}}, {\"id\": \"art_8dea76b1a5d44a6f9384abeb5a80b722\", \"task_id\": \"task_fd7cf4ad58e041da821c13d446f27996\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.280464+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_fd7cf4ad58e041da821c13d446f27996\\\", \\\"intent\\\": \\\"fix in http://127.0.0.1:8082/ UI, in the \\\\\\\"blocked tasks\\\\\\\" input, it looses focus after 1-2 seconds. Doesnt happen for ex on \\\\\\\"NEW INTAKE\\\\\\\"'s input\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"Use Python entrypoints/scripts for any persistence, approvals, and audit logging; reproduce the focus loss at http://127.0.0.1:8082/, inspect the blocked-tasks input path versus NEW INTAKE, fix the rerender/state-reset cause, then run repo checks and capture evidence.\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"codex\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"Local UI focus-loss bug in repo; needs code debugging with brief coordination context.\\\", \\\"secondary_agent\\\": \\\"personal\\\"}, \\\"task_id\\\": \\\"task_fd7cf4ad58e041da821c13d446f27996\\\"}, \\\"reason\\\": \\\"Local UI focus-loss bug in repo; needs code debugging with brief coordination context.\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802613+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802614+00:00\", \"metadata\": {}}, {\"id\": \"art_b805be79c68f41eba8779c2c32f8eba4\", \"task_id\": \"task_3f5eaf670f4749a18aa73281d5ab1722\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.283908+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"repo\\\": \\\"personal-agent\\\", \\\"scope\\\": [\\\"recover stale running task runs after daemon restart\\\", \\\"make scripts/daemon-8082.sh process-safe and health-checked\\\", \\\"reconcile child subtasks when parent deliverable completes\\\", \\\"add explicit artifact classification for deliverables vs intermediate/system\\\", \\\"add task detail API/page from existing task_bundle service\\\", \\\"update dashboard with clear current-run block and epic/subtask separation\\\"], \\\"suggested_order\\\": [\\\"runtime recovery\\\", \\\"daemon helper\\\", \\\"subtask reconciliation\\\", \\\"artifact classification\\\", \\\"task detail page\\\", \\\"dashboard refresh\\\", \\\"tests\\\"], \\\"task_id\\\": \\\"task_3f5eaf670f4749a18aa73281d5ab1722\\\"}, \\\"reason\\\": \\\"Repo changes needed in personal-agent for interrupted-run recovery, daemon helper reliability, task/subtask state clarity, artifact classification, and task detail UI.\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802589+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802590+00:00\", \"metadata\": {}}, {\"id\": \"art_f512f87edb3f4c56bff2767964cdd312\", \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.286009+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\", \\\"intent\\\": \\\"Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (repo)\\\", \\\"secondary_agent\\\": null}, \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\"}, \\\"reason\\\": \\\"code work (repo)\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802544+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802546+00:00\", \"metadata\": {}}, {\"id\": \"art_2778eb43a141444b9f9ea6408e009340\", \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.288035+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\", \\\"intent\\\": \\\"Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (fix)\\\", \\\"secondary_agent\\\": null}, \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\"}, \\\"reason\\\": \\\"code work (fix)\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802511+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802513+00:00\", \"metadata\": {}}, {\"id\": \"art_55b1c9bdee6f4226a81a9160c4703b62\", \"task_id\": \"task_afed9eaf504f42b99348a815d56148cc\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> implementation-agent\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-14T21:29:18.517341+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"artifact_id\\\": \\\"art_598b5da707dd47709fc0fe9bb3e79e28\\\", \\\"epic_id\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"expected_artifacts\\\": [\\\"migration_report\\\", \\\"v1_cross_reference_report\\\"], \\\"idempotency_key\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"remaining_task_ids\\\": [\\\"task_a240986d7c8a4474ba5246536769deed\\\", \\\"task_c0b65186af7d4013b2c1d2906ea6a28e\\\"]}, \\\"reason\\\": \\\"Finish remaining V1 migration and GitHub cross-reference work\\\", \\\"result_summary\\\": \\\"Finished legacy bridge and V1 multi-repo cross-reference work; shared DB now contains the resume anchors and completion artifacts.\\\", \\\"status\\\": \\\"completed\\\", \\\"to_agent\\\": \\\"implementation-agent\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802221+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802225+00:00\", \"metadata\": {}}, {\"id\": \"art_8dcad6182a03483aa914ad451db388fa\", \"task_id\": \"task_ac84e7e584494c2a95ed4a5bd2e1f92b\", \"artifact_type\": \"run_failure\", \"title\": \"Codex failure for links en la UI de 8082 en markdown andan mal cuando son archivos porque se marcan como relative d...\", \"content\": \"# Codex run failed\\n\\n- Exit code: 1\\n\\n## Stderr\\nOpenAI Codex v0.115.0 (research preview)\\n--------\\nworkdir: /Users/sebas/Code/ai-dev-workflow\\nmodel: gpt-5.4\\nprovider: openai\\napproval: never\\nsandbox: danger-full-access\\nreasoning effort: none\\nreasoning summaries: none\\nsession id: 019cfdaa-6e65-7d62-801b-03d94c51ba23\\n--------\\nuser\\nYou are running as a direct Codex execution for personal-agent.\\nWork from this repository root: /Users/sebas/Code/ai-dev-workflow\\nUse the repository's local rules and skills.\\nImplement directly when needed.\\nReturn markdown only.\\nInclude these sections:\\n- Summary\\n- Changes\\n- Verification\\n- Outputs\\nIn Outputs, list any branch, PR, file paths, or artifacts created.\\n\\nTask: links en la UI de  8082 en markdown andan mal cuando son archivos porque se marcan como relative de localhost:8082. deberian ser absolute paths (file://...)\\nRelevant memory:\\n- Task completed: investigar playtomic y armar un mapeo exhaustivo de todas sus features y use cases. Primero un br...: ## Brief ejecutivo\\n\\n**Qu\\u00e9 es Playtomic**\\n\\nPlaytomic es una plataforma de dos caras para deportes de raqueta y, en partes del producto, otros deportes de pista/cancha. Tiene cuatro \\n- Task completed: Improve daemon/UI operator workflow and task communication: # Findings\\n\\n## Already implemented\\n\\n- Interrupted run recovery exists in `personal_agent/runtime.py:930`: stale `running` task runs are marked `interrupted`, an `execution_state` a\\n- Task completed: In /Users/sebas/Code/calistenia, change a tiny UI string in the app and run repo checks: ## Findings\\n- Repo was clean before edit.\\n- Remaining user-visible typo found in filter config: `Musculo`.\\n- No `lint`, `typecheck`, or `test` scripts exist in `package.json`; repo\\nmcp: figma starting\\nmcp: figma ready\\nmcp startup: ready: figma\\nERROR: You've hit your usage limit. Upgrade to Pro (https://chatgpt.com/explore/pro), visit https://chatgpt.com/codex/settings/usage to purchase more credits or try again at Mar 18th, 2026 2:28 PM.\\nWarning: no last agent message; wrote empty content to /Users/sebas/personal-agent/data/runs/run_7895a9b3e900469984a61ea6092271fc.md\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-17T21:18:55.514267+00:00\", \"updated_at\": \"2026-03-17T21:18:55.514267+00:00\", \"metadata\": {\"classification\": \"intermediate\", \"run_id\": \"run_7895a9b3e900469984a61ea6092271fc\"}}, {\"id\": \"art_smoke_markdown_file_links\", \"task_id\": \"task_smoke_markdown_file_links\", \"artifact_type\": \"report\", \"title\": \"smoke markdown file links\", \"content\": \"[tmp](personal-agent-markdown-link-test.md)\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent:smoke\", \"created_at\": \"2026-03-17T21:11:26.188844+00:00\", \"updated_at\": \"2026-03-17T21:11:26.188844+00:00\", \"metadata\": {\"cwd\": \"/tmp\"}}, {\"id\": \"art_5e03c108db5345269e77d02e21d07dd9\", \"task_id\": \"task_84da4829fdea45728971df65c57ff33f\", \"artifact_type\": \"report\", \"title\": \"Codex result for Document the current Raycast-based fullscreen notification and reply implementation for Codex.\", \"content\": \"Hi Sebas. Kept it tight.\\n\\nDocumented current live flow in [docs/raycast-codex-notify.md](/Users/sebas/personal-agent/docs/raycast-codex-notify.md). It captures the actual implementation split between `~/.codex/hooks/notify_macos.sh` and `~/Code/raycast-codex-reply`, including:\\n\\n- notify hook decision path\\n- rollout-message recovery from `~/.codex/state_5.sqlite`\\n- Raycast reply bridge files + deeplinks\\n- approval watcher flow\\n- fallback AppleScript dialog\\n- explicit note that `personal-agent`\\u2019s old blocker/reply path is removed, so this is not handled by the current runtime\\n\\nAdded a README pointer at [README.md](/Users/sebas/personal-agent/README.md).\\n\\nVerification: `./scripts/run-checks.sh` passed.\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-17T02:12:31.505135+00:00\", \"updated_at\": \"2026-03-17T02:12:31.505135+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/personal-agent\", \"output_refs\": {\"paths\": [\"./scripts/run-checks.sh\", \"~/.codex/hooks/notify_macos.sh\", \"~/.codex/state_5.sqlite\", \"~/Code/raycast-codex-reply\"]}, \"run_id\": \"run_9f50b4d09e5842498c055923a851c678\"}}, {\"id\": \"art_d69dff710f97429882a05b0ec979ac5b\", \"task_id\": \"task_be1a1e514a7d43d1b59009ccca9fb57c\", \"artifact_type\": \"report\", \"title\": \"Codex result for Migrate remaining personal-agent legacy persistence/read paths to the shared task/runtime model\", \"content\": \"Hi. Legacy seam closed.\\n\\nCanonical shared-memory model now drives personal-agent surfaces from [shared_memory.py](/Users/sebas/personal-agent/personal_agent/shared_memory.py), with `personal-agent-memory-v1` and `personal-agent-task-v1` helpers, canonical kind detection, and an actual in-place migrator for old memory/task records plus legacy approval memories. [research_store.py](/Users/sebas/personal-agent/personal_agent/research_store.py) now reads/writes canonical run/source/claim/artifact/task records, drops the old bridge fallback for run inspection, uses native approvals rows for request/list/resolve, and includes approvals in search results. [router.py](/Users/sebas/personal-agent/personal_agent/router.py) now mirrors handoffs with canonical memory metadata, and [scripts/personal.py](/Users/sebas/personal-agent/scripts/personal.py) wires `approvals resolve` back into the CLI. [migration.py](/Users/sebas/personal-agent/personal_agent/migration.py) now runs real migration work instead of no-op, and [README.md](/Users/sebas/personal-agent/README.md) reflects the new model.\\n\\nCoverage added in [tests/test_personal.py](/Users/sebas/personal-agent/tests/test_personal.py) for canonical research records, native approval lifecycle, and legacy migration behavior. Verified with `./scripts/run-checks.sh` and `git diff --check`; both passed. No daemon/UI smoke run.\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-17T02:02:06.161088+00:00\", \"updated_at\": \"2026-03-17T02:02:06.161088+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/personal-agent\", \"output_refs\": {\"paths\": [\" helpers, canonical kind detection, and an actual in-place migrator for old memory/task records plus legacy approval memories. [research_store.py](/Users/sebas/personal-agent/personal_agent/research_store.py) now reads/writes canonical run/source/claim/artifact/task records, drops the old bridge fallback for run inspection, uses native approvals rows for request/list/resolve, and includes approvals in search results. [router.py](/Users/sebas/personal-agent/personal_agent/router.py) now mirrors handoffs with canonical memory metadata, and [scripts/personal.py](/Users/sebas/personal-agent/scripts/personal.py) wires \", \"./scripts/run-checks.sh\"]}, \"run_id\": \"run_b694a238b532454bb547c056246a31fd\"}}, {\"id\": \"art_b4f5386484794717b0ddc4c42c265319\", \"task_id\": \"task_871c609196b94132aa686188007f6dea\", \"artifact_type\": \"report\", \"title\": \"Codex result for Consolidate all Ballbox payment-system work from demo validation through production readiness and go-live certification.\", \"content\": \"Consolidated into one active workflow.\\n\\nCreated local repo mapping in [.agents/local-config.json](/Users/sebas/Code/ai-dev-workflow/.agents/local-config.json), set current feature in [.agents/current-feature](/Users/sebas/Code/ai-dev-workflow/.agents/current-feature), and wrote the active feature context in [.agents/feature-contexts/ballbox-payment-system-prod-readiness.json](/Users/sebas/Code/ai-dev-workflow/.agents/feature-contexts/ballbox-payment-system-prod-readiness.json). Added the execution brief/checklist in [docs/ballbox-payment-system-consolidation.md](/Users/sebas/Code/ai-dev-workflow/docs/ballbox-payment-system-consolidation.md).\\n\\nKey consolidation outcome: `ballbox` is stable/demo-facing only; `ballbox-payment-integration` is the real workstream. Demo validation exists now. Production hardening is partially drafted but unstaged in backend. Go-live certification is still blocked by two real gaps: missing TCN/Yunshu live adapter contract, and unvalidated MercadoPago real QR flow.\\n\\nVerification: JSON valid. Shared-memory sync failed because `shared_agent_memory` is not installed/importable in this environment. Control repo also remains behind `origin/main` by 1 commit: `111e974 Feat: accept shared memory run-task handoffs (#2)`. I did not pull because this branch is also 3 commits ahead.\\n\\nBest next move: review and commit the backend readiness slice in `~/Code/ballbox-payment-integration`, then `/status --current`.\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-17T01:55:28.432378+00:00\", \"updated_at\": \"2026-03-17T01:55:28.432378+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/Code/ai-dev-workflow\", \"output_refs\": {\"paths\": [\" is not installed/importable in this environment. Control repo also remains behind \", \" is stable/demo-facing only; \", \"~/Code/ballbox-payment-integration\"]}, \"run_id\": \"run_80140950f2c04d10bd3941d712ab0b25\"}}, {\"id\": \"art_a5cbdcb711234fd3b8306f379678523c\", \"task_id\": \"task_746e1a74f77b4cb0b8e3fd950fad90cb\", \"artifact_type\": \"report\", \"title\": \"link smoke\", \"content\": \"[tct](link) [txt](link.com)\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-16T20:42:13.176072+00:00\", \"updated_at\": \"2026-03-16T20:42:13.176072+00:00\", \"metadata\": {}}, {\"id\": \"art_322e6f0e62724030b8d995053a5ec674\", \"task_id\": \"task_2f22e794212641c49d7f8a600ad3a4bd\", \"artifact_type\": \"report\", \"title\": \"Codex result for en personal-agent que el script que hace restart del daemon pause tareas corriendo (como sea, que...\", \"content\": \"## Summary\\nImplementado en `personal-agent`: el restart del daemon ahora pausa runs activas, las deja en estado reiniciable, y al levantar de nuevo el runtime intenta relanzarlas solo. Tambi\\u00e9n blind\\u00e9 el watcher para que un run ya pausado no termine marcado como fallo cuando llega el `SIGTERM`.\\n\\n## Changes\\n- [`/Users/sebas/personal-agent/personal_agent/runtime.py`](file:///Users/sebas/personal-agent/personal_agent/runtime.py)\\n  - agregado `pause_running_tasks()`\\n  - `stop()` ahora puede pausar antes de terminar procesos\\n  - runs `running` recuperadas tras restart/crash pasan a `paused`, task vuelve a `draft`, metadata marca `auto_restart_on_daemon_start`\\n  - en boot, `_restart_paused_runs()` relanza tasks pausadas con `cwd`/prompt guardados\\n  - `_watch_run()` sale temprano si el run ya no est\\u00e1 `running`, evita falso `failed`\\n- [`/Users/sebas/personal-agent/scripts/personal.py`](file:///Users/sebas/personal-agent/scripts/personal.py)\\n  - nuevo comando `pause-running --reason ...`\\n- [`/Users/sebas/personal-agent/scripts/daemon-8082.sh`](file:///Users/sebas/personal-agent/scripts/daemon-8082.sh)\\n  - `restart` ahora ejecuta `python3 scripts/personal.py pause-running ...` antes de `stop/start`\\n- [`/Users/sebas/personal-agent/tests/test_personal.py`](file:///Users/sebas/personal-agent/tests/test_personal.py)\\n  - actualizado recovery test para validar pause + auto-restart\\n  - nuevo test para pause/shutdown race, sin false failure/artifact\\n\\n## Verification\\n- `python3 -m unittest tests.test_personal` \\u2705\\n- `./scripts/run-checks.sh` \\u2705\\n- `./scripts/daemon-8082.sh restart`\\n  - pause step ejecut\\u00f3 bien\\n  - restart completo bloqueado por entorno actual: `\\\"foreign listener on 127.0.0.1:8082 (pid 18283); refusing to stop it\\\"`\\n- `curl -fsS http://127.0.0.1:8082/api/status` \\u2705\\n  - confirm\\u00f3 que la task activa qued\\u00f3 `draft` con latest run `paused`\\n\\n## Outputs\\n- Branch: none\\n- PR: none\\n- Files changed:\\n  - [`/Users/sebas/personal-agent/personal_agent/runtime.py`](file:///Users/sebas/personal-agent/personal_agent/runtime.py)\\n  - [`/Users/sebas/personal-agent/scripts/personal.py`](file:///Users/sebas/personal-agent/scripts/personal.py)\\n  - [`/Users/sebas/personal-agent/scripts/daemon-8082.sh`](file:///Users/sebas/personal-agent/scripts/daemon-8082.sh)\\n  - [`/Users/sebas/personal-agent/tests/test_personal.py`](file:///Users/sebas/personal-agent/tests/test_personal.py)\\n- Artifacts created: none\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-16T20:25:49.823843+00:00\", \"updated_at\": \"2026-03-16T20:25:49.823843+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/personal-agent\", \"output_refs\": {\"branches\": [\"none\"], \"paths\": [\" recuperadas tras restart/crash pasan a \", \"./scripts/daemon-8082.sh restart\", \"./scripts/run-checks.sh\", \"/Users/sebas/personal-agent/personal_agent/runtime.py\", \"/Users/sebas/personal-agent/scripts/daemon-8082.sh\", \"/Users/sebas/personal-agent/scripts/personal.py\", \"/Users/sebas/personal-agent/tests/test_personal.py\", \"curl -fsS http://127.0.0.1:8082/api/status\", \"python3 scripts/personal.py pause-running ...\", \"stop/start\"]}, \"run_id\": \"run_8ff645c9a9ad4d558df0265495267a41\"}}, {\"id\": \"art_d7f2bb1c7a204e56834b97bc4e77989b\", \"task_id\": \"task_8a866bb8156441ff8cee2f0f9f28e0c5\", \"artifact_type\": \"report\", \"title\": \"Codex result for en un worktree nuevo en personal-agent, agregar soporte markdown para links (ej. aca http://127.0...\", \"content\": \"## Summary\\nAgregado soporte markdown para links en artifacts/report renderizados por el daemon. `[USECASE.md](http://...)` ahora sale como `<a>` clickable, no literal. Hecho en worktree nuevo; adem\\u00e1s dej\\u00e9 un test existente compatible con worktrees.\\n\\n## Changes\\n- Link markdown inline en [personal_agent/daemon.py](/Users/sebas/personal-agent-wt-markdown-links/personal_agent/daemon.py#L630)\\n  - parse `[label](href)`\\n  - `href` allowlist: `http`, `https`, `mailto`, `/`, `./`, `../`, `#`\\n  - links no seguros quedan literales\\n- Tests en [tests/test_personal.py](/Users/sebas/personal-agent-wt-markdown-links/tests/test_personal.py#L1653)\\n  - cubre render de link local `http://127.0.0.1:8082/artifacts/...`\\n  - cubre fallback seguro para `javascript:...`\\n- Fix de test fr\\u00e1gil para worktree en [tests/test_personal.py](/Users/sebas/personal-agent-wt-markdown-links/tests/test_personal.py#L1345)\\n  - deja de asumir path absoluto fijo del repo principal\\n\\n## Verification\\n- `./scripts/run-checks.sh`\\n- Resultado: `Ran 36 tests ... OK`\\n\\n## Outputs\\n- Worktree: `/Users/sebas/personal-agent-wt-markdown-links`\\n- Branch: `fix-artifact-markdown-links`\\n- Files changed:\\n  - [personal_agent/daemon.py](/Users/sebas/personal-agent-wt-markdown-links/personal_agent/daemon.py)\\n  - [tests/test_personal.py](/Users/sebas/personal-agent-wt-markdown-links/tests/test_personal.py)\\n- PR: none\\n- Artifacts: none\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-16T19:15:50.674755+00:00\", \"updated_at\": \"2026-03-16T19:15:50.674755+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/personal-agent\", \"output_refs\": {\"paths\": [\"./scripts/run-checks.sh\", \"/Users/sebas/personal-agent-wt-markdown-links\", \"[USECASE.md](http://...)\"]}, \"run_id\": \"run_cce5f6d82e0148f6a56c9c12d159711e\"}}]}, \"audit\": {\"schema_version\": \"shared-agent-memory-v2\", \"memories\": 344, \"tasks\": 194, \"approvals\": 0, \"handoffs\": 0, \"maintenance_jobs\": 4, \"legacy_memory_rows\": 0, \"legacy_task_rows\": 0}}\n\n[Showing last 50.0KB of line 1 (line is 94.0KB). Full output: /tmp/pi-bash-54b508c7a64febbe.log]"}],"details":{"truncation":{"content":"s11)\\n\\n**Use cases**\\n- 8-week academy, beginner series, seasonal programs, kids programs, transformation programs.\\n- Mantener cohortes con control de min/max y cierres automaticos.\\n\\n**E. Credit packages**\\n- Venta de credits prepago desde el negocio/storefront. [S7](#ref-s7)[S10](#ref-s10)[S11](#ref-s11)\\n- Precio, numero de credits, expiracion, purchase limit, time-limited sales, descripcion. [S11](#ref-s11)\\n- Credits venue/trainer-specific, no cross-merchant. [S7](#ref-s7)[S11](#ref-s11)\\n- Hoy documentados para Classes & Events; client help los presenta tambien para classes/events/courses. [S7](#ref-s7)[S11](#ref-s11)\\n\\n**Use cases**\\n- Session passes, bundles, loyalty/prepay, promo windows, member-only offers.\\n- Cobrar por adelantado y reducir friccion en repeat bookings.\\n\\n### 6. Payments / billing / invoicing\\n\\n**Features confirmadas**\\n- Online payments con Stripe o Mollie; Matchable cobra 1.8% platform fee + processing fees del PSP. [S3](#ref-s3)\\n- Aceptar pagos en 36+ paises. [S3](#ref-s3)\\n- Payment method obligatorio antes de aceptar bookings. [S8](#ref-s8)\\n- Invoices automaticas al cliente; visibles en \\\"My invoices\\\". [S3](#ref-s3)[S7](#ref-s7)[S8](#ref-s8)\\n- Pagos van directo al venue/trainer segun client-facing copy. [S7](#ref-s7)[S17](#ref-s17)\\n- Manual bookings gratis. [S3](#ref-s3)\\n- Manual payment links e in-person payments aparecen como coming soon en pricing. [S3](#ref-s3)\\n\\n**Use cases soportados**\\n- Cobro online nativo sin subscription fee.\\n- Operar mercados internacionales con PSPs estandar.\\n- Emitir comprobantes automaticamente y dar self-serve invoice access al cliente.\\n\\n### 7. Storefront / distribution / embedded commerce\\n\\n**Features confirmadas**\\n- Storefront publico agrega todo lo publicado: private sessions, classes, courses, events, credit packages. [S10](#ref-s10)\\n- Shareable link desde manager; puede usarse en Instagram bio, links directos, etc. [S10](#ref-s10)\\n- Booking widget para incrustar oferta propia en website. [S3](#ref-s3)[S10](#ref-s10)\\n- Marketplace opcional sin fee adicional por descubrir nuevos clientes via marketplace. [S3](#ref-s3)\\n\\n**Use cases soportados**\\n- Negocio sin web propia que usa Matchable como storefront principal.\\n- Negocio con web propia que embebe widget pero usa Matchable para checkout/ops.\\n- Clubs/academies que combinan demanda organica marketplace + trafico propio.\\n\\n### 8. Trainer operations\\n\\n**Features confirmadas**\\n- Crear trainer con nombre, email, funcion/categoria. [S8](#ref-s8)\\n- Asignar trainer a servicios. [S11](#ref-s11)\\n- Gestionar bookings del trainer desde manager. [S11](#ref-s11)\\n- Primary schedule y temporary schedule del trainer. [S11](#ref-s11)\\n- Holidays que override availability y muestran indisponibilidad al cliente. [S11](#ref-s11)\\n- Trainer app con home, weekly schedule, create training, availability, holidays y profile edit. [S11](#ref-s11)\\n- Release note del trainer app documenta manejo entre multiple venues. [S18](#ref-s18)\\n\\n**Use cases soportados**\\n- Coach independiente que se autogestiona.\\n- Academy con multiples coaches y horarios complejos.\\n- Coach itinerante entre varias sedes.\\n\\n### 9. Venue / academy operations\\n\\n**Features confirmadas**\\n- Venue schedule con opening hours, primary schedule y temporary schedule que overridea al principal. [S8](#ref-s8)\\n- Schedule/Calendar operativo donde conviven trainings, classes y courses. [S9](#ref-s9)[S11](#ref-s11)\\n- Day view / week view y quick booking overview. [S9](#ref-s9)\\n- Crear booking manualmente desde manager. [S9](#ref-s9)[S11](#ref-s11)\\n- Multi-venue management y unlimited manager seats para business tier segun pricing. [S3](#ref-s3)\\n\\n**Use cases soportados**\\n- Operar aperturas estacionales o holiday schedules.\\n- Ver conflictos y capacidad del dia en una sola vista.\\n- Administrar academies o grupos con varias sedes y varios managers.\\n\\n### 10. Client CRM / customer operations\\n\\n**Features confirmadas**\\n- Client database con nombre, email, phone. [S11](#ref-s11)\\n- Add Client manual. [S11](#ref-s11)\\n- View profile, add booking y WhatsApp contact desde perfil del cliente. [S11](#ref-s11)\\n- Ver booking history y filtros. [S11](#ref-s11)\\n- En cursos: payment status, payment type, revenue, add guest/manual participant, refund handling. [S11](#ref-s11)\\n\\n**Use cases soportados**\\n- Staff de front desk o academy manager que agrega walk-ins/manual signups.\\n- Seguimiento de recurrentes y soporte posventa.\\n- Gestion de cohortes y refunds en programas.\\n\\n### 11. Promotions / discounts / commercial tooling\\n\\n**Features confirmadas**\\n- Discount codes y vouchers en marketplace checkout. [S6](#ref-s6)\\n- Promotions y vouchers en business homepage. [S2](#ref-s2)\\n- Promotion tools listed as free in pricing. [S3](#ref-s3)\\n- Credit packages con time-limited sales. [S11](#ref-s11)\\n\\n**Use cases soportados**\\n- Acquisition offers, referral codes, Black Friday/new year campaigns.\\n- Prepaid bundles para aumentar retention y cashflow.\\n\\n### 12. Integrations / ecosystem\\n\\n**A. Court-booking / club systems**\\n- MATCHi: reserva cancha automaticamente al reservar private training; sync real-time; evita double-bookings; oculta slots sin court disponible. [S4](#ref-s4)[S12](#ref-s12)\\n- Matchpoint: auto-reserva court, sync availability y updates, cubre tennis/padel/beach tennis/pickleball. [S15](#ref-s15)\\n- KNLTB Meet & Play: auto court reservation y visibilidad de sesiones en KNLTB app. [S16](#ref-s16)\\n- Overview tambien lista AnyBuddy y Taykus con el mismo patron de auto court booking. [S4](#ref-s4)\\n\\n**Use cases**\\n- Club que quiere vender coaching sin hacer booking manual de cancha.\\n- Academia dentro de club que necesita respetar inventario real de courts.\\n- Ecosistemas federativos/club software donde Matchable actua como capa comercial.\\n\\n**Spec para copiar:** Ver la secci\\u00f3n [Ballbox \\u00d7 ATC](#ballbox-atc-spec-de-integraci-n) al final de este documento para el spec detallado de integraci\\u00f3n tipo MATCHi/Matchpoint/KNLTB, adaptado para Ballbox \\u00d7 ATC.\\n\\n**B. Calendar**\\n- Google Calendar integration page promete two-way sync: bloquear tiempo en Google bloquea disponibilidad en Matchable; cambios en Matchable actualizan Calendar. [S13](#ref-s13)\\n- Help article documenta al menos una suscripcion por URL/ICS a Google Calendar. [S11](#ref-s11)\\n\\n**Use cases**\\n- Coach que mezcla agenda personal y comercial.\\n- Reducir no-shows y overlaps.\\n\\n**Nota critica**\\n- Publicamente hay tension entre marketing (two-way sync) y help (ICS subscription, que normalmente es one-way). Conviene tratar el alcance exacto como \\\"publicamente reclamado, no completamente verificado\\\". [S13](#ref-s13)[S11](#ref-s11)\\n\\n**C. Digital sessions**\\n- Google Meet integration agrega links automaticamente a cada booking. [S14](#ref-s14)\\n- Business homepage y pricing listan digital sessions como capability. [S2](#ref-s2)[S3](#ref-s3)\\n\\n**Use cases**\\n- Remote coaching, video review, nutrition coaching, virtual classes.\\n\\n**D. Adjacent value-add tools**\\n- BenFit: entrenadores conectados con soporte nutricional. [S4](#ref-s4)\\n- PadelFast: conectar training clients con torneos/competiciones. [S4](#ref-s4)\\n\\n**Use cases**\\n- Extender ARPU y experiencia mas alla del booking.\\n- Convertir academia en funnel hacia competicion o nutrition upsell.\\n\\n### 13. Mobile products\\n\\n**Client app**\\n- Book next workout, view booked sessions, location and services, no signup/subscription fee. [S7](#ref-s7)\\n- Release notes: localization, courses, credits, top categories, storefront filters/organized overview, support for unplanned sessions. [S17](#ref-s17)\\n\\n**Trainer app**\\n- Daily/weekly schedule, reviews, connected venues, create training, availability, holidays, profile editing. [S11](#ref-s11)\\n- Release notes: manage bookings/schedules/holidays between multiple venues. [S18](#ref-s18)\\n\\n**Use cases soportados**\\n- Consumo movil para clientes repetitivos.\\n- Coach on-court que no quiere operar desde desktop.\\n\\n### 14. Support / onboarding / enablement\\n\\n**Features confirmadas**\\n- Free onboarding y full customer support segun FAQ de business homepage. [S2](#ref-s2)\\n- Chat support desde manager, email support <24h, knowledge base. [S3](#ref-s3)\\n- Help center estructurado para clients, trainer app y business manager. [S5](#ref-s5)\\n\\n**Use cases soportados**\\n- SMB onboarding rapido.\\n- Soporte operativo y autoservicio documental.\\n\\n### 15. Explicit \\\"coming soon\\\" / weakly evidenced areas\\n\\n**Publicamente listados como coming soon o ambiguos**\\n- Courses en pricing (aunque help center y app release notes los muestran activos). [S3](#ref-s3)[S11](#ref-s11)[S17](#ref-s17)\\n- Insights. [S3](#ref-s3)\\n- Manual payment links. [S3](#ref-s3)\\n- In-person payments. [S3](#ref-s3)\\n- Google Analytics y Meta Pixel add-ons. [S3](#ref-s3)\\n- Recruitment en pricing aparece sin detalle publico util. [S3](#ref-s3)\\n\\n**Lectura**\\n- No asumir cobertura plena ni GA/Pixel productizados a todos los clientes.\\n- Courses/credits/storefront parecen en rollout mas avanzado que pricing copy.\\n\\n## Mapa de casos de uso por persona\\n\\n### Cliente casual\\n- Encontrar un coach/venue por deporte.\\n- Comprar un 1:1 o clase puntual.\\n- Elegir attendees/duracion.\\n- Aplicar voucher.\\n- Pagar y reagendar si hace falta. [S6](#ref-s6)[S7](#ref-s7)\\n\\n### Cliente recurrente\\n- Comprar packages o credits.\\n- Dejar sesiones unplanned y consumirlas despues.\\n- Seguir usando el mismo trainer/venue via storefront/app. [S5](#ref-s5)[S7](#ref-s7)[S10](#ref-s10)\\n\\n### Alumno de programa\\n- Inscribirse a course multi-semana.\\n- Ver schedule completo.\\n- Entrar tarde con Smart Courses si quedan sesiones. [S11](#ref-s11)\\n\\n### Trainer independiente\\n- Publicar perfil y disponibilidad.\\n- Vender privados, clases y online sessions.\\n- Gestionar agenda/holidays desde app.\\n- Usar Google Calendar para coordinar vida personal + trabajo. [S2](#ref-s2)[S11](#ref-s11)[S13](#ref-s13)[S14](#ref-s14)\\n\\n### Venue/academy\\n- Publicar oferta completa en storefront.\\n- Gestionar trainers, bookings y clientes.\\n- Vender credits/cursos/eventos.\\n- Cobrar online e invoicing automatico. [S3](#ref-s3)[S10](#ref-s10)[S11](#ref-s11)\\n\\n### Racquet club integrado\\n- Mantener MATCHi/Matchpoint/KNLTB como sistema de courts.\\n- Usar Matchable para captar y vender coaching.\\n- Evitar doble reserva de cancha. [S12](#ref-s12)[S15](#ref-s15)[S16](#ref-s16)\\n\\n### Operador multi-sede\\n- Administrar varias sedes y multiples managers.\\n- Unificar operacion con mismos flujos de booking/clients/trainers. [S3](#ref-s3)\\n\\n## JTBD map\\n\\n**JTBD: \\\"quiero entrenar esta semana con alguien bueno\\\"**\\n- Search marketplace\\n- Trainer profiles\\n- Reviews\\n- Booking + planning\\n- Reschedule/cancel [S1](#ref-s1)[S6](#ref-s6)[S7](#ref-s7)[S19](#ref-s19)\\n\\n**JTBD: \\\"quiero vender mi coaching sin perseguir pagos ni whatsapps\\\"**\\n- Storefront\\n- Online payments\\n- Packages/credits\\n- Automated invoices\\n- Trainer app [S3](#ref-s3)[S10](#ref-s10)[S11](#ref-s11)\\n\\n**JTBD: \\\"quiero correr una academia o programa estructurado\\\"**\\n- Courses\\n- Participant limits\\n- Auto-cancel\\n- Smart Courses\\n- Participant management [S11](#ref-s11)\\n\\n**JTBD: \\\"quiero monetizar horas valle o crear comunidad\\\"**\\n- Classes\\n- Events\\n- Credits/promotions\\n- Public marketplace distribution [S2](#ref-s2)[S3](#ref-s3)[S10](#ref-s10)\\n\\n**JTBD: \\\"quiero que vender coaching no rompa mi operacion de club\\\"**\\n- Court-booking integrations\\n- Availability sync\\n- Booking statuses\\n- Multi-venue / manager operations [S3](#ref-s3)[S9](#ref-s9)[S12](#ref-s12)[S15](#ref-s15)[S16](#ref-s16)\\n\\n**JTBD: \\\"quiero ofrecer coaching online tambien\\\"**\\n- Digital sessions\\n- Google Meet links\\n- Unified profile for in-person + online [S2](#ref-s2)[S14](#ref-s14)\\n\\n## Hallazgos estrategicos\\n\\n1. Matchable parece mas fuerte cuando el negocio vende no solo privados sino un mix de privados + clases + cursos + credits + eventos desde storefront. La plataforma ya esta modelada para ese portfolio, no solo para appointment booking simple. [S2](#ref-s2)[S3](#ref-s3)[S10](#ref-s10)[S11](#ref-s11)\\n2. El wedge mas diferenciador en racquet sports es academy automation conectada a court inventory externo. Eso resuelve un problema operativo real que marketplaces generalistas no resuelven. [S12](#ref-s12)[S15](#ref-s15)[S16](#ref-s16)\\n3. La company esta empujando una evolucion desde \\\"trainer booking\\\" a \\\"sport commerce OS ligero\\\": storefront, widget, apps, credits, courses, integrations, manager. [S2](#ref-s2)[S3](#ref-s3)[S10](#ref-s10)[S17](#ref-s17)[S18](#ref-s18)\\n4. El posicionamiento geografico/publico es multi-country, pero la fuerza de las integraciones y ejemplos visibles sesga hacia Europa y racquet/federation ecosystems. [S12](#ref-s12)[S15](#ref-s15)[S16](#ref-s16)\\n\\n## Riesgos, limites y lectura critica\\n\\n- Parte del marketplace publico devuelve `0 results` sin contexto o depende de query/location; por tanto el mapeo se centra en capacidades de producto confirmadas, no en catalogo disponible en cada region. [S20](#ref-s20)[S21](#ref-s21)\\n- Hay copy inconsistente entre pricing y help center/app release notes sobre courses, credits e insights. No conviene asumir rollout uniforme por cliente/mercado. [S3](#ref-s3)[S11](#ref-s11)[S17](#ref-s17)\\n- La profundidad del BI/reporting no es visible publicamente; \\\"insights\\\" aparece, pero sin detalle funcional verificable. [S3](#ref-s3)\\n- La integracion de Google Calendar se describe como two-way en marketing, pero el help publico solo verifica una integracion via URL/ICS; tratar la sincronizacion bidireccional exacta como probable pero no cerrada. [S13](#ref-s13)[S11](#ref-s11)\\n- No pude verificar exhaustivamente todas las venue pages publicas desde muestra puntual; algunas URLs del sitemap no devolvieron contenido util. [S20](#ref-s20)\\n\\n## Fuentes primarias usadas\\n\\n- [S1] Matchable consumer homepage: https://www.matchable.io/\\n- [S2] Matchable business homepage: https://business.matchable.io/\\n- [S3] Matchable pricing: https://matchable.io/business/pricing\\n- [S4] Matchable integrations overview: https://matchable.io/business/integrations\\n- [S5] Matchable help center index: https://help.matchable.io/en/\\n- [S6] How to book a training: https://help.matchable.io/en/articles/10-how-to-book-a-training\\n- [S7] Client app / credits / FAQ / invoices help pages: https://help.matchable.io/en/articles/15-client-app-iosandroid ; https://help.matchable.io/en/articles/23-how-to-use-credits ; https://help.matchable.io/en/articles/12-faq ; https://help.matchable.io/en/articles/39-invoice-for-my-purchases\\n- [S8] Quick-start manager setup pages: https://help.matchable.io/en/articles/1-add-a-payment-method ; https://help.matchable.io/en/articles/2-create-your-first-class-or-event ; https://help.matchable.io/en/articles/3-create-your-first-private-service ; https://help.matchable.io/en/articles/4-how-to-edit-your-trainer-profile ; https://help.matchable.io/en/articles/5-how-to-edit-your-venue-profile ; https://help.matchable.io/en/articles/6-edit-your-availability ; https://help.matchable.io/en/articles/7-add-your-venue-schedule\\n- [S9] Manager operations pages: https://help.matchable.io/en/articles/17-manage-trainings-and-classes ; https://help.matchable.io/en/articles/18-manage-your-trainers-schedules ; https://help.matchable.io/en/articles/19-manage-your-clients ; https://help.matchable.io/en/articles/22-manage-credits\\n- [S10] Storefront guide: https://help.matchable.io/en/articles/38-getting-started-with-your-storefront\\n- [S11] Courses + trainer app help pages: https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trainer-app ; https://help.matchable.io/en/articles/14-adding-and-managing-your-holidays ; https://help.matchable.io/en/articles/41-getting-started-with-courses ; https://help.matchable.io/en/articles/42-smart-courses-feature ; https://help.matchable.io/en/articles/43-set-up-a-course-page-template ; https://help.matchable.io/en/articles/44-create-and-manage-your-course-schedule ; https://help.matchable.io/en/articles/45-viewing-courses-in-the-calendar ; https://help.matchable.io/en/articles/46-manage-course-participants\\n- [S12] MATCHi integration: https://matchable.io/business/integration/matchi\\n- [S13] Google Calendar integration: https://matchable.io/business/integration/google-calendar\\n- [S14] Google Meet integration: https://matchable.io/business/integration/google-meet\\n- [S15] Matchpoint integration: https://matchable.io/business/integration/matchpoint\\n- [S16] KNLTB Meet & Play integration: https://matchable.io/business/integration/knltb-meet-play\\n- [S17] Client app store pages / release notes: https://apps.apple.com/nl/app/matchable-book-your-training/id6742325816 ; https://play.google.com/store/apps/details?id=com.getmatchable.client\\n- [S18] Trainer app store pages / release notes: https://apps.apple.com/nl/app/matchable-for-trainers/id6468798694 ; https://play.google.com/store/apps/details?id=com.getmatchable.app\\n- [S19] Sample trainer page: https://matchable.io/trainers/alex-hosp\\n- [S20] Matchable sitemap: https://matchable.io/sitemap.xml\\n- [S21] Explore / Activities public pages: https://matchable.io/explore ; https://matchable.io/activities\\n\\n\\n- [S22] Spec Ballbox \\u00d7 ATC: secci\\u00f3n 16 de este documento (Spec de integraci\\u00f3n para copiar patr\\u00f3n MATCHi/Matchpoint/KNLTB en Ballbox)\\n\\n## Ballbox \\u00d7 ATC \\u2014 Spec de Integraci\\u00f3n\\n\\n*Spec de integraci\\u00f3n para copiar el patr\\u00f3n de MATCHi/Matchpoint/KNLTB en Ballbox.*\\n\\n---\\n\\n### 1. Contexto de negocio\\n\\n**Problema que resuelve**\\n\\nUn club de tennis/padel usa ATC como su sistema de gesti\\u00f3n de canchas (reservas, pagos, inventario). Ballbox quiere vender trainings y clases de sus trainers en ese club. El riesgo es vender un entrenamiento cuando la cancha no est\\u00e1 disponible, o duplicar reservas.\\n\\n**Soluci\\u00f3n**\\n\\nCuando alguien reserva un training en Ballbox, el sistema autom\\u00e1ticamente verifica disponibilidad y reserva la cancha en ATC al mismo tiempo. Si el club cancela o modifica la reserva en ATC, Ballbox se entera y act\\u00faa en consecuencia.\\n\\n**Modelo de relaci\\u00f3n**\\n\\n- ATC es el **source of truth** para inventario de canchas\\n- Ballbox es el **canal de venta** de trainings\\n- Ambas plataformas mantienen sync en tiempo real\\n\\n---\\n\\n### 2. Descripci\\u00f3n funcional del flujo\\n\\n#### 2.1. Un cliente busca un entrenamiento\\n\\nEl cliente ve en Ballbox los trainers disponibles de un club. Al seleccionar fecha y horario, Ballbox consulta a ATC qu\\u00e9 canchas est\\u00e1n libres en ese momento. Solo muestra opciones disponibles.\\n\\n#### 2.2. El cliente confirma la reserva\\n\\nCuando el cliente paga o confirma el training en Ballbox:\\n1. Ballbox registra la reserva del training para el trainer\\n2. Simult\\u00e1neamente, Ballbox reserva la cancha en ATC para ese horario\\n3. Ambas reservas quedan linkeadas por un identificador compartido\\n4. Si la cancha no se puede reservar (ya se ocup\\u00f3 entre consulta y confirmaci\\u00f3n), Ballbox informa al cliente y sugiere otro horario\\n\\n#### 2.3. El cliente o el trainer cancela desde Ballbox\\n\\nBallbox cancela la reserva del training y, al mismo tiempo, libera la cancha en ATC.\\n\\n#### 2.4. El club cancela o modifica la reserva desde ATC\\n\\nATC notifica a Ballbox del cambio. Ballbox:\\n- Informa al cliente/trainer\\n- Ofrece reagendar o cancelar sin costo\\n- Marca la reserva como afectada\\n\\n#### 2.5. El club tiene la cancha indisponible (mantenimiento, evento)\\n\\nATC informa a Ballbox. Ballbox marca esas horas como no disponibles para nuevos bookings de trainings.\\n\\n---\\n\\n### 3. Escenarios soportados\\n\\n| Escenario | Comportamiento |\\n|-----------|----------------|\\n| Training reservado en Ballbox, club cierra la cancha | Ballbox notifica al cliente, ofrece reagendar |\\n| Trainer cancela training en Ballbox | La cancha se libera en ATC autom\\u00e1ticamente |\\n| Cliente reserva training, la cancha se ocupa antes de confirmar | Ballbox muestra horarios alternativos |\\n| Ballbox cae durante la reserva | La transacci\\u00f3n se deshace; la cancha en ATC no queda reservada |\\n| ATC tiene mantenimiento | Ballbox lo detecta y muestra \\\"canchas temporalmente no disponibles\\\" |\\n\\n---\\n\\n### 4. Definici\\u00f3n de responsabilidades\\n\\n**Ballbox se encarga de:**\\n- Mostrar la oferta de trainings y trainers\\n- Gestionar el checkout, pago y comunicaci\\u00f3n con el cliente\\n- Mantener el estado del training (confirmado, cancelado, reagendado)\\n- Notificar al cliente ante cambios en disponibilidad de cancha\\n- Persistir la referencia cruzada entre booking de training y booking de cancha\\n\\n**ATC se encarga de:**\\n- Mantener el inventario actualizado de canchas por club\\n- Gestionar reservas de cancha (crear, cancelar, consultar disponibilidad)\\n- Procesar pagos de alquiler de canasta si aplica\\n- Notificar a Ballbox ante cambios en reservas de cancha realizados fuera de Ballbox\\n- Proveer soporte t\\u00e9cnico para la integraci\\u00f3n\\n\\n---\\n\\n### 5. Especificaci\\u00f3n t\\u00e9cnica\\n\\n*Requiere que ATC expose los siguientes endpoints (si no existen, se arman en coordinaci\\u00f3n con ATC).*\\n\\n#### 5.1. Consultar disponibilidad\\n\\n```\\nGET /api/v1/clubs/{club_id}/availability?sport={sport}&date={YYYY-MM-DD}\\n\\nRespuesta:\\n{\\n  \\\"date\\\": \\\"2026-03-25\\\",\\n  \\\"slots\\\": [\\n    {\\n      \\\"court_id\\\": \\\"court-1\\\",\\n      \\\"court_name\\\": \\\"Cancha 1\\\",\\n      \\\"time\\\": \\\"14:00\\\",\\n      \\\"duration_minutes\\\": 60,\\n      \\\"available\\\": true\\n    },\\n    {\\n      \\\"court_id\\\": \\\"court-1\\\",\\n      \\\"time\\\": \\\"15:00\\\",\\n      \\\"available\\\": false,\\n      \\\"reason\\\": \\\"booked\\\"\\n    }\\n  ]\\n}\\n```\\n\\n#### 5.2. Reservar cancha\\n\\n```\\nPOST /api/v1/bookings\\n{\\n  \\\"club_id\\\": \\\"club-123\\\",\\n  \\\"court_id\\\": \\\"court-456\\\",\\n  \\\"datetime\\\": \\\"2026-03-25T14:00:00\\\",\\n  \\\"duration_minutes\\\": 60,\\n  \\\"client_name\\\": \\\"Juan P\\u00e9rez\\\",\\n  \\\"client_phone\\\": \\\"+5491112345678\\\",\\n  \\\"external_ref\\\": \\\"ballbox-bk-789\\\",\\n  \\\"notes\\\": \\\"Training Ballbox\\\"\\n}\\n\\nRespuesta:\\n{\\n  \\\"booking_id\\\": \\\"atc-bk-111\\\",\\n  \\\"status\\\": \\\"confirmed\\\",\\n  \\\"court\\\": \\\"Cancha 1\\\",\\n  \\\"datetime\\\": \\\"2026-03-25T14:00:00\\\"\\n}\\n```\\n\\n#### 5.3. Cancelar reserva de cancha\\n\\n```\\nDELETE /api/v1/bookings/{atc_booking_id}?reason={reason}\\n\\nRespuesta:\\n{\\n  \\\"booking_id\\\": \\\"atc-bk-111\\\",\\n  \\\"status\\\": \\\"cancelled\\\"\\n}\\n```\\n\\n#### 5.4. Consultar estado de una reserva\\n\\n```\\nGET /api/v1/bookings/{atc_booking_id}\\n\\nRespuesta:\\n{\\n  \\\"booking_id\\\": \\\"atc-bk-111\\\",\\n  \\\"status\\\": \\\"confirmed\\\" | \\\"cancelled\\\" | \\\"completed\\\",\\n  \\\"court_id\\\": \\\"court-456\\\",\\n  \\\"datetime\\\": \\\"2026-03-25T14:00:00\\\"\\n}\\n```\\n\\n#### 5.5. Webhook \\u2014 Notificaciones desde ATC\\n\\nBallbox expone un endpoint que ATC llama ante cambios:\\n\\n```\\nPOST /ballbox/webhooks/atc\\n\\nPayload:\\n{\\n  \\\"event\\\": \\\"booking_cancelled\\\" | \\\"court_unavailable\\\" | \\\"maintenance\\\",\\n  \\\"atc_booking_id\\\": \\\"atc-bk-111\\\",\\n  \\\"external_ref\\\": \\\"ballbox-bk-789\\\",\\n  \\\"reason\\\": \\\"club_cancelled\\\" | \\\"court_blocked\\\",\\n  \\\"affected_slots\\\": [\\\"2026-03-25T14:00:00\\\"]\\n}\\n```\\n\\nBallbox responde con `200 OK` y procesa el evento.\\n\\n#### 5.6. Autenticaci\\u00f3n\\n\\n- ATC provee `api_key` por club\\n- Ballbox env\\u00eda `Authorization: Bearer {api_key}` en cada request\\n- Ballbox expone webhook con firma HMAC para verificar origen\\n\\n---\\n\\n### 6. Configuraci\\u00f3n por club\\n\\nPara activar la integraci\\u00f3n, cada club configura en Ballbox:\\n\\n| Campo | Descripci\\u00f3n |\\n|-------|-------------|\\n| `atc_club_id` | Identificador del club en ATC |\\n| `atc_api_key` | Clave de API entregada por ATC |\\n| `sports_enabled` | Lista de deportes habilitados (tennis, padel, etc.) |\\n| `default_duration_minutes` | Duraci\\u00f3n por defecto de un training |\\n\\n---\\n\\n### 7. Roadmap de implementaci\\u00f3n sugerida\\n\\n**Fase 1 \\u2014 MVP (4-6 semanas)**\\n- Disponibilidad consulta\\n- Reserva de cancha\\n- Cancelaci\\u00f3n desde Ballbox\\n- Sincronizaci\\u00f3n unidireccional (Ballbox \\u2192 ATC)\\n\\n**Fase 2 \\u2014 Bidireccional (2-3 semanas)**\\n- Webhooks desde ATC\\n- Notificaciones al cliente\\n- Manejo de cancelaciones externas\\n\\n**Fase 3 \\u2014 Robustez (2 semanas)**\\n- Retry con backoff ante fallos de ATC\\n- Cola de sincronizaci\\u00f3n para fallos\\n- Panel de estado de integraci\\u00f3n por club\\n\\n---\\n\\n*Spec elaborado el 24 de marzo de 2026, basado en el patr\\u00f3n de integraci\\u00f3n de Matchable con MATCHi/Matchpoint/KNLTB.*\\n\\n## Fecha de corte\\n\\nInvestigacion basada en fuentes publicas verificadas disponibles el 24 de marzo de 2026.\\n\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"created_at\": \"2026-03-24T14:26:24.158968+00:00\", \"updated_at\": \"2026-03-24T15:39:47.195210+00:00\", \"metadata\": {\"report_type\": \"feature-use-case-map\", \"run_id\": \"a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"topic\": \"matchable\"}}, {\"id\": \"art_1cb15ded78c141abbfc2abd40a45e776\", \"task_id\": \"task_c20a048b387d4513ac38cf0e67bf83cd\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.259060+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"evidence\\\": [\\\"personal_agent/runtime.py:451-492\\\", \\\"personal_agent/runtime.py:297-300\\\", \\\"tests/test_personal.py:909-914\\\", \\\"tests/test_personal.py:934-947\\\", \\\"docs/system-v1.md:114-115\\\"], \\\"goal\\\": \\\"Make ai-dev-workflow handoffs non-terminal on acceptance and gate parent completion on explicit downstream terminal execution results.\\\", \\\"repo\\\": \\\"personal-agent\\\", \\\"requirements\\\": [\\\"If a task decision creates an ai-dev-workflow handoff, do not mark the parent task completed yet.\\\", \\\"Model accepted as non-terminal for ai-dev-workflow handoffs.\\\", \\\"Require explicit downstream terminal result before handoff completion and parent-task completion.\\\", \\\"Update or add tests covering parent-task gating and accepted-vs-terminal handoff states.\\\", \\\"Update docs to remove the 'accepted JSON' success contract.\\\"], \\\"scope\\\": [\\\"personal_agent/runtime.py\\\", \\\"tests/test_personal.py\\\", \\\"docs/system-v1.md\\\", \\\"README.md\\\"], \\\"task_id\\\": \\\"task_c20a048b387d4513ac38cf0e67bf83cd\\\"}, \\\"reason\\\": \\\"personal-agent currently marks parent tasks complete on handoff acceptance instead of downstream execution result\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802651+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802652+00:00\", \"metadata\": {}}, {\"id\": \"art_a6773502ed4d46d09eed3e41081c0c68\", \"task_id\": \"task_2111a366cf73412eadb396acc1c2d004\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.272531+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_2111a366cf73412eadb396acc1c2d004\\\", \\\"intent\\\": \\\"fix in http://127.0.0.1:8082/ UI, in the \\\\\\\"blocked tasks\\\\\\\" input, it looses focus after 1-2 seconds. Doesnt happen for ex on \\\\\\\"NEW INTAKE\\\\\\\"'s input\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"Use Python as the shell entrypoint for persistence, approvals, auditability, and shared DB state; reproduce on http://127.0.0.1:8082/, inspect the blocked-tasks input vs NEW INTAKE input render/update paths, fix the remount or state-reset causing blur, then run full repo checks.\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"codex\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"UI focus-loss bug in local app; repo debugging and fix first, with personal-agent context kept for stateful workflow.\\\", \\\"secondary_agent\\\": \\\"personal\\\"}, \\\"task_id\\\": \\\"task_2111a366cf73412eadb396acc1c2d004\\\"}, \\\"reason\\\": \\\"UI focus-loss bug in local app; repo debugging and fix first, with personal-agent context kept for stateful workflow.\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. 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Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802589+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802590+00:00\", \"metadata\": {}}, {\"id\": \"art_f512f87edb3f4c56bff2767964cdd312\", \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.286009+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\", \\\"intent\\\": \\\"Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (repo)\\\", \\\"secondary_agent\\\": null}, \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\"}, \\\"reason\\\": \\\"code work (repo)\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802544+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802546+00:00\", \"metadata\": {}}, {\"id\": \"art_2778eb43a141444b9f9ea6408e009340\", \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-16T15:46:39.288035+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\", \\\"intent\\\": \\\"Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (fix)\\\", \\\"secondary_agent\\\": null}, \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\"}, \\\"reason\\\": \\\"code work (fix)\\\", \\\"result_summary\\\": \\\"Deprecated durable ai-dev-workflow handoff. Re-run this work through the code subagent.\\\", \\\"status\\\": \\\"failed\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802511+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802513+00:00\", \"metadata\": {}}, {\"id\": \"art_55b1c9bdee6f4226a81a9160c4703b62\", \"task_id\": \"task_afed9eaf504f42b99348a815d56148cc\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> implementation-agent\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-14T21:29:18.517341+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"artifact_id\\\": \\\"art_598b5da707dd47709fc0fe9bb3e79e28\\\", \\\"epic_id\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"expected_artifacts\\\": [\\\"migration_report\\\", \\\"v1_cross_reference_report\\\"], \\\"idempotency_key\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"remaining_task_ids\\\": [\\\"task_a240986d7c8a4474ba5246536769deed\\\", \\\"task_c0b65186af7d4013b2c1d2906ea6a28e\\\"]}, \\\"reason\\\": \\\"Finish remaining V1 migration and GitHub cross-reference work\\\", \\\"result_summary\\\": \\\"Finished legacy bridge and V1 multi-repo cross-reference work; shared DB now contains the resume anchors and completion artifacts.\\\", \\\"status\\\": \\\"completed\\\", \\\"to_agent\\\": \\\"implementation-agent\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-03-24T14:18:16.802221+00:00\", \"updated_at\": \"2026-03-24T14:18:16.802225+00:00\", \"metadata\": {}}, {\"id\": \"art_8dcad6182a03483aa914ad451db388fa\", \"task_id\": \"task_ac84e7e584494c2a95ed4a5bd2e1f92b\", \"artifact_type\": \"run_failure\", \"title\": \"Codex failure for links en la UI de 8082 en markdown andan mal cuando son archivos porque se marcan como relative d...\", \"content\": \"# Codex run failed\\n\\n- Exit code: 1\\n\\n## Stderr\\nOpenAI Codex v0.115.0 (research preview)\\n--------\\nworkdir: /Users/sebas/Code/ai-dev-workflow\\nmodel: gpt-5.4\\nprovider: openai\\napproval: never\\nsandbox: danger-full-access\\nreasoning effort: none\\nreasoning summaries: none\\nsession id: 019cfdaa-6e65-7d62-801b-03d94c51ba23\\n--------\\nuser\\nYou are running as a direct Codex execution for personal-agent.\\nWork from this repository root: /Users/sebas/Code/ai-dev-workflow\\nUse the repository's local rules and skills.\\nImplement directly when needed.\\nReturn markdown only.\\nInclude these sections:\\n- Summary\\n- Changes\\n- Verification\\n- Outputs\\nIn Outputs, list any branch, PR, file paths, or artifacts created.\\n\\nTask: links en la UI de  8082 en markdown andan mal cuando son archivos porque se marcan como relative de localhost:8082. deberian ser absolute paths (file://...)\\nRelevant memory:\\n- Task completed: investigar playtomic y armar un mapeo exhaustivo de todas sus features y use cases. Primero un br...: ## Brief ejecutivo\\n\\n**Qu\\u00e9 es Playtomic**\\n\\nPlaytomic es una plataforma de dos caras para deportes de raqueta y, en partes del producto, otros deportes de pista/cancha. Tiene cuatro \\n- Task completed: Improve daemon/UI operator workflow and task communication: # Findings\\n\\n## Already implemented\\n\\n- Interrupted run recovery exists in `personal_agent/runtime.py:930`: stale `running` task runs are marked `interrupted`, an `execution_state` a\\n- Task completed: In /Users/sebas/Code/calistenia, change a tiny UI string in the app and run repo checks: ## Findings\\n- Repo was clean before edit.\\n- Remaining user-visible typo found in filter config: `Musculo`.\\n- No `lint`, `typecheck`, or `test` scripts exist in `package.json`; repo\\nmcp: figma starting\\nmcp: figma ready\\nmcp startup: ready: figma\\nERROR: You've hit your usage limit. Upgrade to Pro (https://chatgpt.com/explore/pro), visit https://chatgpt.com/codex/settings/usage to purchase more credits or try again at Mar 18th, 2026 2:28 PM.\\nWarning: no last agent message; wrote empty content to /Users/sebas/personal-agent/data/runs/run_7895a9b3e900469984a61ea6092271fc.md\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-17T21:18:55.514267+00:00\", \"updated_at\": \"2026-03-17T21:18:55.514267+00:00\", \"metadata\": {\"classification\": \"intermediate\", \"run_id\": \"run_7895a9b3e900469984a61ea6092271fc\"}}, {\"id\": \"art_smoke_markdown_file_links\", \"task_id\": \"task_smoke_markdown_file_links\", \"artifact_type\": \"report\", \"title\": \"smoke markdown file links\", \"content\": \"[tmp](personal-agent-markdown-link-test.md)\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent:smoke\", \"created_at\": \"2026-03-17T21:11:26.188844+00:00\", \"updated_at\": \"2026-03-17T21:11:26.188844+00:00\", \"metadata\": {\"cwd\": \"/tmp\"}}, {\"id\": \"art_5e03c108db5345269e77d02e21d07dd9\", \"task_id\": \"task_84da4829fdea45728971df65c57ff33f\", \"artifact_type\": \"report\", \"title\": \"Codex result for Document the current Raycast-based fullscreen notification and reply implementation for Codex.\", \"content\": \"Hi Sebas. Kept it tight.\\n\\nDocumented current live flow in [docs/raycast-codex-notify.md](/Users/sebas/personal-agent/docs/raycast-codex-notify.md). It captures the actual implementation split between `~/.codex/hooks/notify_macos.sh` and `~/Code/raycast-codex-reply`, including:\\n\\n- notify hook decision path\\n- rollout-message recovery from `~/.codex/state_5.sqlite`\\n- Raycast reply bridge files + deeplinks\\n- approval watcher flow\\n- fallback AppleScript dialog\\n- explicit note that `personal-agent`\\u2019s old blocker/reply path is removed, so this is not handled by the current runtime\\n\\nAdded a README pointer at [README.md](/Users/sebas/personal-agent/README.md).\\n\\nVerification: `./scripts/run-checks.sh` passed.\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-17T02:12:31.505135+00:00\", \"updated_at\": \"2026-03-17T02:12:31.505135+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/personal-agent\", \"output_refs\": {\"paths\": [\"./scripts/run-checks.sh\", \"~/.codex/hooks/notify_macos.sh\", \"~/.codex/state_5.sqlite\", \"~/Code/raycast-codex-reply\"]}, \"run_id\": \"run_9f50b4d09e5842498c055923a851c678\"}}, {\"id\": \"art_d69dff710f97429882a05b0ec979ac5b\", \"task_id\": \"task_be1a1e514a7d43d1b59009ccca9fb57c\", \"artifact_type\": \"report\", \"title\": \"Codex result for Migrate remaining personal-agent legacy persistence/read paths to the shared task/runtime model\", \"content\": \"Hi. Legacy seam closed.\\n\\nCanonical shared-memory model now drives personal-agent surfaces from [shared_memory.py](/Users/sebas/personal-agent/personal_agent/shared_memory.py), with `personal-agent-memory-v1` and `personal-agent-task-v1` helpers, canonical kind detection, and an actual in-place migrator for old memory/task records plus legacy approval memories. [research_store.py](/Users/sebas/personal-agent/personal_agent/research_store.py) now reads/writes canonical run/source/claim/artifact/task records, drops the old bridge fallback for run inspection, uses native approvals rows for request/list/resolve, and includes approvals in search results. [router.py](/Users/sebas/personal-agent/personal_agent/router.py) now mirrors handoffs with canonical memory metadata, and [scripts/personal.py](/Users/sebas/personal-agent/scripts/personal.py) wires `approvals resolve` back into the CLI. [migration.py](/Users/sebas/personal-agent/personal_agent/migration.py) now runs real migration work instead of no-op, and [README.md](/Users/sebas/personal-agent/README.md) reflects the new model.\\n\\nCoverage added in [tests/test_personal.py](/Users/sebas/personal-agent/tests/test_personal.py) for canonical research records, native approval lifecycle, and legacy migration behavior. Verified with `./scripts/run-checks.sh` and `git diff --check`; both passed. No daemon/UI smoke run.\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-17T02:02:06.161088+00:00\", \"updated_at\": \"2026-03-17T02:02:06.161088+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/personal-agent\", \"output_refs\": {\"paths\": [\" helpers, canonical kind detection, and an actual in-place migrator for old memory/task records plus legacy approval memories. [research_store.py](/Users/sebas/personal-agent/personal_agent/research_store.py) now reads/writes canonical run/source/claim/artifact/task records, drops the old bridge fallback for run inspection, uses native approvals rows for request/list/resolve, and includes approvals in search results. [router.py](/Users/sebas/personal-agent/personal_agent/router.py) now mirrors handoffs with canonical memory metadata, and [scripts/personal.py](/Users/sebas/personal-agent/scripts/personal.py) wires \", \"./scripts/run-checks.sh\"]}, \"run_id\": \"run_b694a238b532454bb547c056246a31fd\"}}, {\"id\": \"art_b4f5386484794717b0ddc4c42c265319\", \"task_id\": \"task_871c609196b94132aa686188007f6dea\", \"artifact_type\": \"report\", \"title\": \"Codex result for Consolidate all Ballbox payment-system work from demo validation through production readiness and go-live certification.\", \"content\": \"Consolidated into one active workflow.\\n\\nCreated local repo mapping in [.agents/local-config.json](/Users/sebas/Code/ai-dev-workflow/.agents/local-config.json), set current feature in [.agents/current-feature](/Users/sebas/Code/ai-dev-workflow/.agents/current-feature), and wrote the active feature context in [.agents/feature-contexts/ballbox-payment-system-prod-readiness.json](/Users/sebas/Code/ai-dev-workflow/.agents/feature-contexts/ballbox-payment-system-prod-readiness.json). Added the execution brief/checklist in [docs/ballbox-payment-system-consolidation.md](/Users/sebas/Code/ai-dev-workflow/docs/ballbox-payment-system-consolidation.md).\\n\\nKey consolidation outcome: `ballbox` is stable/demo-facing only; `ballbox-payment-integration` is the real workstream. Demo validation exists now. Production hardening is partially drafted but unstaged in backend. Go-live certification is still blocked by two real gaps: missing TCN/Yunshu live adapter contract, and unvalidated MercadoPago real QR flow.\\n\\nVerification: JSON valid. Shared-memory sync failed because `shared_agent_memory` is not installed/importable in this environment. Control repo also remains behind `origin/main` by 1 commit: `111e974 Feat: accept shared memory run-task handoffs (#2)`. I did not pull because this branch is also 3 commits ahead.\\n\\nBest next move: review and commit the backend readiness slice in `~/Code/ballbox-payment-integration`, then `/status --current`.\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-17T01:55:28.432378+00:00\", \"updated_at\": \"2026-03-17T01:55:28.432378+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/Code/ai-dev-workflow\", \"output_refs\": {\"paths\": [\" is not installed/importable in this environment. Control repo also remains behind \", \" is stable/demo-facing only; \", \"~/Code/ballbox-payment-integration\"]}, \"run_id\": \"run_80140950f2c04d10bd3941d712ab0b25\"}}, {\"id\": \"art_a5cbdcb711234fd3b8306f379678523c\", \"task_id\": \"task_746e1a74f77b4cb0b8e3fd950fad90cb\", \"artifact_type\": \"report\", \"title\": \"link smoke\", \"content\": \"[tct](link) [txt](link.com)\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-16T20:42:13.176072+00:00\", \"updated_at\": \"2026-03-16T20:42:13.176072+00:00\", \"metadata\": {}}, {\"id\": \"art_322e6f0e62724030b8d995053a5ec674\", \"task_id\": \"task_2f22e794212641c49d7f8a600ad3a4bd\", \"artifact_type\": \"report\", \"title\": \"Codex result for en personal-agent que el script que hace restart del daemon pause tareas corriendo (como sea, que...\", \"content\": \"## Summary\\nImplementado en `personal-agent`: el restart del daemon ahora pausa runs activas, las deja en estado reiniciable, y al levantar de nuevo el runtime intenta relanzarlas solo. Tambi\\u00e9n blind\\u00e9 el watcher para que un run ya pausado no termine marcado como fallo cuando llega el `SIGTERM`.\\n\\n## Changes\\n- [`/Users/sebas/personal-agent/personal_agent/runtime.py`](file:///Users/sebas/personal-agent/personal_agent/runtime.py)\\n  - agregado `pause_running_tasks()`\\n  - `stop()` ahora puede pausar antes de terminar procesos\\n  - runs `running` recuperadas tras restart/crash pasan a `paused`, task vuelve a `draft`, metadata marca `auto_restart_on_daemon_start`\\n  - en boot, `_restart_paused_runs()` relanza tasks pausadas con `cwd`/prompt guardados\\n  - `_watch_run()` sale temprano si el run ya no est\\u00e1 `running`, evita falso `failed`\\n- [`/Users/sebas/personal-agent/scripts/personal.py`](file:///Users/sebas/personal-agent/scripts/personal.py)\\n  - nuevo comando `pause-running --reason ...`\\n- [`/Users/sebas/personal-agent/scripts/daemon-8082.sh`](file:///Users/sebas/personal-agent/scripts/daemon-8082.sh)\\n  - `restart` ahora ejecuta `python3 scripts/personal.py pause-running ...` antes de `stop/start`\\n- [`/Users/sebas/personal-agent/tests/test_personal.py`](file:///Users/sebas/personal-agent/tests/test_personal.py)\\n  - actualizado recovery test para validar pause + auto-restart\\n  - nuevo test para pause/shutdown race, sin false failure/artifact\\n\\n## Verification\\n- `python3 -m unittest tests.test_personal` \\u2705\\n- `./scripts/run-checks.sh` \\u2705\\n- `./scripts/daemon-8082.sh restart`\\n  - pause step ejecut\\u00f3 bien\\n  - restart completo bloqueado por entorno actual: `\\\"foreign listener on 127.0.0.1:8082 (pid 18283); refusing to stop it\\\"`\\n- `curl -fsS http://127.0.0.1:8082/api/status` \\u2705\\n  - confirm\\u00f3 que la task activa qued\\u00f3 `draft` con latest run `paused`\\n\\n## Outputs\\n- Branch: none\\n- PR: none\\n- Files changed:\\n  - [`/Users/sebas/personal-agent/personal_agent/runtime.py`](file:///Users/sebas/personal-agent/personal_agent/runtime.py)\\n  - [`/Users/sebas/personal-agent/scripts/personal.py`](file:///Users/sebas/personal-agent/scripts/personal.py)\\n  - [`/Users/sebas/personal-agent/scripts/daemon-8082.sh`](file:///Users/sebas/personal-agent/scripts/daemon-8082.sh)\\n  - [`/Users/sebas/personal-agent/tests/test_personal.py`](file:///Users/sebas/personal-agent/tests/test_personal.py)\\n- Artifacts created: none\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-16T20:25:49.823843+00:00\", \"updated_at\": \"2026-03-16T20:25:49.823843+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/personal-agent\", \"output_refs\": {\"branches\": [\"none\"], \"paths\": [\" recuperadas tras restart/crash pasan a \", \"./scripts/daemon-8082.sh restart\", \"./scripts/run-checks.sh\", \"/Users/sebas/personal-agent/personal_agent/runtime.py\", \"/Users/sebas/personal-agent/scripts/daemon-8082.sh\", \"/Users/sebas/personal-agent/scripts/personal.py\", \"/Users/sebas/personal-agent/tests/test_personal.py\", \"curl -fsS http://127.0.0.1:8082/api/status\", \"python3 scripts/personal.py pause-running ...\", \"stop/start\"]}, \"run_id\": \"run_8ff645c9a9ad4d558df0265495267a41\"}}, {\"id\": \"art_d7f2bb1c7a204e56834b97bc4e77989b\", \"task_id\": \"task_8a866bb8156441ff8cee2f0f9f28e0c5\", \"artifact_type\": \"report\", \"title\": \"Codex result for en un worktree nuevo en personal-agent, agregar soporte markdown para links (ej. aca http://127.0...\", \"content\": \"## Summary\\nAgregado soporte markdown para links en artifacts/report renderizados por el daemon. `[USECASE.md](http://...)` ahora sale como `<a>` clickable, no literal. Hecho en worktree nuevo; adem\\u00e1s dej\\u00e9 un test existente compatible con worktrees.\\n\\n## Changes\\n- Link markdown inline en [personal_agent/daemon.py](/Users/sebas/personal-agent-wt-markdown-links/personal_agent/daemon.py#L630)\\n  - parse `[label](href)`\\n  - `href` allowlist: `http`, `https`, `mailto`, `/`, `./`, `../`, `#`\\n  - links no seguros quedan literales\\n- Tests en [tests/test_personal.py](/Users/sebas/personal-agent-wt-markdown-links/tests/test_personal.py#L1653)\\n  - cubre render de link local `http://127.0.0.1:8082/artifacts/...`\\n  - cubre fallback seguro para `javascript:...`\\n- Fix de test fr\\u00e1gil para worktree en [tests/test_personal.py](/Users/sebas/personal-agent-wt-markdown-links/tests/test_personal.py#L1345)\\n  - deja de asumir path absoluto fijo del repo principal\\n\\n## Verification\\n- `./scripts/run-checks.sh`\\n- Resultado: `Ran 36 tests ... OK`\\n\\n## Outputs\\n- Worktree: `/Users/sebas/personal-agent-wt-markdown-links`\\n- Branch: `fix-artifact-markdown-links`\\n- Files changed:\\n  - [personal_agent/daemon.py](/Users/sebas/personal-agent-wt-markdown-links/personal_agent/daemon.py)\\n  - [tests/test_personal.py](/Users/sebas/personal-agent-wt-markdown-links/tests/test_personal.py)\\n- PR: none\\n- Artifacts: none\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-16T19:15:50.674755+00:00\", \"updated_at\": \"2026-03-16T19:15:50.674755+00:00\", \"metadata\": {\"classification\": \"deliverable\", \"cwd\": \"/Users/sebas/personal-agent\", \"output_refs\": {\"paths\": [\"./scripts/run-checks.sh\", \"/Users/sebas/personal-agent-wt-markdown-links\", \"[USECASE.md](http://...)\"]}, \"run_id\": \"run_cce5f6d82e0148f6a56c9c12d159711e\"}}]}, \"audit\": {\"schema_version\": \"shared-agent-memory-v2\", \"memories\": 344, \"tasks\": 194, \"approvals\": 0, \"handoffs\": 0, \"maintenance_jobs\": 4, \"legacy_memory_rows\": 0, \"legacy_task_rows\": 0}}","truncated":true,"truncatedBy":"bytes","totalLines":1,"totalBytes":96304,"outputLines":1,"outputBytes":51200,"lastLinePartial":true,"firstLineExceedsLimit":false,"maxLines":2000,"maxBytes":51200},"fullOutputPath":"/tmp/pi-bash-54b508c7a64febbe.log"},"isError":false,"timestamp":1776868642541}}
{"type":"message","id":"cf84dadc","parentId":"704f570d","timestamp":"2026-04-22T14:37:30.306Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering project notes**\n\nI’m thinking about saving some durable project-specific notes related to the agents-db context. I wonder if I should use the memory-write skill, or if I can use the agents-database API to directly ingest information. I'm considering the curl POST method for simplicity. I might need to review the schema examples from the memory-write skill, but I'm not sure it’s necessary. Still, the instructions suggest using the Agents Database as memory, and this seems like high-signal information to save.","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd24b1688190bd0498811c75f58b\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N0pSocowWhfN8BsC63nO5dUVJGT5k6t9J9LufZ4kfGNbHlWf07Fs27za6jP49b-SAurAq0RTaXzxA4CfAEEZyDUVMmq4-zYAmul9RVSNLvUPSwEK44xp3K3ILjICGZRwmUIsP3eN1BeiJUtTp-963_6tAMQ6VGrNtgF_QHy0JwePxsXwXVTXMCluuFmqHIpH3k7KV_cU_eW4hp4S4LKj47QuxTLEaLimxSs4Eus6RKPhKi6qAXOvSrYNTEBKH4xKvbfnzJXFl3lWurGecQOLF1HiYmVQ2fC7Mh_qbti2LU_Y2c0zqUe4NibHeSJnLmT3qMyNvA9FjuFJd7NWgnTeFO7lQiY2YJEd3N3sa5D4IU9ODOpyNTvtdpVc_UHKh2fbrPembyFdIfRVRRaiLfO6hnnFRaJfVy_ngeaJ-Q0vmr1cpOLohYwz6TqlGE9Jrumh4wDs4ylQK7gzLnK8vLAvHNTtVDq7QL-3KAtgPBZt8MYfYGOlS44A6T1uxERMiL1hK6m44JSH4CUesCFqfVnkTO1ytjEZcPBZVZMipZ13r0GdcE-sMaaVpJFL63wmij6Xd6xqTI4HWEvqkj2Zmh4kZ0G4PVwaJkgMo_9C4tRg7f1zHGh0Q-K0SoK7Y4OSNyXdXKc8lR9523INVguubfyyr9dKfaxryZoVxwPH6JwGYCoVEwkTH8TbfHXt4fNIyaGHCDPsh7LU9CPobIykdZ3ApA4JPrs1Xyly-SGkJzbZC6oRQJohb2-HbCyxF26ddDX1ZmBLk1q9xt5EMp9ySosZJ14KOs6xl2_pGUpyDpReXqt0SEbC0pjHBc7CpV5rA5Bu77EhSJvlNI9eenygflFZ7zTLbszrLqhZkyai3lC8MUA8QCLWmJp9bSy41l-UluD8Qd28DoMoImkYqclv8hvqAGZiWsC0NdyKZy_gbT2PxgsPm7QjbKjylT2EcOuMymZlOfMCbH5pGiiaKHsi6S4f9o-E9xpvF89stp1lbNF0hhLJF83sY_Emc9kUQx2he0x2EISK7QMuUa01reD_4tiW8bR7VgJYHFOHH5S8H9rbcDJ8GhJE7XXDqHnAeuYzgkxO_3-rAAEJvcjBhMzPYpvATdJ6zXYsNvuDq9MDP50ASO7oTjwQFQnHqZa6dUWB0At1jd4hpjJ4EsiKUicXToK9CYH7DU9sy59FZHR4HQwmMkqiruKPK9GSz7hC69NqN_DlhAcvIloUE-mpMOtOqotxbCPEKNDqlFW2Ad2zHxsEjbSHUcPkiHKNXB25e4nwajLrLmsaUdi-1o9JXXBKdc2iDlemBgcPHEubTqgj9RMS_cFZFKqk1h2dmzrA6kAyVFFqOGkIowrH_maQBJHapVyp2uOXuUGp49tvlUJnAeoqlMqNbooOgRTZTOc0OQcFCIyVsDKu0ZXNQjMXd15C-tFot7KujdrT5OZzXnCOVvn3extmoTaU0CTqmkgzkbbENhSD5XdIbu4Nj94Kl-1OmtIq-0E4bVQOkLP5KXCC3k_QgzYzqHB46OmAgDhsMNBMeAFIr2L90vKo13w88Ui3M0RXcTcgZT8uNR-Os7hRVfuhJ-9FsvgAfGkFaWUBDaeLJ36SlqFbWcAU-AXpM7SwVlzS32m7n7bKjaBasUgKQvMba5l71AD_Y6ZBUsIfiNzCq1JaJt_NGtbi3SJywqWhgVpHBrAGwd_aXuQgsZQeudGi0bnW-USJ4eH7WujjjCzV0VapVXah-igi4JFf7MnA3LxCgGtscMhJGg1G-osBfMlj0VKu0LlriFfKbBwHzPHxl1xabeBYHHQkq40Fa4fkPbaD-ds6pwJK2AzZPpzfEQ9szC2L05rh_tH4103cdTCMPGkrtdNHTBcYDmUMUiakw1gbFOqrAy8V4hehA3OFLv4hgwttCRf8TuVA1qPD4sPZkBy9ZrACgoeiHC0-IoqPvjKnG6tePJ47sNuFT8QZ2x_tCH4Zs7DxaqCX9LUAmY2QAVtuNgbTxuaeGN-yCYYsnQBjE7hFklE7lIM2t5m2WNK5n6WtcjogFVlQY_X1wI2GkittpraePk6ZzR9QRslmkDDjJv3rZss1Is9Sbe-QR54kDpZY-l_6OBSLvAWzSXEyTXrZbaQRyI0jaWVGceLrdeJZ_bRHZ5BJum61qQun08jN4Z1kHxfXBq2TXflCRbhQk1ujKrbI-L8sXlgMIJqe6jQYplKySRHPd5zh540-4yeqO5JiWdsejr0Lo67ip0VS8PpVzxbLqkd-8f7PnoSk92l957AF9x6WEcoC84XIVjTM6GZoNAqUMzaXR3sQ_zwiPHOEln7riiNmc6A-m2MQWgKc6aPFSU4fj7OaUr0AqJ1OYC7TvkLS27eOuHW2BEGscOc_S6o9lBOZZJwSSPm-HLhcYCEge2n-MBQxAjq72akH_5L5TBvlh54VQJtoIOLieV-RHT8z05J3GtwREI233pne7AdrqIDphDG18PxtRJ7PHiM52PJ_Hgqm3oCTsoPdOzxUe9NXrb5HG1KstaKahdQU5HAQRHagO7iB3BU54st4WPOAtkjq32mfD87BSsTIFDYJpFol0MWd_-tnQLlto2VShPwGGn6wYsSGx5OFjyHTPJ3-FH9_m1TCBtV2YSCwtPWdsd8Z41gmdg5pVLmen0WJESY21IVb9UQbqvL-YMc0iayvHXoEk2x4PzNrBmxo1QTHaXvDb7DveRZ0AybXQAUdwbQz9fQL4nAMnih54JWnFhaE3z1v0yNXWxSqFwpjitD4v-KMY6k_C_6QLut37T2lvYqlNz7_mSlN49vax5nBQ2VWvk=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering project notes**\\n\\nI’m thinking about saving some durable project-specific notes related to the agents-db context. I wonder if I should use the memory-write skill, or if I can use the agents-database API to directly ingest information. I'm considering the curl POST method for simplicity. I might need to review the schema examples from the memory-write skill, but I'm not sure it’s necessary. Still, the instructions suggest using the Agents Database as memory, and this seems like high-signal information to save.\"}]}"},{"type":"toolCall","id":"call_8PIZGYf9noaL4BKYSAv4qIv8|fc_07b1b9c95ac99ec40169e8dd29f8c081909a6886f54d6861fe","name":"read","arguments":{"path":"/home/sebas/.agents/skills/memory-write/SKILL.md","offset":1,"limit":220}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":15372,"output":314,"cacheRead":82944,"cacheWrite":0,"totalTokens":98630,"cost":{"input":0.011529000000000001,"output":0.001413,"cacheRead":0.006220799999999999,"cacheWrite":0,"total":0.0191628}},"stopReason":"toolUse","timestamp":1776868642552,"responseId":"resp_07b1b9c95ac99ec40169e8dd23772c8190826e8c39e03c3e79"}}
{"type":"message","id":"168f2b8e","parentId":"cf84dadc","timestamp":"2026-04-22T14:37:30.333Z","message":{"role":"toolResult","toolCallId":"call_8PIZGYf9noaL4BKYSAv4qIv8|fc_07b1b9c95ac99ec40169e8dd29f8c081909a6886f54d6861fe","toolName":"read","content":[{"type":"text","text":"---\nname: memory-write\ndescription: Save a durable high-signal memory into agents-database\n---\n\n# memory-write\n\n## Use When\n\n- new information is likely useful beyond current chat or task\n- saving a decision, preference, project fact, reusable source/artifact, or handoff summary\n- user asks to remember something durable\n\n## Do Not Use When\n\n- content is trivial, redundant, ephemeral, or likely useless later\n- content is secret unless user explicitly asked to store it\n\n## Required Workflow\n\n1. If useful context may already exist, optionally check first:\n   - `curl -fsSL \"${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=10\"`\n2. Choose best memory type:\n   - `profile`: stable user preference, priority, recurring constraint\n   - `decision`: chosen approach or operating rule\n   - `project`: durable project fact or invariant\n   - `artifact`: reusable source, claim, reference, output\n   - `episode`: concise session summary, blocked state, handoff context\n3. Build a concise payload with:\n   - required: `type`, `scope`, `title`, `content`\n   - recommended when known: `subtype`, `project_id`, `repo_id`, `task_id`, `run_id`, `source_ref`, `origin_agent`, `url`, `domain`, `metadata`\n4. Save it with the helper:\n   - `~/.agents/skills/memory-write/memory-write --type <type> --scope <scope> --title \"<title>\" --content \"<content>\"`\n5. Confirm the created memory id and what was stored.\n\n## Quick Examples\n\n```bash\n~/.agents/skills/memory-write/memory-write \\\n  --type profile \\\n  --subtype working_preference \\\n  --scope global \\\n  --title \"User prefers concise status updates\" \\\n  --content \"Sebas prefers short, direct updates with business impact first.\" \\\n  --source-ref \"conversation:2026-04-17\" \\\n  --origin-agent personal-agent\n\n~/.agents/skills/memory-write/memory-write \\\n  --type decision \\\n  --subtype operating_rule \\\n  --scope global \\\n  --title \"Agents should save durable high-signal memory opportunistically\" \\\n  --content \"Treat agents-database as shared durable memory, not only search.\" \\\n  --metadata '{\"source\":\"conversation\"}'\n```\n\n## Rules\n\n- prefer one strong memory over many weak ones\n- keep `title` short and searchable\n- keep `content` factual and compact\n- use `episode` for summaries, not for every chat turn\n- include `url` and `domain` for web-derived artifacts when known\n- include `project_id`, `repo_id`, `task_id`, and `run_id` when context exists\n"}],"isError":false,"timestamp":1776868650322}}
{"type":"message","id":"87251538","parentId":"168f2b8e","timestamp":"2026-04-22T14:37:34.638Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting documentation**\n\nI need to write a decision memory, but first, it’s a good idea to check if there are any mentions of the endpoint in the docs. I want to look around in the repository, particularly in the /mnt/rpi/pi-config section and see if the API documentation for the agents-database contains any relevant information. This will help me make sure I have all the necessary details before proceeding.","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd2c87cc8190893bd6f2af776cf1\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N0uRYsaXOYXoPBClcaBduQpjQ2HOWR8PZTwDOJUt42Q2DXLjMa7mFXs_FPeUqh8f4VPzEovPpqT98o6PVToDGfFTaz3cpHlf_z2LNOcjjDiKRe1fqtY1y7-5Y4H8p82X49DsS5Z2ROC6dlwrzTXSJZBxyq8JU9GKXvTiVUBItvSZrxVrBM72x_9kpJAaK5EoOay3fCRfKvyTxtzkz1o0XjsDaelkIdirWaalsLXSE2MxgTSk4YBq0nVXiW2yB3B-PI_mmpjKgft6182meHCtQMrBEUZ3fA8xd9ocydM2El-caivBKFZ3grnKN1S2SHzRBsu1v16cJ6yeJ97h3uaKJagntoxBnGiHBNDy5MZmNo2fAjvx6YBsmP024kiwD-8s2PMwg-2eXF7VUQ_pG835WChEDZ0sQWIXXdqpK5aU3dLn8kjO-DDaHe3Uit0HlqcsGuTYQs9FwaGD9Pa_6NvRZCo4Bf9TM1eolho3nvsI9SJNuTacelqWqIpeQ5jYSlKts9fhMerNJ8McHQ3Ebph_aQww99uw--c2Dw7AyJ-uwFJZD8VMgBBMxBe3LGGTa5i8QMdAM7CLGNXlVkxVKlFaaBeKBB83PxUiYcDumhRV3BLrXlYSicPXmPbU-HozY-McFldq0nauSTql7bDVRnH4AdL5RehjnlQk27n_wOn3rc5Ad4WD5AVxbB6rtxw6XquIViy6NAXZXZX-6ISGcu04JUmJfWqDxrENwPi9B6gDDI4V-NXRZMiz_UOEyhLXZU7Q0vIuSdz6CTcbdoOz5IWCRshu7jSUS3_0BCOLvuI2H3r7YXjfKR-2dW1OxBE_c_7qIBw-GXQfbTfjfvRo1y9cdR0ssvUcpPO_qv8h6QzFENfZkk0In-WbMaQdwpBdFYMZWij5eywfHJB3FLsFfO2FEKlG1ihjkcNNkNgmbtXp3BN1T-iIAe29W6JDAnlbnW9TdZvDvILdZ7h5_OrjoR4jF8I1TOaeRTDU0RWmIr7L97-hJHD1IVOqfv0Ssh15Qfu7mnT5g1Fd4sj-7977GU3LkNdZhj-j3SwsCUEVarS3LlK3ojZ_uAEoBUlf6Ra7OgJI9KBaPgvz_nvzIwz_2p5piBO63Tpt1qr-u67DdVpWwSPP2ZNSHCP-dSgJC_CuJzS0E4sdxcAWh1GZ4i9HUvqr8ueyA==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting documentation**\\n\\nI need to write a decision memory, but first, it’s a good idea to check if there are any mentions of the endpoint in the docs. I want to look around in the repository, particularly in the /mnt/rpi/pi-config section and see if the API documentation for the agents-database contains any relevant information. This will help me make sure I have all the necessary details before proceeding.\"}]}"},{"type":"toolCall","id":"call_ZLwi9ZDaWvnH4WknYMuDPnT7|fc_07b1b9c95ac99ec40169e8dd2e59b481909b5797cf136f3866","name":"bash","arguments":{"command":"rg -n \"api/context|api/search|agents-database|memory-read|memory-write|context endpoint|search endpoint\" /mnt/rpi/pi-config /home/sebas/.agents/skills -S","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":938,"output":104,"cacheRead":98304,"cacheWrite":0,"totalTokens":99346,"cost":{"input":0.0007035,"output":0.000468,"cacheRead":0.0073728,"cacheWrite":0,"total":0.0085443}},"stopReason":"toolUse","timestamp":1776868650335,"responseId":"resp_07b1b9c95ac99ec40169e8dd2b08b0819090cb887f32bc9ea4"}}
{"type":"message","id":"c4df3390","parentId":"87251538","timestamp":"2026-04-22T14:37:34.655Z","message":{"role":"toolResult","toolCallId":"call_ZLwi9ZDaWvnH4WknYMuDPnT7|fc_07b1b9c95ac99ec40169e8dd2e59b481909b5797cf136f3866","toolName":"bash","content":[{"type":"text","text":"/home/sebas/.agents/skills/memory-write/memory-write:20:    p = argparse.ArgumentParser(description='Write memory to agents-database')\n/home/sebas/.agents/skills/memory-write/SKILL.md:2:name: memory-write\n/home/sebas/.agents/skills/memory-write/SKILL.md:3:description: Save a durable high-signal memory into agents-database\n/home/sebas/.agents/skills/memory-write/SKILL.md:6:# memory-write\n/home/sebas/.agents/skills/memory-write/SKILL.md:22:   - `curl -fsSL \"${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=10\"`\n/home/sebas/.agents/skills/memory-write/SKILL.md:33:   - `~/.agents/skills/memory-write/memory-write --type <type> --scope <scope> --title \"<title>\" --content \"<content>\"`\n/home/sebas/.agents/skills/memory-write/SKILL.md:39:~/.agents/skills/memory-write/memory-write \\\n/home/sebas/.agents/skills/memory-write/SKILL.md:48:~/.agents/skills/memory-write/memory-write \\\n/home/sebas/.agents/skills/memory-write/SKILL.md:53:  --content \"Treat agents-database as shared durable memory, not only search.\" \\\n/home/sebas/.agents/skills/memory-read/SKILL.md:2:name: memory-read\n/home/sebas/.agents/skills/memory-read/SKILL.md:11:   - `curl -fsSL \"${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=20\"`\n/mnt/rpi/pi-config/SYSTEM.md:39:Use the `agents-database` HTTP API on `http://100.116.176.16:8091`, especially `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.\n/mnt/rpi/pi-config/SYSTEM.md:58:`agents-database` status: `http://100.116.176.16:8091/api/status`\n/mnt/rpi/pi-config/AGENTS.md:49:- `agents-database` API: `http://100.116.176.16:8091/api/status`\n/mnt/rpi/pi-config/scripts/session-memory-consolidator.py:30:    url = f\"{AGENTS_DB_API}/api/search?\" + parse.urlencode({\"q\": query, \"scope\": \"global\", \"limit\": str(limit)})\n/mnt/rpi/pi-config/README.md:33:- Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.\n/mnt/rpi/pi-config/README.md:35:- A separate consolidator script can batch-search agents-database, detect duplicate clusters, and emit a consolidation report for review.\n"}],"isError":false,"timestamp":1776868654654}}
{"type":"message","id":"272545d1","parentId":"c4df3390","timestamp":"2026-04-22T14:37:38.784Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering documentation improvements**\n\nI think there might be a better way to suggest improvements for the documentation. The reference in SYSTEM.md to GET /api/context could be a bit misleading. I should take a closer look at both SYSTEM.md and README to see how we can make the context clearer. It's vital that users can navigate these documents without confusion! Let’s explore this further, and see if there are specific areas we can enhance.","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd309eb8819081a8146b37bd9bec\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N0yocF-sPxRanRN7JiZtJETfF3jVFJQS89BmZxyk1-d1igVso97qOrTJuFRSXFoiFR8SL6SPQ02WIOWPClBMEfswnILJJt3fpwEiNcn6oTnGnD3Vyvz8xgy3SsIhYrIDCWNe0-CcTHN22oguzZKyHWK6mdqHxuq0sIXoa8ULjW5IPlft-jO6MYo4FCvNNzP5agPmXkStsJ017qtB4EQdyslXBB4-tboh5SEiVWxi2_cIM6BDLZStJTOTUNvujrNG3M2guZtN-L3l1H98i-0BwYT3gVPTYfkoD-Gpd15-R3DH2gQaBnFii-MTtMbWHgqINqKexznRp2VuiM6ZKntEDqluhXBmoSeedvOy6zqX00-YjTSOteBuBDsmkm9fYMZsLzFMub_aSmxmA_ZSKI139lmfhZ9g0ideSF1b-j-J__a-Eoa2cWxDyXaysF0UB-NDbH1DAdhBRtJSeD51_E1FpgSRmoSQ5y16gfhuoA8INB7xAZbu3on4Kluxvq_ybMU2IihPEj5IggX3ybafp2QmGZge14cJWpb_lydFMl-cDpNeaoTomTSTY1CRH-RbRrNG8FhlFPBCZ3BAr6nubmjIAc0z-KTg59_0Gm8Dn5R4B0TnuGukUEpLozgqr7FIC5cIfIt9ar3UjebnDCmvm96z2h-Wxi-HpNj3aelxDSMQNpvWyfFjrQbYPuP2-vkKp3-F_wDyKwggNLPCNhooKARLG0cpf9G1v1RF9Pr2NcONRzB7T3BY71kQxyheY5b52z1fmHNzsC8n5LiDjziJF_XPpk607b1GyMPqCQ4LqOb7g111d3siRy0t5h3JPALXTI_lvUy1m4QRKmyVCyb65gEB0v-ZcRK2kUCOlLlTZaiLWZFxRklTrQiFW9hYx7TlrFb5eWUA0hWaY7ZzLbl2QFPC0mnkOUHN6qGfTGt6GkHcyKnZpOW8CbP3iHNxuvwinmvizRkGl4t8vFKu9Iqq5Bql929LuhRbrdrs5ZjcIadnSGUI4Biy5Gp_6zNrQdRt2CuFS_nOKK8DkWbegaJpHtyA7wpoGlbWLmr5LH6bQUmQJLR_PIjbPRnBW5tjBKaNfu9BqClmXfsOrg19DJM1DQKDZZzLAF7WR9qevEXaAeajCbSZZI=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering documentation improvements**\\n\\nI think there might be a better way to suggest improvements for the documentation. The reference in SYSTEM.md to GET /api/context could be a bit misleading. I should take a closer look at both SYSTEM.md and README to see how we can make the context clearer. It's vital that users can navigate these documents without confusion! Let’s explore this further, and see if there are specific areas we can enhance.\"}]}"},{"type":"toolCall","id":"call_GMEwL8RHxWb4kTPBFc6XCHAt|fc_07b1b9c95ac99ec40169e8dd3296988190b7f301ebbac3d3b7","name":"read","arguments":{"path":"/mnt/rpi/pi-config/SYSTEM.md","offset":1,"limit":120}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":1185,"output":65,"cacheRead":98816,"cacheWrite":0,"totalTokens":100066,"cost":{"input":0.00088875,"output":0.0002925,"cacheRead":0.0074112,"cacheWrite":0,"total":0.00859245}},"stopReason":"toolUse","timestamp":1776868654656,"responseId":"resp_07b1b9c95ac99ec40169e8dd2fa40081909af841ffe84dba85"}}
{"type":"message","id":"525e8bbb","parentId":"272545d1","timestamp":"2026-04-22T14:37:38.797Z","message":{"role":"toolResult","toolCallId":"call_GMEwL8RHxWb4kTPBFc6XCHAt|fc_07b1b9c95ac99ec40169e8dd3296988190b7f301ebbac3d3b7","toolName":"read","content":[{"type":"text","text":"You are Sebas's personal coding agent.\n\n## Communication\n\nDefault style is direct, compressed, and low-noise.\n\nKeep this style active for every new session and every response unless the user explicitly asks for a different communication style.\n\nDo not change style from implication, inferred intent, tone, politeness, or guessed preference.\n\nIf user seems to want a different style but does not ask explicitly, ask for confirmation before switching.\n\nDo not mirror user verbosity, tone, or sentence length.\n\nRemove filler, pleasantries, and hedging unless they materially improve clarity.\n\nPrefer short words, tight phrasing, and direct structure.\n\nUse normal prose when safety, irreversible actions, or sequence clarity matter more than compression. Resume default style after that section.\n\nKeep responses direct and low-noise.\n\n## Core rules\n\nPrioritize business impact.\nReframe technical work toward real objective.\nAsk only questions that change scope, risk, or success criteria.\nExecute fully once clarified.\nPoint out contradictions when they matter.\n\nUse Agents Database as shared durable memory, not just search.\nCreate a memory when new information is likely useful beyond the current chat or task.\nEspecially save: stable user preferences, recurring constraints, important project facts, decisions, reusable sources/artifacts, and notable blocked state or handoff context.\nDo not save obvious trivia, ephemeral chatter, redundant notes, or secrets unless the user explicitly asks for that.\nPrefer a few high-signal memories over many noisy ones.\nWhen useful context may already exist, check Agents Database before or during work.\nWhen writing memory, include the best available scope/type and attach project_id, repo_id, task_id, run_id, source_ref, origin_agent, url, and domain when known.\nDefault memory types: `profile` for user preferences/priorities, `decision` for chosen approaches, `project` for durable project facts, `artifact` for reusable references/sources, `episode` for concise session or handoff summaries.\nUse the `agents-database` HTTP API on `http://100.116.176.16:8091`, especially `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.\n\nIrreversible actions require explicit confirmation.\nNever merge or push to main without user confirmation.\n\n## Machine context\n\nThis machine is `ballbox-first`.\nDo not SSH into `ballbox-first` for normal local work. You are already on it.\nThis Raspberry Pi is effectively both workstation and server for Sebas's day-to-day work.\n`el servidor` usually refers to this same machine unless user clearly says otherwise.\n\n## Server details\n\nHostname: `ballbox-first.emperor-ratio.ts.net`\nTailscale IP: `100.116.176.16`\nRuns 24/7.\nNginx on `:80` routes to other local services.\n`personal-agent` status: `http://100.116.176.16:8082/api/status`\n`agents-database` status: `http://100.116.176.16:8091/api/status`\n\n## Agent temporary storage contract\n\nPrefer the managed temp root (`AGENT_TMP_ROOT`) over `/tmp` for agent temporary files.\nUse `bin/agent-tmp-run --scope scratch|logs|handoff <cmd>` when running tooling that writes temporary files.\nContract details live in `TMP_CONTRACT.md`.\nCleanup command: `bin/agent-tmp-gc`.\nExpected cleanup cadence: every 6 hours.\nUse `.keep` marker files/directories for temporary paths that must survive cleanup.\n\n## Playwright\n\n`playwright-mcp` CLI available.\nPrefer headless Chromium.\nAvoid Firefox/WebKit unless needed.\n"}],"isError":false,"timestamp":1776868658791}}
{"type":"message","id":"3eba1310","parentId":"525e8bbb","timestamp":"2026-04-22T14:37:46.023Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Creating memory for context**\n\nI’m considering creating a memory note about the agents-database and its contextual bundle endpoint. It seems like the exact-ID lookup isn’t what I need; instead, I should use the /api/search for ID lookups. This is durable information, so I should write it down. I need to use a memory-write helper to capture concise content and possibly include a source reference from the project repo pi-config. Let's get started on that!","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd344890819099658aecc997adf3\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N05qq_3quZX_y3s3KC3iZ7aA3SooLB1v_Xo39-8f08EZXRhOsZctkeqza21whX7G4Prznb5PbO-2pncqdKW05rnaCW9IHY89JsP3AbgNjdCUmij1oXY4tqXyIo6z0WKQmVLtN5YT7zgjqOz8JzdWiO2ibLrfppdq_DxwfoFQOwcXwm5KaQQZaqoug8ZVKD8hWGvmyjKTygDCWcfpicfDwXqsMu4d2fzgpcCNguZIi3rsNE9gT2pvR3Lt5ufccta7VrNm3_PgahETwmuT6LdJMv-9R0qFAkUGk0S56Ln97ZfswkvGcqo0bmzBqvqprmtLw6KyzIASl8zkiQnE2qb8D4a85KxkiAZmmRVtSyU0OweX-1O6BhjKCxmC331_Kq9WsGXIGypnHI8QFD7XIlHqICHLV8dXP4y2nqtjrihl-MA8YdWNy2FW1a6DprVYkwutrmADpqkLP5aVXbKb5FlY3qypbisBELXSysjNd-zeXVps7WFip96FnG7czWhInMLdON9HKaw_TAQnhy-0yGufxs2QNXKlyMcMjsb7awlfS79LnGh-xFLM-Dm-Ktvpa6SZg63AVyjy6LEHY1_ag2ve8xXJWrxTfoT6_u_Y-IyFzfDvMDt41qnqgcls1jsOxlq4u71wbWY7mc6ozUbr5o67sxnhm9OijBN7lciUQjoa6a_HkB1odKcjUJqXut8BdrnytGWfuUuqIiY6elFg8ScLHHDAlXSVwf6ky6JPL-dSdGFTa20MoOLvKwt-cCNsFf6wtyLKvnIW5N0-3ajHSmEdocyn5DrWUyFHF58c3vx3r5mX1ahq1nY-e-jHpwgL6j2Rjw976iww4G0RwJQjdQVJ1gK72Wd3mFHGMTBkLNv9V4nNHr_VximkPHU9p-WQp-tNXryJFP2wuCvMVYCh6GUDiaRtGyxKjuurPXYzZOfqmu2afPg4uE6kybF-Cmy9LNaJ9944NbMig68iM-_NMic2IWaaJU12HBEqcwHnrsxwiPTu3ypBZ_hgdDAF_oWmwyKpi2KGF_vvJgf46kfOKbelrn1f2SFhCb9TOGDrFgWbux2V4hJg1jKutY-xgnOuSAmTJCRHltPVGLf2SeOXC75kmy8r2aB6NDwCUwXiVcOV2wiAieoBmZYTUDHOd0o12sFchNl7HukD4ndZQFResblb9z_GmCNzy-ULa47X1-JzELM48-UqSa3-pBAn1hQKVFriOdKV7t-b6OIrn3I8KfBSnLipVM1n-p-Q1HW7W5U_z9T0Pu8a-KYUypkgJbktJFWYDZH0bE-Tb5L8tUtyPtFYr0O-ot0y8DLz-n1DpRGnZY5Qa5yRA-RL9wlAInN6vthKgK6Zs_nkcM5_3BdWFh0w50L5y1juocEZWsuylG8gysKqPSPTzwIUhmtbtqPPM3FRvF4wQfV9PRUK8XQLrHme7Ndj8JuEBHMz2j3z45ZM7D46VxG-Sncgpo-iNbsAcdkdWrnnH3eVmZRr4Z-wIqwcX8ThTRHJjCfgp1uEep6waRt-XMIoQxnFkruEl4VUD_0JdD_MHT381jwdAi82pgX-THSV_JAKNLRoRwIrs4jTf95LsxDeKEx_j2i5UMHTtcrI7zoQvqHwQjPaoKlIFYkQ3zAZpRcKrRxFlSpPr7UNjhiAfUqDMHCfg1Y-NbeSgfHZJXaSCGUZQVLxVDvfxUoLtjk5TfiQDUPcNEfnCtNl0ABP8Anjuu1-s6vghhur6koKvkPG0x0MdpTNtFHNqTBQD0UtDUBIR9DRx0knB-IzPq5Aj6pdeINOVd2ZpB2tkk7gfDnYfonTWpFOpebbK3_kv8BXWLdAr5-Is5LdgusLpLqGpi6p6ftty3FeCrrAnsNvPDnmONffo35YXOFUxb85GNqhOPlFvleTnCKaPWoOZz6MAYQ_4Ysol_AgIGSvL--uTmsnggWmAt20PhgqvW1viO4cXQhu9BLKvUKo7RfVGnPrHvhrKqnE_D8zCXHNJcBDJkz5jGaH1NMEFsxGPAwakuE5hjBco3VsJXB6otj43v4aW-s5FGESSLq9gI6XhN2d7pP7ahAa4jlRcKfelP6bqBkgJ8PgXYGIYxhQ92Z_DHqmE9QJWv_1-qgERI6_wyLYcbBBKQ9qeuPK2-_6fDNgQcqLX3n2XUO0GMDqINb_cMsNyhQy-qrvg6PH1ltLSx3F3naKKcja79oSgL2z_2PczQYONc9RIKxEJngc_luvGvhS7cbLQEjccqPa4UqKbQqLRvNd1BqZfgw2dnet0mtGINp7F28XFyPk-mQ8rYNEXXjLmpC8Amap-Ly02IunRyLQTmMutfctqwu1xT3tfrQXhT1z5vgxNCUNIDKpVH3HQ0oSleNYn4Wu3nJHPGdxW8hR46UrnC3TZVpaEgGrblE9smGtHGsTbnha7sfNQFhtJU06tLyYHlr3JXdwfS7rg9kIfZKGG1oXhbNTr_WG1tGh2-16KwFORXKXdAzYCXPCc9V_nAjFUefuvd2lw5CXyfTEjVTnjG9CwXUB_3F532NzWG8gpJvDjiPwyzzBd8R-cCTa7VrGP6TYcvuXajJIYR7FXkg9lY9pJunjVOLpJIHacH_6TYc023zADfQp-2-zpnRDtjjyH7wwFsBhX2cVb3BmF_E30gopmefcUdUUd-ye_WRoz2aM6NpPSE54frL2ZtGZe0=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Creating memory for context**\\n\\nI’m considering creating a memory note about the agents-database and its contextual bundle endpoint. It seems like the exact-ID lookup isn’t what I need; instead, I should use the /api/search for ID lookups. This is durable information, so I should write it down. I need to use a memory-write helper to capture concise content and possibly include a source reference from the project repo pi-config. Let's get started on that!\"}]}"},{"type":"toolCall","id":"call_RXrQ7FB9YXDVOo9pwreyGznI|fc_07b1b9c95ac99ec40169e8dd3992708190af0af14b96b9cebe","name":"bash","arguments":{"command":"~/.agents/skills/memory-write/memory-write --type decision --subtype operating_rule --scope project --title 'agents-database /api/context is not exact-id lookup' --content 'In agents-database, use /api/search for finding memories by id/content. /api/context returns a contextual bundle and is not a reliable exact-id fetch; docs should state that explicitly to avoid lookup confusion.' --source-ref 'conversation:2026-04-22' --origin-agent 'ballbox-first'","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":979,"output":389,"cacheRead":99840,"cacheWrite":0,"totalTokens":101208,"cost":{"input":0.00073425,"output":0.0017505,"cacheRead":0.007488,"cacheWrite":0,"total":0.009972749999999999}},"stopReason":"toolUse","timestamp":1776868658799,"responseId":"resp_07b1b9c95ac99ec40169e8dd33ad4c819083ab0a4be636d426"}}
{"type":"message","id":"731182b3","parentId":"3eba1310","timestamp":"2026-04-22T14:37:46.208Z","message":{"role":"toolResult","toolCallId":"call_RXrQ7FB9YXDVOo9pwreyGznI|fc_07b1b9c95ac99ec40169e8dd3992708190af0af14b96b9cebe","toolName":"bash","content":[{"type":"text","text":"{\"id\": \"mem_c618bcf49f3c47878beba6a5e5158e16\", \"schema_version\": \"shared-agent-memory-v2\", \"type\": \"decision\", \"subtype\": \"operating_rule\", \"scope\": \"project\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"origin_agent\": \"ballbox-first\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"agents-database /api/context is not exact-id lookup\", \"content\": \"In agents-database, use /api/search for finding memories by id/content. /api/context returns a contextual bundle and is not a reliable exact-id fetch; docs should state that explicitly to avoid lookup confusion.\", \"summary\": \"In agents-database, use /api/search for finding memories by id/content. /api/context returns a contextual bundle and is not a reliable exact-id fetch; docs should state that expli\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-04-22T14:37:46.108565+00:00\", \"updated_at\": \"2026-04-22T14:37:46.108565+00:00\", \"observed_at\": \"2026-04-22T14:37:46.108565+00:00\", \"source_ref\": \"conversation:2026-04-22\", \"evidence_ref\": null, \"metadata\": {}, \"embedding\": null}\n"}],"isError":false,"timestamp":1776868666208}}
{"type":"message","id":"156ceb48","parentId":"731182b3","timestamp":"2026-04-22T14:37:49.775Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd3bbf848190bcd1b8498a3398f1\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N08LF9l3MI27YiCCs4oGhN7TVnrKCsVVLtKJL6DFzNlJxnMOITXGtjvdb-C3G66fTGh5ml9FRvD68iIYGuB_Ag6YvTegGtg20eU_MoO6LkAvvw9IklsqX3jND5TAzMEZCHgV3egfXfRsKkBaxRZDDClOfYPlAUsAKAN1VjWpxpdGNgsWBRQM2DfMg0Zse9aBY3GQVZufFdi08uo6Kr-mq0VtFKczre6zFTnruWYAt3mRW43EGImSmVcrLmS9WFquzpOsHT8OH9v2yfDRM2YJF7EA0Tqq5z-i0DS5t7sjsK6lnXToxId5cCtWOieYE3daNqwcgbvO89Ic4R1xnLKqwSqhY1VgS90cLppwg6IvowO-ghTjSWeJo1UTV8DvMVr-H6nO5jtv8wu_6uYaPBwoJsKwwrN3xbno1p6v313yhjg7TwPZ2g5pCMte96O27bvvmXY9HutZRbC6NNW4pBWPQatc-9sldldTHTx6fvqnTSPtAOZKI_IrwU-Iri_ogIS2ajxW0vPlv-DIPUgtR4NcVH8hzv4K8yWJEgXHDr5HANscTHCAyB_10HImtAp7mJv9JdOoZo_FhYw4ByOHNYMGxGYGmEFcvP5iDUHTDYh8lhtWC5RuGQfnBpNzwNfFJlPjRBJgnSx7E0hoMMWpHKu9NwjF16-0ON_yIhmxex-HKz8yBWjP9mfAvVrTvHk139cF7M08tidcbdhQHjHmUzGGhMpkj6ZVUg4dbtzCc32_ggpAVX-lN68K3T2j4CblKxtD0OvGkAcZ93AIvy-MMufXVFABw_e9vLpPR7UTi8_ZVxWr1klNg0Jf07wCzuI1f5HQjNr2cHl6IjsUGydzCm9iK-4Pr8AYs5OVJdS2DM1D72vrUMllyZLJDUw3bWndTgpExXv8cs6D5U3bilk9slPjo2awPATrPGMXzdoEoTSj72UIDmZ7aYqwL_2FfFWnA5GX5VGLtZlBwW6Ia_tHeAx1vpidEuPupfa6DiktoqRepOvC6J3Zn-3T25rlVrqpMIo7A8-54CJxs1tAqzMsQNpNRPgVkumxL5SqUE9vi2kPE3YGr2Ng4dLSKFfHlwjoV8oF8P4nyTer9sZO8OtYE2AFv74sDlt4Sn7Ui3bmCWob9GWAcQ=\",\"summary\":[]}"},{"type":"text","text":"Yes. Docs need a small fix.\n\nWhy I reached for the wrong endpoint:\n- `SYSTEM.md` says to use `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.\n- That makes `/api/context` sound like a general fetch-by-id endpoint.\n- In practice, `/api/context?id=...` returns a **context bundle**, not a reliable exact-id lookup.\n- `api/search` is the one that actually found the memory by content/id fragment.\n\nSo the docs are ambiguous, not the API necessarily broken.\n\nWhat should change:\n- Spell out that `/api/search` is for lookup.\n- Spell out that `/api/context` returns surrounding memory context, not exact-record retrieval.\n- Add one example of each.\n\nI saved that as a durable decision:\n- `mem_c618bcf49f3c47878beba6a5e5158e16`\n\nIf you want, I can patch the docs now.","textSignature":"{\"v\":1,\"id\":\"msg_07b1b9c95ac99ec40169e8dd3ccdb88190bfe48d50211f43da\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":706,"output":235,"cacheRead":100864,"cacheWrite":0,"totalTokens":101805,"cost":{"input":0.0005295,"output":0.0010575,"cacheRead":0.0075648,"cacheWrite":0,"total":0.0091518}},"stopReason":"stop","timestamp":1776868666209,"responseId":"resp_07b1b9c95ac99ec40169e8dd3aa1c48190984f24682a4aa760"}}
{"type":"message","id":"da0cebed","parentId":"156ceb48","timestamp":"2026-04-22T14:38:03.892Z","message":{"role":"user","content":[{"type":"text","text":"of course, you dont need confirmation"}],"timestamp":1776868683890}}
{"type":"message","id":"8f4e8c55","parentId":"da0cebed","timestamp":"2026-04-22T14:38:11.214Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Updating documentation**\n\nI need to respond and acknowledge the user’s request to update the documentation. They mentioned no confirmation is needed, so I can proceed. I'll look into relevant files, possibly SYSTEM.md or AGENTS, since they asked about patching the docs. I should check the git status first based on our workflow. It seems I'll be editing SYSTEM.md in the repo located at /mnt/rpi/pi-config, and I might also look at the README for context mentions. Let's search around the repo.","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd4e82288190ac51129b52e228b3\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N1ScoctSh-Bh8FIuE7sP2bmL1M8wdaFPvKmxjprGO0CxBX3qQb3aJeStrSFeO_ByonHSLNvgRJKZlLJvyqcjIm3pi6rneQp5bLGWOPBxSV47XNyEZzyF3DBfs2x7bXAs9SdVbjqUsUcRnFe4UjpNx5gOZJ48sEgHR-CWmNIdn3gxOLpbIkgJXTtMgRKKYtgrZ7_1eMnr0XlencWGkB3qJXfFaq5PvoHrgmISRssW907M6xk0pn5_VVM3AIBi_QD5NTCaxLyOm3ta24tLTzFY8aBqjRm-TC1CJ21qVy2Mox5VHEAFvsVKB-y0j7npuIU6_TkKhoBx9slQ--ObjeFjPNU78gV_bea7013EbBXaGFjNLoKHyRfib0TFvteF6KTAZta58YZg-ZqyjD6cJyx5MDnV37yBWQrwKz5WaXGq9kceWbOtfe7eaQhBf9Hc-hjG3kF70QANJkbtwHmp1icDLCd8iOWJVkymXZjl9UXQZU6o5wt5BZ--irEerlrm8VJnbEju8GTYamp2kWKqew3Jkuj6RL-CvB9UIm_RADWM8dnbBkL0B59oe9Flnj1qPrBsMivZL1tiv-n8VlWtglnZBFb8tJUd_Bp3MusevV73rs2Gis_o-Zcx7btzez8Fj9ZexP8XK12wjzSx6MzTHc_65IUPZo6w3o9rl5GQidIKmQDSNASAHeFP3Pg6bBOH1-Dk_clKqVhZsnew6WFwtctIoof_ipAIpP-PVaKmGoysERcEJMVNaL4x6QDGwKPNd3k68vJwOteLkDmC4cOHsrFEcs3_Yb4VQWRrnCuuTzrzG0aJ54n8YA0PK0d3l37eNQCSSLSeamjgoZithoHye7Fbe0qCnzuERddknqC9qhSM8tkUdb8GqtpW_zsT3uM5IPHgSeCfN8apjsSszauICsjksvJ7iziWLRBmB2QV1Tii6vm8uZgeCxaFHHRnNfKTCBB5cOXpkGa4VYr_WW4SpbHhIqcZcZWq93Zxfl2X_QnDLQnbSZ90eaez7h6O8qtpytVgZrhoc4nEYpJI_SKvQFYEzw_oEtB4YF65o3HpvlbfqxeutOuEP21QIKuGK6NBI4nEiomFNIh_vHZdx6T8e1l5ctIW3kIgG5Wba-CR266VeiD-7ezhIUi_gek38Vifm4S_5CGW3O8V8WmdUhWp89eL4Kws_on6zjtVEeW55lfLpd9uTbWLTr3EsdzdmPciswj-7-CHkLKTXyodr-2rFji7QFUPWuNGeqcjkHsct15wdWvdfuNJY5OioafCEQRwBUT_NiJlGEaUm4j2SdYU363YmbT0htr_F-ZwRX1gkX0s3t3h3qXjJycB84Vx6-BuOHUdRVyEiLzStX1m1cV1uIQ0QikV-f9-9414zhosIoDNXBFge2lL4mxBc_b4ia1nk6-zRG4cFh5AvZV2wqDo01m9VPr41hGfKl5_dmhwhepqD-tFEcjFHSBqgrPbHC3iyG9bTRXYjxXoD0LfQgx5r_UMzu6N--dPwxSFvAIdW_O2-78GsRUOsDB7_dAf0k1CqJgUdZ6KySWNGvSyuwISnJGp454XcE4Smf9MLRJTFGizoL-C5za9inNcZeyPf1pWkKBJWGes0ThFEuW6mRhp2GjTlUUiFVyLbfbMyn37wTKF2EBztnH2x23O9fxBuomVM7kR1NNNW4LEvJMSTJmdpCZ5GDADZLBPeXkljPs2hiBNBLo2r1Umiu4qSundFpuBUtyQdQ_Y87pFvsbzlb7S3yzYecxUGmAHrK38IaYHt7GcDZKabbzbd9CmlJIMJpLtgRzI5Zp\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Updating documentation**\\n\\nI need to respond and acknowledge the user’s request to update the documentation. They mentioned no confirmation is needed, so I can proceed. I'll look into relevant files, possibly SYSTEM.md or AGENTS, since they asked about patching the docs. I should check the git status first based on our workflow. It seems I'll be editing SYSTEM.md in the repo located at /mnt/rpi/pi-config, and I might also look at the README for context mentions. Let's search around the repo.\"}]}"},{"type":"toolCall","id":"call_UsKmSrYS7Bpi5Nz2PgJoiexm|fc_07b1b9c95ac99ec40169e8dd523cd88190a08f61a423fd272f","name":"bash","arguments":{"command":"rg -n \"api/context|api/search|POST /api/ingest|context returns|exact-id|context bundle\" /mnt/rpi/pi-config -S","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":101005,"output":195,"cacheRead":0,"cacheWrite":0,"totalTokens":101200,"cost":{"input":0.07575375000000001,"output":0.0008775,"cacheRead":0,"cacheWrite":0,"total":0.07663125000000001}},"stopReason":"toolUse","timestamp":1776868683892,"responseId":"resp_07b1b9c95ac99ec40169e8dd4cb620819081da87c41d54cb49"}}
{"type":"message","id":"675f4ade","parentId":"8f4e8c55","timestamp":"2026-04-22T14:38:11.233Z","message":{"role":"toolResult","toolCallId":"call_UsKmSrYS7Bpi5Nz2PgJoiexm|fc_07b1b9c95ac99ec40169e8dd523cd88190a08f61a423fd272f","toolName":"bash","content":[{"type":"text","text":"/mnt/rpi/pi-config/SYSTEM.md:39:Use the `agents-database` HTTP API on `http://100.116.176.16:8091`, especially `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.\n/mnt/rpi/pi-config/scripts/session-memory-consolidator.py:30:    url = f\"{AGENTS_DB_API}/api/search?\" + parse.urlencode({\"q\": query, \"scope\": \"global\", \"limit\": str(limit)})\n"}],"isError":false,"timestamp":1776868691233}}
{"type":"message","id":"ce915470","parentId":"675f4ade","timestamp":"2026-04-22T14:38:17.102Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering documentation updates**\n\nI might need to update line 39 in SYSTEM.md, and I wonder if the README needs some attention too. It could be worth searching the docs to see what's missing, but I think I’ll just edit directly. Adding an example sentence could really help clarify things. So, I should patch that up to make sure everything is clear and helpful for anyone looking at the documents! Let's get to it!","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd55a5548190af9c6e1bf332c45d\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N1XiSPDNnPQnbLR-yYZFI8a8C08pBkIDXjfRUoc8qgqHGSK8Q9u8NtdMrAUHRt9n3y5mNN2kQfVg5G7OBKpVskuHto9hqbZPfhzeqizxwZ9GG_r2d3eg46ATYRTq0yLkTtA7hhEJ1hGHNtj_VeBKaqFYd555bCtB_JOMqIlXOvJ-yahB4p2hcCsQJday-zo0S5rm5p9ARJivyik_hoZah8kSnMl3rmznO1KYvkk5VvSuMaR_oRybYNsr5UjxuE6klw5T8-OQ9-NpbwvGa8NmyWCiYXBPidPorHPvoJ4NMzSHhkMn8m5qS8bPlMT-bxGFKBFOt95my3V8OlMxRydDAZseagcm4eYx5Owjt9_HOHE2t-myltQEoy8MestRYKeyWqTIJKQET6RI4BfeNpiq7gZhDSA_vQWmjE_nkgiaG9_ZdlcTZRA6Mh5E8QHdvthXmIUksH4FYkKLMxEBR3YpBPqeBl50oVPpDswqKEZ9cMhPanD7uSSgRim82TScR6YHcMv1yLTjHWGaZ6cOqq75VvetsbaH_tyO6CSsyIxfGPDjFoJPHzhB_7BLIJENZB1jemN_fbpy0Ksugt7ngPfn2Wt1huGdqCLChDpWP6Rsz4XScebUpN-eiQF9TyUU6EdLUtuXZi07Q5NoYCsTLSsZhABMDhFzuRGAYQ2x9z58RIdUG2A1C-DkAXUJmK88R6bR9QRTndDIHX88OK7AXzdRoOvhEsv1jpflfcgqoLmHMv_jyuPgdIv-Gc0yCUV60Ejj_hYIcyNSI64FysAHDqk5nHIjdmz0KQkX3X679rxgsjb-RXAqej4qD9ISbEUlsyDKFjgAkViVfmOcxWVZKqtFuIGdipeh8JAxjh85seRlFFmOCLF3QFCEKx38oUzWqP_ehlhpnIbKVW7Gg3DZZje-TdpZEun5Le7X_njNWi5dgyq52WAWm7QTv71X8NW3IMQZZL2hA4biUk4HAho6cDExiGlda49ZTxPpkxp9kK7C7B8PfyBOsZi1DmXYrggSka9esfFO5PWmclv39lHM23pI9U9QTqAwzzj1FZnuf4ZA63BP3gdMCNJ281ZkVQf8uQX6W_VKgR48LmRdsgfS0S06Kz5VMssLhIMr2eeOMdnc4_OHL85KcrILwB17Gao96IcEv6v\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering documentation updates**\\n\\nI might need to update line 39 in SYSTEM.md, and I wonder if the README needs some attention too. It could be worth searching the docs to see what's missing, but I think I’ll just edit directly. Adding an example sentence could really help clarify things. So, I should patch that up to make sure everything is clear and helpful for anyone looking at the documents! Let's get to it!\"}]}"},{"type":"toolCall","id":"call_1NrbhnMkZq70mzpuJSPecytX|fc_07b1b9c95ac99ec40169e8dd57d7788190bd86d15355483218","name":"edit","arguments":{"path":"/mnt/rpi/pi-config/SYSTEM.md","edits":[{"oldText":"Use the `agents-database` HTTP API on `http://100.116.176.16:8091`, especially `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.\n","newText":"Use the `agents-database` HTTP API on `http://100.116.176.16:8091`, especially `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.\n- `GET /api/search` is the normal lookup path for finding memories by id fragment, content, or query.\n- `GET /api/context` returns a contextual bundle around a memory, not a reliable exact-id fetch.\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":455,"output":220,"cacheRead":100864,"cacheWrite":0,"totalTokens":101539,"cost":{"input":0.00034125000000000003,"output":0.00099,"cacheRead":0.0075648,"cacheWrite":0,"total":0.008896049999999999}},"stopReason":"toolUse","timestamp":1776868691234,"responseId":"resp_07b1b9c95ac99ec40169e8dd54a6408190ac09bebb0ede61f7"}}
{"type":"message","id":"a5b7b597","parentId":"ce915470","timestamp":"2026-04-22T14:38:17.115Z","message":{"role":"toolResult","toolCallId":"call_1NrbhnMkZq70mzpuJSPecytX|fc_07b1b9c95ac99ec40169e8dd57d7788190bd86d15355483218","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in /mnt/rpi/pi-config/SYSTEM.md."}],"details":{"diff":"    ...\n 36 When useful context may already exist, check Agents Database before or during work.\n 37 When writing memory, include the best available scope/type and attach project_id, repo_id, task_id, run_id, source_ref, origin_agent, url, and domain when known.\n 38 Default memory types: `profile` for user preferences/priorities, `decision` for chosen approaches, `project` for durable project facts, `artifact` for reusable references/sources, `episode` for concise session or handoff summaries.\n 39 Use the `agents-database` HTTP API on `http://100.116.176.16:8091`, especially `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.\n+40 - `GET /api/search` is the normal lookup path for finding memories by id fragment, content, or query.\n+41 - `GET /api/context` returns a contextual bundle around a memory, not a reliable exact-id fetch.\n 40 \n 41 Irreversible actions require explicit confirmation.\n 42 Never merge or push to main without user confirmation.\n 43 \n    ...","firstChangedLine":40},"isError":false,"timestamp":1776868697115}}
{"type":"message","id":"dcb485ed","parentId":"a5b7b597","timestamp":"2026-04-22T14:38:20.703Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering memory updates**\n\nI need to mention things concisely, and there may not be a need to commit. However, should I update my memory? I’ve already stored the decision, but it might be good to note that I’ve patched the documentation. My earlier mistake was due to ambiguity, which has now been resolved in the docs. I want to ensure I'm keeping communication clear and straightforward.","thinkingSignature":"{\"id\":\"rs_07b1b9c95ac99ec40169e8dd5aab0481909e8580fce8ca7c84\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6N1cQIf9kG8Q1mqvLl4G_Q5fUlZQXtYh9vX8f-zWzwWE9l9eKZYLv0WuTYJ37KiD3RcBy4vcjsB0v1ledD6FA7U_ocWZ4CRtquivQWHHhLC8WaKr5lUEcVEL9Mft69UYq3ShhLWcm3_ZclNefQZzpv9JdKqgRXp-xCvxVvtIYToZ3FkVXu0xPnuLp5ZFJCKqUkB21JcZ6ItOLK7nkL5puygubDjC9srzrpI26ZNMKpU8zf2G7zAntLRzKJg_mLm_pYTWpNfw8xJWftMKjFjriyOaTHQqr2-rvejVKpec8QUy3M2QXXYPqdYWhFH4LnHzJPWiEZu0T2rR2465bvvZ4_I-Cjb5Ihqu6sQPfDtlBpu1M_R2rfnLGqwcF4Y26fDF0a05mM2pMjixjieT-lCQV7xxBu-ROgLAyflIRdRlmKChaECip0dY1b1GHqpASYwM0uyFDVQqG0MDrnl6sKpQcyt_w2c8QeXh_8Yi9gq5NFNdyB0JrI0Vz12-suy9bBGoTxf1-Q1D0USFYh0EcDoVaHhr58wn4VyVP4XmRctoYmcGsRGl8IP-JNEWVRPg168lCz6oU7AzsCMsPC7BdxGUqbrkeEw5gcDiYOAOH8F3IVXBb6STcVfW6aXhFT6Ruc-LDngWOP-fGlB7-WsfjcXppwx6gujLolKPpsNwYdiMl_7djlJ-WgDkd8t6e09T-YyBQRJU_-tVaJ1gQOSHk_ZW4slfydTJTLJKs6pItLPb9F1f-pwBv-1lUr2q0myE3aQmvDJ6u4DXjviSPAnvPhO9j1tcabctY4iRDm0SxMPnw-7MsxS0JyjlRg6uVJAH91AcVoPkQ4WogC7GblrAJIXzebPsgHqM8omLChA7JpBcd8OiwitOKIc0v_NdWdMcijjvtiO5CiLfvKbZZOaqz6ZgKrq8jpN6LWLqMgV4hWgAWNgTLdxt6GhDsvspBPO1_V6qj1cbdViMTSZdwWbtaxRWDYlGI-OvihsbNGt_OSXxgtDeVRRIw2JzQFJqilbeOY0ByO_9astwtzJL4q4n5VUfLk04f753aSeMne07up4-Nr0KADAjJDpXvxOzPXDu7z0JbxIdvNZ4WMwsS7D9_Zw4Zf2YWXWRBAmAjXFPARy1wMJTIEgjYIe2TfKfnE-Hx6eUpIJAYWYx3oo7VDxjBGFE7seh3iO1ZCQ2BwEsESkcSsFstOiyGMd_lUxbJptq43VD7p6m1UXoAjkU2stNI_nMYrMnsBI5REgSR4jNy3WYi-Da6CQxxEbtYgkgNUchDlZ2jOaW\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering memory updates**\\n\\nI need to mention things concisely, and there may not be a need to commit. However, should I update my memory? I’ve already stored the decision, but it might be good to note that I’ve patched the documentation. My earlier mistake was due to ambiguity, which has now been resolved in the docs. I want to ensure I'm keeping communication clear and straightforward.\"}]}"},{"type":"text","text":"Done.\n\nUpdated `SYSTEM.md` to say:\n- `GET /api/search` = lookup path\n- `GET /api/context` = contextual bundle, not exact-id fetch\n\nThat removes the ambiguity that led me to the wrong endpoint.","textSignature":"{\"v\":1,\"id\":\"msg_07b1b9c95ac99ec40169e8dd5c55bc819097d08589f70206a2\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":191,"output":107,"cacheRead":101376,"cacheWrite":0,"totalTokens":101674,"cost":{"input":0.00014325,"output":0.0004815,"cacheRead":0.0076032,"cacheWrite":0,"total":0.00822795}},"stopReason":"stop","timestamp":1776868697118,"responseId":"resp_07b1b9c95ac99ec40169e8dd59d188819084512231ba98cf76"}}