{"type":"session","version":3,"id":"019db54f-ade4-70ca-bccf-03ddd7fc60d9","timestamp":"2026-04-22T13:09:51.204Z","cwd":"/mnt/rpi/repos/ballbox"}
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{"type":"message","id":"804bf34c","parentId":"a5d51a88","timestamp":"2026-04-22T13:10:02.314Z","message":{"role":"user","content":[{"type":"text","text":"read last handoff from memory (mem_7e571be6dcee4a0b8d53c3cf27898c50), then explore api to find it"}],"timestamp":1776863402313}}
{"type":"message","id":"8c7477ba","parentId":"804bf34c","timestamp":"2026-04-22T13:10:03.732Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_H5YqUsR8Tr6DCf30BETin8AH|fc_0c0fef4ddac7e1db0169e8c8ab92b08195962ef2ac33242d72","name":"read","arguments":{"path":"/home/sebas/.agents/skills/memory-read/SKILL.md"}},{"type":"toolCall","id":"call_Mg4XtTRhlInMxYzzQHnSxFYb|fc_0c0fef4ddac7e1db0169e8c8ab92c881958b2cdf14820b8ae4","name":"read","arguments":{"path":"/home/sebas/.agents/skills/ballbox-company/SKILL.md"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":4585,"output":75,"cacheRead":0,"cacheWrite":0,"totalTokens":4660,"cost":{"input":0.00343875,"output":0.0003375,"cacheRead":0,"cacheWrite":0,"total":0.00377625}},"stopReason":"toolUse","timestamp":1776863402315,"responseId":"resp_0c0fef4ddac7e1db0169e8c8aaa1d88195b23d4c1a152201cb"}}
{"type":"message","id":"f2ef36a3","parentId":"8c7477ba","timestamp":"2026-04-22T13:10:03.743Z","message":{"role":"toolResult","toolCallId":"call_H5YqUsR8Tr6DCf30BETin8AH|fc_0c0fef4ddac7e1db0169e8c8ab92b08195962ef2ac33242d72","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":1776863403739}}
{"type":"message","id":"21571ad9","parentId":"f2ef36a3","timestamp":"2026-04-22T13:10:03.747Z","message":{"role":"toolResult","toolCallId":"call_Mg4XtTRhlInMxYzzQHnSxFYb|fc_0c0fef4ddac7e1db0169e8c8ab92c881958b2cdf14820b8ae4","toolName":"read","content":[{"type":"text","text":"---\nname: ballbox-company\ndescription: Load Ballbox company context and operating namespace\n---\n\n# ballbox-company\n\n## Purpose\n\nLoad the Ballbox company operating context when the request is about Ballbox.\n\n## Workflow\n\n1. Use the Ballbox namespace at `/mnt/rpi/repos/ballbox-company-agent` as the working context root.\n2. Read `/mnt/rpi/repos/ballbox-company-agent/AGENTS.md` and follow its rules.\n3. Use `/mnt/rpi/repos/ballbox-company-agent/index.md` as the default dashboard entry point.\n4. Prefer shared-memory search before assuming facts; avoid invented certainty.\n5. If the work implies repo/code changes, delegate to `~/ai-dev-workflow`.\n"}],"isError":false,"timestamp":1776863403743}}
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\"freshness\": 0.75, \"created_at\": \"2026-03-24T14:20:37.594027+00:00\", \"updated_at\": \"2026-03-24T14:20:37.594027+00:00\", \"observed_at\": \"2026-03-24T14:20:37.594027+00:00\", \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"evidence_ref\": \"https://help.matchable.io/en/articles/22-manage-credits\", \"metadata\": {\"step_kind\": \"capture\", \"step_status\": \"completed\"}, \"embedding\": [0.35900326847049996, 0.36884539462961285, 0.3936850463645169, 0.4025898271751429, 0.3515045056826044, 0.349161142311387, 0.31447936441737007, 0.2713614783869706]}, \"score\": 0.22370446278187792, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9913630852125195, \"confidence\": 0.75, \"freshness\": 0.75}, \"matched_scope\": \"global\", \"evidence_ref\": \"https://help.matchable.io/en/articles/22-manage-credits\", \"explanation\": \"facets=0.00 lexical=0.00 semantic=0.99 confidence=0.75 freshness=0.75\"}, {\"memory\": {\"id\": 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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, 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logic and statuses\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-24T14:20:37.383340+00:00\", \"updated_at\": \"2026-03-24T14:20:37.383340+00:00\", \"observed_at\": \"2026-03-24T14:20:37.383340+00:00\", \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"evidence_ref\": \"https://help.matchable.io/en/articles/44-create-and-manage-your-course-schedule\", \"metadata\": {\"notes\": \"Course schedule logic and statuses\\ncontent_type=text/html; charset=utf-8\"}, \"embedding\": [0.4415485035856796, 0.4058855327565152, 0.4118780811335469, 0.37475351313925276, 0.34946788218250907, 0.3268131261229989, 0.2388249767821915, 0.2103238320621625]}, \"score\": 0.22349061731414402, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9899374487609601, \"confidence\": 0.75, \"freshness\": 0.75}, \"matched_scope\": \"global\", \"evidence_ref\": 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charset=utf-8\\nhttps://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trainer-app\", \"summary\": \"Trainer mobile app capabilities\\ncontent_type=text/html; charset=utf-8\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-24T14:20:37.613310+00:00\", \"updated_at\": \"2026-03-24T14:20:37.613310+00:00\", \"observed_at\": \"2026-03-24T14:20:37.613310+00:00\", \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"evidence_ref\": \"https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trainer-app\", \"metadata\": {\"notes\": \"Trainer mobile app capabilities\\ncontent_type=text/html; charset=utf-8\"}, \"embedding\": [0.4468259069118442, 0.40311616624797475, 0.4236693358391359, 0.3562549395801272, 0.32761752328310934, 0.3084345649980256, 0.29048479688841145, 0.21114956226652926]}, \"score\": 0.22345426904962432, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9896951269974955, 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\"freshness\": 0.7, \"created_at\": \"2026-03-13T14:01:39.648368+00:00\", \"updated_at\": \"2026-03-24T14:18:16.797666+00:00\", \"observed_at\": \"2026-03-13T14:01:39.648368+00:00\", \"source_ref\": \"personal-agent:run:9fdccb34-cf1e-47bd-b133-a224f363a6d4\", \"evidence_ref\": \"https://example.com/personal-assistants\", \"metadata\": {\"notes\": \"verification source\", \"schema\": \"shared-agent-memory-v2\", \"subtype\": \"research_source\"}, \"embedding\": [0.4115741445282813, 0.4248507298356452, 0.41385013058097225, 0.38881428400137175, 0.3436738939563344, 0.3395012528597343, 0.26249705807702367, 0.15931902368836695]}, \"score\": 0.22143954015858938, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.0, \"semantic\": 0.9895969343905959, \"confidence\": 0.75, \"freshness\": 0.7}, \"matched_scope\": \"global\", \"evidence_ref\": \"https://example.com/personal-assistants\", \"explanation\": \"facets=0.00 lexical=0.00 semantic=0.99 confidence=0.75 freshness=0.70\"}]}"}],"isError":false,"timestamp":1776863405017}}
{"type":"message","id":"2918779c","parentId":"f4a24c9e","timestamp":"2026-04-22T13:10:07.204Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_gZiznmnOmzs8mKyPloO5QHfe|fc_0c0fef4ddac7e1db0169e8c8ae38148195b767027f2b7f4d65","name":"questionnaire","arguments":{"questions":[{"id":"clarify","label":"Intent","prompt":"No encontré ese id exacto en memory search. Querés que busque el último handoff por contenido/fecha en la API, o tenés otro id/rango para filtrar?","options":[{"value":"search_latest","label":"Buscar último handoff relevante"},{"value":"provide_id","label":"Te paso otro id/rango"},{"value":"stop","label":"Parar"}],"allowOther":false}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":14184,"output":114,"cacheRead":4608,"cacheWrite":0,"totalTokens":18906,"cost":{"input":0.010638,"output":0.000513,"cacheRead":0.0003456,"cacheWrite":0,"total":0.0114966}},"stopReason":"toolUse","timestamp":1776863405021,"responseId":"resp_0c0fef4ddac7e1db0169e8c8adc9e481958bfd553d64617d1d"}}
{"type":"message","id":"36bd78d2","parentId":"2918779c","timestamp":"2026-04-22T13:10:30.436Z","message":{"role":"toolResult","toolCallId":"call_gZiznmnOmzs8mKyPloO5QHfe|fc_0c0fef4ddac7e1db0169e8c8ae38148195b767027f2b7f4d65","toolName":"questionnaire","content":[{"type":"text","text":"Intent: user selected: 3. Parar"}],"details":{"questions":[{"id":"clarify","label":"Intent","prompt":"No encontré ese id exacto en memory search. Querés que busque el último handoff por contenido/fecha en la API, o tenés otro id/rango para filtrar?","options":[{"value":"search_latest","label":"Buscar último handoff relevante"},{"value":"provide_id","label":"Te paso otro id/rango"},{"value":"stop","label":"Parar"}],"allowOther":false}],"answers":[{"id":"clarify","value":"stop","label":"Parar","wasCustom":false,"index":3}],"cancelled":false},"isError":false,"timestamp":1776863430436}}
{"type":"message","id":"ce38b4cf","parentId":"36bd78d2","timestamp":"2026-04-22T13:10:32.042Z","message":{"role":"assistant","content":[{"type":"text","text":"No encontré `mem_7e571be6dcee4a0b8d53c3cf27898c50` en `api/search`.\n\nLo que sí apareció fue una serie de memorias de Matchable y fuentes sueltas, no un handoff con ese id.\n\nPara seguir, necesito una de estas:\n- el texto aproximado del handoff\n- otro id\n- o que busque el “último handoff” por fecha/contenido en la API y te lo traigo\n\nSi querés, hago esa búsqueda ahora.","textSignature":"{\"v\":1,\"id\":\"msg_0c0fef4ddac7e1db0169e8c8c70fa08195b09d506ea3f06e54\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":495,"output":119,"cacheRead":18432,"cacheWrite":0,"totalTokens":19046,"cost":{"input":0.00037125,"output":0.0005355000000000001,"cacheRead":0.0013824,"cacheWrite":0,"total":0.00228915}},"stopReason":"stop","timestamp":1776863430437,"responseId":"resp_0c0fef4ddac7e1db0169e8c8c6b3ec81959eb1d7cc7ba58f74"}}
{"type":"message","id":"d4357c2b","parentId":"ce38b4cf","timestamp":"2026-04-22T13:10:59.107Z","message":{"role":"user","content":[{"type":"text","text":"contiene \"ORDTST01KPPFWZBJD0M1PACY3257J7KH\". seguro que ese id no existe?"}],"timestamp":1776863459106}}
{"type":"message","id":"212d9a83","parentId":"d4357c2b","timestamp":"2026-04-22T13:11:00.446Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_9sYtyuD7U70aPDKercjOTXKh|fc_0c0fef4ddac7e1db0169e8c8e3efc88195a23f5481a0cf0ca8","name":"bash","arguments":{"command":"curl -fsSL 'http://100.116.176.16:8091/api/search?q=ORDTST01KPPFWZBJD0M1PACY3257J7KH&scope=global&scope=project&limit=20'","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":19083,"output":75,"cacheRead":0,"cacheWrite":0,"totalTokens":19158,"cost":{"input":0.01431225,"output":0.0003375,"cacheRead":0,"cacheWrite":0,"total":0.01464975}},"stopReason":"toolUse","timestamp":1776863459107,"responseId":"resp_0c0fef4ddac7e1db0169e8c8e35c9081959fbb61ea15790ad2"}}
{"type":"message","id":"1df250a0","parentId":"212d9a83","timestamp":"2026-04-22T13:11:00.541Z","message":{"role":"toolResult","toolCallId":"call_9sYtyuD7U70aPDKercjOTXKh|fc_0c0fef4ddac7e1db0169e8c8e3efc88195a23f5481a0cf0ca8","toolName":"bash","content":[{"type":"text","text":"{\"retrieval_id\": \"ret_0837c6b3d9ad45cabeafc49c570784f2\", \"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, \"url\": null, \"domain\": null, \"source_kind\": \"manual\", \"title\": \"Research step: capture https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trai\", \"content\": \"Captured https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trainer-app\", \"summary\": \"Captured https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trainer-app\", \"confidence\": 0.75, \"freshness\": 0.75, \"created_at\": \"2026-03-24T14:20:37.617460+00:00\", \"updated_at\": \"2026-03-24T14:20:37.617460+00:00\", \"observed_at\": \"2026-03-24T14:20:37.617460+00:00\", \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\", \"evidence_ref\": \"https://help.matchable.io/en/articles/13-getting-started-with-the-matchable-trainer-app\", \"metadata\": {\"step_kind\": \"capture\", \"step_status\": \"completed\"}, \"embedding\": [0.3291702332392591, 0.35665255102377513, 0.39772163265681587, 0.35974045189843984, 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{"type":"message","id":"ebd2dca8","parentId":"1df250a0","timestamp":"2026-04-22T13:11:03.906Z","message":{"role":"assistant","content":[{"type":"text","text":"Sí, existe. Yo busqué mal el primer id.\n\nHallazgo:\n- `mem_7e571be6dcee4a0b8d53c3cf27898c50`\n- tipo: `project`\n- título: **Ballbox payments validation handoff and open blockers**\n- contiene el orden `ORDTST01KPPFWZBJD0M1PACY3257J7KH`\n\nResumen útil del handoff:\n- `PAY-2` native MercadoPago flow merged y deployed\n- callback legacy removido\n- `external_reference` canónico: `payment-vm_{vendingMachineId}-txn_{machineTxnId}`\n- POS de test válido: `BALLBOXTESTPOS01`\n- orden test aprobada: `ORDTST01KPPFWZBJD0M1PACY3257J7KH` → `processed/accredited`\n- pendientes:\n  - Ballbox no persistió `PaymentSession`\n  - token prod sigue sin acceso a `/users/me` ni `/pos` por `PA_UNAUTHORIZED_RESULT_FROM_POLICIES`\n  - homologator falla con `error_code 1006`\n\nSi querés, ahora exploro la API para sacar:\n1. el run/session completo de ese handoff, o\n2. los logs/claims relacionados con ese `ORDTST...` exacto.","textSignature":"{\"v\":1,\"id\":\"msg_0c0fef4ddac7e1db0169e8c8e5f2f48195b483e029f3e62796\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":15493,"output":285,"cacheRead":18944,"cacheWrite":0,"totalTokens":34722,"cost":{"input":0.01161975,"output":0.0012825,"cacheRead":0.0014207999999999998,"cacheWrite":0,"total":0.01432305}},"stopReason":"stop","timestamp":1776863460542,"responseId":"resp_0c0fef4ddac7e1db0169e8c8e5453c8195aeae9ca9ed0aa29a"}}
{"type":"message","id":"c795fdd6","parentId":"ebd2dca8","timestamp":"2026-04-22T13:15:46.417Z","message":{"role":"user","content":[{"type":"text","text":"arregla la busqueda por id explicito. creo que usaste `q=`. capaz no hay busqueda por id"}],"timestamp":1776863746416}}
{"type":"message","id":"9074f637","parentId":"c795fdd6","timestamp":"2026-04-22T13:15:47.922Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_loyZhAmrtDaT48tlhhlz7nDM|fc_0c0fef4ddac7e1db0169e8ca0333b4819598232a06ec4d6f8e","name":"bash","arguments":{"command":"python - <<'PY'\nimport urllib.parse, requests\nbase='http://100.116.176.16:8091'\nfor path in ['/api/search','/api/context']:\n    try:\n        r=requests.get(base+path, timeout=10)\n        print(path, r.status_code, r.text[:500])\n    except Exception as e:\n        print(path, 'ERR', e)\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":15810,"output":114,"cacheRead":18944,"cacheWrite":0,"totalTokens":34868,"cost":{"input":0.0118575,"output":0.000513,"cacheRead":0.0014207999999999998,"cacheWrite":0,"total":0.0137913}},"stopReason":"toolUse","timestamp":1776863746418,"responseId":"resp_0c0fef4ddac7e1db0169e8ca02be308195b83823d563aafab8"}}
{"type":"message","id":"3bd3c31b","parentId":"9074f637","timestamp":"2026-04-22T13:15:48.130Z","message":{"role":"toolResult","toolCallId":"call_loyZhAmrtDaT48tlhhlz7nDM|fc_0c0fef4ddac7e1db0169e8ca0333b4819598232a06ec4d6f8e","toolName":"bash","content":[{"type":"text","text":"/api/search 400 <!DOCTYPE HTML>\n<html lang=\"en\">\n    <head>\n        <meta charset=\"utf-8\">\n        <title>Error response</title>\n    </head>\n    <body>\n        <h1>Error response</h1>\n        <p>Error code: 400</p>\n        <p>Message: Missing query parameter 'q'.</p>\n        <p>Error code explanation: 400 - Bad request syntax or unsupported method.</p>\n    </body>\n</html>\n\n/api/context 200 {\"profile_facts\": [{\"id\": \"mem_507d4c3e4d84409883a7bf1b31c73e2e\", \"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\": \"Sebas prefers HTML as doc entry point\", \"content\": \"When generating plans, reports, or docs, use HTML as the entry point. Link on\n"}],"isError":false,"timestamp":1776863748130}}
{"type":"message","id":"4261dcd6","parentId":"3bd3c31b","timestamp":"2026-04-22T13:15:49.948Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_eJ0S651nQCLl943GoTnWJSUc|fc_0c0fef4ddac7e1db0169e8ca05279081959dbd29c68e06caf8","name":"bash","arguments":{"command":"curl -fsSL 'http://100.116.176.16:8091/api/context' | python - <<'PY'\nimport sys, json\nobj=json.load(sys.stdin)\nprint(obj.keys())\nprint('profile_facts', len(obj.get('profile_facts',[])))\nprint('project_facts', len(obj.get('project_facts',[])))\nprint('recent_runs', len(obj.get('recent_runs',[])))\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":843,"output":120,"cacheRead":34304,"cacheWrite":0,"totalTokens":35267,"cost":{"input":0.00063225,"output":0.00054,"cacheRead":0.0025728,"cacheWrite":0,"total":0.00374505}},"stopReason":"toolUse","timestamp":1776863748131,"responseId":"resp_0c0fef4ddac7e1db0169e8ca04b7988195825ab6e18bca3a9f"}}
{"type":"message","id":"94ce0d71","parentId":"4261dcd6","timestamp":"2026-04-22T13:15:50.004Z","message":{"role":"toolResult","toolCallId":"call_eJ0S651nQCLl943GoTnWJSUc|fc_0c0fef4ddac7e1db0169e8ca05279081959dbd29c68e06caf8","toolName":"bash","content":[{"type":"text","text":"curl: (23) Failure writing output to destination, passed 16384 returned 0\nTraceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\n  File \"/usr/lib/python3.13/json/__init__.py\", line 293, in load\n    return loads(fp.read(),\n        cls=cls, object_hook=object_hook,\n        parse_float=parse_float, parse_int=parse_int,\n        parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)\n  File \"/usr/lib/python3.13/json/__init__.py\", line 346, in loads\n    return _default_decoder.decode(s)\n           ~~~~~~~~~~~~~~~~~~~~~~~^^^\n  File \"/usr/lib/python3.13/json/decoder.py\", line 345, in decode\n    obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n               ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.13/json/decoder.py\", line 363, in raw_decode\n    raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1776863750003}}
{"type":"message","id":"7f9f8387","parentId":"94ce0d71","timestamp":"2026-04-22T13:15:51.860Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_eJ8XMUVoLQwwuzfJlVkToIlg|fc_0c0fef4ddac7e1db0169e8ca07228481959d5501216146ff01","name":"bash","arguments":{"command":"python - <<'PY'\nimport requests, json\nr=requests.get('http://100.116.176.16:8091/api/context', timeout=20)\nprint(r.status_code)\nobj=r.json()\nprint(obj.keys())\nfor k in ['profile_facts','project_facts','recent_runs']:\n    print(k, len(obj.get(k,[])))\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":726,"output":106,"cacheRead":34816,"cacheWrite":0,"totalTokens":35648,"cost":{"input":0.0005445000000000001,"output":0.000477,"cacheRead":0.0026111999999999997,"cacheWrite":0,"total":0.0036327}},"stopReason":"toolUse","timestamp":1776863750004,"responseId":"resp_0c0fef4ddac7e1db0169e8ca069bb881958184fcf8e842f285"}}
{"type":"message","id":"0f9d375e","parentId":"7f9f8387","timestamp":"2026-04-22T13:15:52.072Z","message":{"role":"toolResult","toolCallId":"call_eJ8XMUVoLQwwuzfJlVkToIlg|fc_0c0fef4ddac7e1db0169e8ca07228481959d5501216146ff01","toolName":"bash","content":[{"type":"text","text":"200\ndict_keys(['profile_facts', 'project_memories', 'active_decisions', 'recent_episodes', 'task_relevant_artifacts', 'citations', 'agent_id'])\nprofile_facts 8\nproject_facts 0\nrecent_runs 0\n"}],"isError":false,"timestamp":1776863752071}}
{"type":"message","id":"ef43acdc","parentId":"0f9d375e","timestamp":"2026-04-22T13:15:54.518Z","message":{"role":"assistant","content":[{"type":"text","text":"Sí: la API de `search` exige `q=`. No parece haber búsqueda por id explícito.\n\nLo que probé:\n- `GET /api/search` sin `q` → `400 Missing query parameter 'q'`\n- `GET /api/context` → devuelve contexto, no búsqueda por id\n\nConclusión:\n- `api/search` es por texto, no por id\n- para encontrar un memory id, hay que buscar por contenido o usar `/api/context` si ya está en contexto reciente\n\nOjo: en este caso `q=ORDTST...` sí devolvió el handoff exacto, así que el id existe y el texto también.\n\nSi querés, hago el fix mental/procedimental:\n- tratar `q=` como búsqueda full-text\n- no asumir lookup por id\n- usar `/api/context` para contexto reciente y `search` para recuperar por texto\n\nSi querés, te dejo una mini nota de uso para no repetir este error.","textSignature":"{\"v\":1,\"id\":\"msg_0c0fef4ddac7e1db0169e8ca08c7b481959ccd5f0f918064dc\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":387,"output":206,"cacheRead":35328,"cacheWrite":0,"totalTokens":35921,"cost":{"input":0.00029025,"output":0.000927,"cacheRead":0.0026496,"cacheWrite":0,"total":0.00386685}},"stopReason":"stop","timestamp":1776863752072,"responseId":"resp_0c0fef4ddac7e1db0169e8ca08632481958287634cffd90854"}}
{"type":"message","id":"3db03b7f","parentId":"ef43acdc","timestamp":"2026-04-22T13:22:18.061Z","message":{"role":"user","content":[{"type":"text","text":"pprefiero que agregues endpoint/param de busqueda por id"}],"timestamp":1776864138060}}
{"type":"message","id":"01612f82","parentId":"3db03b7f","timestamp":"2026-04-22T13:22:34.243Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_sID1s95wx8FSSdBgBdeY0PDB|fc_0c0fef4ddac7e1db0169e8cb968ef0819581737b05279bcc0c","name":"todo","arguments":{"action":"create","subject":"Add explicit memory lookup by id in agents-database API","description":"Investigate agents-database API implementation and add explicit endpoint or query param for direct memory id lookup, then verify behavior and update docs if needed.","activeForm":"adding id lookup","status":"pending"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":101,"output":73,"cacheRead":35840,"cacheWrite":0,"totalTokens":36014,"cost":{"input":0.00007575,"output":0.0003285,"cacheRead":0.002688,"cacheWrite":0,"total":0.00309225}},"stopReason":"toolUse","timestamp":1776864138061,"responseId":"resp_0c0fef4ddac7e1db0169e8cb8c6ebc8195ac999404b38f8991"}}
{"type":"message","id":"6c4369e2","parentId":"01612f82","timestamp":"2026-04-22T13:22:34.258Z","message":{"role":"toolResult","toolCallId":"call_sID1s95wx8FSSdBgBdeY0PDB|fc_0c0fef4ddac7e1db0169e8cb968ef0819581737b05279bcc0c","toolName":"todo","content":[{"type":"text","text":"Created #1: Add explicit memory lookup by id in agents-database API (pending)"}],"details":{"action":"create","params":{"action":"create","subject":"Add explicit memory lookup by id in agents-database API","description":"Investigate agents-database API implementation and add explicit endpoint or query param for direct memory id lookup, then verify behavior and update docs if needed.","activeForm":"adding id lookup","status":"pending"},"tasks":[{"id":1,"subject":"Add explicit memory lookup by id in agents-database API","status":"pending","description":"Investigate agents-database API implementation and add explicit endpoint or query param for direct memory id lookup, then verify behavior and update docs if needed.","activeForm":"adding id lookup"}],"nextId":2},"isError":false,"timestamp":1776864154258}}
{"type":"message","id":"35650916","parentId":"6c4369e2","timestamp":"2026-04-22T13:22:35.944Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_lob6HXd3ZJt7CV9YhmfOctxt|fc_0c0fef4ddac7e1db0169e8cb9bce448195923f39382165549f","name":"read","arguments":{"path":"/mnt/rpi/repos/ballbox/AGENTS.md"}},{"type":"toolCall","id":"call_u2LBTHoxBChUki17GEIY6xyf|fc_0c0fef4ddac7e1db0169e8cb9bce5481959ea9579e4dc03f11","name":"read","arguments":{"path":"/mnt/rpi/repos/ballbox/docs/index.md"}},{"type":"toolCall","id":"call_yIZBNGcXwsjnj6uC5aU4rX7P|fc_0c0fef4ddac7e1db0169e8cb9bce608195bc5574974b2ef9b9","name":"read","arguments":{"path":"/mnt/rpi/repos/ballbox/docs/repo-map.md"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":202,"output":92,"cacheRead":35840,"cacheWrite":0,"totalTokens":36134,"cost":{"input":0.0001515,"output":0.00041400000000000003,"cacheRead":0.002688,"cacheWrite":0,"total":0.0032535}},"stopReason":"toolUse","timestamp":1776864154259,"responseId":"resp_0c0fef4ddac7e1db0169e8cb9ac8d881958e2090e38fd4fc83"}}
{"type":"message","id":"274ebfbe","parentId":"35650916","timestamp":"2026-04-22T13:22:35.970Z","message":{"role":"toolResult","toolCallId":"call_lob6HXd3ZJt7CV9YhmfOctxt|fc_0c0fef4ddac7e1db0169e8cb9bce448195923f39382165549f","toolName":"read","content":[{"type":"text","text":"Owner-managed repo. Telegraph. Min tokens.\n\n## Memory\n- durable prefs/rules/notes to `~/agents-database`\n- do not rely on Codex-only local state as source of truth\n\n## Git / Deploy\n- default branch: `main`\n- this repo auto-deploys to Vercel\n- for every change, start from a fresh branch created from up-to-date `origin/main`\n- do not block local commits on working branches; commit freely when the work is coherent\n- do not push directly to `main` unless the user explicitly asks for it\n- protect `main` from direct changes: no direct push and no merge without explicit user confirmation\n- treat this as near-future client production\n\n## Workflow\n- safe by default: check `git status/diff` first\n- prefer end-to-end verify before handoff\n- for Playwright/e2e, prefer a stable app-like server path over dev-only watcher flows when either can satisfy the test objective; keep the repo default framed as the most repeatable cross-machine setup, not a workaround for one host\n- when a working branch is considered done/ready, open a PR automatically; do not ask the user for permission\n- when user asks for handoff to another agent, leave clean repo state first: docs, tests, and code aligned across whole repo; no half-done, incomplete, stale, or misleading leftovers\n- keep `PLAN.md` as the source of truth for current state and next implementation steps\n- every meaningful change must have its plan and progress documented in repo so work is visible and resumable\n- use `docs/next-steps-queue.md` to pick the next task by priority/dependency (`ready` first)\n- when implementation changes ship, update `PLAN.md` in the same work\n- seeded demo/e2e fixtures used by operator flows should stay valid across normal near-future local runs; avoid near-term hard-coded expiry windows unless the task explicitly tests expiry behavior\n- subsystem docs under `docs/` must describe only what is already implemented; no future promises there\n- whenever code changes materially affect behavior or architecture, update both `PLAN.md` and the relevant implemented docs in the same work\n- push working-branch progress frequently to improve team communication and resumability, but keep commits coherent\n- before proposing or building a demo-oriented feature, read `docs/demo-iteration-mode.md` and follow its L1/L2/L3 delivery model and anti-debt guardrails\n- use `docs/index.md` as the docs entrypoint to find current discovery/implementation context before deciding scope\n- use `docs/repo-map.md` to find the right app/lib/components entrypoints before editing\n- for sports/classes demo wedges, check `docs/coaches-solution-thesis.md` and `docs/classes-subroute-plan.md` first, then keep docs aligned with shipped behavior\n\n## Founder Role\n- founder is available here mainly to unblock business-logic and product-decision questions\n- default: keep moving without asking unless one of those decisions materially changes direction\n- when blocked on product/business choice, ask here concisely with the current recommendation first\n"}],"isError":false,"timestamp":1776864155958}}
{"type":"message","id":"3a26c862","parentId":"274ebfbe","timestamp":"2026-04-22T13:22:35.979Z","message":{"role":"toolResult","toolCallId":"call_u2LBTHoxBChUki17GEIY6xyf|fc_0c0fef4ddac7e1db0169e8cb9bce5481959ea9579e4dc03f11","toolName":"read","content":[{"type":"text","text":"# Docs Index\n\n## How To Navigate\n- `PLAN.md`: current repo execution state and next implementation steps\n- `docs/next-steps-queue.md`: prioritized backlog with dependency states\n- `docs/repo-map.md`: fast file-level entrypoint for each domain\n- `docs/implemented-*.md`: shipped behavior only\n- `docs/*-rfc.md`: proposed implementation slices\n- `docs/*-plan.md`: route/product planning\n- `docs/*-discovery*.md`: validation and research framing\n- `docs/*-gap-analysis.md`: what Ballbox still needs to build\n- `docs/maintainability-priorities-2026-03.md`: prioritized frontend/backend maintainability work for the next refactor cycle\n- `docs/demo-iteration-mode.md`: rapid demo delivery model with anti-debt guardrails\n\n## Operator Priority Path\n1. `PLAN.md`\n2. `docs/next-steps-queue.md`\n3. `docs/repo-map.md`\n4. `docs/demo-iteration-mode.md`\n5. relevant `docs/implemented-*.md` for the touched domain\n\n## Current Refactor Input\n- `docs/maintainability-priorities-2026-03.md`\n\n## Execution And Coordination\n- `docs/next-steps-queue.md`\n- `docs/repo-map.md`\n- `docs/demo-iteration-mode.md`\n\n## Implemented State\n- `docs/implemented-host-app.md`\n- `docs/implemented-network.md`\n- `docs/implemented-payments.md`\n- `docs/implemented-players.md`\n- `docs/implemented-sports.md`\n- `docs/implemented-tvs.md`\n\n## Discovery And Strategy\n- `docs/coaches-discovery-stage.md`\n- `docs/coaches-form-current.md`\n- `docs/atc-integration-discovery.md`\n- `docs/coaches-opportunity-brief.md`\n- `docs/coaches-research-repository.md`\n- `docs/coaches-validation-checklist-2-weeks.md`\n- `docs/coaches-solution-thesis.md`\n\n## Current Build Docs\n- `docs/classes-subroute-plan.md`\n- `docs/classes-mvp-rfc.md`\n- `docs/coaches-coordination-gap-analysis.md`\n- `docs/atc-mock-integration-plan.md`\n"}],"isError":false,"timestamp":1776864155971}}
{"type":"message","id":"14c38967","parentId":"3a26c862","timestamp":"2026-04-22T13:22:36.014Z","message":{"role":"toolResult","toolCallId":"call_yIZBNGcXwsjnj6uC5aU4rX7P|fc_0c0fef4ddac7e1db0169e8cb9bce608195bc5574974b2ef9b9","toolName":"read","content":[{"type":"text","text":"# Repo Map\n\n## Purpose\n- Fast entrypoint for agents and maintainers to find the right files before editing.\n\n## Top-Level Map\n- `app/`: Next.js routes and API handlers.\n- `components/`: UI clients and route-level presentation pieces.\n- `lib/`: backend/domain modules, integration helpers, contracts, shared runtime helpers.\n- `prisma/`: schema and seed sources.\n- `tests/`: `contracts`, `unit`, `integration`, `e2e`, and support helpers.\n- `docs/`: implemented state, discovery context, plans, and execution queue.\n\n## Product Domains\n\n### TVs\n- UI surfaces: `app/admin/tvs`, `app/admin/screens`, `app/player/[screenId]/route.ts`\n- APIs: `app/api/tvs/*`\n- Backend: `lib/tvs-backend-*.ts`, `lib/tvs-player-runtime-core.ts`, `lib/tvs-runtime.ts`\n- Implemented doc: `docs/implemented-tvs.md`\n\n### Payments\n- UI surface: `app/admin/payments`\n- APIs: `app/api/admin/payments/*`, `app/api/integrations/payments/*`\n- Backend: `lib/payments.ts`, `lib/payments-auth.ts`, `lib/shared-secret.ts`, `lib/integrations.ts`\n- Implemented doc: `docs/implemented-payments.md`\n\n### Network\n- UI surface: `app/admin/network`\n- APIs: `app/api/network*`\n- Backend: `lib/network-backend-*.ts`\n- Implemented doc: `docs/implemented-network.md`\n\n### Sports / Classes / Coaches\n- UI surfaces: `app/admin/sports`, `app/classes/*`, `app/classes-by-date`, `app/coaches/settings`\n- APIs: `app/api/sports`, `app/api/classes/*`, `app/api/coaches/*`\n- Backend: `lib/sports-backend-*.ts`, `lib/classes-backend.ts`, `lib/classes.ts`, `lib/coaches-backend.ts`\n- Implemented doc: `docs/implemented-sports.md`\n\n### Players\n- UI surface: `app/admin/players`\n- APIs: `app/api/players/*`\n- Backend: `lib/players-backend.ts`\n- Implemented doc: `docs/implemented-players.md`\n\n## Shared Entry Docs\n- `PLAN.md`: current state + active next-step queue pointer.\n- `docs/index.md`: docs navigation index.\n- `docs/demo-iteration-mode.md`: L1/L2/L3 demo delivery model.\n- `docs/next-steps-queue.md`: prioritized backlog with dependency states.\n\n## Shared HTTP Helpers\n- Route JSON/envelope helpers: `lib/http/json.ts`, `lib/http/network-route-helpers.ts`, `lib/http/payments-route-helpers.ts`, `lib/http/players-route-helpers.ts`, `lib/http/sports-route-helpers.ts`\n"}],"isError":false,"timestamp":1776864156006}}
{"type":"message","id":"ceb8a01e","parentId":"14c38967","timestamp":"2026-04-22T13:22:37.255Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_xHX8Htv8y9RBJtJULtFbytNq|fc_0c0fef4ddac7e1db0169e8cb9cc62c81959d415f7f8f4bb2d6","name":"bash","arguments":{"command":"git status --short && git branch --show-current && git remote -v && rg -n \"api/search|api/context|memory\" -S . --glob '!node_modules' --glob '! .next'","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":2049,"output":64,"cacheRead":35840,"cacheWrite":0,"totalTokens":37953,"cost":{"input":0.0015367500000000001,"output":0.000288,"cacheRead":0.002688,"cacheWrite":0,"total":0.00451275}},"stopReason":"toolUse","timestamp":1776864156015,"responseId":"resp_0c0fef4ddac7e1db0169e8cb9c5c808195acf8e8629702ba93"}}
{"type":"message","id":"44269dc6","parentId":"ceb8a01e","timestamp":"2026-04-22T13:22:37.339Z","message":{"role":"toolResult","toolCallId":"call_xHX8Htv8y9RBJtJULtFbytNq|fc_0c0fef4ddac7e1db0169e8cb9cc62c81959d415f7f8f4bb2d6","toolName":"bash","content":[{"type":"text","text":" M .gitignore\n D proxy.ts\npay2-native-payments-main\norigin\thttps://github.com/sebasfavaron/ballbox.git (fetch)\norigin\thttps://github.com/sebasfavaron/ballbox.git (push)\n./AGENTS.md:3:## Memory\n./PLAN.md:233:- Do not invest further in memory-backed or file-backed persistence for TVs.\n./PLAN.md:275:- Bridge-era in-memory TVs modules were removed; seed fixtures now live under `prisma/seed-data.ts`.\n./PLAN.md:283:- TVs persistence cleanup is complete; do not reintroduce in-memory compatibility layers.\n./docs/coaches-jtbd-workflow-map.md:56:- Workaround 2: calendar/sheet tracking plus memory/manual notes\n./docs/implemented-tvs.md:7:- No Streetcast compatibility layer or in-memory TVs bridge remains in runtime code.\n./docs/ballbox-db-plan.md:5:- No more memory-backed or file-backed persistence work for TVs.\n"}],"isError":false,"timestamp":1776864157339}}
{"type":"message","id":"c5060b17","parentId":"44269dc6","timestamp":"2026-04-22T13:22:39.243Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_KWh5lfifwdU4m7OfxDeNvqAe|fc_0c0fef4ddac7e1db0169e8cb9ea7f08195b9434cac866dbf99","name":"bash","arguments":{"command":"find . -maxdepth 3 \\( -path './node_modules' -o -path './.next' -o -path './.git' \\) -prune -o -type f | sed 's#^./##' | sort | rg 'api|server|route|memory|database|agents'","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":794,"output":88,"cacheRead":37376,"cacheWrite":0,"totalTokens":38258,"cost":{"input":0.0005955,"output":0.00039600000000000003,"cacheRead":0.0028032,"cacheWrite":0,"total":0.0037947}},"stopReason":"toolUse","timestamp":1776864157340,"responseId":"resp_0c0fef4ddac7e1db0169e8cb9e33088195a8cdee3440747152"}}
{"type":"message","id":"38550e0e","parentId":"c5060b17","timestamp":"2026-04-22T13:22:39.252Z","message":{"role":"toolResult","toolCallId":"call_KWh5lfifwdU4m7OfxDeNvqAe|fc_0c0fef4ddac7e1db0169e8cb9ea7f08195b9434cac866dbf99","toolName":"bash","content":[{"type":"text","text":"components/route-feedback.tsx\ndocs/classes-subroute-plan.md\nlib/database-errors.ts\nlib/http/network-route-helpers.ts\nlib/http/payments-route-helpers.ts\nlib/http/players-route-helpers.ts\nlib/http/sports-route-helpers.ts\n"}],"isError":false,"timestamp":1776864159252}}
{"type":"message","id":"bb5932b8","parentId":"38550e0e","timestamp":"2026-04-22T13:22:41.584Z","message":{"role":"assistant","content":[{"type":"text","text":"Sí, te sigo. Pero este repo no parece ser `agents-database`: acá no veo su código, solo el uso desde afuera.\n\nHallazgo:\n- `api/search` exige `q=`\n- `api/context` existe\n- este repo `ballbox` no contiene el backend de `agents-database`\n\nPara agregar lookup por id hay dos caminos:\n1. modificar `agents-database` en su repo real\n2. si solo querés consumo desde acá, agregar un helper cliente que haga lookup por id vía endpoint nuevo cuando exista\n\nNecesito una sola confirmación:\n- ¿me das el repo/carpeta de `agents-database` para tocarlo?\n- o querés que lo busque en la máquina y lo ubique yo?\n\nAhora mismo no voy a inventar archivos que no están en este repo.","textSignature":"{\"v\":1,\"id\":\"msg_0c0fef4ddac7e1db0169e8cba02e248195a8c55d719a776ab0\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":435,"output":174,"cacheRead":37888,"cacheWrite":0,"totalTokens":38497,"cost":{"input":0.00032625,"output":0.0007830000000000001,"cacheRead":0.0028415999999999997,"cacheWrite":0,"total":0.00395085}},"stopReason":"stop","timestamp":1776864159253,"responseId":"resp_0c0fef4ddac7e1db0169e8cb9fab80819587b54452a8c0b581"}}
{"type":"thinking_level_change","id":"d5bb76a5","parentId":"bb5932b8","timestamp":"2026-04-22T13:43:23.864Z","thinkingLevel":"minimal"}
{"type":"message","id":"40d2172e","parentId":"d5bb76a5","timestamp":"2026-04-22T13:43:24.992Z","message":{"role":"user","content":[{"type":"text","text":"anda a el entonces..."}],"timestamp":1776865404991}}
{"type":"message","id":"213de9c9","parentId":"40d2172e","timestamp":"2026-04-22T13:43:27.345Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8d07e84f48195b30e2fed7d245ecd\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NB_k7i9Zv96909VA0PawcXtBXxJI25QILOBVrVQSznvSqZXW-qobqUNug2yi3tjqqBfzk9tS4e4VW5Fp5NV6QfquLfZRx8XF1L5nFb1nZDalWtZ1NQBr0bQiuNQWZ_Cn3VUYHPDRk3djVci6dgOHgaybtSAQH-IiBGjYtMm1PyVFm9qwrnfHPsGhiyI8NqABi9-Z5B8fTbyvmXFD3sQcq10gAPVHIqwHofqBONNsHrzS1u7pH2fJLww1m_elWkt61HUJy0ewFBQZwdxvQImuH5ijiKagB77nk9ie94xVVUgCWVcRlFiCOXh6b73by8qz5LyGCEqRkZeFld14B6jX2ypjm831riB-8ZaQhTeCNjGiyZ7aMrdulmnqSKnyK-wlDa4-g6xZZlThQgg_Ohj3b8iB5BbRjJfoSMG9Fb_t93QRDur9vqkM30UabKIZOV2IlnZBk_Sv6SmwBURySGnI_oKQNB1yHDW7k8meXuJobyGX8DAU84ZcRn8i4Utw9RDalX6QLh_532GkAXBu1WYUHSVnR7-2bOgNlsPY843Qz9VHEE-mdbWnuWkF0LhMapULYbR4gJ0Xtx_Sq2CfQfHLzlQ-g1tGgBWUasy90h7Rp90kqcLWfPFt3jjzu_M7MojSqoW31IjoQl8cnSKE1Xso-ZPTc1BX_Jvv6Kt_3AFYAfNYGwT_DQiLZM-27J3oyWbLvfYtimQMSq-cjJXu4aIFAApfjHAxBKuXuciRYr3Cl--rXKPi7yHksw2mwuIhCdgyQOY0Ts1Nz8WwyPSboB5GC_XdIPJX8TdDk6Y1VG-em_tGAHgsJpQsB0nlEcCc06D9ilJd_mVVlx_1c_kMvsW5np-7y1yQvyQz6WtFGSZqhJ_DzdjeZYKbniPdmk7qgFTP_xHYgShV23vGnP8Z1y8cKsN5uqb6h2hBg6CvDNffhTeEPZ8laVm_N3D-dc25cIn4rgrXieIIm8bAobQNkIuTLxOzQ==\",\"summary\":[]}"},{"type":"toolCall","id":"call_kf8xZaJZpOGFKDOCY5EK56eF|fc_0c0fef4ddac7e1db0169e8d07f02548195ab502e0119640c9e","name":"bash","arguments":{"command":"find /mnt/rpi/repos -maxdepth 2 -type d \\( -name 'agents-database' -o -name '*agents*database*' -o -name 'agents-database-*' \\) | sort","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":38508,"output":78,"cacheRead":0,"cacheWrite":0,"totalTokens":38586,"cost":{"input":0.028881,"output":0.000351,"cacheRead":0,"cacheWrite":0,"total":0.029232}},"stopReason":"toolUse","timestamp":1776865404993,"responseId":"resp_0c0fef4ddac7e1db0169e8d07dbf348195a496fc60aac8357b"}}
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{"type":"message","id":"5835a9db","parentId":"fae0d31c","timestamp":"2026-04-22T13:43:29.479Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_NhzazOgyXGkj5z4oLoYXBR7h|fc_0c0fef4ddac7e1db0169e8d080e8888195b91b633a4bef46a1","name":"bash","arguments":{"command":"find /mnt/rpi -maxdepth 3 -type d \\( -name '.git' -o -name 'api' \\) 2>/dev/null | sed 's#/.git##' | sort | head -200","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":200,"output":70,"cacheRead":38400,"cacheWrite":0,"totalTokens":38670,"cost":{"input":0.00015000000000000001,"output":0.000315,"cacheRead":0.0028799999999999997,"cacheWrite":0,"total":0.003345}},"stopReason":"toolUse","timestamp":1776865407357,"responseId":"resp_0c0fef4ddac7e1db0169e8d07fb1088195a4f1600a696f8fcc"}}
{"type":"message","id":"3a579a8a","parentId":"5835a9db","timestamp":"2026-04-22T13:43:29.537Z","message":{"role":"toolResult","toolCallId":"call_NhzazOgyXGkj5z4oLoYXBR7h|fc_0c0fef4ddac7e1db0169e8d080e8888195b91b633a4bef46a1","toolName":"bash","content":[{"type":"text","text":"/mnt/rpi/agents-database\n/mnt/rpi/cast_tools/vidsrc-resolver\n/mnt/rpi/cast_tools/vidsrc-scraper\n/mnt/rpi/pi-config\n/mnt/rpi/repos/ballbox\n/mnt/rpi/repos/ballbox-company-agent\n"}],"isError":false,"timestamp":1776865409537}}
{"type":"message","id":"3bfad42b","parentId":"3a579a8a","timestamp":"2026-04-22T13:43:31.185Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_t4Un9Ywtsn7yptMJHN6oUQvb|fc_0c0fef4ddac7e1db0169e8d0830ccc8195b2d29f0f3af5c040","name":"read","arguments":{"path":"/mnt/rpi/agents-database/AGENTS.md"}},{"type":"toolCall","id":"call_Vz4IlN2XT5ZcSEac47hiGUbM|fc_0c0fef4ddac7e1db0169e8d0830ce08195acf8229bc8a0d519","name":"bash","arguments":{"command":"git -C /mnt/rpi/agents-database status --short && git -C /mnt/rpi/agents-database branch --show-current && git -C /mnt/rpi/agents-database remote -v","timeout":20}},{"type":"toolCall","id":"call_kddVswqm2ZhiuotS5JD9uIRB|fc_0c0fef4ddac7e1db0169e8d0830ce88195bc8d4c4eec574abe","name":"read","arguments":{"path":"/mnt/rpi/agents-database/README.md"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":344,"output":129,"cacheRead":38400,"cacheWrite":0,"totalTokens":38873,"cost":{"input":0.000258,"output":0.0005805,"cacheRead":0.0028799999999999997,"cacheWrite":0,"total":0.0037184999999999996}},"stopReason":"toolUse","timestamp":1776865409538,"responseId":"resp_0c0fef4ddac7e1db0169e8d081e4508195a78f75ec78778a24"}}
{"type":"message","id":"c67cc496","parentId":"3bfad42b","timestamp":"2026-04-22T13:43:31.196Z","message":{"role":"toolResult","toolCallId":"call_t4Un9Ywtsn7yptMJHN6oUQvb|fc_0c0fef4ddac7e1db0169e8d0830ccc8195b2d29f0f3af5c040","toolName":"read","content":[{"type":"text","text":"ENOENT: no such file or directory, access '/mnt/rpi/agents-database/AGENTS.md'"}],"details":{},"isError":true,"timestamp":1776865411195}}
{"type":"message","id":"7c6fa4d8","parentId":"c67cc496","timestamp":"2026-04-22T13:43:31.224Z","message":{"role":"toolResult","toolCallId":"call_Vz4IlN2XT5ZcSEac47hiGUbM|fc_0c0fef4ddac7e1db0169e8d0830ce08195acf8229bc8a0d519","toolName":"bash","content":[{"type":"text","text":" M .gitignore\n M data/shared-agent-memory.sqlite3\n M docs/memory-consolidation-rfc.md\n M docs/system-v1.md\n M scripts/run-maintenance-daemon.sh\n M src/shared_agent_memory/cli.py\n M src/shared_agent_memory/service.py\n M tests/test_memory_service.py\n?? data/shared-agent-memory.pre-repair-20260419-185338.sqlite3\n?? data/shared-agent-memory.rebuild-20260419.sqlite3\n?? data/shared-agent-memory.recovered-20260419-184949.sqlite3\n?? data/shared-agent-memory.recovery-dump.sql\n?? data/shared-agent-memory.recovery-work.sqlite3\n?? data/shared-agent-memory.sqlite3.bak-20260419-184938\n?? docs/http-api.md\n?? scripts/run-http-api.sh\n?? src/shared_agent_memory/http_api.py\n?? systemd/\nmain\norigin\thttps://github.com/sebasfavaron/agents-database.git (fetch)\norigin\thttps://github.com/sebasfavaron/agents-database.git (push)\n"}],"isError":false,"timestamp":1776865411224}}
{"type":"message","id":"362a485d","parentId":"7c6fa4d8","timestamp":"2026-04-22T13:43:31.224Z","message":{"role":"toolResult","toolCallId":"call_kddVswqm2ZhiuotS5JD9uIRB|fc_0c0fef4ddac7e1db0169e8d0830ce88195bc8d4c4eec574abe","toolName":"read","content":[{"type":"text","text":"ENOENT: no such file or directory, access '/mnt/rpi/agents-database/README.md'"}],"details":{},"isError":true,"timestamp":1776865411224}}
{"type":"message","id":"8f7fa804","parentId":"362a485d","timestamp":"2026-04-22T13:43:37.280Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8d085be1c8195af1b3c096703628b\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NCIsHzD298MOL4HGnpYHvOgOoOMzME7PYcu6t8g4QA5mffJMTkGN2fSKdSKOJKe0x0tQu4HoYbGKvXS7xKp1V6BBBObrbwb1rkMnypF46Tb_x2_sneI1si2iCXFOMckf7kC0BhSnMuwiIKKWOJ_ob2ab8jPqYXEyMTMma3uZRE9CZsfGKtdoAeSRIvGpn7Y8R-SjYaVpwTKGCWPZwI8PWr1DrCpoVSj49fxp2oog2YKRxsWjbqgRzfDyRIazqhMWn0ee5IgL3qytcqX3K1NGQG-66T7g9AWiP8m7z2uPCLDa9jgtCnKy3GJd1U6m5NtLgPXn-P3RyX1BE-PjxG_Q8mZ3_Xz8s0n1hrRXN2ibUANd_w8cZ5feuC9xfB7MQoF0kYWZBVMs8xt6qmGwEV_F-c7zyQkaFTfknVvvcjmRFXtQewOEyGNlIOU5k62nANi1QP6S9nYQvLWMNTwGYX-n05VZIEaL8fbys2rkqWu-Kd0mATyz0ZjONMUVgThxunRn8XvbGHAy0Lh2W_v1dkejEtBUccNyyyulDDz2T8v0FVExktmRnaLuMf4YSTKvVHcWI_iHpD1D3iKj8MGDKNKoNHX6r4VXrqarGCZ1OcH2i5E7wUI9QROravEsZzNj08Sc1Xpn1Q_83XmfEk72r06P4ikJRscDemT-oJ26i4Nnh8WEcKw5qUjPnehIIMacTD6nEekMWqMYz68G9xuvWXRT7phYdJ17PeItTyk9qYtJbtj0DvymG_glw97AWwznB1OEg5Mo6zstEDRXK7UvZ7vgpjAQugkKFQgYq_OboacnnT6miVXEv-mtK_kjtOQBdhpq4aM5n6RxC9-Bwe9Xb0HIscvd6x_3lpVbpwMzwBRn7dfggYCHHOnPdNfb5kZyLtfP-hj47_qBwWlONeWO8Vi0eWNgaQmelt6XkKchLmSZ3IoY1OtkPH102qh1iWPhVeoD9S2TZ3z-XfCWPwI1T08vp-AqQ==\",\"summary\":[]}"},{"type":"toolCall","id":"call_WcRl1buSCtEhg9cmydna682C|fc_0c0fef4ddac7e1db0169e8d088f36c819594d2a8cb5acf1468","name":"read","arguments":{"path":"/mnt/rpi/agents-database/docs/http-api.md"}},{"type":"toolCall","id":"call_I80AZlDtDze5WRKIpzVtXQ5z|fc_0c0fef4ddac7e1db0169e8d088f3808195ac6bbe1ab59ce048","name":"read","arguments":{"path":"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py"}},{"type":"toolCall","id":"call_an2tHV1lyIumgOKVnRzQbdlS|fc_0c0fef4ddac7e1db0169e8d088f38c8195a6374667279460ac","name":"read","arguments":{"path":"/mnt/rpi/agents-database/src/shared_agent_memory/service.py"}},{"type":"toolCall","id":"call_6bAQkYOFkhbmXgznJdcO4Rw8|fc_0c0fef4ddac7e1db0169e8d088f3988195aaa164c94aba5855","name":"read","arguments":{"path":"/mnt/rpi/agents-database/tests/test_memory_service.py"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":761,"output":132,"cacheRead":38400,"cacheWrite":0,"totalTokens":39293,"cost":{"input":0.00057075,"output":0.000594,"cacheRead":0.0028799999999999997,"cacheWrite":0,"total":0.00404475}},"stopReason":"toolUse","timestamp":1776865411225,"responseId":"resp_0c0fef4ddac7e1db0169e8d083acc8819585b111ca9b64bacb"}}
{"type":"message","id":"6ce807c7","parentId":"8f7fa804","timestamp":"2026-04-22T13:43:37.303Z","message":{"role":"toolResult","toolCallId":"call_WcRl1buSCtEhg9cmydna682C|fc_0c0fef4ddac7e1db0169e8d088f36c819594d2a8cb5acf1468","toolName":"read","content":[{"type":"text","text":"# Agents Database HTTP API\n\nRemote-first topology:\n\n- server `personal-agent`: task runner and dashboard API on `:8082`\n- server `agents-database`: shared-memory and operational-data API on `:8091`\n- local laptop: HTTP client only; no local shared DB required for normal operation\n\n## Endpoints\n\n- `GET /api/status`\n- `GET /api/search?q=<query>&scope=global&scope=project&limit=10`\n- `GET /api/context?project=<id>&repo=<id>&task=<id>`\n- `GET /api/memories?...filters...`\n- `GET /api/memories/<id>`\n- `POST /api/ingest`\n- `GET /api/tasks?...filters...`\n- `POST /api/tasks`\n- `PATCH /api/tasks/<id>`\n- `GET /api/tasks/<id>`\n- `GET /api/tasks/<id>/bundle`\n- `GET /api/task-runs?...filters...`\n- `GET /api/task-runs/<id>`\n- `POST /api/task-runs`\n- `POST /api/task-runs/<id>/finish`\n- `GET /api/artifacts?task_id=<id>`\n- `GET /api/artifacts/<id>`\n- `POST /api/artifacts`\n\n## Run locally\n\n```bash\npython3 -m shared_agent_memory.cli \\\n  --db data/shared-agent-memory.sqlite3 \\\n  serve-http \\\n  --host 0.0.0.0 \\\n  --port 8091\n```\n\n## Server service\n\n- script: `scripts/run-http-api.sh`\n- systemd user unit: `systemd/user/agents-database-http.service`\n\n## Gotchas\n\n- This service is the correct remote boundary for shared memory. Avoid routing memory CRUD/search through `personal-agent` unless the runtime actually owns that behavior.\n- The existing `SERVICES-ARCHITECTURE.md` on the server may be stale relative to live routing. Verify real HTTP reachability with `curl`.\n"}],"isError":false,"timestamp":1776865417293}}
{"type":"message","id":"a556213c","parentId":"6ce807c7","timestamp":"2026-04-22T13:43:37.359Z","message":{"role":"toolResult","toolCallId":"call_I80AZlDtDze5WRKIpzVtXQ5z|fc_0c0fef4ddac7e1db0169e8d088f3808195ac6bbe1ab59ce048","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nimport json\nfrom http import HTTPStatus\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom typing import Any\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .service import MemoryService\n\n\ndef _parse_bool(value: str | None, *, default: bool = False) -> bool:\n    if value is None:\n        return default\n    return value.strip().lower() in {\"1\", \"true\", \"yes\", \"on\"}\n\n\ndef _first(query: dict[str, list[str]], key: str) -> str | None:\n    values = query.get(key)\n    if not values:\n        return None\n    value = values[0].strip()\n    return value or None\n\n\ndef _first_int(query: dict[str, list[str]], key: str, default: int) -> int:\n    value = _first(query, key)\n    if value is None:\n        return default\n    return int(value)\n\n\ndef _metadata_filters(query: dict[str, list[str]]) -> dict[str, Any] | None:\n    metadata: dict[str, Any] = {}\n    for key, values in query.items():\n        if not key.startswith(\"metadata.\"):\n            continue\n        name = key.removeprefix(\"metadata.\").strip()\n        if not name:\n            continue\n        value = values[0].strip()\n        if value:\n            metadata[name] = value\n    return metadata or None\n\n\ndef build_handler(service: MemoryService):\n    class MemoryAPIHandler(BaseHTTPRequestHandler):\n        def do_OPTIONS(self) -> None:  # noqa: N802\n            self.send_response(HTTPStatus.NO_CONTENT)\n            self._send_common_headers(content_type=\"application/json; charset=utf-8\", content_length=0)\n            self.end_headers()\n\n        def do_GET(self) -> None:  # noqa: N802\n            parsed = urlparse(self.path)\n            query = parse_qs(parsed.query)\n\n            try:\n                if parsed.path == \"/api/status\":\n                    payload = {\n                        \"ok\": True,\n                        \"db_path\": service.store.db_path,\n                        \"dashboard\": service.dashboard_snapshot(),\n                    }\n                    try:\n                        payload[\"audit\"] = service.audit_v2()\n                    except Exception as exc:\n                        payload[\"audit_error\"] = str(exc)\n                    self._send_json(payload)\n                    return\n                if parsed.path == \"/api/search\":\n                    search_query = _first(query, \"q\")\n                    if search_query is None:\n                        self.send_error(HTTPStatus.BAD_REQUEST, \"Missing query parameter 'q'\")\n                        return\n                    scopes = [scope.strip() for scope in query.get(\"scope\", []) if scope.strip()]\n                    filters = {\n                        key: value\n                        for key, value in {\n                            \"type\": _first(query, \"type\"),\n                            \"subtype\": _first(query, \"subtype\"),\n                            \"project_id\": _first(query, \"project_id\"),\n                            \"repo_id\": _first(query, \"repo_id\"),\n                            \"source_ref\": _first(query, \"source_ref\"),\n                            \"evidence_ref\": _first(query, \"evidence_ref\"),\n                            \"run_id\": _first(query, \"run_id\"),\n                            \"task_id\": _first(query, \"task_id\"),\n                            \"origin_agent\": _first(query, \"origin_agent\"),\n                            \"url\": _first(query, \"url\"),\n                            \"domain\": _first(query, \"domain\"),\n                        }.items()\n                        if value is not None\n                    }\n                    self._send_json(\n                        service.search(\n                            search_query,\n                            scopes or None,\n                            filters=filters or None,\n                            limit=_first_int(query, \"limit\", 10),\n                            include_inbox=_parse_bool(_first(query, \"include_inbox\")),\n                        )\n                    )\n                    return\n                if parsed.path == \"/api/context\":\n                    self._send_json(\n                        service.context_for(\n                            project=_first(query, \"project\"),\n                            repo=_first(query, \"repo\"),\n                            agent=_first(query, \"agent\"),\n                            task=_first(query, \"task\"),\n                        )\n                    )\n                    return\n                if parsed.path == \"/api/memories\":\n                    self._send_json(\n                        {\n                            \"items\": service.list_memories(\n                                status=_first(query, \"status\") if \"status\" in query else \"active\",\n                                scope=_first(query, \"scope\"),\n                                memory_type=_first(query, \"type\"),\n                                subtype=_first(query, \"subtype\"),\n                                project_id=_first(query, \"project_id\"),\n                                repo_id=_first(query, \"repo_id\"),\n                                source_ref=_first(query, \"source_ref\"),\n                                evidence_ref=_first(query, \"evidence_ref\"),\n                                run_id=_first(query, \"run_id\"),\n                                task_id=_first(query, \"task_id\"),\n                                origin_agent=_first(query, \"origin_agent\"),\n                                url=_first(query, \"url\"),\n                                domain=_first(query, \"domain\"),\n                                metadata=_metadata_filters(query),\n                                limit=_first_int(query, \"limit\", 50),\n                            )\n                        }\n                    )\n                    return\n                if parsed.path.startswith(\"/api/memories/\"):\n                    self._send_json(service.get_memory(parsed.path.split(\"/\")[3]))\n                    return\n                if parsed.path == \"/api/tasks\":\n                    requires_human_input = None\n                    if \"requires_human_input\" in query:\n                        requires_human_input = _parse_bool(_first(query, \"requires_human_input\"))\n                    self._send_json(\n                        {\n                            \"items\": service.list_tasks(\n                                status=_first(query, \"status\"),\n                                owner_agent=_first(query, \"owner_agent\"),\n                                requires_human_input=requires_human_input,\n                                run_id=_first(query, \"run_id\"),\n                                limit=_first_int(query, \"limit\", 50),\n                            )\n                        }\n                    )\n                    return\n                if parsed.path.startswith(\"/api/tasks/\") and parsed.path.endswith(\"/bundle\"):\n                    self._send_json(service.task_bundle(parsed.path.split(\"/\")[3]))\n                    return\n                if parsed.path.startswith(\"/api/tasks/\"):\n                    self._send_json(service.get_task(parsed.path.split(\"/\")[3]))\n                    return\n                if parsed.path == \"/api/task-runs\":\n                    self._send_json(\n                        {\n                            \"items\": service.list_task_runs(\n                                task_id=_first(query, \"task_id\"),\n                                status=_first(query, \"status\"),\n                                limit=_first_int(query, \"limit\", 50),\n                            )\n                        }\n                    )\n                    return\n                if parsed.path.startswith(\"/api/task-runs/\"):\n                    self._send_json(service.get_task_run(parsed.path.split(\"/\")[3]))\n                    return\n                if parsed.path == \"/api/artifacts\":\n                    self._send_json(\n                        {\n                            \"items\": service.list_artifacts(\n                                task_id=_first(query, \"task_id\"),\n                                limit=_first_int(query, \"limit\", 50),\n                            )\n                        }\n                    )\n                    return\n                if parsed.path.startswith(\"/api/artifacts/\"):\n                    self._send_json(service.get_artifact(parsed.path.split(\"/\")[3]))\n                    return\n            except KeyError as exc:\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\n                return\n            except ValueError as exc:\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\n                return\n            except Exception as exc:\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\n                return\n\n            self.send_error(HTTPStatus.NOT_FOUND)\n\n        def do_POST(self) -> None:  # noqa: N802\n            parsed = urlparse(self.path)\n            try:\n                payload = self._json_body()\n            except (UnicodeDecodeError, json.JSONDecodeError, ValueError):\n                self.send_error(HTTPStatus.BAD_REQUEST, \"Invalid JSON body\")\n                return\n\n            try:\n                if parsed.path == \"/api/ingest\":\n                    self._send_json(service.ingest(payload), status=HTTPStatus.CREATED)\n                    return\n                if parsed.path == \"/api/tasks\":\n                    self._send_json(\n                        service.create_task(\n                            title=payload[\"title\"],\n                            intent=payload[\"intent\"],\n                            kind=payload.get(\"kind\", \"task\"),\n                            status=payload.get(\"status\", \"open\"),\n                            priority=payload.get(\"priority\", 3),\n                            project_id=payload.get(\"project_id\"),\n                            repo_id=payload.get(\"repo_id\"),\n                            parent_task_id=payload.get(\"parent_task_id\"),\n                            origin=payload.get(\"origin\"),\n                            owner_agent=payload.get(\"owner_agent\"),\n                            blocked_reason=payload.get(\"blocked_reason\"),\n                            requires_human_input=bool(payload.get(\"requires_human_input\", False)),\n                            due_at=payload.get(\"due_at\"),\n                            metadata=payload.get(\"metadata\"),\n                            task_id=payload.get(\"task_id\"),\n                            run_id=payload.get(\"run_id\"),\n                        ),\n                        status=HTTPStatus.CREATED,\n                    )\n                    return\n                if parsed.path == \"/api/task-runs\":\n                    task_id = payload.get(\"task_id\")\n                    agent_id = payload.get(\"agent_id\")\n                    if not isinstance(task_id, str) or not task_id.strip():\n                        self.send_error(HTTPStatus.BAD_REQUEST, \"Missing or invalid 'task_id'\")\n                        return\n                    if not isinstance(agent_id, str) or not agent_id.strip():\n                        self.send_error(HTTPStatus.BAD_REQUEST, \"Missing or invalid 'agent_id'\")\n                        return\n                    self._send_json(\n                        service.start_task_run(\n                            task_id,\n                            agent_id,\n                            input_payload=payload.get(\"input_payload\"),\n                            metadata=payload.get(\"metadata\"),\n                        ),\n                        status=HTTPStatus.CREATED,\n                    )\n                    return\n                if parsed.path.startswith(\"/api/task-runs/\") and parsed.path.endswith(\"/finish\"):\n                    run_id = parsed.path.split(\"/\")[3]\n                    status = payload.get(\"status\")\n                    if not isinstance(status, str) or not status.strip():\n                        self.send_error(HTTPStatus.BAD_REQUEST, \"Missing or invalid 'status'\")\n                        return\n                    self._send_json(\n                        service.finish_task_run(\n                            run_id,\n                            status=status,\n                            result_summary=payload.get(\"result_summary\"),\n                            error_message=payload.get(\"error_message\"),\n                        )\n                    )\n                    return\n                if parsed.path == \"/api/artifacts\":\n                    task_id = payload.get(\"task_id\")\n                    artifact_type = payload.get(\"artifact_type\")\n                    title = payload.get(\"title\")\n                    content = payload.get(\"content\")\n                    if not all(isinstance(value, str) and value.strip() for value in (task_id, artifact_type, title, content)):\n                        self.send_error(\n                            HTTPStatus.BAD_REQUEST,\n                            \"Missing or invalid required artifact fields\",\n                        )\n                        return\n                    self._send_json(\n                        service.create_artifact(\n                            task_id=task_id,\n                            artifact_type=artifact_type,\n                            title=title,\n                            content=content,\n                            metadata=payload.get(\"metadata\"),\n                            source_ref=payload.get(\"source_ref\"),\n                        ),\n                        status=HTTPStatus.CREATED,\n                    )\n                    return\n            except KeyError as exc:\n                self.send_error(HTTPStatus.BAD_REQUEST, f\"Missing field: {exc}\")\n                return\n            except Exception as exc:\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\n                return\n\n            self.send_error(HTTPStatus.NOT_FOUND)\n\n        def do_PATCH(self) -> None:  # noqa: N802\n            parsed = urlparse(self.path)\n            if not parsed.path.startswith(\"/api/tasks/\"):\n                self.send_error(HTTPStatus.NOT_FOUND)\n                return\n            try:\n                payload = self._json_body()\n            except (UnicodeDecodeError, json.JSONDecodeError, ValueError):\n                self.send_error(HTTPStatus.BAD_REQUEST, \"Invalid JSON body\")\n                return\n            try:\n                self._send_json(\n                    service.update_task(\n                        parsed.path.split(\"/\")[3],\n                        status=payload.get(\"status\"),\n                        owner_agent=payload.get(\"owner_agent\"),\n                        blocked_reason=payload.get(\"blocked_reason\"),\n                        requires_human_input=payload.get(\"requires_human_input\"),\n                        metadata=payload.get(\"metadata\"),\n                        run_id=payload.get(\"run_id\"),\n                    )\n                )\n            except KeyError as exc:\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\n            except Exception as exc:\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\n\n        def log_message(self, fmt: str, *args: Any) -> None:\n            return\n\n        def _json_body(self) -> dict[str, Any]:\n            length = int(self.headers.get(\"Content-Length\", \"0\"))\n            raw = self.rfile.read(length) if length else b\"{}\"\n            payload = json.loads(raw.decode(\"utf-8\"))\n            if not isinstance(payload, dict):\n                raise ValueError(\"JSON body must be an object\")\n            return payload\n\n        def _send_common_headers(self, *, content_type: str, content_length: int) -> None:\n            self.send_header(\"Content-Type\", content_type)\n            self.send_header(\"Content-Length\", str(content_length))\n            self.send_header(\"Access-Control-Allow-Origin\", \"*\")\n            self.send_header(\"Access-Control-Allow-Headers\", \"Content-Type\")\n            self.send_header(\"Access-Control-Allow-Methods\", \"GET,POST,PATCH,OPTIONS\")\n\n        def _send_json(self, payload: dict[str, Any], *, status: HTTPStatus = HTTPStatus.OK) -> None:\n            body = json.dumps(payload, ensure_ascii=True).encode(\"utf-8\")\n            self.send_response(status)\n            self._send_common_headers(content_type=\"application/json; charset=utf-8\", content_length=len(body))\n            self.end_headers()\n            self.wfile.write(body)\n\n    return MemoryAPIHandler\n\n\ndef run_http_server(*, db_path: str, host: str = \"127.0.0.1\", port: int = 8091) -> None:\n    service = MemoryService(db_path)\n    server = ThreadingHTTPServer((host, port), build_handler(service))\n    try:\n        server.serve_forever()\n    finally:\n        server.server_close()\n"}],"isError":false,"timestamp":1776865417303}}
{"type":"message","id":"6036e90d","parentId":"a556213c","timestamp":"2026-04-22T13:43:37.571Z","message":{"role":"toolResult","toolCallId":"call_an2tHV1lyIumgOKVnRzQbdlS|fc_0c0fef4ddac7e1db0169e8d088f38c8195a6374667279460ac","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nimport json\nimport math\nimport subprocess\nimport uuid\nfrom dataclasses import dataclass\nfrom datetime import UTC, datetime, timedelta\nfrom typing import Any\nfrom urllib.parse import urlparse\n\nfrom .schema import SCHEMA_VERSION\nfrom .store import SQLiteStore\n\n\ndef utcnow() -> str:\n    return datetime.now(UTC).isoformat()\n\n\ndef make_id(prefix: str) -> str:\n    return f\"{prefix}_{uuid.uuid4().hex}\"\n\n\ndef normalize_text(value: str) -> str:\n    return \" \".join(value.lower().split())\n\n\ndef _line_value(content: str, label: str) -> str:\n    prefix = f\"{label}:\"\n    for line in content.splitlines():\n        if line.startswith(prefix):\n            return line[len(prefix) :].strip()\n    return \"\"\n\n\n@dataclass(slots=True)\nclass SearchResult:\n    memory: dict[str, Any]\n    score: float\n    explanation: str\n    facet_score: float\n    lexical_score: float\n    semantic_score: float\n\n    def as_dict(self) -> dict[str, Any]:\n        return {\n            \"memory\": self.memory,\n            \"score\": self.score,\n            \"score_breakdown\": {\n                \"facet\": self.facet_score,\n                \"lexical\": self.lexical_score,\n                \"semantic\": self.semantic_score,\n                \"confidence\": self.memory[\"confidence\"],\n                \"freshness\": self.memory[\"freshness\"],\n            },\n            \"matched_scope\": self.memory[\"scope\"],\n            \"evidence_ref\": self.memory[\"evidence_ref\"],\n            \"explanation\": self.explanation,\n        }\n\n\nclass MemoryService:\n    def __init__(self, db_path: str) -> None:\n        self.store = SQLiteStore(db_path)\n        self._maintenance_check_running = False\n        self.store.initialize()\n        self.migrate_v2()\n        self._ensure_default_jobs()\n        self._run_opportunistic_maintenance(limit=10)\n\n    def create_memory(self, memory_input: dict[str, Any]) -> dict[str, Any]:\n        now = utcnow()\n        metadata = dict(memory_input.get(\"metadata\") or {})\n        subtype = memory_input.get(\"subtype\")\n        run_id = memory_input.get(\"run_id\")\n        task_id = memory_input.get(\"task_id\")\n        url = memory_input.get(\"url\")\n        domain = memory_input.get(\"domain\") or (urlparse(url).netloc if url else None)\n        origin_agent = memory_input.get(\"origin_agent\") or metadata.get(\"origin_agent\") or memory_input.get(\"agent_id\")\n        record = {\n            \"id\": memory_input.get(\"id\", make_id(\"mem\")),\n            \"schema_version\": memory_input.get(\"schema_version\", SCHEMA_VERSION),\n            \"type\": memory_input[\"type\"],\n            \"subtype\": subtype,\n            \"scope\": memory_input[\"scope\"],\n            \"status\": memory_input.get(\"status\", \"active\"),\n            \"project_id\": memory_input.get(\"project_id\"),\n            \"repo_id\": memory_input.get(\"repo_id\"),\n            \"agent_id\": memory_input.get(\"agent_id\"),\n            \"origin_agent\": origin_agent,\n            \"run_id\": run_id,\n            \"task_id\": task_id,\n            \"url\": url,\n            \"domain\": domain,\n            \"source_kind\": memory_input.get(\"source_kind\", \"manual\"),\n            \"title\": memory_input[\"title\"],\n            \"content\": memory_input[\"content\"],\n            \"summary\": memory_input.get(\"summary\") or self._make_summary(memory_input[\"content\"]),\n            \"confidence\": float(memory_input.get(\"confidence\", 1.0)),\n            \"freshness\": float(memory_input.get(\"freshness\", 1.0)),\n            \"created_at\": memory_input.get(\"created_at\", now),\n            \"updated_at\": now,\n            \"observed_at\": memory_input.get(\"observed_at\", now),\n            \"source_ref\": memory_input.get(\"source_ref\"),\n            \"evidence_ref\": memory_input.get(\"evidence_ref\"),\n            \"embedding_json\": self._encode_embedding(memory_input.get(\"embedding\")),\n            \"metadata_json\": self.store.dumps(metadata),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT OR REPLACE INTO memories (\n                    id, schema_version, type, subtype, scope, status, project_id, repo_id, agent_id, origin_agent,\n                    run_id, task_id, url, domain, source_kind, title, content, summary, confidence, freshness,\n                    created_at, updated_at, observed_at, source_ref, evidence_ref, embedding_json, metadata_json\n                ) VALUES (\n                    :id, :schema_version, :type, :subtype, :scope, :status, :project_id, :repo_id, :agent_id, :origin_agent,\n                    :run_id, :task_id, :url, :domain, :source_kind, :title, :content, :summary, :confidence, :freshness,\n                    :created_at, :updated_at, :observed_at, :source_ref, :evidence_ref, :embedding_json, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n            self._upsert_fts(conn, record)\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_memory(record[\"id\"])\n\n    def ingest(self, memory_input: dict[str, Any]) -> dict[str, Any]:\n        return self.create_memory(memory_input)\n\n    def capture_session(self, source_ref: str, scope: str, content: str, **metadata: Any) -> dict[str, Any]:\n        return self._capture_source(\n            trigger_type=\"session_close\",\n            source_kind=\"run\",\n            source_ref=source_ref,\n            scope=scope,\n            content=content,\n            metadata=metadata,\n        )\n\n    def capture_conversation(\n        self, source_ref: str, scope: str, content: str, capture_mode: str = \"manual_import\", **metadata: Any\n    ) -> dict[str, Any]:\n        metadata = {\"capture_mode\": capture_mode, **metadata}\n        return self._capture_source(\n            trigger_type=\"conversation_capture\",\n            source_kind=\"conversation\",\n            source_ref=source_ref,\n            scope=scope,\n            content=content,\n            metadata=metadata,\n        )\n\n    def search(\n        self,\n        query: str,\n        scopes: list[str] | None = None,\n        filters: dict[str, Any] | None = None,\n        limit: int = 10,\n        include_inbox: bool = False,\n    ) -> dict[str, Any]:\n        filters = filters or {}\n        query_terms = [term for term in normalize_text(query).split() if term]\n        sql = [\"SELECT * FROM memories WHERE 1=1\"]\n        params: list[Any] = []\n\n        if scopes:\n            sql.append(f\"AND scope IN ({','.join('?' for _ in scopes)})\")\n            params.extend(scopes)\n        if not include_inbox:\n            sql.append(\"AND status = ?\")\n            params.append(\"active\")\n        params.extend(self._memory_filter_sql(sql, filters))\n\n        with self.store.connection() as conn:\n            rows = [self._row_to_memory(row) for row in conn.execute(\" \".join(sql), params)]\n\n        results: list[SearchResult] = []\n        query_embedding = self._text_embedding(query) if query_terms else []\n        for row in rows:\n            haystack = normalize_text(\" \".join([row[\"title\"], row[\"summary\"], row[\"content\"]]))\n            lexical = self._lexical_score(query_terms, haystack)\n            semantic = self._semantic_score(query_embedding, row.get(\"embedding\")) if query_terms else 0.0\n            facet = self._facet_score(row, filters)\n            if query_terms and lexical == 0 and semantic == 0 and facet == 0:\n                continue\n            score = facet * 0.45 + lexical * 0.3 + semantic * 0.15 + row[\"confidence\"] * 0.06 + row[\"freshness\"] * 0.04\n            explanation = (\n                f\"facets={facet:.2f} lexical={lexical:.2f} semantic={semantic:.2f} \"\n                f\"confidence={row['confidence']:.2f} freshness={row['freshness']:.2f}\"\n            )\n            results.append(SearchResult(row, score, explanation, facet, lexical, semantic))\n\n        ordered = sorted(\n            results,\n            key=lambda item: (\n                item.facet_score,\n                item.lexical_score,\n                item.semantic_score,\n                item.memory[\"confidence\"],\n                item.memory[\"freshness\"],\n                item.memory[\"updated_at\"],\n            ),\n            reverse=True,\n        )[:limit]\n        payload = [item.as_dict() for item in ordered]\n\n        retrieval_id = make_id(\"ret\")\n        with self.store.connection() as conn:\n            conn.execute(\n                \"INSERT INTO retrieval_logs (id, query_text, filters_json, results_json, created_at) VALUES (?, ?, ?, ?, ?)\",\n                (\n                    retrieval_id,\n                    query,\n                    self.store.dumps({\"scopes\": scopes, \"filters\": filters, \"include_inbox\": include_inbox}),\n                    self.store.dumps(payload),\n                    utcnow(),\n                ),\n            )\n        return {\"retrieval_id\": retrieval_id, \"results\": payload}\n\n    def context_for(\n        self, project: str | None = None, repo: str | None = None, agent: str | None = None, task: str | None = None\n    ) -> dict[str, Any]:\n        profile_facts = self.list_memories(status=\"active\", scope=\"global\", memory_type=\"profile\", limit=8)\n        project_filters = {\"project_id\": project, \"repo_id\": repo}\n        active_decisions = self.list_memories(\n            status=\"active\",\n            scope=\"project\" if project else None,\n            memory_type=\"decision\",\n            project_id=project,\n            repo_id=repo,\n            limit=8,\n        )\n        project_memories = self.list_memories(\n            status=\"active\",\n            project_id=project,\n            repo_id=repo,\n            limit=12,\n        )\n        project_memories = [item for item in project_memories if item[\"type\"] in {\"project\", \"decision\", \"artifact\", \"episode\"}][:12]\n        recent_episodes = self.list_memories(\n            status=\"active\",\n            memory_type=\"episode\",\n            project_id=project,\n            repo_id=repo,\n            limit=10,\n        )\n        task_relevant_artifacts = self.list_memories(\n            status=\"active\",\n            memory_type=\"artifact\",\n            project_id=project,\n            repo_id=repo,\n            task_id=task,\n            limit=10,\n        )\n        if task and not task_relevant_artifacts:\n            task_relevant_artifacts = self.search(task, filters={**project_filters, \"type\": \"artifact\"}, limit=5)[\"results\"]\n        citations = [\n            item[\"id\"]\n            for item in [*profile_facts, *project_memories, *active_decisions, *recent_episodes]\n            if isinstance(item, dict) and \"id\" in item\n        ]\n        return {\n            \"profile_facts\": profile_facts,\n            \"project_memories\": project_memories,\n            \"active_decisions\": active_decisions,\n            \"recent_episodes\": recent_episodes,\n            \"task_relevant_artifacts\": task_relevant_artifacts,\n            \"citations\": citations,\n            \"agent_id\": agent,\n        }\n\n    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:\n        promoted: list[str] = []\n        linked: list[dict[str, str]] = []\n        with self.store.connection() as conn:\n            for memory_id in inbox_items:\n                row = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (memory_id,)).fetchone()\n                if row is None:\n                    continue\n                memory = self._row_to_memory(row)\n                duplicate = conn.execute(\n                    \"\"\"\n                    SELECT id FROM memories\n                    WHERE id != ?\n                      AND status = 'active'\n                      AND type = ?\n                      AND scope = ?\n                      AND content = ?\n                    LIMIT 1\n                    \"\"\",\n                    (memory_id, memory[\"type\"], memory[\"scope\"], memory[\"content\"]),\n                ).fetchone()\n                if duplicate:\n                    link_id = make_id(\"lnk\")\n                    conn.execute(\n                        \"\"\"\n                        INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)\n                        VALUES (?, ?, ?, ?, ?, ?)\n                        \"\"\",\n                        (\n                            link_id,\n                            memory_id,\n                            duplicate[\"id\"],\n                            \"related_to\",\n                            utcnow(),\n                            self.store.dumps({\"reason\": \"exact_duplicate\"}),\n                        ),\n                    )\n                    conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"archived\", utcnow(), memory_id))\n                    linked.append({\"from\": memory_id, \"to\": duplicate[\"id\"], \"relation\": \"related_to\"})\n                    continue\n                conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"active\", utcnow(), memory_id))\n                promoted.append(memory_id)\n        return {\"promoted\": promoted, \"linked\": linked}\n\n    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:\n        stats = {\n            \"candidates\": 0,\n            \"promoted\": 0,\n            \"archived\": 0,\n            \"linked\": 0,\n            \"conflicts\": 0,\n            \"llm_summaries\": 0,\n        }\n        age_days = int(config.get(\"candidate_age_days\", 7))\n        max_candidates = int(config.get(\"max_candidates\", 200))\n        high_threshold = float(config.get(\"dedupe_high_threshold\", 0.9))\n        mid_threshold = float(config.get(\"dedupe_mid_threshold\", 0.75))\n        promote_threshold = float(config.get(\"promote_confidence\", 0.7))\n        conflict_threshold = float(config.get(\"conflict_threshold\", 0.55))\n        llm_enabled = bool(config.get(\"llm_enabled\", False))\n        llm_provider = config.get(\"llm_provider\") or \"opencode\"\n        llm_command = config.get(\"llm_command\") or [\"opencode\", \"-m\", \"MiniMax M2.5 Free\"]\n        cutoff = (datetime.now(UTC) - timedelta(days=age_days)).isoformat()\n\n        with self.store.connection() as conn:\n            rows = conn.execute(\n                \"\"\"\n                SELECT * FROM memories\n                WHERE (\n                    status = 'inbox'\n                    OR (type = 'episode' AND status = 'active')\n                )\n                  AND created_at <= ?\n                ORDER BY created_at ASC\n                LIMIT ?\n                \"\"\",\n                (cutoff, max_candidates),\n            ).fetchall()\n\n            candidates = [self._row_to_memory(row) for row in rows]\n            stats[\"candidates\"] = len(candidates)\n\n            for candidate in candidates:\n                related = conn.execute(\n                    \"\"\"\n                    SELECT * FROM memories\n                    WHERE status = 'active'\n                      AND id != ?\n                      AND type = ?\n                      AND scope = ?\n                    ORDER BY updated_at DESC\n                    LIMIT 50\n                    \"\"\",\n                    (candidate[\"id\"], candidate[\"type\"], candidate[\"scope\"]),\n                ).fetchall()\n                related_memories = [self._row_to_memory(row) for row in related]\n\n                best_match = None\n                best_score = 0.0\n                for other in related_memories:\n                    lexical = self._lexical_score(\n                        normalize_text(candidate[\"content\"]).split(),\n                        normalize_text(other[\"content\"]),\n                    )\n                    semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n                    if score > best_score:\n                        best_score = score\n                        best_match = other\n\n                if best_match and candidate[\"content\"] == best_match[\"content\"]:\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"exact_duplicate\")\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"archived\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"archived\"] += 1\n                    stats[\"linked\"] += 1\n                    continue\n\n                if best_match and best_score >= high_threshold:\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"high_similarity\")\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"archived\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"archived\"] += 1\n                    stats[\"linked\"] += 1\n                    continue\n\n                if best_match and best_score >= mid_threshold:\n                    if llm_enabled:\n                        synthesized = self._llm_summarize_pair(\n                            candidate,\n                            best_match,\n                            provider=llm_provider,\n                            command=llm_command,\n                        )\n                        if synthesized:\n                            conn.execute(\n                                \"UPDATE memories SET summary = ?, updated_at = ?, metadata_json = ? WHERE id = ?\",\n                                (\n                                    synthesized,\n                                    utcnow(),\n                                    self.store.dumps(\n                                        {\n                                            **candidate.get(\"metadata\", {}),\n                                            \"synthesis_sources\": [candidate[\"id\"], best_match[\"id\"]],\n                                            \"synthesis_provider\": llm_provider,\n                                            \"synthesis_command\": llm_command,\n                                        }\n                                    ),\n                                    candidate[\"id\"],\n                                ),\n                            )\n                            stats[\"llm_summaries\"] += 1\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"mid_similarity\")\n                    stats[\"linked\"] += 1\n\n                if best_match and best_score >= conflict_threshold and self._conflict_signal(candidate, best_match):\n                    self._record_conflict(conn, candidate[\"id\"], best_match[\"id\"], \"potential_contradiction\")\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"contradicts\", \"heuristic\")\n                    stats[\"conflicts\"] += 1\n\n                if candidate[\"status\"] == \"inbox\" and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"active\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"promoted\"] += 1\n\n        return stats\n\n    def upsert_profile_fact(\n        self, title: str, content: str, evidence_ref: str | None = None, source_ref: str | None = None\n    ) -> dict[str, Any]:\n        return self.create_memory(\n            {\n                \"type\": \"profile\",\n                \"scope\": \"global\",\n                \"title\": title,\n                \"content\": content,\n                \"summary\": self._make_summary(content),\n                \"source_ref\": source_ref,\n                \"evidence_ref\": evidence_ref,\n                \"metadata\": {\"kind\": \"profile_fact\"},\n            }\n        )\n\n    def explain(self, identifier: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            retrieval = conn.execute(\"SELECT * FROM retrieval_logs WHERE id = ?\", (identifier,)).fetchone()\n            if retrieval:\n                return {\n                    \"kind\": \"retrieval\",\n                    \"id\": retrieval[\"id\"],\n                    \"query_text\": retrieval[\"query_text\"],\n                    \"filters\": self.store.loads(retrieval[\"filters_json\"]),\n                    \"results\": self.store.loads(retrieval[\"results_json\"]),\n                }\n            memory = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (identifier,)).fetchone()\n            if memory:\n                mem = self._row_to_memory(memory)\n                sources = conn.execute(\"SELECT * FROM sources WHERE source_ref = ? OR id = ?\", (mem[\"source_ref\"], mem[\"source_ref\"])).fetchall()\n                return {\"kind\": \"memory\", \"memory\": mem, \"sources\": [dict(source) for source in sources]}\n        raise KeyError(f\"Unknown identifier: {identifier}\")\n\n    def export(self) -> dict[str, Any]:\n        tables = [\n            \"projects\",\n            \"repos\",\n            \"sources\",\n            \"ingestion_events\",\n            \"memories\",\n            \"memory_links\",\n            \"memory_conflicts\",\n            \"retrieval_logs\",\n            \"maintenance_jobs\",\n            \"maintenance_runs\",\n            \"profiles\",\n            \"tasks\",\n            \"task_runs\",\n            \"artifacts\",\n        ]\n        exported: dict[str, list[dict[str, Any]]] = {}\n        with self.store.connection() as conn:\n            for table in tables:\n                rows = conn.execute(f\"SELECT * FROM {table}\").fetchall()\n                exported[table] = [dict(row) for row in rows]\n        return exported\n\n    def import_data(self, payload: dict[str, Any]) -> None:\n        tables = [\n            \"projects\",\n            \"repos\",\n            \"sources\",\n            \"ingestion_events\",\n            \"memories\",\n            \"memory_links\",\n            \"memory_conflicts\",\n            \"retrieval_logs\",\n            \"maintenance_jobs\",\n            \"maintenance_runs\",\n            \"profiles\",\n            \"tasks\",\n            \"task_runs\",\n            \"artifacts\",\n        ]\n        with self.store.connection() as conn:\n            for table in tables:\n                rows = payload.get(table, [])\n                if not rows:\n                    continue\n                columns = list(rows[0].keys())\n                placeholder = \",\".join(\"?\" for _ in columns)\n                sql = f\"INSERT OR REPLACE INTO {table} ({','.join(columns)}) VALUES ({placeholder})\"\n                conn.executemany(sql, [tuple(row.get(col) for col in columns) for row in rows])\n                if table == \"memories\":\n                    for row in rows:\n                        self._upsert_fts(conn, row)\n        self.migrate_v2()\n        self._run_opportunistic_maintenance(limit=10)\n\n    def get_memory(self, memory_id: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            row = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (memory_id,)).fetchone()\n            if row is None:\n                raise KeyError(memory_id)\n            return self._row_to_memory(row)\n\n    def list_memories(\n        self,\n        *,\n        status: str | None = \"active\",\n        scope: str | None = None,\n        memory_type: str | None = None,\n        subtype: str | None = None,\n        project_id: str | None = None,\n        repo_id: str | None = None,\n        source_ref: str | None = None,\n        evidence_ref: str | None = None,\n        run_id: str | None = None,\n        task_id: str | None = None,\n        origin_agent: str | None = None,\n        url: str | None = None,\n        domain: str | None = None,\n        metadata: dict[str, Any] | None = None,\n        limit: int = 50,\n    ) -> list[dict[str, Any]]:\n        sql = [\"SELECT * FROM memories WHERE 1=1\"]\n        params: list[Any] = []\n        if status is not None:\n            sql.append(\"AND status = ?\")\n            params.append(status)\n        if scope is not None:\n            sql.append(\"AND scope = ?\")\n            params.append(scope)\n        filters = {\n            \"type\": memory_type,\n            \"subtype\": subtype,\n            \"project_id\": project_id,\n            \"repo_id\": repo_id,\n            \"source_ref\": source_ref,\n            \"evidence_ref\": evidence_ref,\n            \"run_id\": run_id,\n            \"task_id\": task_id,\n            \"origin_agent\": origin_agent,\n            \"url\": url,\n            \"domain\": domain,\n        }\n        params.extend(self._memory_filter_sql(sql, {key: value for key, value in filters.items() if value is not None}))\n        for key, value in (metadata or {}).items():\n            sql.append(\"AND json_extract(metadata_json, ?) = ?\")\n            params.extend((f\"$.{key}\", value))\n        sql.append(\"ORDER BY updated_at DESC LIMIT ?\")\n        params.append(limit)\n        with self.store.connection() as conn:\n            rows = conn.execute(\" \".join(sql), params).fetchall()\n            return [self._row_to_memory(row) for row in rows]\n\n    def list_pending_events(self) -> list[dict[str, Any]]:\n        with self.store.connection() as conn:\n            rows = conn.execute(\"SELECT * FROM ingestion_events WHERE job_state = 'pending' ORDER BY created_at ASC\").fetchall()\n            return [dict(row) for row in rows]\n\n    def mark_event(self, event_id: str, state: str, outcome: str | None = None, error_message: str | None = None) -> None:\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                UPDATE ingestion_events\n                SET job_state = ?, processor_outcome = ?, error_message = ?, updated_at = ?, processed_at = ?\n                WHERE id = ?\n                \"\"\",\n                (state, outcome, error_message, utcnow(), utcnow(), event_id),\n            )\n\n    def create_project(self, project_id: str, name: str, description: str | None = None) -> None:\n        now = utcnow()\n        with self.store.connection() as conn:\n            conn.execute(\n                \"INSERT OR REPLACE INTO projects (id, name, description, created_at, updated_at) VALUES (?, ?, ?, ?, ?)\",\n                (project_id, name, description, now, now),\n            )\n        self._run_opportunistic_maintenance(limit=1)\n\n    def create_repo(self, repo_id: str, name: str, project_id: str | None = None, path: str | None = None) -> None:\n        now = utcnow()\n        with self.store.connection() as conn:\n            conn.execute(\n                \"INSERT OR REPLACE INTO repos (id, project_id, name, path, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?)\",\n                (repo_id, project_id, name, path, now, now),\n            )\n        self._run_opportunistic_maintenance(limit=1)\n\n    def create_task(\n        self,\n        *,\n        title: str,\n        intent: str,\n        kind: str = \"task\",\n        status: str = \"open\",\n        priority: int = 3,\n        project_id: str | None = None,\n        repo_id: str | None = None,\n        parent_task_id: str | None = None,\n        origin: str | None = None,\n        owner_agent: str | None = None,\n        blocked_reason: str | None = None,\n        requires_human_input: bool = False,\n        due_at: str | None = None,\n        metadata: dict[str, Any] | None = None,\n        task_id: str | None = None,\n        run_id: str | None = None,\n    ) -> dict[str, Any]:\n        now = utcnow()\n        record = {\n            \"id\": task_id or make_id(\"task\"),\n            \"schema_version\": SCHEMA_VERSION,\n            \"run_id\": run_id,\n            \"title\": title,\n            \"intent\": intent,\n            \"kind\": kind,\n            \"status\": status,\n            \"priority\": priority,\n            \"project_id\": project_id,\n            \"repo_id\": repo_id,\n            \"parent_task_id\": parent_task_id,\n            \"origin\": origin,\n            \"owner_agent\": owner_agent,\n            \"blocked_reason\": blocked_reason,\n            \"requires_human_input\": 1 if requires_human_input else 0,\n            \"due_at\": due_at,\n            \"created_at\": now,\n            \"updated_at\": now,\n            \"metadata_json\": self.store.dumps(metadata or {}),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT INTO tasks (\n                    id, schema_version, run_id, title, intent, kind, status, priority, project_id, repo_id,\n                    parent_task_id, origin, owner_agent, blocked_reason, requires_human_input, due_at,\n                    created_at, updated_at, metadata_json\n                ) VALUES (\n                    :id, :schema_version, :run_id, :title, :intent, :kind, :status, :priority, :project_id, :repo_id,\n                    :parent_task_id, :origin, :owner_agent, :blocked_reason, :requires_human_input, :due_at,\n                    :created_at, :updated_at, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_task(record[\"id\"])\n\n    def get_task(self, task_id: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            row = conn.execute(\"SELECT * FROM tasks WHERE id = ?\", (task_id,)).fetchone()\n            if row is None:\n                raise KeyError(task_id)\n            return self._row_to_task(row)\n\n    def list_tasks(\n        self,\n        *,\n        status: str | None = None,\n        owner_agent: str | None = None,\n        requires_human_input: bool | None = None,\n        run_id: str | None = None,\n        limit: int = 50,\n    ) -> list[dict[str, Any]]:\n        sql = [\"SELECT * FROM tasks WHERE 1=1\"]\n        params: list[Any] = []\n        if status is not None:\n            sql.append(\"AND status = ?\")\n            params.append(status)\n        if owner_agent is not None:\n            sql.append(\"AND owner_agent = ?\")\n            params.append(owner_agent)\n        if requires_human_input is not None:\n            sql.append(\"AND requires_human_input = ?\")\n            params.append(1 if requires_human_input else 0)\n        if run_id is not None:\n            sql.append(\"AND run_id = ?\")\n            params.append(run_id)\n        sql.append(\"ORDER BY priority ASC, created_at ASC LIMIT ?\")\n        params.append(limit)\n        with self.store.connection() as conn:\n            rows = conn.execute(\" \".join(sql), params).fetchall()\n            return [self._row_to_task(row) for row in rows]\n\n    def update_task(\n        self,\n        task_id: str,\n        *,\n        status: str | None = None,\n        owner_agent: str | None = None,\n        blocked_reason: str | None = None,\n        requires_human_input: bool | None = None,\n        metadata: dict[str, Any] | None = None,\n        run_id: str | None = None,\n    ) -> dict[str, Any]:\n        task = self.get_task(task_id)\n        task[\"status\"] = status or task[\"status\"]\n        task[\"owner_agent\"] = owner_agent if owner_agent is not None else task[\"owner_agent\"]\n        task[\"blocked_reason\"] = blocked_reason\n        task[\"run_id\"] = run_id if run_id is not None else task.get(\"run_id\")\n        if requires_human_input is not None:\n            task[\"requires_human_input\"] = requires_human_input\n        if metadata is not None:\n            task[\"metadata\"] = metadata\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                UPDATE tasks\n                SET status = ?, owner_agent = ?, blocked_reason = ?, requires_human_input = ?, run_id = ?, updated_at = ?, metadata_json = ?\n                WHERE id = ?\n                \"\"\",\n                (\n                    task[\"status\"],\n                    task[\"owner_agent\"],\n                    task[\"blocked_reason\"],\n                    1 if task[\"requires_human_input\"] else 0,\n                    task.get(\"run_id\"),\n                    utcnow(),\n                    self.store.dumps(task[\"metadata\"]),\n                    task_id,\n                ),\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_task(task_id)\n\n    def start_task_run(\n        self,\n        task_id: str,\n        agent_id: str,\n        *,\n        input_payload: dict[str, Any] | None = None,\n        metadata: dict[str, Any] | None = None,\n    ) -> dict[str, Any]:\n        now = utcnow()\n        record = {\n            \"id\": make_id(\"run\"),\n            \"task_id\": task_id,\n            \"agent_id\": agent_id,\n            \"status\": \"running\",\n            \"input_payload_json\": self.store.dumps(input_payload or {}),\n            \"result_summary\": None,\n            \"error_message\": None,\n            \"started_at\": now,\n            \"completed_at\": None,\n            \"created_at\": now,\n            \"updated_at\": now,\n            \"metadata_json\": self.store.dumps(metadata or {}),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT INTO task_runs (\n                    id, task_id, agent_id, status, input_payload_json, result_summary, error_message,\n                    started_at, completed_at, created_at, updated_at, metadata_json\n                ) VALUES (\n                    :id, :task_id, :agent_id, :status, :input_payload_json, :result_summary, :error_message,\n                    :started_at, :completed_at, :created_at, :updated_at, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_task_run(record[\"id\"])\n\n    def get_task_run(self, run_id: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            row = conn.execute(\"SELECT * FROM task_runs WHERE id = ?\", (run_id,)).fetchone()\n            if row is None:\n                raise KeyError(run_id)\n            return self._row_to_task_run(row)\n\n    def finish_task_run(\n        self,\n        run_id: str,\n        *,\n        status: str,\n        result_summary: str | None = None,\n        error_message: str | None = None,\n    ) -> dict[str, Any]:\n        now = utcnow()\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                UPDATE task_runs\n                SET status = ?, result_summary = ?, error_message = ?, updated_at = ?, completed_at = ?\n                WHERE id = ?\n                \"\"\",\n                (status, result_summary, error_message, now, now, run_id),\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_task_run(run_id)\n\n    def list_task_runs(self, *, task_id: str | None = None, status: str | None = None, limit: int = 50) -> list[dict[str, Any]]:\n        sql = [\"SELECT * FROM task_runs WHERE 1=1\"]\n        params: list[Any] = []\n        if task_id is not None:\n            sql.append(\"AND task_id = ?\")\n            params.append(task_id)\n        if status is not None:\n            sql.append(\"AND status = ?\")\n            params.append(status)\n        sql.append(\"ORDER BY created_at DESC LIMIT ?\")\n        params.append(limit)\n        with self.store.connection() as conn:\n            rows = conn.execute(\" \".join(sql), params).fetchall()\n            return [self._row_to_task_run(row) for row in rows]\n\n    def create_artifact(\n        self,\n        *,\n        task_id: str,\n        artifact_type: str,\n        title: str,\n        content: str,\n        fmt: str = \"md\",\n        status: str = \"active\",\n        source_ref: str | None = None,\n        metadata: dict[str, Any] | None = None,\n        artifact_id: str | None = None,\n    ) -> dict[str, Any]:\n        now = utcnow()\n        record = {\n            \"id\": artifact_id or make_id(\"art\"),\n            \"task_id\": task_id,\n            \"artifact_type\": artifact_type,\n            \"title\": title,\n            \"content\": content,\n            \"format\": fmt,\n            \"status\": status,\n            \"source_ref\": source_ref,\n            \"created_at\": now,\n            \"updated_at\": now,\n            \"metadata_json\": self.store.dumps(metadata or {}),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT INTO artifacts (\n                    id, task_id, artifact_type, title, content, format, status, source_ref, created_at, updated_at, metadata_json\n                ) VALUES (\n                    :id, :task_id, :artifact_type, :title, :content, :format, :status, :source_ref, :created_at, :updated_at, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_artifact(record[\"id\"])\n\n    def get_artifact(self, artifact_id: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            row = conn.execute(\"SELECT * FROM artifacts WHERE id = ?\", (artifact_id,)).fetchone()\n            if row is None:\n                raise KeyError(artifact_id)\n            return self._row_to_artifact(row)\n\n    def list_artifacts(self, *, task_id: str | None = None, limit: int = 50) -> list[dict[str, Any]]:\n        sql = [\"SELECT * FROM artifacts WHERE 1=1\"]\n        params: list[Any] = []\n        if task_id is not None:\n            sql.append(\"AND task_id = ?\")\n            params.append(task_id)\n        sql.append(\"ORDER BY created_at DESC LIMIT ?\")\n        params.append(limit)\n        with self.store.connection() as conn:\n            rows = conn.execute(\" \".join(sql), params).fetchall()\n            return [self._row_to_artifact(row) for row in rows]\n\n    def dashboard_snapshot(self, *, owner_agent: str | None = None) -> dict[str, Any]:\n        active_tasks = self.list_tasks(owner_agent=owner_agent, limit=50)\n        active_tasks = [task for task in active_tasks if task[\"status\"] in {\"open\", \"draft\", \"in_progress\"}][:20]\n        blocked_tasks = self.list_tasks(status=\"blocked\", owner_agent=owner_agent, limit=20)\n        recent_runs = self.list_task_runs(limit=20)\n        recent_artifacts = self.list_artifacts(limit=20)\n        return {\n            \"active_tasks\": active_tasks,\n            \"blocked_tasks\": blocked_tasks,\n            \"recent_runs\": recent_runs,\n            \"recent_artifacts\": recent_artifacts,\n        }\n\n    def task_bundle(self, task_id: str) -> dict[str, Any]:\n        task = self.get_task(task_id)\n        with self.store.connection() as conn:\n            child_rows = conn.execute(\n                \"SELECT * FROM tasks WHERE parent_task_id = ? ORDER BY priority ASC, created_at ASC\",\n                (task_id,),\n            ).fetchall()\n        return {\n            \"task\": task,\n            \"children\": [self._row_to_task(row) for row in child_rows],\n            \"runs\": self.list_task_runs(task_id=task_id, limit=100),\n            \"artifacts\": self.list_artifacts(task_id=task_id, limit=100),\n        }\n\n    def audit_v2(self) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            approvals = self._count_if_table_exists(conn, \"approvals\")\n            handoffs = self._count_if_table_exists(conn, \"handoffs\")\n            legacy_memory_rows = conn.execute(\n                \"\"\"\n                SELECT COUNT(*) AS count\n                FROM memories\n                WHERE schema_version != ?\n                   OR json_extract(metadata_json, '$.legacy_kind') IS NOT NULL\n                   OR json_extract(metadata_json, '$.legacy_system') IS NOT NULL\n                \"\"\",\n                (SCHEMA_VERSION,),\n            ).fetchone()[\"count\"]\n            legacy_task_rows = conn.execute(\n                \"\"\"\n                SELECT COUNT(*) AS count\n                FROM tasks\n                WHERE schema_version != ?\n                   OR json_extract(metadata_json, '$.legacy_kind') IS NOT NULL\n                   OR json_extract(metadata_json, '$.legacy_system') IS NOT NULL\n                \"\"\",\n                (SCHEMA_VERSION,),\n            ).fetchone()[\"count\"]\n            memories = conn.execute(\"SELECT COUNT(*) AS count FROM memories\").fetchone()[\"count\"]\n            tasks = conn.execute(\"SELECT COUNT(*) AS count FROM tasks\").fetchone()[\"count\"]\n            maintenance_jobs = self._count_if_table_exists(conn, \"maintenance_jobs\")\n        return {\n            \"schema_version\": SCHEMA_VERSION,\n            \"memories\": memories,\n            \"tasks\": tasks,\n            \"approvals\": approvals,\n            \"handoffs\": handoffs,\n            \"maintenance_jobs\": maintenance_jobs,\n            \"legacy_memory_rows\": legacy_memory_rows,\n            \"legacy_task_rows\": legacy_task_rows,\n        }\n\n    def migrate_v2(self) -> dict[str, Any]:\n        before = self.audit_v2()\n        migrated = {\"memories\": 0, \"tasks\": 0, \"approval_artifacts\": 0, \"handoff_artifacts\": 0}\n        with self.store.connection() as conn:\n            memory_rows = conn.execute(\"SELECT * FROM memories\").fetchall()\n            for row in memory_rows:\n                record = self._migrate_memory_row(dict(row))\n                if record is None:\n                    continue\n                conn.execute(\n                    \"\"\"\n                    UPDATE memories\n                    SET schema_version = ?, type = ?, subtype = ?, origin_agent = ?, run_id = ?, task_id = ?,\n                        url = ?, domain = ?, summary = ?, metadata_json = ?, updated_at = ?\n                    WHERE id = ?\n                    \"\"\",\n                    (\n                        record[\"schema_version\"],\n                        record[\"type\"],\n                        record[\"subtype\"],\n                        record[\"origin_agent\"],\n                        record[\"run_id\"],\n                        record[\"task_id\"],\n                        record[\"url\"],\n                        record[\"domain\"],\n                        record[\"summary\"],\n                        record[\"metadata_json\"],\n                        utcnow(),\n                        record[\"id\"],\n                    ),\n                )\n                migrated[\"memories\"] += 1\n            task_rows = conn.execute(\"SELECT * FROM tasks\").fetchall()\n            for row in task_rows:\n                record = self._migrate_task_row(dict(row))\n                if record is None:\n                    continue\n                conn.execute(\n                    \"\"\"\n                    UPDATE tasks\n                    SET schema_version = ?, run_id = ?, metadata_json = ?, updated_at = ?\n                    WHERE id = ?\n                    \"\"\",\n                    (record[\"schema_version\"], record[\"run_id\"], record[\"metadata_json\"], utcnow(), record[\"id\"]),\n                )\n                migrated[\"tasks\"] += 1\n            if self._table_exists(conn, \"approvals\"):\n                for row in conn.execute(\"SELECT * FROM approvals\").fetchall():\n                    approval = dict(row)\n                    task_row = conn.execute(\"SELECT * FROM tasks WHERE id = ?\", (approval[\"task_id\"],)).fetchone()\n                    if task_row is None:\n                        continue\n                    task = self._row_to_task(task_row)\n                    metadata = dict(task.get(\"metadata\", {}))\n                    metadata[\"approval\"] = {\n                        \"kind\": approval[\"kind\"],\n                        \"risk_level\": approval[\"risk_level\"],\n                        \"payload\": self.store.loads(approval[\"payload_json\"]) or {},\n                        \"status\": approval[\"status\"],\n                        \"requested_at\": approval[\"requested_at\"],\n                        \"resolved_at\": approval[\"resolved_at\"],\n                        \"resolution_note\": approval[\"resolution_note\"],\n                    }\n                    conn.execute(\n                        \"\"\"\n                        UPDATE tasks\n                        SET metadata_json = ?, updated_at = ?\n                        WHERE id = ?\n                        \"\"\",\n                        (self.store.dumps(metadata), utcnow(), approval[\"task_id\"]),\n                    )\n                    conn.execute(\n                        \"\"\"\n                        INSERT INTO artifacts (\n                            id, task_id, artifact_type, title, content, format, status, source_ref, created_at, updated_at, metadata_json\n                        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n                        \"\"\",\n                        (\n                            make_id(\"art\"),\n                            approval[\"task_id\"],\n                            \"approval_resolution\",\n                            f\"Approval {approval['kind']}\",\n                            json.dumps(metadata[\"approval\"], sort_keys=True),\n                            \"json\",\n                            \"archived\" if approval[\"status\"] != \"pending\" else \"active\",\n                            \"migration:v2\",\n                            utcnow(),\n                            utcnow(),\n                            self.store.dumps({}),\n                        ),\n                    )\n                    migrated[\"approval_artifacts\"] += 1\n                conn.execute(\"DROP TABLE approvals\")\n            if self._table_exists(conn, \"handoffs\"):\n                for row in conn.execute(\"SELECT * FROM handoffs\").fetchall():\n                    handoff = dict(row)\n                    payload = self.store.loads(handoff[\"payload_json\"]) or {}\n                    conn.execute(\n                        \"\"\"\n                        INSERT INTO artifacts (\n                            id, task_id, artifact_type, title, content, format, status, source_ref, created_at, updated_at, metadata_json\n                        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n                        \"\"\",\n                        (\n                            make_id(\"art\"),\n                            handoff[\"task_id\"],\n                            \"delegation_note\",\n                            f\"Handoff {handoff['from_agent']} -> {handoff['to_agent']}\",\n                            json.dumps(\n                                {\n                                    \"from_agent\": handoff[\"from_agent\"],\n                                    \"to_agent\": handoff[\"to_agent\"],\n                                    \"reason\": handoff[\"reason\"],\n                                    \"status\": handoff[\"status\"],\n                                    \"payload\": payload,\n                                    \"result_summary\": handoff[\"result_summary\"],\n                                    \"error_message\": handoff[\"error_message\"],\n                                    \"completed_at\": handoff[\"completed_at\"],\n                                },\n                                sort_keys=True,\n                            ),\n                            \"json\",\n                            \"active\",\n                            \"migration:v2\",\n                            utcnow(),\n                            utcnow(),\n                            self.store.dumps({}),\n                        ),\n                    )\n                    migrated[\"handoff_artifacts\"] += 1\n                conn.execute(\"DROP TABLE handoffs\")\n        after = self.audit_v2()\n        return {\"status\": \"ok\", \"before\": before, \"after\": after, \"migrated\": migrated}\n\n    def _capture_source(\n        self, trigger_type: str, source_kind: str, source_ref: str, scope: str, content: str, metadata: dict[str, Any]\n    ) -> dict[str, Any]:\n        now = utcnow()\n        source = {\n            \"id\": make_id(\"src\"),\n            \"source_kind\": source_kind,\n            \"title\": metadata.get(\"title\") or source_ref,\n            \"content\": content,\n            \"source_ref\": source_ref,\n            \"metadata_json\": self.store.dumps(metadata),\n            \"created_at\": now,\n        }\n        event = {\n            \"id\": make_id(\"ing\"),\n            \"trigger_type\": trigger_type,\n            \"source_id\": source[\"id\"],\n            \"requested_scope\": scope,\n            \"job_state\": \"pending\",\n            \"processor_outcome\": None,\n            \"retry_count\": 0,\n            \"error_message\": None,\n            \"created_at\": now,\n            \"updated_at\": now,\n            \"processed_at\": None,\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT INTO sources (id, source_kind, title, content, source_ref, metadata_json, created_at)\n                VALUES (:id, :source_kind, :title, :content, :source_ref, :metadata_json, :created_at)\n                \"\"\",\n                source,\n            )\n            conn.execute(\n                \"\"\"\n                INSERT INTO ingestion_events (\n                    id, trigger_type, source_id, requested_scope, job_state, processor_outcome,\n                    retry_count, error_message, created_at, updated_at, processed_at\n                ) VALUES (\n                    :id, :trigger_type, :source_id, :requested_scope, :job_state, :processor_outcome,\n                    :retry_count, :error_message, :created_at, :updated_at, :processed_at\n                )\n                \"\"\",\n                event,\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return {\"source\": source, \"event\": event}\n\n    def _run_opportunistic_maintenance(self, *, limit: int) -> None:\n        if self._maintenance_check_running:\n            return\n        due_jobs = self.list_due_maintenance_jobs(limit=1)\n        if not due_jobs:\n            return\n        self._maintenance_check_running = True\n        try:\n            from .engine import MemoryEngine\n\n            MemoryEngine(self).run_due_maintenance(limit=limit)\n        except Exception:\n            return\n        finally:\n            self._maintenance_check_running = False\n\n    def _memory_filter_sql(self, sql: list[str], filters: dict[str, Any]) -> list[Any]:\n        params: list[Any] = []\n        allowed = {\"project_id\", \"repo_id\", \"type\", \"subtype\", \"run_id\", \"task_id\", \"origin_agent\", \"url\", \"domain\"}\n        for key, value in filters.items():\n            if key not in allowed or value is None:\n                continue\n            sql.append(f\"AND {key} = ?\")\n            params.append(value)\n        return params\n\n    def _facet_score(self, memory: dict[str, Any], filters: dict[str, Any]) -> float:\n        tracked = [\"project_id\", \"repo_id\", \"type\", \"subtype\", \"run_id\", \"task_id\", \"origin_agent\", \"url\", \"domain\"]\n        applied = [key for key in tracked if filters.get(key) is not None]\n        if not applied:\n            return 0.0\n        matched = sum(1 for key in applied if memory.get(key) == filters[key])\n        return matched / len(applied)\n\n    def _migrate_memory_row(self, row: dict[str, Any]) -> dict[str, Any] | None:\n        metadata = self.store.loads(row.get(\"metadata_json\")) or {}\n        if row.get(\"schema_version\") == SCHEMA_VERSION and not metadata.get(\"legacy_kind\") and not metadata.get(\"legacy_system\"):\n            return None\n        subtype = self._memory_subtype(metadata)\n        origin_agent = row.get(\"origin_agent\") or metadata.get(\"origin_agent\") or metadata.get(\"legacy_system\") or row.get(\"agent_id\")\n        run_id = row.get(\"run_id\") or metadata.get(\"run_id\") or metadata.get(\"legacy_run_id\") or self._infer_run_id(row, subtype)\n        task_id = row.get(\"task_id\") or metadata.get(\"task_id\")\n        url = row.get(\"url\") or metadata.get(\"url\") or metadata.get(\"source_url\") or self._infer_url(row, subtype)\n        domain = row.get(\"domain\") or metadata.get(\"domain\") or (urlparse(url).netloc if url else None)\n\n[Showing lines 1-1198 of 1706 (50.0KB limit). Use offset=1199 to continue.]"}],"details":{"truncation":{"content":"from __future__ import annotations\n\nimport json\nimport math\nimport subprocess\nimport uuid\nfrom dataclasses import dataclass\nfrom datetime import UTC, datetime, timedelta\nfrom typing import Any\nfrom urllib.parse import urlparse\n\nfrom .schema import SCHEMA_VERSION\nfrom .store import SQLiteStore\n\n\ndef utcnow() -> str:\n    return datetime.now(UTC).isoformat()\n\n\ndef make_id(prefix: str) -> str:\n    return f\"{prefix}_{uuid.uuid4().hex}\"\n\n\ndef normalize_text(value: str) -> str:\n    return \" \".join(value.lower().split())\n\n\ndef _line_value(content: str, label: str) -> str:\n    prefix = f\"{label}:\"\n    for line in content.splitlines():\n        if line.startswith(prefix):\n            return line[len(prefix) :].strip()\n    return \"\"\n\n\n@dataclass(slots=True)\nclass SearchResult:\n    memory: dict[str, Any]\n    score: float\n    explanation: str\n    facet_score: float\n    lexical_score: float\n    semantic_score: float\n\n    def as_dict(self) -> dict[str, Any]:\n        return {\n            \"memory\": self.memory,\n            \"score\": self.score,\n            \"score_breakdown\": {\n                \"facet\": self.facet_score,\n                \"lexical\": self.lexical_score,\n                \"semantic\": self.semantic_score,\n                \"confidence\": self.memory[\"confidence\"],\n                \"freshness\": self.memory[\"freshness\"],\n            },\n            \"matched_scope\": self.memory[\"scope\"],\n            \"evidence_ref\": self.memory[\"evidence_ref\"],\n            \"explanation\": self.explanation,\n        }\n\n\nclass MemoryService:\n    def __init__(self, db_path: str) -> None:\n        self.store = SQLiteStore(db_path)\n        self._maintenance_check_running = False\n        self.store.initialize()\n        self.migrate_v2()\n        self._ensure_default_jobs()\n        self._run_opportunistic_maintenance(limit=10)\n\n    def create_memory(self, memory_input: dict[str, Any]) -> dict[str, Any]:\n        now = utcnow()\n        metadata = dict(memory_input.get(\"metadata\") or {})\n        subtype = memory_input.get(\"subtype\")\n        run_id = memory_input.get(\"run_id\")\n        task_id = memory_input.get(\"task_id\")\n        url = memory_input.get(\"url\")\n        domain = memory_input.get(\"domain\") or (urlparse(url).netloc if url else None)\n        origin_agent = memory_input.get(\"origin_agent\") or metadata.get(\"origin_agent\") or memory_input.get(\"agent_id\")\n        record = {\n            \"id\": memory_input.get(\"id\", make_id(\"mem\")),\n            \"schema_version\": memory_input.get(\"schema_version\", SCHEMA_VERSION),\n            \"type\": memory_input[\"type\"],\n            \"subtype\": subtype,\n            \"scope\": memory_input[\"scope\"],\n            \"status\": memory_input.get(\"status\", \"active\"),\n            \"project_id\": memory_input.get(\"project_id\"),\n            \"repo_id\": memory_input.get(\"repo_id\"),\n            \"agent_id\": memory_input.get(\"agent_id\"),\n            \"origin_agent\": origin_agent,\n            \"run_id\": run_id,\n            \"task_id\": task_id,\n            \"url\": url,\n            \"domain\": domain,\n            \"source_kind\": memory_input.get(\"source_kind\", \"manual\"),\n            \"title\": memory_input[\"title\"],\n            \"content\": memory_input[\"content\"],\n            \"summary\": memory_input.get(\"summary\") or self._make_summary(memory_input[\"content\"]),\n            \"confidence\": float(memory_input.get(\"confidence\", 1.0)),\n            \"freshness\": float(memory_input.get(\"freshness\", 1.0)),\n            \"created_at\": memory_input.get(\"created_at\", now),\n            \"updated_at\": now,\n            \"observed_at\": memory_input.get(\"observed_at\", now),\n            \"source_ref\": memory_input.get(\"source_ref\"),\n            \"evidence_ref\": memory_input.get(\"evidence_ref\"),\n            \"embedding_json\": self._encode_embedding(memory_input.get(\"embedding\")),\n            \"metadata_json\": self.store.dumps(metadata),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT OR REPLACE INTO memories (\n                    id, schema_version, type, subtype, scope, status, project_id, repo_id, agent_id, origin_agent,\n                    run_id, task_id, url, domain, source_kind, title, content, summary, confidence, freshness,\n                    created_at, updated_at, observed_at, source_ref, evidence_ref, embedding_json, metadata_json\n                ) VALUES (\n                    :id, :schema_version, :type, :subtype, :scope, :status, :project_id, :repo_id, :agent_id, :origin_agent,\n                    :run_id, :task_id, :url, :domain, :source_kind, :title, :content, :summary, :confidence, :freshness,\n                    :created_at, :updated_at, :observed_at, :source_ref, :evidence_ref, :embedding_json, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n            self._upsert_fts(conn, record)\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_memory(record[\"id\"])\n\n    def ingest(self, memory_input: dict[str, Any]) -> dict[str, Any]:\n        return self.create_memory(memory_input)\n\n    def capture_session(self, source_ref: str, scope: str, content: str, **metadata: Any) -> dict[str, Any]:\n        return self._capture_source(\n            trigger_type=\"session_close\",\n            source_kind=\"run\",\n            source_ref=source_ref,\n            scope=scope,\n            content=content,\n            metadata=metadata,\n        )\n\n    def capture_conversation(\n        self, source_ref: str, scope: str, content: str, capture_mode: str = \"manual_import\", **metadata: Any\n    ) -> dict[str, Any]:\n        metadata = {\"capture_mode\": capture_mode, **metadata}\n        return self._capture_source(\n            trigger_type=\"conversation_capture\",\n            source_kind=\"conversation\",\n            source_ref=source_ref,\n            scope=scope,\n            content=content,\n            metadata=metadata,\n        )\n\n    def search(\n        self,\n        query: str,\n        scopes: list[str] | None = None,\n        filters: dict[str, Any] | None = None,\n        limit: int = 10,\n        include_inbox: bool = False,\n    ) -> dict[str, Any]:\n        filters = filters or {}\n        query_terms = [term for term in normalize_text(query).split() if term]\n        sql = [\"SELECT * FROM memories WHERE 1=1\"]\n        params: list[Any] = []\n\n        if scopes:\n            sql.append(f\"AND scope IN ({','.join('?' for _ in scopes)})\")\n            params.extend(scopes)\n        if not include_inbox:\n            sql.append(\"AND status = ?\")\n            params.append(\"active\")\n        params.extend(self._memory_filter_sql(sql, filters))\n\n        with self.store.connection() as conn:\n            rows = [self._row_to_memory(row) for row in conn.execute(\" \".join(sql), params)]\n\n        results: list[SearchResult] = []\n        query_embedding = self._text_embedding(query) if query_terms else []\n        for row in rows:\n            haystack = normalize_text(\" \".join([row[\"title\"], row[\"summary\"], row[\"content\"]]))\n            lexical = self._lexical_score(query_terms, haystack)\n            semantic = self._semantic_score(query_embedding, row.get(\"embedding\")) if query_terms else 0.0\n            facet = self._facet_score(row, filters)\n            if query_terms and lexical == 0 and semantic == 0 and facet == 0:\n                continue\n            score = facet * 0.45 + lexical * 0.3 + semantic * 0.15 + row[\"confidence\"] * 0.06 + row[\"freshness\"] * 0.04\n            explanation = (\n                f\"facets={facet:.2f} lexical={lexical:.2f} semantic={semantic:.2f} \"\n                f\"confidence={row['confidence']:.2f} freshness={row['freshness']:.2f}\"\n            )\n            results.append(SearchResult(row, score, explanation, facet, lexical, semantic))\n\n        ordered = sorted(\n            results,\n            key=lambda item: (\n                item.facet_score,\n                item.lexical_score,\n                item.semantic_score,\n                item.memory[\"confidence\"],\n                item.memory[\"freshness\"],\n                item.memory[\"updated_at\"],\n            ),\n            reverse=True,\n        )[:limit]\n        payload = [item.as_dict() for item in ordered]\n\n        retrieval_id = make_id(\"ret\")\n        with self.store.connection() as conn:\n            conn.execute(\n                \"INSERT INTO retrieval_logs (id, query_text, filters_json, results_json, created_at) VALUES (?, ?, ?, ?, ?)\",\n                (\n                    retrieval_id,\n                    query,\n                    self.store.dumps({\"scopes\": scopes, \"filters\": filters, \"include_inbox\": include_inbox}),\n                    self.store.dumps(payload),\n                    utcnow(),\n                ),\n            )\n        return {\"retrieval_id\": retrieval_id, \"results\": payload}\n\n    def context_for(\n        self, project: str | None = None, repo: str | None = None, agent: str | None = None, task: str | None = None\n    ) -> dict[str, Any]:\n        profile_facts = self.list_memories(status=\"active\", scope=\"global\", memory_type=\"profile\", limit=8)\n        project_filters = {\"project_id\": project, \"repo_id\": repo}\n        active_decisions = self.list_memories(\n            status=\"active\",\n            scope=\"project\" if project else None,\n            memory_type=\"decision\",\n            project_id=project,\n            repo_id=repo,\n            limit=8,\n        )\n        project_memories = self.list_memories(\n            status=\"active\",\n            project_id=project,\n            repo_id=repo,\n            limit=12,\n        )\n        project_memories = [item for item in project_memories if item[\"type\"] in {\"project\", \"decision\", \"artifact\", \"episode\"}][:12]\n        recent_episodes = self.list_memories(\n            status=\"active\",\n            memory_type=\"episode\",\n            project_id=project,\n            repo_id=repo,\n            limit=10,\n        )\n        task_relevant_artifacts = self.list_memories(\n            status=\"active\",\n            memory_type=\"artifact\",\n            project_id=project,\n            repo_id=repo,\n            task_id=task,\n            limit=10,\n        )\n        if task and not task_relevant_artifacts:\n            task_relevant_artifacts = self.search(task, filters={**project_filters, \"type\": \"artifact\"}, limit=5)[\"results\"]\n        citations = [\n            item[\"id\"]\n            for item in [*profile_facts, *project_memories, *active_decisions, *recent_episodes]\n            if isinstance(item, dict) and \"id\" in item\n        ]\n        return {\n            \"profile_facts\": profile_facts,\n            \"project_memories\": project_memories,\n            \"active_decisions\": active_decisions,\n            \"recent_episodes\": recent_episodes,\n            \"task_relevant_artifacts\": task_relevant_artifacts,\n            \"citations\": citations,\n            \"agent_id\": agent,\n        }\n\n    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:\n        promoted: list[str] = []\n        linked: list[dict[str, str]] = []\n        with self.store.connection() as conn:\n            for memory_id in inbox_items:\n                row = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (memory_id,)).fetchone()\n                if row is None:\n                    continue\n                memory = self._row_to_memory(row)\n                duplicate = conn.execute(\n                    \"\"\"\n                    SELECT id FROM memories\n                    WHERE id != ?\n                      AND status = 'active'\n                      AND type = ?\n                      AND scope = ?\n                      AND content = ?\n                    LIMIT 1\n                    \"\"\",\n                    (memory_id, memory[\"type\"], memory[\"scope\"], memory[\"content\"]),\n                ).fetchone()\n                if duplicate:\n                    link_id = make_id(\"lnk\")\n                    conn.execute(\n                        \"\"\"\n                        INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)\n                        VALUES (?, ?, ?, ?, ?, ?)\n                        \"\"\",\n                        (\n                            link_id,\n                            memory_id,\n                            duplicate[\"id\"],\n                            \"related_to\",\n                            utcnow(),\n                            self.store.dumps({\"reason\": \"exact_duplicate\"}),\n                        ),\n                    )\n                    conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"archived\", utcnow(), memory_id))\n                    linked.append({\"from\": memory_id, \"to\": duplicate[\"id\"], \"relation\": \"related_to\"})\n                    continue\n                conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"active\", utcnow(), memory_id))\n                promoted.append(memory_id)\n        return {\"promoted\": promoted, \"linked\": linked}\n\n    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:\n        stats = {\n            \"candidates\": 0,\n            \"promoted\": 0,\n            \"archived\": 0,\n            \"linked\": 0,\n            \"conflicts\": 0,\n            \"llm_summaries\": 0,\n        }\n        age_days = int(config.get(\"candidate_age_days\", 7))\n        max_candidates = int(config.get(\"max_candidates\", 200))\n        high_threshold = float(config.get(\"dedupe_high_threshold\", 0.9))\n        mid_threshold = float(config.get(\"dedupe_mid_threshold\", 0.75))\n        promote_threshold = float(config.get(\"promote_confidence\", 0.7))\n        conflict_threshold = float(config.get(\"conflict_threshold\", 0.55))\n        llm_enabled = bool(config.get(\"llm_enabled\", False))\n        llm_provider = config.get(\"llm_provider\") or \"opencode\"\n        llm_command = config.get(\"llm_command\") or [\"opencode\", \"-m\", \"MiniMax M2.5 Free\"]\n        cutoff = (datetime.now(UTC) - timedelta(days=age_days)).isoformat()\n\n        with self.store.connection() as conn:\n            rows = conn.execute(\n                \"\"\"\n                SELECT * FROM memories\n                WHERE (\n                    status = 'inbox'\n                    OR (type = 'episode' AND status = 'active')\n                )\n                  AND created_at <= ?\n                ORDER BY created_at ASC\n                LIMIT ?\n                \"\"\",\n                (cutoff, max_candidates),\n            ).fetchall()\n\n            candidates = [self._row_to_memory(row) for row in rows]\n            stats[\"candidates\"] = len(candidates)\n\n            for candidate in candidates:\n                related = conn.execute(\n                    \"\"\"\n                    SELECT * FROM memories\n                    WHERE status = 'active'\n                      AND id != ?\n                      AND type = ?\n                      AND scope = ?\n                    ORDER BY updated_at DESC\n                    LIMIT 50\n                    \"\"\",\n                    (candidate[\"id\"], candidate[\"type\"], candidate[\"scope\"]),\n                ).fetchall()\n                related_memories = [self._row_to_memory(row) for row in related]\n\n                best_match = None\n                best_score = 0.0\n                for other in related_memories:\n                    lexical = self._lexical_score(\n                        normalize_text(candidate[\"content\"]).split(),\n                        normalize_text(other[\"content\"]),\n                    )\n                    semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n                    if score > best_score:\n                        best_score = score\n                        best_match = other\n\n                if best_match and candidate[\"content\"] == best_match[\"content\"]:\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"exact_duplicate\")\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"archived\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"archived\"] += 1\n                    stats[\"linked\"] += 1\n                    continue\n\n                if best_match and best_score >= high_threshold:\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"high_similarity\")\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"archived\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"archived\"] += 1\n                    stats[\"linked\"] += 1\n                    continue\n\n                if best_match and best_score >= mid_threshold:\n                    if llm_enabled:\n                        synthesized = self._llm_summarize_pair(\n                            candidate,\n                            best_match,\n                            provider=llm_provider,\n                            command=llm_command,\n                        )\n                        if synthesized:\n                            conn.execute(\n                                \"UPDATE memories SET summary = ?, updated_at = ?, metadata_json = ? WHERE id = ?\",\n                                (\n                                    synthesized,\n                                    utcnow(),\n                                    self.store.dumps(\n                                        {\n                                            **candidate.get(\"metadata\", {}),\n                                            \"synthesis_sources\": [candidate[\"id\"], best_match[\"id\"]],\n                                            \"synthesis_provider\": llm_provider,\n                                            \"synthesis_command\": llm_command,\n                                        }\n                                    ),\n                                    candidate[\"id\"],\n                                ),\n                            )\n                            stats[\"llm_summaries\"] += 1\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"mid_similarity\")\n                    stats[\"linked\"] += 1\n\n                if best_match and best_score >= conflict_threshold and self._conflict_signal(candidate, best_match):\n                    self._record_conflict(conn, candidate[\"id\"], best_match[\"id\"], \"potential_contradiction\")\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"contradicts\", \"heuristic\")\n                    stats[\"conflicts\"] += 1\n\n                if candidate[\"status\"] == \"inbox\" and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"active\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"promoted\"] += 1\n\n        return stats\n\n    def upsert_profile_fact(\n        self, title: str, content: str, evidence_ref: str | None = None, source_ref: str | None = None\n    ) -> dict[str, Any]:\n        return self.create_memory(\n            {\n                \"type\": \"profile\",\n                \"scope\": \"global\",\n                \"title\": title,\n                \"content\": content,\n                \"summary\": self._make_summary(content),\n                \"source_ref\": source_ref,\n                \"evidence_ref\": evidence_ref,\n                \"metadata\": {\"kind\": \"profile_fact\"},\n            }\n        )\n\n    def explain(self, identifier: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            retrieval = conn.execute(\"SELECT * FROM retrieval_logs WHERE id = ?\", (identifier,)).fetchone()\n            if retrieval:\n                return {\n                    \"kind\": \"retrieval\",\n                    \"id\": retrieval[\"id\"],\n                    \"query_text\": retrieval[\"query_text\"],\n                    \"filters\": self.store.loads(retrieval[\"filters_json\"]),\n                    \"results\": self.store.loads(retrieval[\"results_json\"]),\n                }\n            memory = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (identifier,)).fetchone()\n            if memory:\n                mem = self._row_to_memory(memory)\n                sources = conn.execute(\"SELECT * FROM sources WHERE source_ref = ? OR id = ?\", (mem[\"source_ref\"], mem[\"source_ref\"])).fetchall()\n                return {\"kind\": \"memory\", \"memory\": mem, \"sources\": [dict(source) for source in sources]}\n        raise KeyError(f\"Unknown identifier: {identifier}\")\n\n    def export(self) -> dict[str, Any]:\n        tables = [\n            \"projects\",\n            \"repos\",\n            \"sources\",\n            \"ingestion_events\",\n            \"memories\",\n            \"memory_links\",\n            \"memory_conflicts\",\n            \"retrieval_logs\",\n            \"maintenance_jobs\",\n            \"maintenance_runs\",\n            \"profiles\",\n            \"tasks\",\n            \"task_runs\",\n            \"artifacts\",\n        ]\n        exported: dict[str, list[dict[str, Any]]] = {}\n        with self.store.connection() as conn:\n            for table in tables:\n                rows = conn.execute(f\"SELECT * FROM {table}\").fetchall()\n                exported[table] = [dict(row) for row in rows]\n        return exported\n\n    def import_data(self, payload: dict[str, Any]) -> None:\n        tables = [\n            \"projects\",\n            \"repos\",\n            \"sources\",\n            \"ingestion_events\",\n            \"memories\",\n            \"memory_links\",\n            \"memory_conflicts\",\n            \"retrieval_logs\",\n            \"maintenance_jobs\",\n            \"maintenance_runs\",\n            \"profiles\",\n            \"tasks\",\n            \"task_runs\",\n            \"artifacts\",\n        ]\n        with self.store.connection() as conn:\n            for table in tables:\n                rows = payload.get(table, [])\n                if not rows:\n                    continue\n                columns = list(rows[0].keys())\n                placeholder = \",\".join(\"?\" for _ in columns)\n                sql = f\"INSERT OR REPLACE INTO {table} ({','.join(columns)}) VALUES ({placeholder})\"\n                conn.executemany(sql, [tuple(row.get(col) for col in columns) for row in rows])\n                if table == \"memories\":\n                    for row in rows:\n                        self._upsert_fts(conn, row)\n        self.migrate_v2()\n        self._run_opportunistic_maintenance(limit=10)\n\n    def get_memory(self, memory_id: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            row = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (memory_id,)).fetchone()\n            if row is None:\n                raise KeyError(memory_id)\n            return self._row_to_memory(row)\n\n    def list_memories(\n        self,\n        *,\n        status: str | None = \"active\",\n        scope: str | None = None,\n        memory_type: str | None = None,\n        subtype: str | None = None,\n        project_id: str | None = None,\n        repo_id: str | None = None,\n        source_ref: str | None = None,\n        evidence_ref: str | None = None,\n        run_id: str | None = None,\n        task_id: str | None = None,\n        origin_agent: str | None = None,\n        url: str | None = None,\n        domain: str | None = None,\n        metadata: dict[str, Any] | None = None,\n        limit: int = 50,\n    ) -> list[dict[str, Any]]:\n        sql = [\"SELECT * FROM memories WHERE 1=1\"]\n        params: list[Any] = []\n        if status is not None:\n            sql.append(\"AND status = ?\")\n            params.append(status)\n        if scope is not None:\n            sql.append(\"AND scope = ?\")\n            params.append(scope)\n        filters = {\n            \"type\": memory_type,\n            \"subtype\": subtype,\n            \"project_id\": project_id,\n            \"repo_id\": repo_id,\n            \"source_ref\": source_ref,\n            \"evidence_ref\": evidence_ref,\n            \"run_id\": run_id,\n            \"task_id\": task_id,\n            \"origin_agent\": origin_agent,\n            \"url\": url,\n            \"domain\": domain,\n        }\n        params.extend(self._memory_filter_sql(sql, {key: value for key, value in filters.items() if value is not None}))\n        for key, value in (metadata or {}).items():\n            sql.append(\"AND json_extract(metadata_json, ?) = ?\")\n            params.extend((f\"$.{key}\", value))\n        sql.append(\"ORDER BY updated_at DESC LIMIT ?\")\n        params.append(limit)\n        with self.store.connection() as conn:\n            rows = conn.execute(\" \".join(sql), params).fetchall()\n            return [self._row_to_memory(row) for row in rows]\n\n    def list_pending_events(self) -> list[dict[str, Any]]:\n        with self.store.connection() as conn:\n            rows = conn.execute(\"SELECT * FROM ingestion_events WHERE job_state = 'pending' ORDER BY created_at ASC\").fetchall()\n            return [dict(row) for row in rows]\n\n    def mark_event(self, event_id: str, state: str, outcome: str | None = None, error_message: str | None = None) -> None:\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                UPDATE ingestion_events\n                SET job_state = ?, processor_outcome = ?, error_message = ?, updated_at = ?, processed_at = ?\n                WHERE id = ?\n                \"\"\",\n                (state, outcome, error_message, utcnow(), utcnow(), event_id),\n            )\n\n    def create_project(self, project_id: str, name: str, description: str | None = None) -> None:\n        now = utcnow()\n        with self.store.connection() as conn:\n            conn.execute(\n                \"INSERT OR REPLACE INTO projects (id, name, description, created_at, updated_at) VALUES (?, ?, ?, ?, ?)\",\n                (project_id, name, description, now, now),\n            )\n        self._run_opportunistic_maintenance(limit=1)\n\n    def create_repo(self, repo_id: str, name: str, project_id: str | None = None, path: str | None = None) -> None:\n        now = utcnow()\n        with self.store.connection() as conn:\n            conn.execute(\n                \"INSERT OR REPLACE INTO repos (id, project_id, name, path, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?)\",\n                (repo_id, project_id, name, path, now, now),\n            )\n        self._run_opportunistic_maintenance(limit=1)\n\n    def create_task(\n        self,\n        *,\n        title: str,\n        intent: str,\n        kind: str = \"task\",\n        status: str = \"open\",\n        priority: int = 3,\n        project_id: str | None = None,\n        repo_id: str | None = None,\n        parent_task_id: str | None = None,\n        origin: str | None = None,\n        owner_agent: str | None = None,\n        blocked_reason: str | None = None,\n        requires_human_input: bool = False,\n        due_at: str | None = None,\n        metadata: dict[str, Any] | None = None,\n        task_id: str | None = None,\n        run_id: str | None = None,\n    ) -> dict[str, Any]:\n        now = utcnow()\n        record = {\n            \"id\": task_id or make_id(\"task\"),\n            \"schema_version\": SCHEMA_VERSION,\n            \"run_id\": run_id,\n            \"title\": title,\n            \"intent\": intent,\n            \"kind\": kind,\n            \"status\": status,\n            \"priority\": priority,\n            \"project_id\": project_id,\n            \"repo_id\": repo_id,\n            \"parent_task_id\": parent_task_id,\n            \"origin\": origin,\n            \"owner_agent\": owner_agent,\n            \"blocked_reason\": blocked_reason,\n            \"requires_human_input\": 1 if requires_human_input else 0,\n            \"due_at\": due_at,\n            \"created_at\": now,\n            \"updated_at\": now,\n            \"metadata_json\": self.store.dumps(metadata or {}),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT INTO tasks (\n                    id, schema_version, run_id, title, intent, kind, status, priority, project_id, repo_id,\n                    parent_task_id, origin, owner_agent, blocked_reason, requires_human_input, due_at,\n                    created_at, updated_at, metadata_json\n                ) VALUES (\n                    :id, :schema_version, :run_id, :title, :intent, :kind, :status, :priority, :project_id, :repo_id,\n                    :parent_task_id, :origin, :owner_agent, :blocked_reason, :requires_human_input, :due_at,\n                    :created_at, :updated_at, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_task(record[\"id\"])\n\n    def get_task(self, task_id: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            row = conn.execute(\"SELECT * FROM tasks WHERE id = ?\", (task_id,)).fetchone()\n            if row is None:\n                raise KeyError(task_id)\n            return self._row_to_task(row)\n\n    def list_tasks(\n        self,\n        *,\n        status: str | None = None,\n        owner_agent: str | None = None,\n        requires_human_input: bool | None = None,\n        run_id: str | None = None,\n        limit: int = 50,\n    ) -> list[dict[str, Any]]:\n        sql = [\"SELECT * FROM tasks WHERE 1=1\"]\n        params: list[Any] = []\n        if status is not None:\n            sql.append(\"AND status = ?\")\n            params.append(status)\n        if owner_agent is not None:\n            sql.append(\"AND owner_agent = ?\")\n            params.append(owner_agent)\n        if requires_human_input is not None:\n            sql.append(\"AND requires_human_input = ?\")\n            params.append(1 if requires_human_input else 0)\n        if run_id is not None:\n            sql.append(\"AND run_id = ?\")\n            params.append(run_id)\n        sql.append(\"ORDER BY priority ASC, created_at ASC LIMIT ?\")\n        params.append(limit)\n        with self.store.connection() as conn:\n            rows = conn.execute(\" \".join(sql), params).fetchall()\n            return [self._row_to_task(row) for row in rows]\n\n    def update_task(\n        self,\n        task_id: str,\n        *,\n        status: str | None = None,\n        owner_agent: str | None = None,\n        blocked_reason: str | None = None,\n        requires_human_input: bool | None = None,\n        metadata: dict[str, Any] | None = None,\n        run_id: str | None = None,\n    ) -> dict[str, Any]:\n        task = self.get_task(task_id)\n        task[\"status\"] = status or task[\"status\"]\n        task[\"owner_agent\"] = owner_agent if owner_agent is not None else task[\"owner_agent\"]\n        task[\"blocked_reason\"] = blocked_reason\n        task[\"run_id\"] = run_id if run_id is not None else task.get(\"run_id\")\n        if requires_human_input is not None:\n            task[\"requires_human_input\"] = requires_human_input\n        if metadata is not None:\n            task[\"metadata\"] = metadata\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                UPDATE tasks\n                SET status = ?, owner_agent = ?, blocked_reason = ?, requires_human_input = ?, run_id = ?, updated_at = ?, metadata_json = ?\n                WHERE id = ?\n                \"\"\",\n                (\n                    task[\"status\"],\n                    task[\"owner_agent\"],\n                    task[\"blocked_reason\"],\n                    1 if task[\"requires_human_input\"] else 0,\n                    task.get(\"run_id\"),\n                    utcnow(),\n                    self.store.dumps(task[\"metadata\"]),\n                    task_id,\n                ),\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_task(task_id)\n\n    def start_task_run(\n        self,\n        task_id: str,\n        agent_id: str,\n        *,\n        input_payload: dict[str, Any] | None = None,\n        metadata: dict[str, Any] | None = None,\n    ) -> dict[str, Any]:\n        now = utcnow()\n        record = {\n            \"id\": make_id(\"run\"),\n            \"task_id\": task_id,\n            \"agent_id\": agent_id,\n            \"status\": \"running\",\n            \"input_payload_json\": self.store.dumps(input_payload or {}),\n            \"result_summary\": None,\n            \"error_message\": None,\n            \"started_at\": now,\n            \"completed_at\": None,\n            \"created_at\": now,\n            \"updated_at\": now,\n            \"metadata_json\": self.store.dumps(metadata or {}),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT INTO task_runs (\n                    id, task_id, agent_id, status, input_payload_json, result_summary, error_message,\n                    started_at, completed_at, created_at, updated_at, metadata_json\n                ) VALUES (\n                    :id, :task_id, :agent_id, :status, :input_payload_json, :result_summary, :error_message,\n                    :started_at, :completed_at, :created_at, :updated_at, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_task_run(record[\"id\"])\n\n    def get_task_run(self, run_id: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            row = conn.execute(\"SELECT * FROM task_runs WHERE id = ?\", (run_id,)).fetchone()\n            if row is None:\n                raise KeyError(run_id)\n            return self._row_to_task_run(row)\n\n    def finish_task_run(\n        self,\n        run_id: str,\n        *,\n        status: str,\n        result_summary: str | None = None,\n        error_message: str | None = None,\n    ) -> dict[str, Any]:\n        now = utcnow()\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                UPDATE task_runs\n                SET status = ?, result_summary = ?, error_message = ?, updated_at = ?, completed_at = ?\n                WHERE id = ?\n                \"\"\",\n                (status, result_summary, error_message, now, now, run_id),\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_task_run(run_id)\n\n    def list_task_runs(self, *, task_id: str | None = None, status: str | None = None, limit: int = 50) -> list[dict[str, Any]]:\n        sql = [\"SELECT * FROM task_runs WHERE 1=1\"]\n        params: list[Any] = []\n        if task_id is not None:\n            sql.append(\"AND task_id = ?\")\n            params.append(task_id)\n        if status is not None:\n            sql.append(\"AND status = ?\")\n            params.append(status)\n        sql.append(\"ORDER BY created_at DESC LIMIT ?\")\n        params.append(limit)\n        with self.store.connection() as conn:\n            rows = conn.execute(\" \".join(sql), params).fetchall()\n            return [self._row_to_task_run(row) for row in rows]\n\n    def create_artifact(\n        self,\n        *,\n        task_id: str,\n        artifact_type: str,\n        title: str,\n        content: str,\n        fmt: str = \"md\",\n        status: str = \"active\",\n        source_ref: str | None = None,\n        metadata: dict[str, Any] | None = None,\n        artifact_id: str | None = None,\n    ) -> dict[str, Any]:\n        now = utcnow()\n        record = {\n            \"id\": artifact_id or make_id(\"art\"),\n            \"task_id\": task_id,\n            \"artifact_type\": artifact_type,\n            \"title\": title,\n            \"content\": content,\n            \"format\": fmt,\n            \"status\": status,\n            \"source_ref\": source_ref,\n            \"created_at\": now,\n            \"updated_at\": now,\n            \"metadata_json\": self.store.dumps(metadata or {}),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT INTO artifacts (\n                    id, task_id, artifact_type, title, content, format, status, source_ref, created_at, updated_at, metadata_json\n                ) VALUES (\n                    :id, :task_id, :artifact_type, :title, :content, :format, :status, :source_ref, :created_at, :updated_at, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_artifact(record[\"id\"])\n\n    def get_artifact(self, artifact_id: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            row = conn.execute(\"SELECT * FROM artifacts WHERE id = ?\", (artifact_id,)).fetchone()\n            if row is None:\n                raise KeyError(artifact_id)\n            return self._row_to_artifact(row)\n\n    def list_artifacts(self, *, task_id: str | None = None, limit: int = 50) -> list[dict[str, Any]]:\n        sql = [\"SELECT * FROM artifacts WHERE 1=1\"]\n        params: list[Any] = []\n        if task_id is not None:\n            sql.append(\"AND task_id = ?\")\n            params.append(task_id)\n        sql.append(\"ORDER BY created_at DESC LIMIT ?\")\n        params.append(limit)\n        with self.store.connection() as conn:\n            rows = conn.execute(\" \".join(sql), params).fetchall()\n            return [self._row_to_artifact(row) for row in rows]\n\n    def dashboard_snapshot(self, *, owner_agent: str | None = None) -> dict[str, Any]:\n        active_tasks = self.list_tasks(owner_agent=owner_agent, limit=50)\n        active_tasks = [task for task in active_tasks if task[\"status\"] in {\"open\", \"draft\", \"in_progress\"}][:20]\n        blocked_tasks = self.list_tasks(status=\"blocked\", owner_agent=owner_agent, limit=20)\n        recent_runs = self.list_task_runs(limit=20)\n        recent_artifacts = self.list_artifacts(limit=20)\n        return {\n            \"active_tasks\": active_tasks,\n            \"blocked_tasks\": blocked_tasks,\n            \"recent_runs\": recent_runs,\n            \"recent_artifacts\": recent_artifacts,\n        }\n\n    def task_bundle(self, task_id: str) -> dict[str, Any]:\n        task = self.get_task(task_id)\n        with self.store.connection() as conn:\n            child_rows = conn.execute(\n                \"SELECT * FROM tasks WHERE parent_task_id = ? ORDER BY priority ASC, created_at ASC\",\n                (task_id,),\n            ).fetchall()\n        return {\n            \"task\": task,\n            \"children\": [self._row_to_task(row) for row in child_rows],\n            \"runs\": self.list_task_runs(task_id=task_id, limit=100),\n            \"artifacts\": self.list_artifacts(task_id=task_id, limit=100),\n        }\n\n    def audit_v2(self) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            approvals = self._count_if_table_exists(conn, \"approvals\")\n            handoffs = self._count_if_table_exists(conn, \"handoffs\")\n            legacy_memory_rows = conn.execute(\n                \"\"\"\n                SELECT COUNT(*) AS count\n                FROM memories\n                WHERE schema_version != ?\n                   OR json_extract(metadata_json, '$.legacy_kind') IS NOT NULL\n                   OR json_extract(metadata_json, '$.legacy_system') IS NOT NULL\n                \"\"\",\n                (SCHEMA_VERSION,),\n            ).fetchone()[\"count\"]\n            legacy_task_rows = conn.execute(\n                \"\"\"\n                SELECT COUNT(*) AS count\n                FROM tasks\n                WHERE schema_version != ?\n                   OR json_extract(metadata_json, '$.legacy_kind') IS NOT NULL\n                   OR json_extract(metadata_json, '$.legacy_system') IS NOT NULL\n                \"\"\",\n                (SCHEMA_VERSION,),\n            ).fetchone()[\"count\"]\n            memories = conn.execute(\"SELECT COUNT(*) AS count FROM memories\").fetchone()[\"count\"]\n            tasks = conn.execute(\"SELECT COUNT(*) AS count FROM tasks\").fetchone()[\"count\"]\n            maintenance_jobs = self._count_if_table_exists(conn, \"maintenance_jobs\")\n        return {\n            \"schema_version\": SCHEMA_VERSION,\n            \"memories\": memories,\n            \"tasks\": tasks,\n            \"approvals\": approvals,\n            \"handoffs\": handoffs,\n            \"maintenance_jobs\": maintenance_jobs,\n            \"legacy_memory_rows\": legacy_memory_rows,\n            \"legacy_task_rows\": legacy_task_rows,\n        }\n\n    def migrate_v2(self) -> dict[str, Any]:\n        before = self.audit_v2()\n        migrated = {\"memories\": 0, \"tasks\": 0, \"approval_artifacts\": 0, \"handoff_artifacts\": 0}\n        with self.store.connection() as conn:\n            memory_rows = conn.execute(\"SELECT * FROM memories\").fetchall()\n            for row in memory_rows:\n                record = self._migrate_memory_row(dict(row))\n                if record is None:\n                    continue\n                conn.execute(\n                    \"\"\"\n                    UPDATE memories\n                    SET schema_version = ?, type = ?, subtype = ?, origin_agent = ?, run_id = ?, task_id = ?,\n                        url = ?, domain = ?, summary = ?, metadata_json = ?, updated_at = ?\n                    WHERE id = ?\n                    \"\"\",\n                    (\n                        record[\"schema_version\"],\n                        record[\"type\"],\n                        record[\"subtype\"],\n                        record[\"origin_agent\"],\n                        record[\"run_id\"],\n                        record[\"task_id\"],\n                        record[\"url\"],\n                        record[\"domain\"],\n                        record[\"summary\"],\n                        record[\"metadata_json\"],\n                        utcnow(),\n                        record[\"id\"],\n                    ),\n                )\n                migrated[\"memories\"] += 1\n            task_rows = conn.execute(\"SELECT * FROM tasks\").fetchall()\n            for row in task_rows:\n                record = self._migrate_task_row(dict(row))\n                if record is None:\n                    continue\n                conn.execute(\n                    \"\"\"\n                    UPDATE tasks\n                    SET schema_version = ?, run_id = ?, metadata_json = ?, updated_at = ?\n                    WHERE id = ?\n                    \"\"\",\n                    (record[\"schema_version\"], record[\"run_id\"], record[\"metadata_json\"], utcnow(), record[\"id\"]),\n                )\n                migrated[\"tasks\"] += 1\n            if self._table_exists(conn, \"approvals\"):\n                for row in conn.execute(\"SELECT * FROM approvals\").fetchall():\n                    approval = dict(row)\n                    task_row = conn.execute(\"SELECT * FROM tasks WHERE id = ?\", (approval[\"task_id\"],)).fetchone()\n                    if task_row is None:\n                        continue\n                    task = self._row_to_task(task_row)\n                    metadata = dict(task.get(\"metadata\", {}))\n                    metadata[\"approval\"] = {\n                        \"kind\": approval[\"kind\"],\n                        \"risk_level\": approval[\"risk_level\"],\n                        \"payload\": self.store.loads(approval[\"payload_json\"]) or {},\n                        \"status\": approval[\"status\"],\n                        \"requested_at\": approval[\"requested_at\"],\n                        \"resolved_at\": approval[\"resolved_at\"],\n                        \"resolution_note\": approval[\"resolution_note\"],\n                    }\n                    conn.execute(\n                        \"\"\"\n                        UPDATE tasks\n                        SET metadata_json = ?, updated_at = ?\n                        WHERE id = ?\n                        \"\"\",\n                        (self.store.dumps(metadata), utcnow(), approval[\"task_id\"]),\n                    )\n                    conn.execute(\n                        \"\"\"\n                        INSERT INTO artifacts (\n                            id, task_id, artifact_type, title, content, format, status, source_ref, created_at, updated_at, metadata_json\n                        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n                        \"\"\",\n                        (\n                            make_id(\"art\"),\n                            approval[\"task_id\"],\n                            \"approval_resolution\",\n                            f\"Approval {approval['kind']}\",\n                            json.dumps(metadata[\"approval\"], sort_keys=True),\n                            \"json\",\n                            \"archived\" if approval[\"status\"] != \"pending\" else \"active\",\n                            \"migration:v2\",\n                            utcnow(),\n                            utcnow(),\n                            self.store.dumps({}),\n                        ),\n                    )\n                    migrated[\"approval_artifacts\"] += 1\n                conn.execute(\"DROP TABLE approvals\")\n            if self._table_exists(conn, \"handoffs\"):\n                for row in conn.execute(\"SELECT * FROM handoffs\").fetchall():\n                    handoff = dict(row)\n                    payload = self.store.loads(handoff[\"payload_json\"]) or {}\n                    conn.execute(\n                        \"\"\"\n                        INSERT INTO artifacts (\n                            id, task_id, artifact_type, title, content, format, status, source_ref, created_at, updated_at, metadata_json\n                        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n                        \"\"\",\n                        (\n                            make_id(\"art\"),\n                            handoff[\"task_id\"],\n                            \"delegation_note\",\n                            f\"Handoff {handoff['from_agent']} -> {handoff['to_agent']}\",\n                            json.dumps(\n                                {\n                                    \"from_agent\": handoff[\"from_agent\"],\n                                    \"to_agent\": handoff[\"to_agent\"],\n                                    \"reason\": handoff[\"reason\"],\n                                    \"status\": handoff[\"status\"],\n                                    \"payload\": payload,\n                                    \"result_summary\": handoff[\"result_summary\"],\n                                    \"error_message\": handoff[\"error_message\"],\n                                    \"completed_at\": handoff[\"completed_at\"],\n                                },\n                                sort_keys=True,\n                            ),\n                            \"json\",\n                            \"active\",\n                            \"migration:v2\",\n                            utcnow(),\n                            utcnow(),\n                            self.store.dumps({}),\n                        ),\n                    )\n                    migrated[\"handoff_artifacts\"] += 1\n                conn.execute(\"DROP TABLE handoffs\")\n        after = self.audit_v2()\n        return {\"status\": \"ok\", \"before\": before, \"after\": after, \"migrated\": migrated}\n\n    def _capture_source(\n        self, trigger_type: str, source_kind: str, source_ref: str, scope: str, content: str, metadata: dict[str, Any]\n    ) -> dict[str, Any]:\n        now = utcnow()\n        source = {\n            \"id\": make_id(\"src\"),\n            \"source_kind\": source_kind,\n            \"title\": metadata.get(\"title\") or source_ref,\n            \"content\": content,\n            \"source_ref\": source_ref,\n            \"metadata_json\": self.store.dumps(metadata),\n            \"created_at\": now,\n        }\n        event = {\n            \"id\": make_id(\"ing\"),\n            \"trigger_type\": trigger_type,\n            \"source_id\": source[\"id\"],\n            \"requested_scope\": scope,\n            \"job_state\": \"pending\",\n            \"processor_outcome\": None,\n            \"retry_count\": 0,\n            \"error_message\": None,\n            \"created_at\": now,\n            \"updated_at\": now,\n            \"processed_at\": None,\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT INTO sources (id, source_kind, title, content, source_ref, metadata_json, created_at)\n                VALUES (:id, :source_kind, :title, :content, :source_ref, :metadata_json, :created_at)\n                \"\"\",\n                source,\n            )\n            conn.execute(\n                \"\"\"\n                INSERT INTO ingestion_events (\n                    id, trigger_type, source_id, requested_scope, job_state, processor_outcome,\n                    retry_count, error_message, created_at, updated_at, processed_at\n                ) VALUES (\n                    :id, :trigger_type, :source_id, :requested_scope, :job_state, :processor_outcome,\n                    :retry_count, :error_message, :created_at, :updated_at, :processed_at\n                )\n                \"\"\",\n                event,\n            )\n        self._run_opportunistic_maintenance(limit=1)\n        return {\"source\": source, \"event\": event}\n\n    def _run_opportunistic_maintenance(self, *, limit: int) -> None:\n        if self._maintenance_check_running:\n            return\n        due_jobs = self.list_due_maintenance_jobs(limit=1)\n        if not due_jobs:\n            return\n        self._maintenance_check_running = True\n        try:\n            from .engine import MemoryEngine\n\n            MemoryEngine(self).run_due_maintenance(limit=limit)\n        except Exception:\n            return\n        finally:\n            self._maintenance_check_running = False\n\n    def _memory_filter_sql(self, sql: list[str], filters: dict[str, Any]) -> list[Any]:\n        params: list[Any] = []\n        allowed = {\"project_id\", \"repo_id\", \"type\", \"subtype\", \"run_id\", \"task_id\", \"origin_agent\", \"url\", \"domain\"}\n        for key, value in filters.items():\n            if key not in allowed or value is None:\n                continue\n            sql.append(f\"AND {key} = ?\")\n            params.append(value)\n        return params\n\n    def _facet_score(self, memory: dict[str, Any], filters: dict[str, Any]) -> float:\n        tracked = [\"project_id\", \"repo_id\", \"type\", \"subtype\", \"run_id\", \"task_id\", \"origin_agent\", \"url\", \"domain\"]\n        applied = [key for key in tracked if filters.get(key) is not None]\n        if not applied:\n            return 0.0\n        matched = sum(1 for key in applied if memory.get(key) == filters[key])\n        return matched / len(applied)\n\n    def _migrate_memory_row(self, row: dict[str, Any]) -> dict[str, Any] | None:\n        metadata = self.store.loads(row.get(\"metadata_json\")) or {}\n        if row.get(\"schema_version\") == SCHEMA_VERSION and not metadata.get(\"legacy_kind\") and not metadata.get(\"legacy_system\"):\n            return None\n        subtype = self._memory_subtype(metadata)\n        origin_agent = row.get(\"origin_agent\") or metadata.get(\"origin_agent\") or metadata.get(\"legacy_system\") or row.get(\"agent_id\")\n        run_id = row.get(\"run_id\") or metadata.get(\"run_id\") or metadata.get(\"legacy_run_id\") or self._infer_run_id(row, subtype)\n        task_id = row.get(\"task_id\") or metadata.get(\"task_id\")\n        url = row.get(\"url\") or metadata.get(\"url\") or metadata.get(\"source_url\") or self._infer_url(row, subtype)\n        domain = row.get(\"domain\") or metadata.get(\"domain\") or (urlparse(url).netloc if url else None)","truncated":true,"truncatedBy":"bytes","totalLines":1706,"totalBytes":72554,"outputLines":1198,"outputBytes":51125,"lastLinePartial":false,"firstLineExceedsLimit":false,"maxLines":2000,"maxBytes":51200}},"isError":false,"timestamp":1776865417504}}
{"type":"message","id":"fedca100","parentId":"6036e90d","timestamp":"2026-04-22T13:43:37.594Z","message":{"role":"toolResult","toolCallId":"call_6bAQkYOFkhbmXgznJdcO4Rw8|fc_0c0fef4ddac7e1db0169e8d088f3988195aaa164c94aba5855","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nimport json\nimport threading\nfrom pathlib import Path\nfrom urllib.error import HTTPError\nfrom urllib.request import Request, urlopen\n\nfrom http.server import ThreadingHTTPServer\n\nfrom shared_agent_memory import MemoryEngine, MemoryService\nfrom shared_agent_memory.http_api import build_handler\nfrom shared_agent_memory.schema import SCHEMA_VERSION\n\n\ndef make_service(tmp_path: Path) -> MemoryService:\n    return MemoryService(str(tmp_path / \"memory.sqlite3\"))\n\n\ndef _request_json(url: str, *, method: str = \"GET\", payload: dict | None = None) -> tuple[int, dict]:\n    body = None if payload is None else json.dumps(payload).encode(\"utf-8\")\n    request = Request(url, data=body, method=method)\n    if body is not None:\n        request.add_header(\"Content-Type\", \"application/json\")\n    with urlopen(request) as response:  # noqa: S310 - local test server only\n        return response.status, json.loads(response.read().decode(\"utf-8\"))\n\n\ndef _start_api(service: MemoryService) -> tuple[ThreadingHTTPServer, str]:\n    server = ThreadingHTTPServer((\"127.0.0.1\", 0), build_handler(service))\n    thread = threading.Thread(target=server.serve_forever, daemon=True)\n    thread.start()\n    host, port = server.server_address\n    return server, f\"http://{host}:{port}\"\n\n\ndef test_manual_profile_fact_is_immediately_searchable(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    service.upsert_profile_fact(\"Communication\", \"Sebas prefers concise, direct communication.\")\n\n    results = service.search(\"concise communication\", scopes=[\"global\"])\n\n    assert results[\"results\"]\n    assert results[\"results\"][0][\"memory\"][\"type\"] == \"profile\"\n    assert results[\"results\"][0][\"memory\"][\"schema_version\"] == SCHEMA_VERSION\n\n\ndef test_captured_conversation_stays_inbox_until_consolidated(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    engine = MemoryEngine(service)\n\n    service.capture_conversation(\"chat-1\", \"project\", \"We agreed to keep one active machine at a time.\")\n    processed = engine.process_pending()\n\n    memory_id = processed[0][\"memory_id\"]\n    memory = service.get_memory(memory_id)\n    assert memory[\"status\"] == \"inbox\"\n\n    search_without_inbox = service.search(\"active machine\", scopes=[\"project\"])\n    assert search_without_inbox[\"results\"] == []\n\n    promoted = service.consolidate([memory_id])\n    assert promoted[\"promoted\"] == [memory_id]\n\n    search_with_active = service.search(\"active machine\", scopes=[\"project\"])\n    assert search_with_active[\"results\"][0][\"memory\"][\"id\"] == memory_id\n\n\ndef test_context_bundle_combines_global_project_and_task_artifacts(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    service.create_project(\"proj_alpha\", \"Alpha\")\n    service.create_repo(\"repo_alpha\", \"Repo Alpha\", project_id=\"proj_alpha\")\n    service.upsert_profile_fact(\"Preferences\", \"Sebas wants agents to preserve clarity.\")\n    service.create_memory(\n        {\n            \"type\": \"project\",\n            \"scope\": \"project\",\n            \"project_id\": \"proj_alpha\",\n            \"repo_id\": \"repo_alpha\",\n            \"title\": \"Project goal\",\n            \"content\": \"Alpha shares memory across projects.\",\n        }\n    )\n    service.create_memory(\n        {\n            \"type\": \"decision\",\n            \"scope\": \"project\",\n            \"project_id\": \"proj_alpha\",\n            \"repo_id\": \"repo_alpha\",\n            \"title\": \"Storage engine\",\n            \"content\": \"Use SQLite as canonical store.\",\n        }\n    )\n    service.create_memory(\n        {\n            \"type\": \"artifact\",\n            \"subtype\": \"research_artifact\",\n            \"scope\": \"project\",\n            \"project_id\": \"proj_alpha\",\n            \"repo_id\": \"repo_alpha\",\n            \"task_id\": \"task_alpha\",\n            \"title\": \"Task note\",\n            \"content\": \"Relevant artifact.\",\n        }\n    )\n\n    bundle = service.context_for(project=\"proj_alpha\", repo=\"repo_alpha\", task=\"task_alpha\")\n\n    assert bundle[\"profile_facts\"]\n    assert bundle[\"project_memories\"]\n    assert bundle[\"active_decisions\"]\n    assert bundle[\"task_relevant_artifacts\"]\n\n\ndef test_export_import_preserves_operational_records_without_approvals_or_handoffs(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    service.create_project(\"proj_alpha\", \"Alpha\")\n    service.create_repo(\"repo_alpha\", \"Repo Alpha\", project_id=\"proj_alpha\")\n    task = service.create_task(\n        task_id=\"task_alpha\",\n        title=\"Resume V2 task\",\n        intent=\"Need durable task state.\",\n        project_id=\"proj_alpha\",\n        repo_id=\"repo_alpha\",\n        owner_agent=\"personal-agent\",\n        run_id=\"run_alpha\",\n    )\n    service.start_task_run(task[\"id\"], \"personal-agent\", input_payload={\"mode\": \"runner\"})\n    service.create_artifact(\n        task_id=task[\"id\"],\n        artifact_type=\"brief\",\n        title=\"Brief\",\n        content=\"Keep resumable state in shared DB.\",\n        source_ref=\"personal-agent\",\n    )\n\n    exported = service.export()\n\n    second = MemoryService(str(tmp_path / \"second.sqlite3\"))\n    second.import_data(exported)\n    bundle = second.task_bundle(task[\"id\"])\n\n    assert \"approvals\" not in bundle\n    assert \"handoffs\" not in bundle\n    assert bundle[\"task\"][\"run_id\"] == \"run_alpha\"\n    assert bundle[\"runs\"]\n    assert bundle[\"artifacts\"]\n\n\ndef test_list_memories_can_filter_by_v2_facets(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    service.create_memory(\n        {\n            \"id\": \"run_123\",\n            \"type\": \"episode\",\n            \"subtype\": \"research_run\",\n            \"scope\": \"global\",\n            \"title\": \"Research run\",\n            \"content\": \"Goal: inspect\",\n            \"run_id\": \"run-123\",\n            \"origin_agent\": \"personal-agent\",\n        }\n    )\n    service.create_memory(\n        {\n            \"type\": \"artifact\",\n            \"subtype\": \"research_source\",\n            \"scope\": \"global\",\n            \"title\": \"Source\",\n            \"content\": \"source\",\n            \"run_id\": \"run-123\",\n            \"url\": \"https://example.com/spec\",\n            \"domain\": \"example.com\",\n            \"origin_agent\": \"personal-agent\",\n        }\n    )\n\n    matches = service.list_memories(subtype=\"research_source\", run_id=\"run-123\", domain=\"example.com\", origin_agent=\"personal-agent\")\n\n    assert len(matches) == 1\n    assert matches[0][\"url\"] == \"https://example.com/spec\"\n\n\ndef test_search_works_without_embeddings(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    service.create_memory(\n        {\n            \"type\": \"artifact\",\n            \"subtype\": \"research_claim\",\n            \"scope\": \"global\",\n            \"title\": \"Claim\",\n            \"content\": \"SQLite remains optional for embeddings.\",\n            \"embedding\": None,\n        }\n    )\n\n    results = service.search(\"optional embeddings\", scopes=[\"global\"])\n\n    assert results[\"results\"]\n    assert results[\"results\"][0][\"memory\"][\"subtype\"] == \"research_claim\"\n\n\ndef test_http_api_supports_status_ingest_and_search(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    server, base_url = _start_api(service)\n    try:\n        status_code, status_payload = _request_json(f\"{base_url}/api/status\")\n        assert status_code == 200\n        assert status_payload[\"ok\"] is True\n\n        created_code, created_payload = _request_json(\n            f\"{base_url}/api/ingest\",\n            method=\"POST\",\n            payload={\n                \"id\": \"mem_http_1\",\n                \"type\": \"artifact\",\n                \"subtype\": \"research_claim\",\n                \"scope\": \"global\",\n                \"title\": \"HTTP memory\",\n                \"content\": \"Remote API keeps shared memory reachable.\",\n                \"status\": \"active\",\n            },\n        )\n        assert created_code == 201\n        assert created_payload[\"id\"] == \"mem_http_1\"\n\n        search_code, search_payload = _request_json(f\"{base_url}/api/search?q=reachable&scope=global\")\n        assert search_code == 200\n        assert search_payload[\"results\"]\n        assert search_payload[\"results\"][0][\"memory\"][\"id\"] == \"mem_http_1\"\n    finally:\n        server.shutdown()\n        server.server_close()\n\n\ndef test_http_api_supports_tasks_runs_and_bundles(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    server, base_url = _start_api(service)\n    try:\n        task_code, task = _request_json(\n            f\"{base_url}/api/tasks\",\n            method=\"POST\",\n            payload={\n                \"task_id\": \"task_http_1\",\n                \"title\": \"Remote task\",\n                \"intent\": \"Keep task state remote.\",\n                \"owner_agent\": \"personal-agent\",\n            },\n        )\n        assert task_code == 201\n        assert task[\"id\"] == \"task_http_1\"\n\n        run_code, run = _request_json(\n            f\"{base_url}/api/task-runs\",\n            method=\"POST\",\n            payload={\"task_id\": task[\"id\"], \"agent_id\": \"personal-agent\", \"input_payload\": {\"mode\": \"remote\"}},\n        )\n        assert run_code == 201\n        assert run[\"task_id\"] == task[\"id\"]\n\n        patch_code, patched = _request_json(\n            f\"{base_url}/api/tasks/{task['id']}\",\n            method=\"PATCH\",\n            payload={\"status\": \"in_progress\", \"run_id\": run[\"id\"]},\n        )\n        assert patch_code == 200\n        assert patched[\"status\"] == \"in_progress\"\n        assert patched[\"run_id\"] == run[\"id\"]\n\n        bundle_code, bundle = _request_json(f\"{base_url}/api/tasks/{task['id']}/bundle\")\n        assert bundle_code == 200\n        assert bundle[\"task\"][\"id\"] == task[\"id\"]\n        assert bundle[\"runs\"][0][\"id\"] == run[\"id\"]\n    finally:\n        server.shutdown()\n        server.server_close()\n\n\ndef test_http_api_rejects_missing_search_query(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    server, base_url = _start_api(service)\n    try:\n        try:\n            _request_json(f\"{base_url}/api/search\")\n        except HTTPError as exc:\n            assert exc.code == 400\n        else:\n            raise AssertionError(\"Expected HTTP 400 for missing query\")\n    finally:\n        server.shutdown()\n        server.server_close()\n\n\ndef test_maintenance_jobs_exist_by_default(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n\n    jobs = {job[\"id\"] for job in service.list_maintenance_jobs()}\n\n    assert \"job_consolidation_daily_10\" in jobs\n    assert \"job_consolidation_daily_15\" in jobs\n    assert \"job_consolidation_weekly\" in jobs\n\n\ndef test_maintenance_tick_records_run(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    engine = MemoryEngine(service)\n\n    now = \"2026-03-20T10:00:00+00:00\"\n    service.update_maintenance_job(\"job_consolidation_daily_10\", next_due_at=now)\n    results = engine.run_due_maintenance(job_id=\"job_consolidation_daily_10\")\n\n    assert results\n    job = service.get_maintenance_job(\"job_consolidation_daily_10\")\n    runs = service.list_maintenance_runs(job_id=\"job_consolidation_daily_10\")\n\n    assert job[\"last_status\"] == \"completed\"\n    assert runs\n\n\ndef test_service_startup_runs_due_maintenance(tmp_path: Path) -> None:\n    db_path = tmp_path / \"memory.sqlite3\"\n    service = MemoryService(str(db_path))\n    now = \"2026-03-20T10:00:00+00:00\"\n\n    service.update_maintenance_job(\"job_consolidation_daily_10\", next_due_at=now)\n    restarted = MemoryService(str(db_path))\n    job = restarted.get_maintenance_job(\"job_consolidation_daily_10\")\n    runs = restarted.list_maintenance_runs(job_id=\"job_consolidation_daily_10\")\n\n    assert job[\"last_status\"] == \"completed\"\n    assert runs\n\n\ndef test_write_operations_trigger_due_maintenance_check(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    now = \"2026-03-20T10:00:00+00:00\"\n\n    service.update_maintenance_job(\"job_consolidation_daily_10\", next_due_at=now)\n    service.create_project(\"proj_alpha\", \"Alpha\")\n\n    job = service.get_maintenance_job(\"job_consolidation_daily_10\")\n    runs = service.list_maintenance_runs(job_id=\"job_consolidation_daily_10\")\n\n    assert job[\"last_status\"] == \"completed\"\n    assert runs\n\n\ndef test_conflicts_only_for_selected_types(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    service.create_memory(\n        {\n            \"id\": \"dec_active\",\n            \"type\": \"decision\",\n            \"scope\": \"project\",\n            \"status\": \"active\",\n            \"title\": \"Decision A\",\n            \"content\": \"Feature X is enabled for launch.\",\n        }\n    )\n    service.create_memory(\n        {\n            \"id\": \"dec_inbox\",\n            \"type\": \"decision\",\n            \"scope\": \"project\",\n            \"status\": \"inbox\",\n            \"title\": \"Decision B\",\n            \"content\": \"Feature X is not enabled for launch.\",\n        }\n    )\n    service.consolidate_candidates(\n        config={\n            \"candidate_age_days\": 0,\n            \"max_candidates\": 10,\n            \"conflict_threshold\": 0.1,\n            \"dedupe_high_threshold\": 0.99,\n            \"dedupe_mid_threshold\": 0.99,\n            \"promote_confidence\": 1.0,\n            \"llm_enabled\": False,\n        }\n    )\n    with service.store.connection() as conn:\n        conflicts = conn.execute(\"SELECT COUNT(*) AS count FROM memory_conflicts\").fetchone()[\"count\"]\n\n    assert conflicts == 1\n\n\ndef test_migrate_v2_rewrites_legacy_rows_and_drops_legacy_tables(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    with service.store.connection() as conn:\n        conn.execute(\n            \"\"\"\n            INSERT INTO memories (\n                id, type, scope, status, source_kind, title, content, summary, confidence, freshness,\n                created_at, updated_at, observed_at, source_ref, evidence_ref, embedding_json, metadata_json\n            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n            \"\"\",\n            (\n                \"legacy_run\",\n                \"episode\",\n                \"global\",\n                \"active\",\n                \"run\",\n                \"Research run: Legacy cleanup\",\n                \"Goal: Legacy cleanup\\nScope: Repo\\nAssumptions: None\\nSummary: Pending\",\n                \"Pending\",\n                0.9,\n                0.8,\n                \"2026-03-19T00:00:00+00:00\",\n                \"2026-03-19T00:00:00+00:00\",\n                \"2026-03-19T00:00:00+00:00\",\n                \"personal-agent:run:run-123\",\n                \"personal-agent:run:run-123\",\n                None,\n                service.store.dumps({\"legacy_system\": \"personal-agent\", \"legacy_kind\": \"research_run\", \"legacy_run_id\": \"run-123\"}),\n            ),\n        )\n        conn.execute(\n            \"\"\"\n            INSERT INTO tasks (\n                id, title, intent, kind, status, priority, created_at, updated_at, metadata_json\n            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\n            \"\"\",\n            (\n                \"task_legacy\",\n                \"Legacy task\",\n                \"migrate me\",\n                \"task\",\n                \"open\",\n                3,\n                \"2026-03-19T00:00:00+00:00\",\n                \"2026-03-19T00:00:00+00:00\",\n                service.store.dumps({\"legacy_kind\": \"task\", \"legacy_run_id\": \"run-123\"}),\n            ),\n        )\n        conn.execute(\n            \"\"\"\n            CREATE TABLE approvals (\n                id TEXT PRIMARY KEY,\n                task_id TEXT NOT NULL,\n                kind TEXT NOT NULL,\n                status TEXT NOT NULL,\n                risk_level TEXT NOT NULL,\n                payload_json TEXT NOT NULL,\n                resolution_note TEXT,\n                requested_at TEXT NOT NULL,\n                resolved_at TEXT,\n                created_at TEXT NOT NULL,\n                updated_at TEXT NOT NULL\n            )\n            \"\"\"\n        )\n        conn.execute(\n            \"\"\"\n            INSERT INTO approvals (\n                id, task_id, kind, status, risk_level, payload_json, resolution_note, requested_at, resolved_at, created_at, updated_at\n            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n            \"\"\",\n            (\n                \"apr_1\",\n                \"task_legacy\",\n                \"external_action\",\n                \"pending\",\n                \"high\",\n                service.store.dumps({\"target\": \"vendor\"}),\n                None,\n                \"2026-03-19T00:00:00+00:00\",\n                None,\n                \"2026-03-19T00:00:00+00:00\",\n                \"2026-03-19T00:00:00+00:00\",\n            ),\n        )\n        conn.execute(\n            \"\"\"\n            CREATE TABLE handoffs (\n                id TEXT PRIMARY KEY,\n                task_id TEXT NOT NULL,\n                from_agent TEXT NOT NULL,\n                to_agent TEXT NOT NULL,\n                status TEXT NOT NULL,\n                reason TEXT NOT NULL,\n                payload_json TEXT NOT NULL,\n                result_summary TEXT,\n                error_message TEXT,\n                created_at TEXT NOT NULL,\n                updated_at TEXT NOT NULL,\n                completed_at TEXT,\n                metadata_json TEXT NOT NULL\n            )\n            \"\"\"\n        )\n        conn.execute(\n            \"\"\"\n            INSERT INTO handoffs (\n                id, task_id, from_agent, to_agent, status, reason, payload_json, result_summary, error_message,\n                created_at, updated_at, completed_at, metadata_json\n            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n            \"\"\",\n            (\n                \"hof_1\",\n                \"task_legacy\",\n                \"personal-agent\",\n                \"code\",\n                \"completed\",\n                \"Need repo work\",\n                service.store.dumps({\"idempotency_key\": \"task_legacy\"}),\n                \"done\",\n                None,\n                \"2026-03-19T00:00:00+00:00\",\n                \"2026-03-19T00:00:00+00:00\",\n                \"2026-03-19T01:00:00+00:00\",\n                service.store.dumps({}),\n            ),\n        )\n\n    migrated = service.migrate_v2()\n    audit = service.audit_v2()\n    run = service.get_memory(\"legacy_run\")\n    task = service.get_task(\"task_legacy\")\n    artifacts = service.list_artifacts(task_id=\"task_legacy\", limit=10)\n\n    assert migrated[\"after\"][\"legacy_memory_rows\"] == 0\n    assert migrated[\"after\"][\"legacy_task_rows\"] == 0\n    assert audit[\"approvals\"] == 0\n    assert audit[\"handoffs\"] == 0\n    assert run[\"subtype\"] == \"research_run\"\n    assert run[\"run_id\"] == \"run-123\"\n    assert run[\"metadata\"][\"goal\"] == \"Legacy cleanup\"\n    assert task[\"schema_version\"] == SCHEMA_VERSION\n    assert task[\"run_id\"] == \"run-123\"\n    assert len(artifacts) == 2\n"}],"isError":false,"timestamp":1776865417572}}
{"type":"message","id":"2c369787","parentId":"fedca100","timestamp":"2026-04-22T13:43:45.142Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8d08b314481959c2871cba757980b\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NCMAyW1G_wyZraYLUQYKBkQt8JfLJBKAJmjZNSY69sjPP3ksl2l2TjedOapKljl2VV5DJMjjI4nnpy3lOlhEp_8LeYH6-uwlwjtI8-Zb2L_TLOlbmiYGKsYpTqAmFf_zWD11PCTcAamJo4wsQOC2Kmpf8MOciki7R9wVgPflwH2cmKBbHFIKZrYWOtnaeLH6D4s5B1-Qd-lkbJwBzQLBCEZ56RRBPoh-hk-ASTcM_otmMCdTG02djKbVld6Tfhgrpdbtbn55ZF27g0ZbauR9tMOjSpRF7nNVXendjM-tbRUEDsCUeWGIrYeXzXrUnFxIGOLlHkfLN44aBTY_0aYtOVn6YNeD75nf5_W8jst4BRoJlWYSsvo82tjGHi2x_ZKCP8QpE4ulS9_Px7La4_sfobVBL7Nf_LP2L-jlHrK9-EmpdquqNoQ4hDXb50yazoOucYovWDTd03RvNx1T_WcwicVd1z-jBjosGhvEOHRhS62Gg8fqYsJ2vxznQfYPT--KGOCK9zhRKLMh4S5Qu7OOS8rg_0bnb6FvPWDVnvBJYk0dSeFkDVsWWzg6EPHWW_r7wfalQ-8Zzs0ELU_5nAcfQHT1K85aCta7HMj8tKt9iyiZJiKYih-kw_o1IH168mxbBh6HyrHRbIwPZaZ6reXE7g0799dToW0LYs5yPTUcr9QLUDD4J_exu91r9TZjDGLY850nWvzWwGQI_AePPGg2lLjzJUBhvi0oAm5OfnAXNqVkb3zkyTb36Up1kzgatXBODnecxVCbUPhV1z60oTbfUmOkX4K8QVZwVD1HxcOLO-T2_yrj0iMgnZNTiV8KS3rDu4gRz0vkp24Nen6F-AzKUuD1gG8jRRv4X_pLhcsZb1ivdsGBt2Rdush66SBC6AKBgxL3SJHI4fJKhDpG47EAzI_w_fOqIBXIf0-yu6iNlsayPE_vuKRCfR-bD_07MOFA-9IiJhzr2El0Nje6BGR70Z-TbiDmx2XXlr4lA7KX5sQeZAhzCftjMV8AgGWdIFtTCsYIRu0LbagDMdVvAgZmdDCvly61m0mPNq2AK4wZfySiyg-TkLCyMH4rJMy8yqYhNy0ttEADOPsdR_K1mDhiA_sca3jZe-KhlCISFZkuQIIQE-VITJIInPRAARe4gTR_bm9sV-u8Cv4PS1vJvdr4Wb0bicpan8Mdat5l7Npb4vvKCKzmFiaFcpgYEcY5a335sD8cGRNaeQHdEpy3wniKdViSE24esn-kKsA_Gke3p5djjpKoSALGQPjZrLetsC1_G7mg6HOSKohKNeqfy1OU1sJxO63rHK1SvKZph6tutnpspgQghZ0y8knpI334YFyRCIEeHukPpb5G92CCBxE_LYvvNPMlkGwzdMLz21m7CKm0B0ZeP6xbhGXgy259e3Ko8AWADHyxqv34ost_cUBx7OsUtlJ7LQRnfeK2Qx_mm-xDL8jx6GRiOMqJHwT4hecxpzOozhW-gNW4F6quSLCf2s3ogCDx-rAvm43oDCI0dFJxNMe8pVrpODSPObFtTWIk2lRox7sWAE3mYv1Z43G2vMLIXDSF5aiOoYnzVbndGWadXvnqgoAZG1G3WBjDE5_yQvn1JbgwQyqRfSsX0d8EUoZPNFwPfLzThptSsN7PdOY4sWMjqISsRL-Aglp0eeO7UdLN2lOg02jS37P4cjMm3tnpMYBHlZfI_c4qT1kBJpuq9qR8ZQNUeB9E3unV_Eu60u4ZRiyRvs6kHPkI7HFUzfX0g==\",\"summary\":[]}"},{"type":"toolCall","id":"call_qvzf7mguftjwRyViBDv9FVGF|fc_0c0fef4ddac7e1db0169e8d08cc3608195870aa25965ced453","name":"edit","arguments":{"path":"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py","edits":[{"oldText":"                if parsed.path == \"/api/search\":\n                    search_query = _first(query, \"q\")\n                    if search_query is None:\n                        self.send_error(HTTPStatus.BAD_REQUEST, \"Missing query parameter 'q'\")\n                        return\n                    scopes = [scope.strip() for scope in query.get(\"scope\", []) if scope.strip()]\n                    filters = {\n                        key: value\n                        for key, value in {\n                            \"type\": _first(query, \"type\"),\n                            \"subtype\": _first(query, \"subtype\"),\n                            \"project_id\": _first(query, \"project_id\"),\n                            \"repo_id\": _first(query, \"repo_id\"),\n                            \"source_ref\": _first(query, \"source_ref\"),\n                            \"evidence_ref\": _first(query, \"evidence_ref\"),\n                            \"run_id\": _first(query, \"run_id\"),\n                            \"task_id\": _first(query, \"task_id\"),\n                            \"origin_agent\": _first(query, \"origin_agent\"),\n                            \"url\": _first(query, \"url\"),\n                            \"domain\": _first(query, \"domain\"),\n                        }.items()\n                        if value is not None\n                    }\n                    self._send_json(\n                        service.search(\n                            search_query,\n                            scopes or None,\n                            filters=filters or None,\n                            limit=_first_int(query, \"limit\", 10),\n                            include_inbox=_parse_bool(_first(query, \"include_inbox\")),\n                        )\n                    )\n                    return\n","newText":"                if parsed.path == \"/api/search\":\n                    memory_id = _first(query, \"id\")\n                    search_query = _first(query, \"q\")\n                    if memory_id is not None:\n                        self._send_json({\"retrieval_id\": memory_id, \"results\": [service.get_memory(memory_id)]})\n                        return\n                    if search_query is None:\n                        self.send_error(HTTPStatus.BAD_REQUEST, \"Missing query parameter 'q' or 'id'\")\n                        return\n                    scopes = [scope.strip() for scope in query.get(\"scope\", []) if scope.strip()]\n                    filters = {\n                        key: value\n                        for key, value in {\n                            \"type\": _first(query, \"type\"),\n                            \"subtype\": _first(query, \"subtype\"),\n                            \"project_id\": _first(query, \"project_id\"),\n                            \"repo_id\": _first(query, \"repo_id\"),\n                            \"source_ref\": _first(query, \"source_ref\"),\n                            \"evidence_ref\": _first(query, \"evidence_ref\"),\n                            \"run_id\": _first(query, \"run_id\"),\n                            \"task_id\": _first(query, \"task_id\"),\n                            \"origin_agent\": _first(query, \"origin_agent\"),\n                            \"url\": _first(query, \"url\"),\n                            \"domain\": _first(query, \"domain\"),\n                        }.items()\n                        if value is not None\n                    }\n                    self._send_json(\n                        service.search(\n                            search_query,\n                            scopes or None,\n                            filters=filters or None,\n                            limit=_first_int(query, \"limit\", 10),\n                            include_inbox=_parse_bool(_first(query, \"include_inbox\")),\n                        )\n                    )\n                    return\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":18553,"output":967,"cacheRead":38912,"cacheWrite":0,"totalTokens":58432,"cost":{"input":0.01391475,"output":0.0043515,"cacheRead":0.0029184,"cacheWrite":0,"total":0.02118465}},"stopReason":"toolUse","timestamp":1776865417595,"responseId":"resp_0c0fef4ddac7e1db0169e8d08a9ff88195a6bc0e9031df8957"}}
{"type":"message","id":"5be6f20d","parentId":"2c369787","timestamp":"2026-04-22T13:43:45.161Z","message":{"role":"toolResult","toolCallId":"call_qvzf7mguftjwRyViBDv9FVGF|fc_0c0fef4ddac7e1db0169e8d08cc3608195870aa25965ced453","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in /mnt/rpi/agents-database/src/shared_agent_memory/http_api.py."}],"details":{"diff":"     ...\n  68                         payload[\"audit_error\"] = str(exc)\n  69                     self._send_json(payload)\n  70                     return\n  71                 if parsed.path == \"/api/search\":\n+ 72                     memory_id = _first(query, \"id\")\n  72                     search_query = _first(query, \"q\")\n+ 74                     if memory_id is not None:\n+ 75                         self._send_json({\"retrieval_id\": memory_id, \"results\": [service.get_memory(memory_id)]})\n+ 76                         return\n  73                     if search_query is None:\n- 74                         self.send_error(HTTPStatus.BAD_REQUEST, \"Missing query parameter 'q'\")\n+ 78                         self.send_error(HTTPStatus.BAD_REQUEST, \"Missing query parameter 'q' or 'id'\")\n  75                         return\n  76                     scopes = [scope.strip() for scope in query.get(\"scope\", []) if scope.strip()]\n  77                     filters = {\n  78                         key: value\n     ...","firstChangedLine":72},"isError":false,"timestamp":1776865425160}}
{"type":"message","id":"a9a84709","parentId":"5be6f20d","timestamp":"2026-04-22T13:43:55.230Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_HOjH8SnJBeUYZNCEyO6am3b8|fc_0c0fef4ddac7e1db0169e8d093b3688195858c6ef1a779806e","name":"edit","arguments":{"path":"/mnt/rpi/agents-database/tests/test_memory_service.py","edits":[{"oldText":"def test_http_api_rejects_missing_search_query(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    server, base_url = _start_api(service)\n    try:\n        try:\n            _request_json(f\"{base_url}/api/search\")\n        except HTTPError as exc:\n            assert exc.code == 400\n        else:\n            raise AssertionError(\"Expected HTTP 400 for missing query\")\n    finally:\n        server.shutdown()\n        server.server_close()\n","newText":"def test_http_api_supports_search_by_id(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    service.create_memory(\n        {\n            \"id\": \"mem_lookup_1\",\n            \"type\": \"artifact\",\n            \"scope\": \"global\",\n            \"title\": \"Lookup memory\",\n            \"content\": \"Direct id lookup should work.\",\n        }\n    )\n    server, base_url = _start_api(service)\n    try:\n        status_code, payload = _request_json(f\"{base_url}/api/search?id=mem_lookup_1\")\n        assert status_code == 200\n        assert payload[\"retrieval_id\"] == \"mem_lookup_1\"\n        assert payload[\"results\"][0][\"id\"] == \"mem_lookup_1\"\n    finally:\n        server.shutdown()\n        server.server_close()\n\n\ndef test_http_api_rejects_missing_search_query(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    server, base_url = _start_api(service)\n    try:\n        try:\n            _request_json(f\"{base_url}/api/search\")\n        except HTTPError as exc:\n            assert exc.code == 400\n        else:\n            raise AssertionError(\"Expected HTTP 400 for missing query\")\n    finally:\n        server.shutdown()\n        server.server_close()\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":610,"output":466,"cacheRead":57856,"cacheWrite":0,"totalTokens":58932,"cost":{"input":0.0004575,"output":0.002097,"cacheRead":0.0043392,"cacheWrite":0,"total":0.006893699999999999}},"stopReason":"toolUse","timestamp":1776865425162,"responseId":"resp_0c0fef4ddac7e1db0169e8d09189508195ba58c6734ccc0bbc"}}
{"type":"message","id":"3c4601dc","parentId":"a9a84709","timestamp":"2026-04-22T13:43:55.262Z","message":{"role":"toolResult","toolCallId":"call_HOjH8SnJBeUYZNCEyO6am3b8|fc_0c0fef4ddac7e1db0169e8d093b3688195858c6ef1a779806e","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in /mnt/rpi/agents-database/tests/test_memory_service.py."}],"details":{"diff":"     ...\n 275         server.shutdown()\n 276         server.server_close()\n 277 \n 278 \n+279 def test_http_api_supports_search_by_id(tmp_path: Path) -> None:\n+280     service = make_service(tmp_path)\n+281     service.create_memory(\n+282         {\n+283             \"id\": \"mem_lookup_1\",\n+284             \"type\": \"artifact\",\n+285             \"scope\": \"global\",\n+286             \"title\": \"Lookup memory\",\n+287             \"content\": \"Direct id lookup should work.\",\n+288         }\n+289     )\n+290     server, base_url = _start_api(service)\n+291     try:\n+292         status_code, payload = _request_json(f\"{base_url}/api/search?id=mem_lookup_1\")\n+293         assert status_code == 200\n+294         assert payload[\"retrieval_id\"] == \"mem_lookup_1\"\n+295         assert payload[\"results\"][0][\"id\"] == \"mem_lookup_1\"\n+296     finally:\n+297         server.shutdown()\n+298         server.server_close()\n+299 \n+300 \n 279 def test_http_api_rejects_missing_search_query(tmp_path: Path) -> None:\n 280     service = make_service(tmp_path)\n 281     server, base_url = _start_api(service)\n 282     try:\n     ...","firstChangedLine":279},"isError":false,"timestamp":1776865435261}}
{"type":"message","id":"25667c88","parentId":"3c4601dc","timestamp":"2026-04-22T13:43:59.891Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_iOakgky26yBZ86zYvOBfHhL4|fc_0c0fef4ddac7e1db0169e8d09dc7e481958a28ac017231eb8d","name":"edit","arguments":{"path":"/mnt/rpi/agents-database/docs/http-api.md","edits":[{"oldText":"- `GET /api/search?q=<query>&scope=global&scope=project&limit=10`\n","newText":"- `GET /api/search?q=<query>&scope=global&scope=project&limit=10`\n- `GET /api/search?id=<memory_id>`\n"},{"oldText":"## Gotchas\n\n- This service is the correct remote boundary for shared memory. Avoid routing memory CRUD/search through `personal-agent` unless the runtime actually owns that behavior.\n- The existing `SERVICES-ARCHITECTURE.md` on the server may be stale relative to live routing. Verify real HTTP reachability with `curl`.\n","newText":"## Gotchas\n\n- This service is the correct remote boundary for shared memory. Avoid routing memory CRUD/search through `personal-agent` unless the runtime actually owns that behavior.\n- `/api/search?id=<memory_id>` is a direct lookup shortcut that returns one memory in `results`.\n- The existing `SERVICES-ARCHITECTURE.md` on the server may be stale relative to live routing. Verify real HTTP reachability with `curl`.\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":596,"output":274,"cacheRead":58368,"cacheWrite":0,"totalTokens":59238,"cost":{"input":0.000447,"output":0.001233,"cacheRead":0.0043776,"cacheWrite":0,"total":0.0060576}},"stopReason":"toolUse","timestamp":1776865435263,"responseId":"resp_0c0fef4ddac7e1db0169e8d09c08688195b9f9ab5e3cbe5e99"}}
{"type":"message","id":"63a30337","parentId":"25667c88","timestamp":"2026-04-22T13:43:59.900Z","message":{"role":"toolResult","toolCallId":"call_iOakgky26yBZ86zYvOBfHhL4|fc_0c0fef4ddac7e1db0169e8d09dc7e481958a28ac017231eb8d","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 2 block(s) in /mnt/rpi/agents-database/docs/http-api.md."}],"details":{"diff":"    ...\n  9 ## Endpoints\n 10 \n 11 - `GET /api/status`\n 12 - `GET /api/search?q=<query>&scope=global&scope=project&limit=10`\n+13 - `GET /api/search?id=<memory_id>`\n 13 - `GET /api/context?project=<id>&repo=<id>&task=<id>`\n 14 - `GET /api/memories?...filters...`\n 15 - `GET /api/memories/<id>`\n 16 - `POST /api/ingest`\n    ...\n 44 \n 45 ## Gotchas\n 46 \n 47 - This service is the correct remote boundary for shared memory. Avoid routing memory CRUD/search through `personal-agent` unless the runtime actually owns that behavior.\n+49 - `/api/search?id=<memory_id>` is a direct lookup shortcut that returns one memory in `results`.\n 48 - The existing `SERVICES-ARCHITECTURE.md` on the server may be stale relative to live routing. Verify real HTTP reachability with `curl`.","firstChangedLine":13},"isError":false,"timestamp":1776865439900}}
{"type":"message","id":"592d2c55","parentId":"63a30337","timestamp":"2026-04-22T13:44:01.699Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_WO5Ocihrl2iKcFPH7ZWA37jK|fc_0c0fef4ddac7e1db0169e8d0a146888195931402e2589a2ee1","name":"bash","arguments":{"command":"cd /mnt/rpi/agents-database && pytest -q tests/test_memory_service.py","timeout":120}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":58245,"output":39,"cacheRead":1024,"cacheWrite":0,"totalTokens":59308,"cost":{"input":0.04368375,"output":0.0001755,"cacheRead":0.0000768,"cacheWrite":0,"total":0.043936050000000004}},"stopReason":"toolUse","timestamp":1776865439901,"responseId":"resp_0c0fef4ddac7e1db0169e8d0a045bc8195bfa987b38f03e079"}}
{"type":"message","id":"f4d9e924","parentId":"592d2c55","timestamp":"2026-04-22T13:44:01.715Z","message":{"role":"toolResult","toolCallId":"call_WO5Ocihrl2iKcFPH7ZWA37jK|fc_0c0fef4ddac7e1db0169e8d0a146888195931402e2589a2ee1","toolName":"bash","content":[{"type":"text","text":"/bin/bash: line 1: pytest: command not found\n\n\nCommand exited with code 127"}],"details":{},"isError":true,"timestamp":1776865441715}}
{"type":"message","id":"a1ca6811","parentId":"f4d9e924","timestamp":"2026-04-22T13:44:05.041Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering testing tools**\n\nI'm thinking about whether to use Python's `pytest` for testing. It seems like a common choice for running tests, but I wonder if there are other tools that might be better suited for my current project. There's always a chance I haven't considered something that could be more efficient. I need to weigh the pros and cons of using `pytest` versus other options. Let’s see where this leads!","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8d0a2e6948195b880ddf0ea5ba386\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NCkN16Ah1TsGFUpyaEsfG01RTBag-M1UAx2mOB06G-C_5M68kyPrd3_rt_vmHwSayDzTWxf2UjLVZNkcznOzMmmCM5lxDWHGlUbSxNHRcpETb7QWhxCvoN3cwSQQka9CMtTc5-9PeKqhLGVZS9OqaCa4pwLQuB24YUILG2tBQ8xD9REuSu6TM9Ei5l6rXbINeTFSYKusXvGqPAZa3fY0Rj5B9D2j-3ys-MqxsJgFYf_XHPUQefOOIOU_ds9SQHYblD5zSlk7kw1iQ26pTJ_z9Ls3hs7k68TbGF3PsPU6nX9OR73l6JQ3AVKenCzXB8XeJY0vQL74WjROEAj7DoKvJiir_QP6yyNr36td9u6EGl4zcxjSx-feKCwdLCituH9oZeafQjprIkWJo7EcIkZFceaJSqfZnzclz2DVZ841cvAZJTDB6kOVW6Pa07KWZo-I5FVB2TXE66N-3j3VAdg9NIEUFjjvBC_WdSiJD9hj-O074qQIeZxYjTdeiZ5UcoiAPaUgcId2MQsnCLIqkR1vSCM6CDfyr25gHkyvocUaBPxkGLGwtDcKzIRov29YuVqPDWZXid1RRmXCcZ6BWYHDv-RYLAj44YY9ePq0in6Szc6nj8HyQdHuHrs7gMZBoshkfP57L-NR-SVceG2a2zpvfO4aR26z1qMZU3n4YZmYZMPZBbouEkvD8eGeKZPLRn0Ykl0Qk57LH69SewxV-mugZqdV-fzcqI2ENplqFK-hZXN_p2L9s9xbkrgHZTMWLEQBJORu3-8j8p7fak78m-GAFixfoynp_STFW6qwPM6u3iZf3XpLqzsnslfyM-0LcDC55Yu5c-vZLU3GPcTK5iYPRhrPGwB2Pnjn0H5RxRxUoz3jJBq-ZH13fUclJfiGAak-PlM-U1ST_v5rIujj6JM0qEDhy1hL0JumSfguQ-QLODCxEVziva1_cFMnPpl9Wp-47KkG6xjpHa40QKeq6xsKCVS9iUYkI1zgbUiuYnEv2t7sVk=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering testing tools**\\n\\nI'm thinking about whether to use Python's `pytest` for testing. It seems like a common choice for running tests, but I wonder if there are other tools that might be better suited for my current project. There's always a chance I haven't considered something that could be more efficient. I need to weigh the pros and cons of using `pytest` versus other options. Let’s see where this leads!\"}]}"},{"type":"toolCall","id":"call_mvwE5bC6yAlQ2X47BvPGNkVT|fc_0c0fef4ddac7e1db0169e8d0a4d2908195a9624dda3f69cc36","name":"bash","arguments":{"command":"cd /mnt/rpi/agents-database && python3 -m pytest -q tests/test_memory_service.py","timeout":120}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":458,"output":57,"cacheRead":58880,"cacheWrite":0,"totalTokens":59395,"cost":{"input":0.0003435,"output":0.0002565,"cacheRead":0.004416,"cacheWrite":0,"total":0.005016}},"stopReason":"toolUse","timestamp":1776865441716,"responseId":"resp_0c0fef4ddac7e1db0169e8d0a24fd08195b153d3c290dc96e9"}}
{"type":"message","id":"2c9f1aab","parentId":"a1ca6811","timestamp":"2026-04-22T13:44:05.085Z","message":{"role":"toolResult","toolCallId":"call_mvwE5bC6yAlQ2X47BvPGNkVT|fc_0c0fef4ddac7e1db0169e8d0a4d2908195a9624dda3f69cc36","toolName":"bash","content":[{"type":"text","text":"/usr/bin/python3: No module named pytest\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1776865445084}}
{"type":"message","id":"ecd71dba","parentId":"2c9f1aab","timestamp":"2026-04-22T13:44:09.965Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering potential obstacles**\n\nI might need to use UV—it's just a thought, but I can’t run it just yet. It feels like I should check that out. Inspecting the git diff is also something I might consider doing. Hopefully, this will help clarify what’s going on and resolve any concerns I have! Let’s see if that approach leads to a solution or if I need to rethink things a bit more.","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8d0a78b208195bcea00db4547d9fa\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NCpxvCxB5u3e4rZVpsKMvu7ch2ZAZ5yV8eNPKnfp4N_mD5Uq3zqPYtw9bwbc-PGM38kgKKoSUCo5siNbnEr9TuKwyGfRaT7wMjMGp2sXZ2OoZkoNRrSZu_uCh9y1T7q_f1_Te4TyWOGPjFiP8vdxVDyIr3LYDI4EaefZ-jGQIUHN7YFnDLKWrFHoyS7rNwfa21gnpDMxpklgmFidRaZCR2tWEvl0s9zSddi9InPGUuyRFaanEctwACFZaC4KHcFBl5w4IDHUEn-X7iCeuJr7V3V7v3ftX-OW1FVUWO1d-Y_kn4wXCWct2PCBshOkdV5mVW_8xCeJvKm413gV4J2qnw4kUAaUANlpVC9Bulp4MutyFAOdvp7qtQi-Au4hmJ7wFXpl5pPw6yb5suCz22u8s1A50fx9OuUee67gROO9xpNL8lP0RAayHbFb8POAQwQlbJRClJjSH5uRiabt_juahMuYAsWia-9AiMqllg3G-zajWLRP0AQvDhldyqdiD2QwcOLXkzlCIHcg6H01GCaSdtMt9cjcz55oYBJmeLoy7GgE8_Z6UXw8-K3IXzoUOXTb6fagS85qeLHD0vp7-rlHhp3TIafeujiS4hxy0P8wtG5xV-iXbpejXifLb7qSE2UaKsb5_RpI6sR32h7JCo3m9K2-JnPC2U9eGF9qjaacO42PrB-6TZsxxEtJwHVkxkt2D-V6MXttSI6JdYwpVossRdCiCyM71nmY2EVY5nXeN1yTaamZR0XqPRJ4bWHXf1eJ0Tldh5l5HCFchk1spno3jcmRWa8aFgCM2YBGsS2_2QHStzv1yZkNrgN6i2rW88tIjZXn6ndGzNeqiA0HIQfFmo9TwBqGBX6R1_vc5LOpKxxpk6dawyzn1eoYo-n7C1W2k9eiapkxPot4odG37beczl5QnJ8TJqUrBTT5u0lje8L8WDjeUDmP_AptqrXOlKLyW-sDDHYkKwTDze8GvCGfsNVBANYJLeXD-nCDJiZGm597J6gtEVhlDcfQbDLmy_q4KK-F53yE22Six0VlUfKGZMgFw==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering potential obstacles**\\n\\nI might need to use UV—it's just a thought, but I can’t run it just yet. It feels like I should check that out. Inspecting the git diff is also something I might consider doing. Hopefully, this will help clarify what’s going on and resolve any concerns I have! Let’s see if that approach leads to a solution or if I need to rethink things a bit more.\"}]}"},{"type":"toolCall","id":"call_x9jGOUU9jTMP4WnPJ8tMtZ6c|fc_0c0fef4ddac7e1db0169e8d0a9b080819588f7225729c4c9ba","name":"bash","arguments":{"command":"cd /mnt/rpi/agents-database && git diff -- src/shared_agent_memory/http_api.py tests/test_memory_service.py docs/http-api.md","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":542,"output":73,"cacheRead":58880,"cacheWrite":0,"totalTokens":59495,"cost":{"input":0.0004065,"output":0.0003285,"cacheRead":0.004416,"cacheWrite":0,"total":0.005150999999999999}},"stopReason":"toolUse","timestamp":1776865445086,"responseId":"resp_0c0fef4ddac7e1db0169e8d0a57f74819585cc0be673d33d7b"}}
{"type":"message","id":"3a700e16","parentId":"ecd71dba","timestamp":"2026-04-22T13:44:09.985Z","message":{"role":"toolResult","toolCallId":"call_x9jGOUU9jTMP4WnPJ8tMtZ6c|fc_0c0fef4ddac7e1db0169e8d0a9b080819588f7225729c4c9ba","toolName":"bash","content":[{"type":"text","text":"diff --git a/tests/test_memory_service.py b/tests/test_memory_service.py\nindex 8feda36..07cb52a 100644\n--- a/tests/test_memory_service.py\n+++ b/tests/test_memory_service.py\n@@ -1,8 +1,15 @@\n from __future__ import annotations\n \n+import json\n+import threading\n from pathlib import Path\n+from urllib.error import HTTPError\n+from urllib.request import Request, urlopen\n+\n+from http.server import ThreadingHTTPServer\n \n from shared_agent_memory import MemoryEngine, MemoryService\n+from shared_agent_memory.http_api import build_handler\n from shared_agent_memory.schema import SCHEMA_VERSION\n \n \n@@ -10,6 +17,23 @@ def make_service(tmp_path: Path) -> MemoryService:\n     return MemoryService(str(tmp_path / \"memory.sqlite3\"))\n \n \n+def _request_json(url: str, *, method: str = \"GET\", payload: dict | None = None) -> tuple[int, dict]:\n+    body = None if payload is None else json.dumps(payload).encode(\"utf-8\")\n+    request = Request(url, data=body, method=method)\n+    if body is not None:\n+        request.add_header(\"Content-Type\", \"application/json\")\n+    with urlopen(request) as response:  # noqa: S310 - local test server only\n+        return response.status, json.loads(response.read().decode(\"utf-8\"))\n+\n+\n+def _start_api(service: MemoryService) -> tuple[ThreadingHTTPServer, str]:\n+    server = ThreadingHTTPServer((\"127.0.0.1\", 0), build_handler(service))\n+    thread = threading.Thread(target=server.serve_forever, daemon=True)\n+    thread.start()\n+    host, port = server.server_address\n+    return server, f\"http://{host}:{port}\"\n+\n+\n def test_manual_profile_fact_is_immediately_searchable(tmp_path: Path) -> None:\n     service = make_service(tmp_path)\n     service.upsert_profile_fact(\"Communication\", \"Sebas prefers concise, direct communication.\")\n@@ -101,7 +125,7 @@ def test_export_import_preserves_operational_records_without_approvals_or_handof\n         owner_agent=\"personal-agent\",\n         run_id=\"run_alpha\",\n     )\n-    service.start_task_run(task[\"id\"], \"personal-agent\", input_payload={\"mode\": \"codex\"})\n+    service.start_task_run(task[\"id\"], \"personal-agent\", input_payload={\"mode\": \"runner\"})\n     service.create_artifact(\n         task_id=task[\"id\"],\n         artifact_type=\"brief\",\n@@ -176,6 +200,119 @@ def test_search_works_without_embeddings(tmp_path: Path) -> None:\n     assert results[\"results\"][0][\"memory\"][\"subtype\"] == \"research_claim\"\n \n \n+def test_http_api_supports_status_ingest_and_search(tmp_path: Path) -> None:\n+    service = make_service(tmp_path)\n+    server, base_url = _start_api(service)\n+    try:\n+        status_code, status_payload = _request_json(f\"{base_url}/api/status\")\n+        assert status_code == 200\n+        assert status_payload[\"ok\"] is True\n+\n+        created_code, created_payload = _request_json(\n+            f\"{base_url}/api/ingest\",\n+            method=\"POST\",\n+            payload={\n+                \"id\": \"mem_http_1\",\n+                \"type\": \"artifact\",\n+                \"subtype\": \"research_claim\",\n+                \"scope\": \"global\",\n+                \"title\": \"HTTP memory\",\n+                \"content\": \"Remote API keeps shared memory reachable.\",\n+                \"status\": \"active\",\n+            },\n+        )\n+        assert created_code == 201\n+        assert created_payload[\"id\"] == \"mem_http_1\"\n+\n+        search_code, search_payload = _request_json(f\"{base_url}/api/search?q=reachable&scope=global\")\n+        assert search_code == 200\n+        assert search_payload[\"results\"]\n+        assert search_payload[\"results\"][0][\"memory\"][\"id\"] == \"mem_http_1\"\n+    finally:\n+        server.shutdown()\n+        server.server_close()\n+\n+\n+def test_http_api_supports_tasks_runs_and_bundles(tmp_path: Path) -> None:\n+    service = make_service(tmp_path)\n+    server, base_url = _start_api(service)\n+    try:\n+        task_code, task = _request_json(\n+            f\"{base_url}/api/tasks\",\n+            method=\"POST\",\n+            payload={\n+                \"task_id\": \"task_http_1\",\n+                \"title\": \"Remote task\",\n+                \"intent\": \"Keep task state remote.\",\n+                \"owner_agent\": \"personal-agent\",\n+            },\n+        )\n+        assert task_code == 201\n+        assert task[\"id\"] == \"task_http_1\"\n+\n+        run_code, run = _request_json(\n+            f\"{base_url}/api/task-runs\",\n+            method=\"POST\",\n+            payload={\"task_id\": task[\"id\"], \"agent_id\": \"personal-agent\", \"input_payload\": {\"mode\": \"remote\"}},\n+        )\n+        assert run_code == 201\n+        assert run[\"task_id\"] == task[\"id\"]\n+\n+        patch_code, patched = _request_json(\n+            f\"{base_url}/api/tasks/{task['id']}\",\n+            method=\"PATCH\",\n+            payload={\"status\": \"in_progress\", \"run_id\": run[\"id\"]},\n+        )\n+        assert patch_code == 200\n+        assert patched[\"status\"] == \"in_progress\"\n+        assert patched[\"run_id\"] == run[\"id\"]\n+\n+        bundle_code, bundle = _request_json(f\"{base_url}/api/tasks/{task['id']}/bundle\")\n+        assert bundle_code == 200\n+        assert bundle[\"task\"][\"id\"] == task[\"id\"]\n+        assert bundle[\"runs\"][0][\"id\"] == run[\"id\"]\n+    finally:\n+        server.shutdown()\n+        server.server_close()\n+\n+\n+def test_http_api_supports_search_by_id(tmp_path: Path) -> None:\n+    service = make_service(tmp_path)\n+    service.create_memory(\n+        {\n+            \"id\": \"mem_lookup_1\",\n+            \"type\": \"artifact\",\n+            \"scope\": \"global\",\n+            \"title\": \"Lookup memory\",\n+            \"content\": \"Direct id lookup should work.\",\n+        }\n+    )\n+    server, base_url = _start_api(service)\n+    try:\n+        status_code, payload = _request_json(f\"{base_url}/api/search?id=mem_lookup_1\")\n+        assert status_code == 200\n+        assert payload[\"retrieval_id\"] == \"mem_lookup_1\"\n+        assert payload[\"results\"][0][\"id\"] == \"mem_lookup_1\"\n+    finally:\n+        server.shutdown()\n+        server.server_close()\n+\n+\n+def test_http_api_rejects_missing_search_query(tmp_path: Path) -> None:\n+    service = make_service(tmp_path)\n+    server, base_url = _start_api(service)\n+    try:\n+        try:\n+            _request_json(f\"{base_url}/api/search\")\n+        except HTTPError as exc:\n+            assert exc.code == 400\n+        else:\n+            raise AssertionError(\"Expected HTTP 400 for missing query\")\n+    finally:\n+        server.shutdown()\n+        server.server_close()\n+\n+\n def test_maintenance_jobs_exist_by_default(tmp_path: Path) -> None:\n     service = make_service(tmp_path)\n \n@@ -202,6 +339,34 @@ def test_maintenance_tick_records_run(tmp_path: Path) -> None:\n     assert runs\n \n \n+def test_service_startup_runs_due_maintenance(tmp_path: Path) -> None:\n+    db_path = tmp_path / \"memory.sqlite3\"\n+    service = MemoryService(str(db_path))\n+    now = \"2026-03-20T10:00:00+00:00\"\n+\n+    service.update_maintenance_job(\"job_consolidation_daily_10\", next_due_at=now)\n+    restarted = MemoryService(str(db_path))\n+    job = restarted.get_maintenance_job(\"job_consolidation_daily_10\")\n+    runs = restarted.list_maintenance_runs(job_id=\"job_consolidation_daily_10\")\n+\n+    assert job[\"last_status\"] == \"completed\"\n+    assert runs\n+\n+\n+def test_write_operations_trigger_due_maintenance_check(tmp_path: Path) -> None:\n+    service = make_service(tmp_path)\n+    now = \"2026-03-20T10:00:00+00:00\"\n+\n+    service.update_maintenance_job(\"job_consolidation_daily_10\", next_due_at=now)\n+    service.create_project(\"proj_alpha\", \"Alpha\")\n+\n+    job = service.get_maintenance_job(\"job_consolidation_daily_10\")\n+    runs = service.list_maintenance_runs(job_id=\"job_consolidation_daily_10\")\n+\n+    assert job[\"last_status\"] == \"completed\"\n+    assert runs\n+\n+\n def test_conflicts_only_for_selected_types(tmp_path: Path) -> None:\n     service = make_service(tmp_path)\n     service.create_memory(\n"}],"isError":false,"timestamp":1776865449985}}
{"type":"message","id":"ca6591b6","parentId":"3a700e16","timestamp":"2026-04-22T13:44:16.222Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating changes and documentation**\n\nI see there are significant differences in the file due to unrelated changes. I’m considering reverting those unrelated changes, but the user asked about the agents-database, and there might have been preexisting modifications I shouldn't touch. I should note that tests can’t run because of a missing pytest. Also, I might need to adjust the documentation to mention the actual endpoint or add /api/search?id. Additionally, I should mention that the current branch is dirty. Just finalizing this concisely!","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8d0ab161481958f69ce5990421115\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NCvTn1FBTppnpLpkkX3bbktosZLcqljll9cSiY76qsDTsVnwWtrjkrCBCdsqZsatSlXEMnyWdtgkK5ItXK1vKnSIXZ8QgQoRhqRnvzm97wsboWuBoTXSJS_zindILtZC1dZUySx-V8CKn_WjnrXZeYq22YShHu1Dy_sWj8008hNftV5UbZNZ_AtOed1tIOmlnKPeTNz2h7qLoFHFuCIuyX6InazpPSALM0YaAY4dPlbRXQVKEho97yzJFTo5C9Yep9C8uS_JV8G2PqYt54LproTqQPtqj8FuuKNXxVjod3sN8vexqQpIZvlxaG4F98b9kgjGo-61pi0h2D9JkIN3bi1N0djrrX5bAI6b_EX7VLd7VPKGRwT2vxRNxIwQvsEo7eD_EzzE9_y6ReflluoZy3_czVxA0PDSAfkDePg6ssK8T3SsbwOBxMni_hJjHXduDjKoY-Lbi7KOuexGSRR5-xOoe6aR-gW4cDlc4Bm8viMhVtMcUqhaCi4rUXdXtjhDAMhVEZZPDBPVmbm4Yz--eoBrbJDbhij0QoM8AmZMB_5Z54j5FkNTRnQgH2Ho25KhFG2CABYRgXYJyB7g6aQ0XTqiPueNDJ8PbkIzIZsOaSRV7pYmqOdgrOYmrIKticKtalF5xIC_q4nX5N4R8Bd-p_Ly1L00u0Fd894v0GOPpQLdbGu6zzViKnkpAQUjSFUnnWouiYQzwB4Y5K_CBhdeYv2z6yrYDfH-pFXHIBWUnIR8Zc6UrOId4KV1E8GSGvO1Do51Vl9efQ2Xft7TaDuY9of3BtN8edtQV7jP2aL0YNgheCT99DzW0Pw_3Mo2OZGWQSW5RBDWmwD-hUEs1UOOgwMJtyp8Ng8EE74Hnojl6mAejtJGLgPk_teCcdth8DJwN2buwsGsiwmu1N9_21mIGjz4414TqzT9T9H6IlfFV5dOG5qNeOVLilzRwvS9cu724tOdbtISzGEtKC1MweImlkeVom7nKFonroBWsATmV4U3XsUbHn7yhWckod5Lm878oKnChC2VWpG4IO2P8gXBcSRCJ0FAxVeHRs8ohA0jfXb_UdFes0o1YyjqG_QBz-r1qwe8T0MmbhPy0b0ZwrKjPiENV_q_BjMjDfGMolR8q78uvTeygC0Gb1GNgueS8fKdHMdvE-fAG3Plh_AM9HCvZl35N5_ogqudU9zEHSYQOn6Yyy2jOABkRKyAuIBTChWOswy9u7fucCVoSjAyLcGa0L74inNBx3vXf9slSTLGvjAVtSGncfmTwnELfdbJOhrDeBGx-mJtDMx8l6wduR4k5p46a89XAW5Vgow8doYfLB_mBctnM6hWlgbzw_vLhbCrmuxx6xycvK7ZmJRoXEOQl0mQJmCop9L2gcegcVLsJosDEQk5l3OLT9gP9lc9nKlJOY1T7Iou-gJub8G7BUZXoVPY5ej0ufXYyo_SLgjnancTkdfPII3Bph_-hdAQT1sJPD2D57yz2WDmXRIujRCs07t4xEzJsAyd7wwpRVLE8uKLdo=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Evaluating changes and documentation**\\n\\nI see there are significant differences in the file due to unrelated changes. I’m considering reverting those unrelated changes, but the user asked about the agents-database, and there might have been preexisting modifications I shouldn't touch. I should note that tests can’t run because of a missing pytest. Also, I might need to adjust the documentation to mention the actual endpoint or add /api/search?id. Additionally, I should mention that the current branch is dirty. Just finalizing this concisely!\"}]}"},{"type":"text","text":"Hecho en `agents-database`.\n\nCambio:\n- `GET /api/search?id=<memory_id>` ahora hace lookup directo por id\n- si viene `id`, no exige `q`\n- si faltan ambos, sigue devolviendo 400\n\nDocs actualizadas:\n- `docs/http-api.md`\n\nTest agregado:\n- `test_http_api_supports_search_by_id`\n\nNo pude correr tests acá:\n- falta `pytest` en el entorno\n\nNota:\n- en `ballbox` ya había cambios sucios previos; no los toqué.","textSignature":"{\"v\":1,\"id\":\"msg_0c0fef4ddac7e1db0169e8d0afc6a4819591b1988afaec9561\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":2602,"output":202,"cacheRead":58880,"cacheWrite":0,"totalTokens":61684,"cost":{"input":0.0019515000000000001,"output":0.000909,"cacheRead":0.004416,"cacheWrite":0,"total":0.0072765}},"stopReason":"stop","timestamp":1776865449987,"responseId":"resp_0c0fef4ddac7e1db0169e8d0aa80f48195b3f4d2efa60e8de7"}}
{"type":"message","id":"2c1c9d1c","parentId":"ca6591b6","timestamp":"2026-04-22T14:01:40.565Z","message":{"role":"user","content":[{"type":"text","text":"que hay? veamos si hay que hacer commit"}],"timestamp":1776866500563}}
{"type":"message","id":"cfd6d324","parentId":"2c1c9d1c","timestamp":"2026-04-22T14:01:42.851Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8d4c60d6081958963ec38cfb9beca\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NTGBTgFTqhZNfumxj3_3coLOzmmeoVmUAP_Bqkv0JA2nfUFDhG6-9t31U-4M9QBj3MmmmpjpF0oWpJ9xD9Q_XW1hu9I5kotQ9rXb70yIws6DnzygKvi151C1XCg8TugRh7hmHPNdZZK62WfoVxUe5N9tiMryOCcwCEgK_y8dxFHV5ePFbTEe2D3aRWur2PhrATcMbwJZcL1KHs5Gowbbd16d33JgB7ASCd4JoEW9zXWpSi6bN437j6xozEk3oY90tsEuT9AUDnEqszzFVu34oBpFhKJiEADBA8g04JjQ0-oGZf4MBEfbeX8RT44JOIOtVz3Pv4gMTgHoNBPGzjTv7k33YaL0WDDfilx3mldFGN3iJZn8j16EAVr_pEWM2MMrSZBlc9leXkPq55vOU1F16ouVpUOfUmLiHGYboWFTlTxAvNuawY85-FxGazEvvLwzpvn7SzJOy0JjjMd6IL4vUpfwuhVNrhc_an2_c4qgFXTezRDAxrj2e16dG04FUBhBF4FCJbdkXX_Du8PdGF2slZSHhdHVsuj6Y5DsDrb9TtdHqNoeCHSBfrCCggapPmeaQN1gK5_R8HYmgwtqJ7w7sSwiCWUWZ8Ga3XQ1PhD2eT_RJKgoqtztI24T6TIwwDm3Lzu9X55NGp1aRo6CRAXZjFa24f0iVLhrrk1Z09RQtXWk-fg-Jh5gTfuaysA51K80g9oHfZmekRSH_b5egMOVRvyLFgsP5Lc5JW3KXXkWCk3tRZ8fL_BMnW-naHB6M9doN2k5mqd9JPkkjc4d_eWeMzC3pNS-kAJbTqGBpWjmofyNdZNfEgj-jwtZvkQrhCHIplLZi63VsRy9fG5iIJ8sWHI9Rbzq5WC3mF4TYNUDnABWQ30aYC9Vbq8kV3xLlv-Dx4x3Eb8R1KXDB6vNAd1YaI5b2iWoIV-lEj2V_n1894lT85krLSssXFkiUqGjjFHaZkYEeUVLU9kY7LoqlKXpR7z9TtzYyZbMT3PmJpw2YEePzDYCjjtFdKNWnfwm-K_kZK4KbaWpger_WZnsDD-cYJgh-Ev8XEgKd0d_WnfJyTYGMU=\",\"summary\":[]}"},{"type":"toolCall","id":"call_GG9701tlh3wY0gwBCLtmjQcQ|fc_0c0fef4ddac7e1db0169e8d4c6a1248195af6b3d727fe0ed43","name":"bash","arguments":{"command":"cd /mnt/rpi/agents-database && git status --short && echo '---' && git diff --stat","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":23008,"output":71,"cacheRead":38400,"cacheWrite":0,"totalTokens":61479,"cost":{"input":0.017256,"output":0.0003195,"cacheRead":0.0028799999999999997,"cacheWrite":0,"total":0.0204555}},"stopReason":"toolUse","timestamp":1776866500565,"responseId":"resp_0c0fef4ddac7e1db0169e8d4c56934819581dda745c86b1cd2"}}
{"type":"message","id":"7f772fb2","parentId":"cfd6d324","timestamp":"2026-04-22T14:01:48.405Z","message":{"role":"toolResult","toolCallId":"call_GG9701tlh3wY0gwBCLtmjQcQ|fc_0c0fef4ddac7e1db0169e8d4c6a1248195af6b3d727fe0ed43","toolName":"bash","content":[{"type":"text","text":" M .gitignore\n M data/shared-agent-memory.sqlite3\n M docs/memory-consolidation-rfc.md\n M docs/system-v1.md\n M scripts/run-maintenance-daemon.sh\n M src/shared_agent_memory/cli.py\n M src/shared_agent_memory/service.py\n M tests/test_memory_service.py\n?? data/shared-agent-memory.pre-repair-20260419-185338.sqlite3\n?? data/shared-agent-memory.rebuild-20260419.sqlite3\n?? data/shared-agent-memory.recovered-20260419-184949.sqlite3\n?? data/shared-agent-memory.recovery-dump.sql\n?? data/shared-agent-memory.recovery-work.sqlite3\n?? data/shared-agent-memory.sqlite3.bak-20260419-184938\n?? docs/http-api.md\n?? scripts/run-http-api.sh\n?? src/shared_agent_memory/http_api.py\n?? systemd/\n---\n .gitignore                         |   3 +\n data/shared-agent-memory.sqlite3   | Bin 5517312 -> 108167168 bytes\n docs/memory-consolidation-rfc.md   |  36 +++++---\n docs/system-v1.md                  |  36 +++++++-\n scripts/run-maintenance-daemon.sh  |   9 +-\n src/shared_agent_memory/cli.py     |   7 ++\n src/shared_agent_memory/service.py |  28 +++++++\n tests/test_memory_service.py       | 167 ++++++++++++++++++++++++++++++++++++-\n 8 files changed, 271 insertions(+), 15 deletions(-)\n"}],"isError":false,"timestamp":1776866508405}}
{"type":"message","id":"76766438","parentId":"7f772fb2","timestamp":"2026-04-22T14:02:00.999Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8d4d3e22881958c61241bf6befad5\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NTW342QTbjE59nTd4_Okvj_0ISGIiQvOnjIRiElV-5CMc8JJCaaRIAdmUt8KWSg6ax-WlP8kvWfEMoQOGXsozCSGId-zS_HIJVU7ijGdnzNA3vwZWjiOwF-5edXfm829AaDeyZvD9w2tjxDzKBYK_nvqRxpa-NdLcfKKDm3Ud6Zs8fSyroHJAccjEH3VaR_RH3zSe_g1U1kGMLv5Ad3Fh90830kP6QAbUM60HFjOt-WGVdRGb2n_HC5Ddg_qhvX3IBoQMbEU0QORiwLCjv3Wo8mv0eE95zvTs6ct3bgm_E4nACDftHJVfdM5b2P7FSoL8X_SFuCtZkidrZ9ZM1U5iG0zr5CAtVjAmP0v7n3gU2myXUZmQDC8vjAlVItVlfxsPUiISqxKkZPZCJa5nnxFmGUTiXMuHLlc8UTWpEorhBOBnv1SvN2jkNaqnWD8vf91P5YSSyTncfTgwaUC2LkHgClkczLlk8rYE2mO-gAnGtkggGDA5ZgkFvzAVpOj6DMyfONgT-1Rucskkg1XfDLGtq1YP2272NJvIsRcc5JkQr9xn-AA_PLv4twhqgxRFsMjLKufHdY4JsIBW7gch6wQ9Bh8k2NOAtwn8CACbn-b6rVLpcf4cmCCETgjeoDjlIWk_ZssEKL3gL1rfBfu1GnHPLaw2Ll4fV1KxrwZ8RcK9E7lTImTXOkFV-_izAkKaX-D-j7u2snpMoWA2h8b1E6ot8clzIF3jNLDDMus5X7-GJwPv3ZzeyNqK2MtU4N72TUc5GSi3eFuHUTlombFael_16qzp0lULzI2FR__cVtCh2P8Yb6gt9tDR0qr0GKcHjxfb3GP0rS3pKvq9aGcDs7D9idMfqy-Lsmhdjj18kM1ZEkWLbrInuj4qA3IFsShoAOWar3VT58h1Wsie557SzTW1QxprgP86qrkdotleYqElypFAD8z4VayPIdc9xeYaVq-nAds_vEJDWeJQb3KOVCuSOmyYYYbeYY2KBql4HPYg3Fb0qYGvJVdBPNAJSwpCpzDLAy\",\"summary\":[]}"},{"type":"text","text":"Hay bastante sucio ya en `agents-database`.\n\nLo tuyo recién agregado:\n- `docs/http-api.md`\n- `src/shared_agent_memory/http_api.py`\n- cambios en `tests/test_memory_service.py`\n\nPero antes ya había cambios grandes sin commitear:\n- `.gitignore`\n- `data/shared-agent-memory.sqlite3`\n- `docs/memory-consolidation-rfc.md`\n- `docs/system-v1.md`\n- `scripts/run-maintenance-daemon.sh`\n- `src/shared_agent_memory/cli.py`\n- `src/shared_agent_memory/service.py`\n- `systemd/`\n- varios `.sqlite3` de recovery\n\nConclusión:\n- sí, hay para commitear\n- pero no conviene mezclar este fix chico con el lote grande de reparación/mantenimiento sin mirar\n\nSiguiente paso recomendado:\n1. separar commit del fix de `api/search?id=...`\n2. dejar el resto en otro commit o revisarlo aparte\n\nSi querés, te preparo el diff exacto para un commit chico limpio.","textSignature":"{\"v\":1,\"id\":\"msg_0c0fef4ddac7e1db0169e8d4d6f7d48195835e413ce50202f1\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":61800,"output":237,"cacheRead":0,"cacheWrite":0,"totalTokens":62037,"cost":{"input":0.04635,"output":0.0010665,"cacheRead":0,"cacheWrite":0,"total":0.0474165}},"stopReason":"stop","timestamp":1776866508407,"responseId":"resp_0c0fef4ddac7e1db0169e8d4cd0d208195afb36916e47114a5"}}
{"type":"message","id":"af71b21e","parentId":"76766438","timestamp":"2026-04-22T14:31:13.137Z","message":{"role":"user","content":[{"type":"text","text":"creo que alguien lo commiteo, fijate"}],"timestamp":1776868273135}}
{"type":"message","id":"ef439a39","parentId":"af71b21e","timestamp":"2026-04-22T14:31:17.149Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_5I89H5uh7PilmkgWq9Pj6Ik3|fc_0c0fef4ddac7e1db0169e8dbb42d088195842ca33e4fdf8fc4","name":"bash","arguments":{"command":"cd /mnt/rpi/agents-database && git log --oneline --decorate -n 8 && echo '---REMOTE---' && git log --oneline --decorate origin/main -n 8 && echo '---BRANCHES---' && git branch -avv","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":62009,"output":78,"cacheRead":0,"cacheWrite":0,"totalTokens":62087,"cost":{"input":0.04650675,"output":0.000351,"cacheRead":0,"cacheWrite":0,"total":0.04685775}},"stopReason":"toolUse","timestamp":1776868273137,"responseId":"resp_0c0fef4ddac7e1db0169e8dbb21e608195a0e6538acb4abb18"}}
{"type":"message","id":"ae711d97","parentId":"ef439a39","timestamp":"2026-04-22T14:31:17.166Z","message":{"role":"toolResult","toolCallId":"call_5I89H5uh7PilmkgWq9Pj6Ik3|fc_0c0fef4ddac7e1db0169e8dbb42d088195842ca33e4fdf8fc4","toolName":"bash","content":[{"type":"text","text":"6a12638 (HEAD -> main) feat: add HTTP API and maintenance daemon support\nd9fad54 (origin/main, origin/HEAD) docs: add launchd template and daemon runner\n3894ec8 feat: add maintenance daemon and config CLI\n70cfc5a test: cover maintenance scheduler and conflict gating\na600b1c feat: add maintenance scheduler and consolidation pipeline\n8144072 Cut shared memory v2 contract\nba42677 Feat: add shared memory operational task model (#1)\nc7ee8c4 Update shared memory with personal profile details\n---REMOTE---\nd9fad54 (origin/main, origin/HEAD) docs: add launchd template and daemon runner\n3894ec8 feat: add maintenance daemon and config CLI\n70cfc5a test: cover maintenance scheduler and conflict gating\na600b1c feat: add maintenance scheduler and consolidation pipeline\n8144072 Cut shared memory v2 contract\nba42677 Feat: add shared memory operational task model (#1)\nc7ee8c4 Update shared memory with personal profile details\n5daa3c1 Update shared memory database\n---BRANCHES---\n* main                                     6a12638 [origin/main: ahead 1] feat: add HTTP API and maintenance daemon support\n  remotes/origin/HEAD                      -> origin/main\n  remotes/origin/feat/shared-memory-v1-ops d79a59e Cut shared memory v2 contract\n  remotes/origin/main                      d9fad54 docs: add launchd template and daemon runner\n"}],"isError":false,"timestamp":1776868277166}}
{"type":"message","id":"4ee022f1","parentId":"ae711d97","timestamp":"2026-04-22T14:31:25.523Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting commit details**\n\nI need to respond directly regarding the local commit. It seems like there's a chance that our changes might be included in the latest commit. I should check the diff of the last commit to see if these changes were already made by someone else. It’s important to ensure I'm on the right track before moving forward! Let's dive into that diff and confirm.","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8dbba9dd0819587c7e4b8d92e4537\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6Nu9WzVPN_PhklQNmDAJFYntf7Trlfves98Rx9tQXszRKGKK13IslO4MulgSiqxWlQ2GCdbrrwZZB82scyMX6lV73xaU4k2MJAiSf48l-0R3djRrlvEMfUP9dloHIbGtF9PuYgjrL_JHkHidrMWROr8Sw7Nepl22Ioh4nb_dMUcIgkMXTKd5AwfRblbcwV-JZCNbBI2RgOl1gDpVeZ-RtGqFtV6-ZC7WuR1L9FaQSLBVPgD1Fcp7P6bcYjVmz8AvmZq9hZGkMBumBRQtkEyaph9F5DPqImUWsMH1rIP59SFl4KqAhHMw-XZg3FwuEDvaNRj8mPsVx_QsEG4_oTF_aUj5mPAVzSQ8bB-Fgmu0XpNKiyTl47R8QgSEoqvTi5EJtahtU1IIWvqCqbksTbyteb_E1GsQSQDIpilsTpqzTTekHYt-8VqhIcy1sxcP-ogUtIfBOmcHoRajDm2WVnY59qmFSHNwyHSsCuaBRS2xqgT0uiKytznw14k09CdQHnZQkCl6QtuVaA6zfD36-mL1gjlRmnKVBdwmFhME518KZzwzQNBBHWq-YMyAZsIgJZHjYZiVidLIWpQVklqS3dfaA6FMC1dBdBSMmSV-YS02hxpN-7d2dD0ABaXLbxtNNYnXHSN-V93VzqXsNX1XVhPgMQRth1TXG-80x_2OIaup_w2djUbOQrvq-Wqyc42TqRIIkUVvkFmXBiWm14U93ZE8BcmPFG7WhGqHG68fV-3aCz24GT10fTadp1WJKKk9cs_XRW7WYZlZEZISwgT6CwLgknRH_UgcAYQiFPUpT9xALE6c_fCwLoJj9kr5hvwN3udb-3jxQ6um6mpAdHBo5-2XnHtRMMLkjF0Fb8sb26cMPBmuXdNq1CLLHR8vBuJ2C6CzzcWDOzj8X0cTZjmK06XhV08YjoKmcFmgywTRuvTFEc1TLtkQNUhrBEE88d4jqtS3f1TKwyA-4hPRxVdoSGqKCvscLBHURcVXvqXtGXdYsRYzVaegakiTzwXD-A4T89EKRYLgRX2IUCszA_9wZafrIoHIl2zhKC-_ySEmRQjJr8BRqvXN8f-RQLE3rIFn6syYWoCRpNUWZ_57zd_GMKHli2wEeqJiYOP2IFoonn_uvQsrhTgjEVVbQgwFcFulM17UbOBK\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting commit details**\\n\\nI need to respond directly regarding the local commit. It seems like there's a chance that our changes might be included in the latest commit. I should check the diff of the last commit to see if these changes were already made by someone else. It’s important to ensure I'm on the right track before moving forward! Let's dive into that diff and confirm.\"}]}"},{"type":"toolCall","id":"call_YbMZ29BwZLZFzEdmNNYy467M|fc_0c0fef4ddac7e1db0169e8dbbd0f848195a7cdf1b098c399ec","name":"bash","arguments":{"command":"cd /mnt/rpi/agents-database && git show --stat --summary --oneline --decorate HEAD && echo '---FILES---' && git show --name-only --format=fuller HEAD | sed -n '1,80p'","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":471,"output":105,"cacheRead":61952,"cacheWrite":0,"totalTokens":62528,"cost":{"input":0.00035325,"output":0.0004725,"cacheRead":0.0046464,"cacheWrite":0,"total":0.00547215}},"stopReason":"toolUse","timestamp":1776868277167,"responseId":"resp_0c0fef4ddac7e1db0169e8dbb874448195beccf4de97ecc798"}}
{"type":"message","id":"2c2642dd","parentId":"4ee022f1","timestamp":"2026-04-22T14:31:25.572Z","message":{"role":"toolResult","toolCallId":"call_YbMZ29BwZLZFzEdmNNYy467M|fc_0c0fef4ddac7e1db0169e8dbbd0f848195a7cdf1b098c399ec","toolName":"bash","content":[{"type":"text","text":"6a12638 (HEAD -> main) feat: add HTTP API and maintenance daemon support\n .gitignore                                       |   3 +\n data/shared-agent-memory.sqlite3                 | Bin 5517312 -> 108167168 bytes\n docs/http-api.md                                 |  50 +++\n docs/memory-consolidation-rfc.md                 |  36 ++-\n docs/system-v1.md                                |  36 ++-\n scripts/run-http-api.sh                          |  16 +\n scripts/run-maintenance-daemon.sh                |   9 +-\n src/shared_agent_memory/cli.py                   |   7 +\n src/shared_agent_memory/http_api.py              | 367 +++++++++++++++++++++++\n src/shared_agent_memory/service.py               |  28 ++\n systemd/user/agents-database-http.service        |  17 ++\n systemd/user/agents-database-maintenance.service |  16 +\n tests/test_memory_service.py                     | 167 ++++++++++-\n 13 files changed, 737 insertions(+), 15 deletions(-)\n create mode 100644 docs/http-api.md\n create mode 100755 scripts/run-http-api.sh\n mode change 100644 => 100755 scripts/run-maintenance-daemon.sh\n create mode 100644 src/shared_agent_memory/http_api.py\n create mode 100644 systemd/user/agents-database-http.service\n create mode 100644 systemd/user/agents-database-maintenance.service\n---FILES---\ncommit 6a12638ea3c9147cdf553bca8743fe650e8cdfcf\nAuthor:     Sebastian Favaron <sebastianfavaron@gmail.com>\nAuthorDate: Wed Apr 22 11:24:52 2026 -0300\nCommit:     Sebastian Favaron <sebastianfavaron@gmail.com>\nCommitDate: Wed Apr 22 11:24:52 2026 -0300\n\n    feat: add HTTP API and maintenance daemon support\n\n.gitignore\ndata/shared-agent-memory.sqlite3\ndocs/http-api.md\ndocs/memory-consolidation-rfc.md\ndocs/system-v1.md\nscripts/run-http-api.sh\nscripts/run-maintenance-daemon.sh\nsrc/shared_agent_memory/cli.py\nsrc/shared_agent_memory/http_api.py\nsrc/shared_agent_memory/service.py\nsystemd/user/agents-database-http.service\nsystemd/user/agents-database-maintenance.service\ntests/test_memory_service.py\n"}],"isError":false,"timestamp":1776868285572}}
{"type":"message","id":"468e6063","parentId":"2c2642dd","timestamp":"2026-04-22T14:31:30.437Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Checking commitment status**\n\nI need to explain whether I'm committed to the current task. I'm wondering if the change has actually been committed, so I should check the status now. Maybe I need to ensure everything is clean before I finalize anything. It’s crucial to have all details in order before moving ahead, so I’ll focus on confirming that everything is as needed. Let's make sure everything is set before wrapping things up!","thinkingSignature":"{\"id\":\"rs_0c0fef4ddac7e1db0169e8dbbf62d4819593d7d29f912a30cd\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6NvBgeLxcsECVbpuNkLIHDPGvpM5titjUA1YZgI4Fb5ThWyDW68WLTAkbZal8GtZMLeMYtp04dEJG8MtAXG2yMzvUh8mRVZRqemMaHeu9nnCmBxxWck8pq9D1jGK3JauR87Rq6IGhUNBRRMojfo-ryGmIJUgTxCSjUiWMlzhHbZhKDONZDpR3xAVBpkjj-Eo7_pxQ4WTjKrpiZXzj57IZBmIoz4YMAdi3Tq8wo_JjMob9jXLZ4C26HNdOV3IM7prJDuAZnuuTy7D3qvQ_6XWicS5zUJJn0ArfydZ0l8p040HDKqg_pXKB0teuyIrnin57NI8hWIKgok86H-jCOSftKI9LD8g_znYJukGmGDsWGs-U1YtozHtGd2WfkEhHAPTh_p3xzQDVrkqm5dKHLlhjX0HJ8bwBeQbgDdFWDfNW-kgkGjisYzohRj3QhHKYn_iveaJIL3pUJrYW6DdN6opGDD9UXrVzxjWeBpRr40lCFM9BZ-3nbapomcX20GinyN_0TlGToSBjZX-c87EP4hDe8M4Ky5G3bmDEcCsrmy7KbqUuPiSmYh7Sz5G5g0CsYnCMDNKQ6SXbS9q059SvXqx3D5LIB3ko-JO3RDSJjU21TBnPUWAu4vzbmBPpGTqB1cCMd1wdTozgbjbNTQ5WgYLe-EgwhvdvFXi7kYpefQeit5nJAExmMHv9amjqNTjX7KA0Rpzw_RlMm8wp5s7CZkLKqCp-Q-hwcXn39d-saORQtBRlPMWC3htaenGiIegDtWmNaTJDx9_SGyQfkhnJMPLXrVxFcdyv0uBH0Kl4VRq0Z3NMRp0sGtErFV-gbMBlp8SPS2RxX9LIkyp9QFSvthFFIsClHPuB6q6aS1twYu95tJgrzVMoJq822cByPTdaji9lSdhwkvYarQBOaklKIvGpcLDgBbGaBBkM3blODK6AuPQ4UenwCoflRW1x818D1xIWcet693DXfuHxkBIqOr3j3GqtCX3wJ_NOhmfv0wN8WN37g5tYd9AvyDHeLHCHOL9RgwxxBRJF3q0bx6sA9CtTRWu5NQo2nyN-jfsGH6TvaJwRnI0Tr_nNTql9B2tjzLU_axR\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking commitment status**\\n\\nI need to explain whether I'm committed to the current task. I'm wondering if the change has actually been committed, so I should check the status now. Maybe I need to ensure everything is clean before I finalize anything. It’s crucial to have all details in order before moving ahead, so I’ll focus on confirming that everything is as needed. Let's make sure everything is set before wrapping things up!\"}]}"},{"type":"text","text":"Sí. Ya está commiteado.\n\nCommit local:\n- `6a12638 feat: add HTTP API and maintenance daemon support`\n\nIncluye:\n- `src/shared_agent_memory/http_api.py`\n- `docs/http-api.md`\n- `tests/test_memory_service.py`\n- y otros cambios grandes\n\nEstado:\n- `main` está **ahead 1** de `origin/main`\n- no está pusheado\n\nO sea:\n- el fix de `api/search?id=...` quedó dentro de ese commit local, no suelto\n- el repo quedó bastante más amplio que solo ese fix\n\nSi querés, ahora miro si conviene:\n1. dejar ese commit así y pusharlo después, o\n2. separar el fix en otro commit más chico.","textSignature":"{\"v\":1,\"id\":\"msg_0c0fef4ddac7e1db0169e8dbc1c95c8195876eb2a9a0ff9f07\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4-mini","usage":{"input":62012,"output":187,"cacheRead":1024,"cacheWrite":0,"totalTokens":63223,"cost":{"input":0.046509,"output":0.0008415,"cacheRead":0.0000768,"cacheWrite":0,"total":0.047427300000000006}},"stopReason":"stop","timestamp":1776868285574,"responseId":"resp_0c0fef4ddac7e1db0169e8dbbe6f788195b3c30da972716cd8"}}