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{"type":"thinking_level_change","id":"ac6b0192","parentId":"288f6076","timestamp":"2026-05-05T17:26:02.411Z","thinkingLevel":"low"}
{"type":"message","id":"1602bef2","parentId":"ac6b0192","timestamp":"2026-05-05T17:27:54.319Z","message":{"role":"user","content":[{"type":"text","text":"En otra sesión vimos que el servicio de memoria prácticamente no usa inbox sino que promueve automáticamente todas las memorias que ingresan y esto de por sí no está mal pero parecería que estamos ingresando pocas memorias de buena calidad y perdiendo una mayor cantidad de memorias de inicialmente menor calidad que podrían ser promovidas y generar más insights. Este claim de que hay pocas memorias igual también puede estar relacionado al hecho de que uno estaba andando el hook de session end de la gente y recientemente se arregló así que deberían haber corrido unos cinco o seis eventos de estos. ¿Quieres extrapolar si de haber estado andando esto hubieran sido muchas más memorias o no tantas?\nEmpecemos por ahí el análisis y si es el caso de que igual no hubieran sido tantas memorias podemos repensar la manera en que se ingiren memorias para obtener más a pesar de que puedan ser de menor calidad."}],"timestamp":1778002074309}}
{"type":"message","id":"7dd259b5","parentId":"1602bef2","timestamp":"2026-05-05T17:27:58.845Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Analyzing memory service**\n\nI need to analyze the memory service and possibly inspect the agents-database data. The user is asking if the session-end hook could have created more memories, so I should maybe query the service stats and check the current counts in the memory database. It might be helpful to look into recent events too. I’ll also consider using the skill memory-read, as that could be useful for this inspection. Let’s see what I find!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa289be9bc8195a7ac4ea3d2bda539\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-iieJKzFTeSygb-OZkkqAm5KLdjPzrg-tUlUc9T7cifnrm2XLL1z0b8atJ-Eb5XRFpnrNQkSPyTkY00CtTDPqrO9_CBBBPkys6xXaAaMYxKh7xsA6-i8x9qS62q64a0msGLYD9m_wALvIJgQ3Y6I_8qTibqgd-3-u4AbOkmA2ClZ3qusvhL8wRPCE8iQUA_-Z8Ph43KUhrFpuyDnzkuL7Vvqafk3nAqDiGDDkGFO-k4STVvf2I7vn3xGqgg1mEGYsVBI7EYuwfttJIWJy4jZymvs96nTTRQNTsFez3Db_b2pKchPenDdA8p_x-POIRY8KYcv0P2nR-JL02m3iSJfXpe2LBi65nZJtVFqcXbJqHAiLkf3ksM9zzHYozD-09YwQaqMPIhYqRLGB9jD_mMuCeYvrYct7E6S94UZfMBoy15F0BtPnxNgFq_ASiSje-HqmAakjBledii2E3xxo2JjD5dmMExAGe_ya5VfWcDw9Uj6i978jm1noEfMvgv5AL6ftEvqciZ7Br5irX8wLHhaHPgvBQce4HlE7iWkCm0LjVibRv2LDkihT8fMs5K9bhYkiGRQP4Bui1K5sYr4y-DOyNsWG2iFQXNfCNpc6PfrdMuWrIqhxAzKGcrVNLtpftkx5nxaMFIgRnaRmkTf_55PNcDA5qulUa0M6pkxBKa9XRDGJtMXVvvTKGISZNbGVl9rifbHksMCNYco1ijinxwnHNK_96wQ_Rv-sz6DIYLkLIW9O8S0KAa7pzpAIHKA1QkpNN7itZAwx1qciYeXVB7w5r8QcWGBODRYYMkqJpxac-xAbuVEMGgftyFn4-K8Unh2CfFJdriRECcmoIPTSJpuuZ-KZr3oYAAgY1aMdWXrIdJCF8IkhKj2Qeoh16TsvX9_U5bBAIBDgn787SFw9Mg45oLiQSA2EhxNDyDKkTXVZbciveFUjHoSelEg7h_kPZ4LVArd2OELvKHtsht84Vlo4SFmsszXXw8n7mCxNYBrLtKPsnnc4TmLyHHuoJPCKPRZYVMbnVjwJtl_RGcUigikOj-oHULkv1oyBZXd0W1Q4S3pVU_LmXVuASo1ROzBaznVFqBzZho3oUWN6YBxPDbOOzEQZQDWqYVISXgQAciXLHzFye5mmEikJi-yZNoE13tCn2SSLIk_bv_pfdyu8TrSGu1c60nYdHNFNYL1fND3jZSJWkC6lD9Y5nG5zlRHZ7us2Mhf_oNd--wEmnf79PIL9PSajoeJiNaqOJ8QbUElXmBPoLMl510624Oa9Jmv6N-tGtNUOFg-evPiDVkDN3gPGBRaLvJasLrtiQAdTf0WuMX6ShV_1_tv0G6DRvQqa4y5q5Gs2nqq23B4CUPeDhcabwR0AUWmG1FH2meZr1rampEnhEM=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Analyzing memory service**\\n\\nI need to analyze the memory service and possibly inspect the agents-database data. The user is asking if the session-end hook could have created more memories, so I should maybe query the service stats and check the current counts in the memory database. It might be helpful to look into recent events too. I’ll also consider using the skill memory-read, as that could be useful for this inspection. Let’s see what I find!\"}]}"},{"type":"toolCall","id":"call_4pRjUlNrHD5x02Txh8Fqokaq|fc_0d46162620b774590169fa289e978c8195820670ecc2dcb354","name":"read","arguments":{"path":"/home/sebas/.agents/skills/memory-read/SKILL.md"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":4143,"output":103,"cacheRead":0,"cacheWrite":0,"totalTokens":4246,"cost":{"input":0.0103575,"output":0.001545,"cacheRead":0,"cacheWrite":0,"total":0.0119025}},"stopReason":"toolUse","timestamp":1778002074322,"responseId":"resp_0d46162620b774590169fa289aa66c81958332ac0952ea83ad"}}
{"type":"message","id":"051cc650","parentId":"7dd259b5","timestamp":"2026-05-05T17:27:58.857Z","message":{"role":"toolResult","toolCallId":"call_4pRjUlNrHD5x02Txh8Fqokaq|fc_0d46162620b774590169fa289e978c8195820670ecc2dcb354","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":1778002078856}}
{"type":"message","id":"5a47fe47","parentId":"051cc650","timestamp":"2026-05-05T17:28:04.032Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Looking into service API**\n\nI’m thinking I might need to use a service API here. I should also inspect the repo to get more context. Locating the agents-database could be the next step, maybe in /mnt/rpi? Using bash with the `rg` command could help me search for what I need! It's important to ensure I'm on the right track for this. Let's see what I can find!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa289ffd708195942bb546c3975ff9\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-iijUqtnYXBhCQDMuv3rtPUaY7buLCvEnk1P4VPDQm45Ra5ZMBZlbOrHEf_myLThPDHmGzzwjof3gHQmW-c1PQm7G9TC14ZE6qe4xc7QpuTyrYXI0VVj-1OG-YppbTaJcdzNe0iwxyPYv3BSVYAULKnYTyn9qNHE7UnpxmQxY6mUFR6Zq56W3stwtpvsW5RpCKeRJdKWDDcNoZ0zKlg5VPiOWnVUB88_MVmDpRodshLwh4O9gKoKUoMTBr2OuLaLNd6mytzut6wWDGeeSCHnujImzGZ_dJ3Lr-8jfeZiZRRs_n2vnbVXriZS00hEKpyroykhXodcm1ugZ_g8skiOhMheA8mT28Jai5_LjfypWqs9Gsfoy6h0eUYY7iClLopX21f-XdrGfk0y4Sfgfje0CpuypkpHYIoVEBtVvaaTGBoZkBNgh8cVrpCmpDUpu5GvlS_O8FeFW5zAMAjgf9ELWz_TBkCQN5dZUxGoaID1fvxGUuffx4xSjn4qJOnBWvmy6RhuEr6c-8VgPC07PVigBmmG9vimNcmpKXCLLKwDQwHsCtMa6rwBBlT4BxI3xlSXbowVOQQILHkOKeW-BDuOaBbxZYxcDNxQL_k5bEh5v3Mqp4fXMxTRo4JX9116sDM3-CYlCyFCu9yaPycO8CO3Q6LD_Zqq4G4FD5cnzr3Y59sacgpZuoPYaZHkTOrf6CdhTPNY7j2mHvLtJWyAGVQC-GIe8OWzBGvKVCZ3CCUOexVK2ALkDKg4Avb00mst_hNl_5qY9ZEQdNdvq1ruJdyQSCWrmZDR9yggUe51NGtlkzywvjK2zZ4lnFLtVDAQN7FVr6t2-07Pr3xQTTs4gbmG-RvBJahkmdJNsiSAXDVZHi7ydYL1SWsuEkFpr9s_-4cFv2P-zEsqQznkjfdRmxxrJHdmrJ8FP6bGiJJ3SraBFDVTHx2ppiWZwkwVnWe4oV48NijPPS1PT0tIH1rhyWZTmF4J7NsLssARDK2Pn5HqWc2nWp9iQog-pUKJYiYwraJyfzQ8nWdvML9EYkLUVhy4wSy_U0KQRxwAoDXMXtrsduQ4CSX1hTQ_gZjr9zqg7TcSkLJl2Uq04cV3Cim60S2wFj2XjQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Looking into service API**\\n\\nI’m thinking I might need to use a service API here. I should also inspect the repo to get more context. Locating the agents-database could be the next step, maybe in /mnt/rpi? Using bash with the `rg` command could help me search for what I need! It's important to ensure I'm on the right track for this. Let's see what I can find!\"}]}"},{"type":"toolCall","id":"call_hRiYTW7JAxDRfrcsetpwdk14|fc_0d46162620b774590169fa28a3cd2c8195b0eac672432fd71e","name":"bash","arguments":{"command":"find /home/sebas -maxdepth 3 -type d \\( -name 'agents-database' -o -name '*memory*' \\) 2>/dev/null | sed 's#^#/##' | head -200"}},{"type":"toolCall","id":"call_BzBknF9UpGz7pDsd5Boiul8f|fc_0d46162620b774590169fa28a3cd44819593af4f0c04b9d3c3","name":"bash","arguments":{"command":"curl -fsSL http://100.116.176.16:8091/api/status || true"}},{"type":"toolCall","id":"call_64jVxMM2YDyrlOX0pwWbhmqS|fc_0d46162620b774590169fa28a3cd4c819592833f0c286cc281","name":"bash","arguments":{"command":"curl -fsSL 'http://100.116.176.16:8091/api/search?q=session%20end&scope=global&scope=project&limit=10' || true","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":775,"output":209,"cacheRead":3584,"cacheWrite":0,"totalTokens":4568,"cost":{"input":0.0019375000000000002,"output":0.003135,"cacheRead":0.000896,"cacheWrite":0,"total":0.005968500000000001}},"stopReason":"toolUse","timestamp":1778002078858,"responseId":"resp_0d46162620b774590169fa289f282c81959fc53e3455f3197a"}}
{"type":"message","id":"7e1d7be9","parentId":"5a47fe47","timestamp":"2026-05-05T17:28:04.178Z","message":{"role":"toolResult","toolCallId":"call_hRiYTW7JAxDRfrcsetpwdk14|fc_0d46162620b774590169fa28a3cd2c8195b0eac672432fd71e","toolName":"bash","content":[{"type":"text","text":"//home/sebas/pi-config/sessions/session-memory-jobs\n//home/sebas/pi-jobs/jobs/20260430T235040Z-validate-memory-promotion\n//home/sebas/.agents/skills/memory-read\n//home/sebas/.agents/skills/memory-write\n//home/sebas/.agents/skills.bck/personal-memory-search\n//home/sebas/agents-database\n//home/sebas/agents-database/src/shared_agent_memory\n"}],"isError":false,"timestamp":1778002084176}}
{"type":"message","id":"2ae07b35","parentId":"7e1d7be9","timestamp":"2026-05-05T17:28:04.178Z","message":{"role":"toolResult","toolCallId":"call_BzBknF9UpGz7pDsd5Boiul8f|fc_0d46162620b774590169fa28a3cd44819593af4f0c04b9d3c3","toolName":"bash","content":[{"type":"text","text":"{\"ok\": true, \"db_path\": \"/home/sebas/agents-database/data/shared-agent-memory.sqlite3\", \"dashboard\": {\"active_tasks\": [{\"id\": \"task_33d854e72d094660a2fb293a8723f5c5\", \"title\": \"Fix GitHub credential automation so repo pushes and PR creation use the intended account reliably\", \"intent\": \"The current credential helper flow can select the wrong GitHub identity during multi-repo push and PR automation. Investigate the root cause across gh auth git credential helper and repo remote handling, then design a safer simpler script or convention.\", \"kind\": \"subtask\", \"status\": \"open\", \"priority\": 9, \"project_id\": \"proj_personal_agent\", \"repo_id\": \"repo_personal_agent\", \"parent_task_id\": \"task_afed9eaf504f42b99348a815d56148cc\", \"origin\": \"implementation\", \"owner_agent\": \"personal-agent\", \"blocked_reason\": null, \"requires_human_input\": false, \"due_at\": null, \"created_at\": \"2026-03-14T22:07:02.464013+00:00\", \"updated_at\": \"2026-03-15T14:13:45.052882+00:00\", \"schema_version\": \"shared-agent-memory-v2\", \"run_id\": null, \"metadata\": {\"acceptance\": [\"push uses intended GitHub account without token workarounds\", \"PR creation works consistently across public and private repos\", \"documented operator flow is simpler than the current state\"], \"area\": \"git_credentials\", \"classification_note\": \"User confirmed this credential automation task is post-V1 and not a blocker for V1 closeout.\", \"kind\": \"low_priority_followup\", \"milestone\": \"post-V1\", \"relevant_repos\": [\"/Users/sebas/personal-agent\", \"/Users/sebas/agents-database\", \"/Users/sebas/ai-dev-workflow\", \"/Users/sebas/ballbox-company-agent\"], \"requested_on\": \"2026-03-14\", \"v1_closeout_epic_id\": \"task_afed9eaf504f42b99348a815d56148cc\", \"v1_excluded\": true, \"why_now\": \"PR creation required direct credential workarounds; automation should simplify this later\"}}], \"blocked_tasks\": [], \"recent_runs\": [{\"id\": \"run_d37c8577c9674333b7157a945274385a\", \"task_id\": \"task_3d7271f7d19e456791b5796695fbe2fa\", \"agent_id\": \"personal-agent\", \"status\": \"completed\", \"result_summary\": \"Local readiness is partial: repo docs and runtime surface exist, current status API works, but full verification is not yet proven in this sandbox.\", \"error_message\": null, \"started_at\": \"2026-03-15T14:21:34.691945+00:00\", \"completed_at\": \"2026-03-15T14:22:58.675711+00:00\", \"created_at\": \"2026-03-15T14:21:34.691945+00:00\", \"updated_at\": \"2026-03-15T14:22:58.675711+00:00\", \"input_payload\": {\"mode\": \"codex-agentic\"}, \"metadata\": {}}, {\"id\": \"run_ce032d1c6fbf4e3aa23a26df10d8d91f\", \"task_id\": \"task_46bb6d81471e47e39340198a889252a1\", \"agent_id\": \"personal-agent\", \"status\": \"failed\", \"result_summary\": null, \"error_message\": \"error: unexpected argument '--ask-for-approval' found\\n\\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\\n\\nUsage: codex exec --sandbox <SANDBOX_MODE> [PROMPT]\\n\\nFor more information, try '--help'.\", \"started_at\": \"2026-03-15T14:19:23.634939+00:00\", \"completed_at\": \"2026-03-15T14:19:23.690219+00:00\", \"created_at\": \"2026-03-15T14:19:23.634939+00:00\", \"updated_at\": \"2026-03-15T14:19:23.690219+00:00\", \"input_payload\": {\"mode\": \"codex-agentic\"}, \"metadata\": {}}, {\"id\": \"run_d7ea2a60bbd74e9b8fdaa628115d7cbd\", \"task_id\": \"task_4a80a42f5d424f7191b151c3640db934\", \"agent_id\": \"personal-agent\", \"status\": \"failed\", \"result_summary\": null, \"error_message\": \"error: unexpected argument '--ask-for-approval' found\\n\\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\\n\\nUsage: codex exec --sandbox <SANDBOX_MODE> [PROMPT]\\n\\nFor more information, try '--help'.\", \"started_at\": \"2026-03-15T14:16:58.205690+00:00\", \"completed_at\": \"2026-03-15T14:16:58.257806+00:00\", \"created_at\": \"2026-03-15T14:16:58.205690+00:00\", \"updated_at\": \"2026-03-15T14:16:58.257806+00:00\", \"input_payload\": {\"mode\": \"codex-agentic\"}, \"metadata\": {}}], \"recent_artifacts\": [{\"id\": \"art_d9ed019809c24381919c280f28481652\", \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-15T14:18:48.588043+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\", \\\"intent\\\": \\\"Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (repo)\\\", \\\"secondary_agent\\\": null}, \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\"}, \\\"reason\\\": \\\"code work (repo)\\\", \\\"result_summary\\\": \\\"ai-dev-workflow accepted the handoff and persisted intake context.\\\", \\\"status\\\": \\\"accepted\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-04-30T17:32:27.377428+00:00\", \"updated_at\": \"2026-04-30T17:32:27.377432+00:00\", \"metadata\": {}}, {\"id\": \"art_4e83c1c6fdf94b3ca70f6e0dbb9be2d7\", \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> ai-dev-workflow\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-15T14:18:33.506326+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\", \\\"intent\\\": \\\"Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (fix)\\\", \\\"secondary_agent\\\": null}, \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\"}, \\\"reason\\\": \\\"code work (fix)\\\", \\\"result_summary\\\": \\\"ai-dev-workflow accepted the handoff and persisted intake context.\\\", \\\"status\\\": \\\"accepted\\\", \\\"to_agent\\\": \\\"ai-dev-workflow\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-04-30T17:32:27.377374+00:00\", \"updated_at\": \"2026-04-30T17:32:27.377379+00:00\", \"metadata\": {}}, {\"id\": \"art_7ed9c6867a814f7fb9ad4f45fc9a578e\", \"task_id\": \"task_afed9eaf504f42b99348a815d56148cc\", \"artifact_type\": \"delegation_note\", \"title\": \"Handoff personal-agent -> implementation-agent\", \"content\": \"{\\\"completed_at\\\": \\\"2026-03-14T21:29:18.517341+00:00\\\", \\\"error_message\\\": null, \\\"from_agent\\\": \\\"personal-agent\\\", \\\"payload\\\": {\\\"artifact_id\\\": \\\"art_598b5da707dd47709fc0fe9bb3e79e28\\\", \\\"epic_id\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"expected_artifacts\\\": [\\\"migration_report\\\", \\\"v1_cross_reference_report\\\"], \\\"idempotency_key\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"remaining_task_ids\\\": [\\\"task_a240986d7c8a4474ba5246536769deed\\\", \\\"task_c0b65186af7d4013b2c1d2906ea6a28e\\\"]}, \\\"reason\\\": \\\"Finish remaining V1 migration and GitHub cross-reference work\\\", \\\"result_summary\\\": \\\"Finished legacy bridge and V1 multi-repo cross-reference work; shared DB now contains the resume anchors and completion artifacts.\\\", \\\"status\\\": \\\"completed\\\", \\\"to_agent\\\": \\\"implementation-agent\\\"}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"migration:v2\", \"created_at\": \"2026-04-30T17:32:27.377239+00:00\", \"updated_at\": \"2026-04-30T17:32:27.377247+00:00\", \"metadata\": {}}, {\"id\": \"art_eb70bf77f5524240a4782e1e6e5eddb3\", \"task_id\": \"task_3d7271f7d19e456791b5796695fbe2fa\", \"artifact_type\": \"execution_state\", \"title\": \"Execution state snapshot\", \"content\": \"{\\n  \\\"action_results\\\": [\\n    {\\n      \\\"task_id\\\": \\\"task_561a1444260c4c23b716ab65ad2ebe3d\\\",\\n      \\\"type\\\": \\\"create_followup_task\\\"\\n    }\\n  ],\\n  \\\"outcome\\\": \\\"complete\\\",\\n  \\\"run_id\\\": \\\"run_d37c8577c9674333b7157a945274385a\\\",\\n  \\\"summary\\\": \\\"Local readiness is partial: repo docs and runtime surface exist, current status API works, but full verification is not yet proven in this sandbox.\\\"\\n}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:22:58.674979+00:00\", \"updated_at\": \"2026-03-15T14:22:58.674979+00:00\", \"metadata\": {\"outcome\": \"complete\", \"task_run_id\": \"run_d37c8577c9674333b7157a945274385a\"}}, {\"id\": \"art_f2e3c950a9b8427e89e083457683077e\", \"task_id\": \"task_3d7271f7d19e456791b5796695fbe2fa\", \"artifact_type\": \"report\", \"title\": \"CLI Smoke Readiness Summary\", \"content\": \"## Findings\\n- Confirmed locally on 2026-03-15: `python3 scripts/personal.py --json status` works and returns live task/run state. Current snapshot shows `active_task_count=19`, `blocked_task_count=2`, `pending_approval_count=0`.\\n- Confirmed locally on 2026-03-15: `codex exec --help` matches the newer CLI shape and supports `-C/--cd`, `--add-dir`, `--output-last-message`; it does not advertise the older `--ask-for-approval` flow.\\n- Confirmed from repo state: local edits in `personal_agent/planner.py`, `personal_agent/runtime.py`, and `tests/test_personal.py` replace the obsolete `--ask-for-approval` and `--search` flags with `-C`, and add test coverage for that command shape.\\n- Confirmed from runtime evidence: recent stored runs failed with exact error `unexpected argument '--ask-for-approval' found`, so the earlier smoke failures were real and explainable.\\n- Confirmed from docs/scripts: the only declared repo gate is `./scripts/run-checks.sh`, which runs `python3 -m unittest discover -s tests`.\\n- Not confirmed: full repo test pass. In this read-only sandbox, the test suite fails before exercising app behavior because Python cannot create temp dirs: `FileNotFoundError: No usable temporary directory found`.\\n- Not confirmed: shared-memory operational readiness under this sandbox. Targeted memory search failed with `sqlite3.OperationalError: attempt to write a readonly database`, so shared DB interaction is not proven here.\\n- Not confirmed: production readiness. Docs still describe dashboard, daemon, worker, approvals, and multi-repo handoff topology, but this run did not validate dashboard serving, worker completion, or end-to-end shared-memory writes.\\n\\n## Risks\\n- Main near-term risk is false confidence from static docs plus partial command success while writable-path and temp-dir assumptions remain unverified.\\n- Working tree is dirty, so the codex CLI fix appears in progress but not yet fully validated by a successful full gate.\\n\\n## Recommended Next Action\\n- Queue one internal follow-up task: rerun the full local smoke gate in a writable environment after the current codex CLI flag fix lands, with focus on two proofs only: tests pass and shared-memory-backed worker flow completes without the old CLI error.\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:22:58.671693+00:00\", \"updated_at\": \"2026-03-15T14:22:58.671693+00:00\", \"metadata\": {\"outcome\": \"complete\", \"task_run_id\": \"run_d37c8577c9674333b7157a945274385a\"}}, {\"id\": \"art_504acf83d6fe481f82082c3fa518b6cb\", \"task_id\": \"task_3d7271f7d19e456791b5796695fbe2fa\", \"artifact_type\": \"plan\", \"title\": \"Execution Plan\", \"content\": \"# Plan for Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordinati...\\n\\n- Route: personal\\n- Secondary route: code\\n- Planning source: codex\\n- Codex instruction: Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\\n\\n## Subtasks\\n- Collect readiness signals\\n- Write smoke summary\\n- Propose one coordination step\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:21:32.622366+00:00\", \"updated_at\": \"2026-03-15T14:21:32.622366+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\", \"delegation_target\": null, \"planning_source\": \"codex\", \"primary_agent\": \"personal\", \"reason\": \"Planning/synthesis task with light repo-readiness context; no external action.\", \"secondary_agent\": \"code\"}}}, {\"id\": \"art_52e95ab045ea45df8cd59f44707194ef\", \"task_id\": \"task_3d7271f7d19e456791b5796695fbe2fa\", \"artifact_type\": \"normalized_intent\", \"title\": \"Normalized Intent\", \"content\": \"Request: Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordination step only. No external actions.\\nPrimary route: personal\\nSecondary route: code\\nReason: Planning/synthesis task with light repo-readiness context; no external action.\\nPlanning source: codex\\nCodex instruction: Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\\nRelevant memory:\\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]\\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\\n- Sebas desired relationship with money [mem_sebas_money_relationship]\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:21:32.621453+00:00\", \"updated_at\": \"2026-03-15T14:21:32.621453+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\", \"delegation_target\": null, \"planning_source\": \"codex\", \"primary_agent\": \"personal\", \"reason\": \"Planning/synthesis task with light repo-readiness context; no external action.\", \"secondary_agent\": \"code\"}}}, {\"id\": \"art_64eb875c9be041c2bf56ef7825e7b49d\", \"task_id\": \"task_46bb6d81471e47e39340198a889252a1\", \"artifact_type\": \"plan\", \"title\": \"Execution Plan\", \"content\": \"# Plan for Smoke task: summarize current system readiness and suggest one safe internal coordination step on...\\n\\n- Route: personal\\n- Planning source: fallback\\n\\n## Subtasks\\n- Clarify objective and constraints\\n- Gather context and evidence\\n- Produce final recommendation\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:19:21.818641+00:00\", \"updated_at\": \"2026-03-15T14:19:21.818641+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"\", \"delegation_target\": null, \"planning_source\": \"fallback\", \"primary_agent\": \"personal\", \"reason\": \"default personal route\", \"secondary_agent\": null}}}, {\"id\": \"art_0f68333747c744728f5291e4e64d563a\", \"task_id\": \"task_46bb6d81471e47e39340198a889252a1\", \"artifact_type\": \"normalized_intent\", \"title\": \"Normalized Intent\", \"content\": \"Request: Smoke task: summarize current system readiness and suggest one safe internal coordination step only. No external actions.\\nPrimary route: personal\\nSecondary route: none\\nReason: default personal route\\nPlanning source: fallback\\nRelevant memory:\\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]\\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\\n- Sebas desired relationship with money [mem_sebas_money_relationship]\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:19:21.818049+00:00\", \"updated_at\": \"2026-03-15T14:19:21.818049+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"\", \"delegation_target\": null, \"planning_source\": \"fallback\", \"primary_agent\": \"personal\", \"reason\": \"default personal route\", \"secondary_agent\": null}}}, {\"id\": \"art_a60c0d4bffb14d67be795c5f45124318\", \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\", \"artifact_type\": \"handoff_result\", \"title\": \"Handoff result from ai-dev-workflow\", \"content\": \"{\\n  \\\"artifacts\\\": [],\\n  \\\"memory_id\\\": \\\"mem_17eea5c0658f4cd3beece90a93d35e36\\\",\\n  \\\"status\\\": \\\"accepted\\\",\\n  \\\"summary\\\": \\\"ai-dev-workflow accepted the handoff and persisted intake context.\\\",\\n  \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\"\\n}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"ai-dev-workflow\", \"created_at\": \"2026-03-15T14:18:48.589136+00:00\", \"updated_at\": \"2026-03-15T14:18:48.589136+00:00\", \"metadata\": {\"handoff_id\": \"hof_bdc2e7b08a7c4cdb803c986fc6ec84fc\"}}, {\"id\": \"art_ecadce16c6ce44e9b8b534630a2aea26\", \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\", \"artifact_type\": \"plan\", \"title\": \"Execution Plan\", \"content\": \"# Plan for Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and...\\n\\n- Route: code\\n- Planning source: fallback\\n- Delegation target: ai-dev-workflow\\n\\n## Subtasks\\n- Inspect technical context\\n- Delegate code execution\\n- Summarize technical outcome\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:18:47.342014+00:00\", \"updated_at\": \"2026-03-15T14:18:47.342014+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"\", \"delegation_target\": \"ai-dev-workflow\", \"planning_source\": \"fallback\", \"primary_agent\": \"code\", \"reason\": \"code work (repo)\", \"secondary_agent\": null}}}, {\"id\": \"art_73f79012e7f0471085d2749541f5748b\", \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\", \"artifact_type\": \"normalized_intent\", \"title\": \"Normalized Intent\", \"content\": \"Request: Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\\nPrimary route: code\\nSecondary route: none\\nReason: code work (repo)\\nPlanning source: fallback\\nRelevant memory:\\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]\\n- Research run: Production-readiness plan for ballbox-payment-integration [legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54]\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:18:47.341454+00:00\", \"updated_at\": \"2026-03-15T14:18:47.341454+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"\", \"delegation_target\": \"ai-dev-workflow\", \"planning_source\": \"fallback\", \"primary_agent\": \"code\", \"reason\": \"code work (repo)\", \"secondary_agent\": null}}}, {\"id\": \"art_1accabbcec71437d8592292c9e412153\", \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\", \"artifact_type\": \"handoff_result\", \"title\": \"Handoff result from ai-dev-workflow\", \"content\": \"{\\n  \\\"artifacts\\\": [],\\n  \\\"memory_id\\\": \\\"mem_04dc02a90cad40cc826ed1e8f93aff36\\\",\\n  \\\"status\\\": \\\"accepted\\\",\\n  \\\"summary\\\": \\\"ai-dev-workflow accepted the handoff and persisted intake context.\\\",\\n  \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\"\\n}\", \"format\": \"json\", \"status\": \"active\", \"source_ref\": \"ai-dev-workflow\", \"created_at\": \"2026-03-15T14:18:33.507460+00:00\", \"updated_at\": \"2026-03-15T14:18:33.507460+00:00\", \"metadata\": {\"handoff_id\": \"hof_d7a1028b907341fa93a5cb8cb460e2c7\"}}, {\"id\": \"art_39c706c5d09f421a9198fc938020c79f\", \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\", \"artifact_type\": \"plan\", \"title\": \"Execution Plan\", \"content\": \"# Plan for Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal ne...\\n\\n- Route: code\\n- Planning source: fallback\\n- Delegation target: ai-dev-workflow\\n\\n## Subtasks\\n- Inspect technical context\\n- Delegate code execution\\n- Summarize technical outcome\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:18:28.454377+00:00\", \"updated_at\": \"2026-03-15T14:18:28.454377+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"\", \"delegation_target\": \"ai-dev-workflow\", \"planning_source\": \"fallback\", \"primary_agent\": \"code\", \"reason\": \"code work (fix)\", \"secondary_agent\": null}}}, {\"id\": \"art_8225d4c69b224564ba55a14f20519717\", \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\", \"artifact_type\": \"normalized_intent\", \"title\": \"Normalized Intent\", \"content\": \"Request: Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\\nPrimary route: code\\nSecondary route: none\\nReason: code work (fix)\\nPlanning source: fallback\\nRelevant memory:\\n- Refactor runtime toward Codex-agentic orchestration [mem_runtime_agentic_refactor_priority]\\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]\\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:18:28.453697+00:00\", \"updated_at\": \"2026-03-15T14:18:28.453697+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"\", \"delegation_target\": \"ai-dev-workflow\", \"planning_source\": \"fallback\", \"primary_agent\": \"code\", \"reason\": \"code work (fix)\", \"secondary_agent\": null}}}, {\"id\": \"art_4b872101defe41188e12e81c2c5e0ffc\", \"task_id\": \"task_4a80a42f5d424f7191b151c3640db934\", \"artifact_type\": \"plan\", \"title\": \"Execution Plan\", \"content\": \"# Plan for Smoke test: summarize current V1 readiness and recommend the next safe internal step without exte...\\n\\n- Route: personal\\n- Planning source: fallback\\n\\n## Subtasks\\n- Clarify objective and constraints\\n- Gather context and evidence\\n- Produce final recommendation\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:16:55.509883+00:00\", \"updated_at\": \"2026-03-15T14:16:55.509883+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"\", \"delegation_target\": null, \"planning_source\": \"fallback\", \"primary_agent\": \"personal\", \"reason\": \"default personal route\", \"secondary_agent\": null}}}, {\"id\": \"art_0f65fad23c6e4f698fa606feead854e1\", \"task_id\": \"task_4a80a42f5d424f7191b151c3640db934\", \"artifact_type\": \"normalized_intent\", \"title\": \"Normalized Intent\", \"content\": \"Request: Smoke test: summarize current V1 readiness and recommend the next safe internal step without external side effects.\\nPrimary route: personal\\nSecondary route: none\\nReason: default personal route\\nPlanning source: fallback\\nRelevant memory:\\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\\n- Refactor runtime toward Codex-agentic orchestration [mem_runtime_agentic_refactor_priority]\\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:16:55.509067+00:00\", \"updated_at\": \"2026-03-15T14:16:55.509067+00:00\", \"metadata\": {\"route\": {\"codex_instruction\": \"\", \"delegation_target\": null, \"planning_source\": \"fallback\", \"primary_agent\": \"personal\", \"reason\": \"default personal route\", \"secondary_agent\": null}}}, {\"id\": \"art_f7fad73c89674ef1b9f9248c7b829ca1\", \"task_id\": \"task_afed9eaf504f42b99348a815d56148cc\", \"artifact_type\": \"closeout\", \"title\": \"V1 final closeout\", \"content\": \"# V1 Closeout\\n\\n## Outcome\\nV1 is complete.\\n\\n`personal-agent` now operates as the shared-memory front door with:\\n- shared operational state in `agents-database`\\n- intake planning and runtime orchestration backed by Codex outputs\\n- daemon/dashboard surfaces for status, blockers, approvals, and next actions\\n- approval resolution with automatic resume\\n- stable subagent handoff contract for `ai-dev-workflow` and `ballbox-company-agent`\\n- legacy bridge for old personal-agent research/task inspection\\n- documented sandbox write convention for canonical shared DB access\\n\\n## Verification\\n- local repo gate: `./scripts/run-checks.sh` passed with 26 tests\\n- direct suite: `python3 -m pytest tests/test_personal.py` passed with 26 tests\\n- PR CI: run `23112079488` passed on 2026-03-15 (`unit-tests`)\\n\\n## Runtime use cases covered by tests\\n- intake creates operational task, subtasks, and specialist handoff\\n- Codex planning path and fallback path\\n- worker terminal outcomes: `complete`, `needs_approval`, `blocked`\\n- approval resolution paths: `approved` resumes and completes; `rejected` stays blocked\\n- structured runtime actions: follow-up task, handoff, artifact\\n- dashboard snapshot enrichment: summary, next action, latest run, pending approval, route summary\\n- end-to-end flow: intake -> approval -> resume -> complete\\n- Codex command wiring includes `--add-dir /Users/sebas/agents-database`\\n- handoff dispatch completion persists result artifact\\n- preference blocker can resolve from memory without human input\\n- daemon API surface covers status, intake, blocker response, and approval resolution\\n- CLI accepts `--json` before or after subcommands\\n\\n## Scope boundary\\n- task `task_33d854e72d094660a2fb293a8723f5c5` is explicitly post-V1 follow-up, not a V1 blocker\\n- remaining work after V1 should continue as follow-up capabilities and operator hardening, not as part of this milestone\\n\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-15T14:13:45.050563+00:00\", \"updated_at\": \"2026-03-15T14:13:45.050563+00:00\", \"metadata\": {\"completed_on\": \"2026-03-15\", \"kind\": \"v1_closeout\", \"post_v1_followup_task_id\": \"task_33d854e72d094660a2fb293a8723f5c5\", \"verification\": {\"pr_ci_run_id\": \"23112079488\", \"pr_ci_status\": \"success\", \"pytest\": \"26 passed\", \"run_checks\": \"passed\"}}}, {\"id\": \"art_4151fdb5c75c48449199130022f7a909\", \"task_id\": \"task_eaf005df03fb441ab2b84df7ea05178d\", \"artifact_type\": \"continuation_brief\", \"title\": \"Phase 2 closeout brief\", \"content\": \"Phase 2 closeout:\\n- structured runtime actions implemented\\n- approval loop resolves and resumes\\n- execution_state snapshots persisted\\n- dashboard/status surfaces expose next actions and latest run state\\n- end-to-end runtime tests cover intake -> approval -> resume -> complete\\n\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-14T22:18:49.489775+00:00\", \"updated_at\": \"2026-03-14T22:18:49.489775+00:00\", \"metadata\": {\"kind\": \"phase2_closeout\"}}, {\"id\": \"art_b4b8c8b4bcc344ae81c206fa3ca57724\", \"task_id\": \"task_33d854e72d094660a2fb293a8723f5c5\", \"artifact_type\": \"task_note\", \"title\": \"Credential automation follow-up note\", \"content\": \"Low-priority follow-up. During multi-repo PR prep, git push and gh PR creation hit mixed-account behavior. Temporary workaround used direct credential injection. Future work should simplify and harden this flow instead of relying on manual recovery steps.\", \"format\": \"md\", \"status\": \"active\", \"source_ref\": \"personal-agent\", \"created_at\": \"2026-03-14T22:07:02.464680+00:00\", \"updated_at\": \"2026-03-14T22:07:02.464680+00:00\", \"metadata\": {\"kind\": \"followup_context\"}}]}, \"audit\": {\"schema_version\": \"shared-agent-memory-v2\", \"memories\": 232, \"tasks\": 39, \"approvals\": 0, \"handoffs\": 0, \"maintenance_jobs\": 3, \"legacy_memory_rows\": 0, \"legacy_task_rows\": 0}}"}],"isError":false,"timestamp":1778002084178}}
{"type":"message","id":"ce1b6f42","parentId":"2ae07b35","timestamp":"2026-05-05T17:28:04.182Z","message":{"role":"toolResult","toolCallId":"call_64jVxMM2YDyrlOX0pwWbhmqS|fc_0d46162620b774590169fa28a3cd4c819592833f0c286cc281","toolName":"bash","content":[{"type":"text","text":"{\"retrieval_id\": \"ret_1454b8a9884147a1a84227ee9ab3d89d\", \"results\": [{\"memory\": {\"id\": \"mem_93c0669e3d064fc29b8142d201f923af\", \"type\": \"project\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"source_kind\": \"manual\", \"title\": \"ballbox-first no longer depends on /mnt/rpi for home symlinks and core services\", \"content\": \"On 2026-05-05, symlinked home paths and core service/runtime paths were repatriated from /mnt/rpi to internal storage under /home/sebas. Moved ballbox, caches, browser profiles, eiffel-monitor venv, agents-database, pi-config service paths, browser-harness, pi-jobs, and qBittorrent media dirs. Removed /mnt/rpi mount from /etc/fstab and unmounted it. Verified agents-database, session-memory-dispatcher, eiffel-monitor, and pi-job-status-writer run from internal paths. ttyd-browser remains broken because ttyd binary is missing, which was already failing before migration.\", \"summary\": \"On 2026-05-05, symlinked home paths and core service/runtime paths were repatriated from /mnt/rpi to internal storage under /home/sebas. Moved ballbox, caches, browser profiles, e\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-05-05T15:35:05.790012+00:00\", \"updated_at\": \"2026-05-05T15:35:05.790012+00:00\", \"observed_at\": \"2026-05-05T15:35:05.790012+00:00\", \"source_ref\": \"conversation:2026-05-05-/mnt-rpi-repatriation\", \"evidence_ref\": null, \"schema_version\": \"shared-agent-memory-v2\", \"subtype\": \"infrastructure\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"origin_agent\": \"personal-agent\", \"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\": \"global\", \"evidence_ref\": null, \"explanation\": \"facets=0.00 lexical=1.00 semantic=0.00 confidence=1.00 freshness=1.00\"}, {\"memory\": {\"id\": \"mem_90f4bcce6d284ed0b6f6edb9a6ec6cc2\", \"type\": \"decision\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"source_kind\": \"manual\", \"title\": \"Prefer session-end memory writes\", \"content\": \"Prefer storing durable memories at session end via session analysis instead of mid-conversation. Write mid-conversation only when the user explicitly asks, handoff risk is high, or the information would likely be lost before session end.\", \"summary\": \"Prefer storing durable memories at session end via session analysis instead of mid-conversation. Write mid-conversation only when the user explicitly asks, handoff risk is high, o\\u2026\", \"confidence\": 1.0, \"freshness\": 1.0, \"created_at\": \"2026-05-01T19:46:36.793893+00:00\", \"updated_at\": \"2026-05-01T19:46:36.793893+00:00\", \"observed_at\": \"2026-05-01T19:46:36.793893+00:00\", \"source_ref\": \"conversation:2026-05-01\", \"evidence_ref\": null, \"schema_version\": \"shared-agent-memory-v2\", \"subtype\": \"operating_rule\", \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"origin_agent\": \"personal-agent\", \"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\": \"global\", \"evidence_ref\": null, \"explanation\": \"facets=0.00 lexical=1.00 semantic=0.00 confidence=1.00 freshness=1.00\"}, {\"memory\": {\"id\": \"mem_ballbox_company_overview\", \"type\": \"profile\", \"scope\": \"global\", \"status\": \"active\", \"project_id\": null, \"repo_id\": null, \"agent_id\": null, \"source_kind\": \"manual\", \"title\": \"Ballbox company overview\", \"content\": \"Ballbox is an early-stage company focused on selling advertising on screens and selling vending machines for ball tubes. The machines themselves also display ads. Initial go-to-market is padel centers.\", \"summary\": \"Ballbox is an early-stage company focused on selling advertising on screens and selling vending machines for ball tubes. The machines themselves also display ads. Initial go-to-ma\\u2026\", \"confidence\": 0.97, \"freshness\": 0.95, \"created_at\": \"2026-03-13T14:42:32.059069+00:00\", \"updated_at\": \"2026-03-13T14:42:32.059069+00:00\", \"observed_at\": \"2026-03-13T14:42:32.059069+00:00\", \"source_ref\": \"conversation:2026-03-13:ballbox-context\", \"evidence_ref\": \"conversation:2026-03-13:ballbox-context\", \"schema_version\": \"shared-agent-memory-v2\", \"subtype\": null, \"run_id\": null, \"task_id\": null, \"url\": null, \"domain\": null, \"origin_agent\": null, \"metadata\": {\"kind\": \"project_fact\", \"project\": \"ballbox\"}, \"embedding\": [0.504676405243449, 0.504676405243449, 0.40843713669616294, 0.33669250125805056, 0.2803113635097158, 0.23813421419155276, 0.2533527732238796, 0.10464571067942854]}, \"score\": 0.39425373792870977, \"score_breakdown\": {\"facet\": 0.0, \"lexical\": 0.5, \"semantic\": 0.9870249195247324, \"confidence\": 0.97, \"freshness\": 0.95}, \"matched_scope\": \"global\", \"evidence_ref\": 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{"type":"message","id":"3cdfa7e5","parentId":"2c171437","timestamp":"2026-05-05T17:28:14.236Z","message":{"role":"toolResult","toolCallId":"call_zAbHGM0JE5uNB8ijjZvbf7Ob|fc_0d46162620b774590169fa28adfe888195808b6e3edd5263bb","toolName":"bash","content":[{"type":"text","text":"/bin/bash: line 1: sqlite3: command not found\n\n\nCommand exited with code 127"}],"details":{},"isError":true,"timestamp":1778002094235}}
{"type":"message","id":"db13ebb6","parentId":"3cdfa7e5","timestamp":"2026-05-05T17:28:14.236Z","message":{"role":"toolResult","toolCallId":"call_FuA9Gu7o4TGr3JHr11bUILkv|fc_0d46162620b774590169fa28adfe9c8195b77bfcb6255b70e3","toolName":"read","content":[{"type":"text","text":"EISDIR: illegal operation on a directory, read"}],"details":{},"isError":true,"timestamp":1778002094236}}
{"type":"message","id":"d2a51900","parentId":"db13ebb6","timestamp":"2026-05-05T17:28:14.239Z","message":{"role":"toolResult","toolCallId":"call_VvwYEyWZX24BXKhpRV51wEeC|fc_0d46162620b774590169fa28adfea48195ad92618c88e0a6ac","toolName":"bash","content":[{"type":"text","text":"./src/shared_agent_memory/http_api.py:100:                            include_inbox=_parse_bool(_first(query, \"include_inbox\")),\n./docs/system-v1.md:12:- authoritative place to implement memory promotion, consolidation, and conflict handling\n./docs/system-v1.md:38:- `consolidate_candidates(config)` handles dedup, conflict detection, promotion, and optional synthesis\n./docs/system-v1.md:42:Promotion and conflict semantics:\n./docs/system-v1.md:44:- `episode` and `inbox` records are candidates, not truth by default\n./docs/system-v1.md:47:- inbox memories can be promoted to `active` once they pass the configured threshold\n./docs/shared-agent-memory-rfc.md:105:- promotion of raw conversation snippets directly into durable truth\n./docs/shared-agent-memory-rfc.md:127:   The engine derives candidate memories from the raw material. These land as `inbox` records.\n./docs/shared-agent-memory-rfc.md:132:4. `promote`\n./docs/shared-agent-memory-rfc.md:148:- Agents may auto-create `episode` records and candidate `inbox` facts.\n./docs/shared-agent-memory-rfc.md:149:- Durable `profile` facts about Sebas should require explicit confirmation or a promotion rule.\n./docs/shared-agent-memory-rfc.md:157:- Every promoted memory must retain source/evidence references.\n./docs/shared-agent-memory-rfc.md:224:- `inbox`\n./docs/shared-agent-memory-rfc.md:380:These writes first create `inbox` records. They are candidates, not durable truth.\n./docs/shared-agent-memory-rfc.md:390:- promote only sufficiently grounded memories to `active`\n./docs/shared-agent-memory-rfc.md:395:- candidate facts extracted from episodes stay `inbox` until consolidation.\n./docs/shared-agent-memory-rfc.md:396:- profile/worldview memories should be promoted with explicit confirmation or policy.\n./docs/shared-agent-memory-rfc.md:408:- `consolidate(inbox_items)`\n./docs/shared-agent-memory-rfc.md:455:4. A conversation imported from outside the system stays raw until explicitly captured, then becomes `inbox`, then later may be promoted.\n./docs/http-api.md:51:- `episode` and `inbox` records are candidates, not canonical truth, until consolidation or explicit promotion.\n./src/shared_agent_memory/cli.py:91:    search.add_argument(\"--include-inbox\", action=\"store_true\")\n./src/shared_agent_memory/cli.py:235:        print(json.dumps(service.search(args.query, args.scopes, filters, include_inbox=args.include_inbox), indent=2))\n./docs/memory-consolidation-rfc.md:13:- Keep the existing capture→extract→consolidate→promote→index flow.\n./docs/memory-consolidation-rfc.md:25:- Autonomous truth rewriting without explicit promotion rules.\n./docs/memory-consolidation-rfc.md:52:1. **Select candidates** from `memories` with status `inbox` or `episode` older than a minimum age.\n./docs/memory-consolidation-rfc.md:59:4. **Promotion**: only promote to `active` when above confidence threshold.\n./docs/memory-consolidation-rfc.md:145:- Candidate age: >= 7 days for `episode` and `inbox`.\n./docs/memory-consolidation-rfc.md:151:- Promotion threshold: confidence >= 0.70\n./docs/memory-consolidation-rfc.md:169:- Daily light runs at 10:00 and 15:00: dedupe + promotion checks (no LLM by default).\n./src/shared_agent_memory/engine.py:94:                \"status\": \"inbox\",\n./tests/test_memory_service.py:50:def test_captured_conversation_stays_inbox_until_consolidated(tmp_path: Path) -> None:\n./tests/test_memory_service.py:59:    assert memory[\"status\"] == \"inbox\"\n./tests/test_memory_service.py:61:    search_without_inbox = service.search(\"active machine\", scopes=[\"project\"])\n./tests/test_memory_service.py:62:    assert search_without_inbox[\"results\"] == []\n./tests/test_memory_service.py:64:    promoted = service.consolidate([memory_id])\n./tests/test_memory_service.py:65:    assert promoted[\"promoted\"] == [memory_id]\n./tests/test_memory_service.py:395:def test_high_confidence_inbox_memory_auto_promotes_on_write(tmp_path: Path) -> None:\n./tests/test_memory_service.py:401:            \"id\": \"mem_auto_promote\",\n./tests/test_memory_service.py:404:            \"status\": \"inbox\",\n./tests/test_memory_service.py:405:            \"title\": \"Auto promote me\",\n./tests/test_memory_service.py:429:            \"id\": \"dec_inbox\",\n./tests/test_memory_service.py:432:            \"status\": \"inbox\",\n./tests/test_memory_service.py:444:            \"promote_confidence\": 1.0,\n./src/shared_agent_memory/service.py:160:        include_inbox: bool = False,\n./src/shared_agent_memory/service.py:170:        if not include_inbox:\n./src/shared_agent_memory/service.py:215:                    self.store.dumps({\"scopes\": scopes, \"filters\": filters, \"include_inbox\": include_inbox}),\n./src/shared_agent_memory/service.py:274:    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:\n./src/shared_agent_memory/service.py:275:        promoted: list[str] = []\n./src/shared_agent_memory/service.py:278:            for memory_id in inbox_items:\n./src/shared_agent_memory/service.py:315:                promoted.append(memory_id)\n./src/shared_agent_memory/service.py:316:        return {\"promoted\": promoted, \"linked\": linked}\n./src/shared_agent_memory/service.py:321:            \"promoted\": 0,\n./src/shared_agent_memory/service.py:331:        promote_threshold = float(config.get(\"promote_confidence\", 0.7))\n./src/shared_agent_memory/service.py:343:                    status = 'inbox'\n./src/shared_agent_memory/service.py:438:                if candidate[\"status\"] == \"inbox\" and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n./src/shared_agent_memory/service.py:443:                    stats[\"promoted\"] += 1\n./src/shared_agent_memory/service.py:444:                elif candidate[\"status\"] == \"active\" and candidate[\"type\"] in {\"episode\", \"artifact\", \"decision\", \"project\", \"procedural\", \"semantic\"} and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n./src/shared_agent_memory/service.py:445:                    # Normalize qualifying active records as already-promoted durable memories.\n./src/shared_agent_memory/service.py:446:                    stats[\"promoted\"] += 1\n./src/shared_agent_memory/service.py:1258:                    \"promote_confidence\": 0.7,\n./src/shared_agent_memory/service.py:1278:                    \"promote_confidence\": 0.7,\n./src/shared_agent_memory/service.py:1298:                    \"promote_confidence\": 0.7,\n"}],"isError":false,"timestamp":1778002094236}}
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{"type":"message","id":"63651117","parentId":"9675521a","timestamp":"2026-05-05T17:28:19.569Z","message":{"role":"toolResult","toolCallId":"call_xVmebiSl8P4ENLZDhgN6zJfo|fc_0d46162620b774590169fa28b352c4819595d66838d215fbce","toolName":"read","content":[{"type":"text","text":"        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                elif candidate[\"status\"] == \"active\" and candidate[\"type\"] in {\"episode\", \"artifact\", \"decision\", \"project\", \"procedural\", \"semantic\"} and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    # Normalize qualifying active records as already-promoted durable memories.\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\n[1250 more lines in file. Use offset=460 to continue.]"}],"isError":false,"timestamp":1778002099568}}
{"type":"message","id":"30d31b91","parentId":"63651117","timestamp":"2026-05-05T17:28:19.574Z","message":{"role":"toolResult","toolCallId":"call_FK1HIsbXemKSrlT1UOMqU0si|fc_0d46162620b774590169fa28b352cc8195b93276f1d9b9ae03","toolName":"read","content":[{"type":"text","text":"    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_high_confidence_inbox_memory_auto_promotes_on_write(tmp_path: Path) -> None:\n    service = make_service(tmp_path)\n    service.update_maintenance_job(\"job_consolidation_daily_10\", next_due_at=\"2026-03-20T10:00:00+00:00\")\n\n    memory = service.create_memory(\n        {\n            \"id\": \"mem_auto_promote\",\n            \"type\": \"episode\",\n            \"scope\": \"project\",\n            \"status\": \"inbox\",\n            \"title\": \"Auto promote me\",\n            \"content\": \"A durable session note that should become active automatically.\",\n            \"confidence\": 0.95,\n            \"freshness\": 0.9,\n        }\n    )\n\n    assert memory[\"status\"] == \"active\"\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_run_maintenance_daemon_wrapper_runs_from_repo_checkout(tmp_path: Path) -> None:\n    db_path = tmp_path / \"memory.sqlite3\"\n    service = MemoryService(str(db_path))\n    service.update_maintenance_job(\"job_consolidation_daily_10\", next_due_at=\"2026-03-20T10:00:00+00:00\")\n\n    script = Path(__file__).resolve().parents[1] / \"scripts\" / \"run-maintenance-daemon.sh\"\n    env = os.environ.copy()\n    env[\"DB_PATH\"] = str(db_path)\n    env[\"INTERVAL_SECONDS\"] = \"1\"\n    env[\"JITTER_SECONDS\"] = \"0\"\n    result = subprocess.run(\n        [str(script), \"--once\", \"--job-id\", \"job_consolidation_daily_10\"],\n        capture_output=True,\n        text=True,\n\n[120 more lines in file. Use offset=468 to continue.]"}],"isError":false,"timestamp":1778002099568}}
{"type":"message","id":"81d48b95","parentId":"30d31b91","timestamp":"2026-05-05T17:28:25.065Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28b54224819584aab3e6b0dd64d7\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ii4OhHJPI4DVkSO_pb6XIRbYnIVd5vBJQ2lmEQhMpMXfCQb1ooOU7I-43Vqg3A1Nj4VuaIpPf2t7JCovaphVbBKkumT-aAUlosmttWZuOijOTCrdv9alvPChviLNp6gzrIj2eYquBD-SjuoChPz7wdAe652dSmIWPfykJ7ciHm2e3bFg996Jis-WhqEHqdQx1laPD5z8GQi18KFR3dsfoeMU3Cd0HUD9DtD7RgisiE2Ge-5vM5FX06DOj_Jw7bwDuNNt6tFUMof7mehxHtNHbS4SCuJVBmB3J9qtWui-qwkiJkZylKrZvMiipgNXkQ3_rqQkyyEPwdWoWgohOM6OMlY5lzqj6RS0jZOmzZ4Nj2StCix2IULKwWW9WyTrCD1WCe-x3CAQMQ91QbbWkioJ1jZihRSk_L511hTicNpG5B_E8RILPIhjrVQY3wPDjgbCCCM6hBe2a3-mU7Er5eRzOvHmCgsJpGTiSbxd-hWDnezOHsc3NE0LAWhiEf5K3rU8TioyLE2x-uYfZM8QFpsvfGE5ThkyCEX05Pk3LsPY2pA9qnFMOpusTvvBO6Y5hQhLU8y9ZDMswoSUtSTOllfig5w5SDe40bhmB49QWb3E41oz5WA3Vb88mfaJAFVstkL8xSvCsG7kG9pNR_8oWclvTw9gGZEW5YVyGh6KvjDwZ5FzVq1qsrQV9B6q1QXknPigJjNY7N4wpkR_IgmmBIO6b-orPR2ti7jgCgBNkh6zSpKvYAXwgb9mVnCsrr444jwW7orhVR91Et5VDmzyFcGVnQL00nV6Kx06_EQkugZqbO0wmZmrK9RRzD_MG08x5Ep6zGX590xJr1-U5ACgBRuPlXrVK9po43iHECCOcyZU0KaebDTV8KOozpf-BDo9bQmqFd9p2OqqW22i8caPEcHzeMQpgTCaFsuO1So7hBzvdMycAyD5qfEu7zEoQ8YcWCoq9ojS5aYCECTjcODsNgjwCcnWBB6t_VMDUff7i--b5fclrM=\",\"summary\":[]}"},{"type":"toolCall","id":"call_CKohfwNV3VyxhOnM0R27pRiv|fc_0d46162620b774590169fa28b8e86c8195adc445e76b9757c9","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json\nconn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\nconn.row_factory=sqlite3.Row\nprint([r[0] for r in conn.execute(\"select name from sqlite_master where type='table' order by name\")])\nPY"}},{"type":"toolCall","id":"call_X0aVOTy4CoiRS7h9gWg82lLe|fc_0d46162620b774590169fa28b8e880819585c504c35c1215fe","name":"bash","arguments":{"command":"cd /home/sebas/agents-database && rg -n \"auto_promote|status = 'active'|status\\\": \\\"active\\\"|promote_confidence\" src/shared_agent_memory/service.py | head -80"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":9467,"output":186,"cacheRead":20480,"cacheWrite":0,"totalTokens":30133,"cost":{"input":0.0236675,"output":0.00279,"cacheRead":0.0051199999999999996,"cacheWrite":0,"total":0.0315775}},"stopReason":"toolUse","timestamp":1778002099569,"responseId":"resp_0d46162620b774590169fa28b4446081959d648d06d548e784"}}
{"type":"message","id":"0a31e500","parentId":"81d48b95","timestamp":"2026-05-05T17:28:25.125Z","message":{"role":"toolResult","toolCallId":"call_CKohfwNV3VyxhOnM0R27pRiv|fc_0d46162620b774590169fa28b8e86c8195adc445e76b9757c9","toolName":"bash","content":[{"type":"text","text":"['artifacts', 'ingestion_events', 'maintenance_jobs', 'maintenance_runs', 'memories', 'memories_fts', 'memories_fts_config', 'memories_fts_content', 'memories_fts_data', 'memories_fts_docsize', 'memories_fts_idx', 'memory_conflicts', 'memory_links', 'profiles', 'projects', 'repos', 'retrieval_logs', 'sources', 'task_runs', 'tasks']\n"}],"isError":false,"timestamp":1778002105125}}
{"type":"message","id":"ca8e4405","parentId":"0a31e500","timestamp":"2026-05-05T17:28:25.125Z","message":{"role":"toolResult","toolCallId":"call_X0aVOTy4CoiRS7h9gWg82lLe|fc_0d46162620b774590169fa28b8e880819585c504c35c1215fe","toolName":"bash","content":[{"type":"text","text":"287:                      AND status = 'active'\n331:        promote_threshold = float(config.get(\"promote_confidence\", 0.7))\n344:                    OR (type = 'episode' AND status = 'active')\n360:                    WHERE status = 'active'\n1258:                    \"promote_confidence\": 0.7,\n1278:                    \"promote_confidence\": 0.7,\n1298:                    \"promote_confidence\": 0.7,\n"}],"isError":false,"timestamp":1778002105125}}
{"type":"message","id":"ff932f87","parentId":"ca8e4405","timestamp":"2026-05-05T17:28:31.424Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting job files**\n\nI need to query counts by source kind or manually check something. It seems essential to look into session end ingestion events. I’ll inspect the job files and also review the ingestion_events table since that might include the source reference. I’m planning to use Python for this, but I want to ensure I'm covering all necessary aspects for effective data retrieval and analysis. Let's get into this!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28ba5d308195acb57e6b5dbcac16\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ii_V07WThiIfoik5FQUW-uxGduGYgoHPFc1qhPxezq9cH72vFS8UFrNXVMRpL88GqiO2-Ve9ZLM2s0CVV_7nGaptqE2FUC-PhjrHrTtERGXp7MCbnflPuLnaD6vi2U1jJ8aepwYO9hDTKs2FlVIGF1bjVUEQ6zMpUsNqbc3_oDJAzLROp_3MD8-WJpzZxoENoa70aCaR38nXYdDydZyUCWvHschSzuvIOeybaWLPKlC16193l33EISheICppRXgz2yg4kiQmuxZngo3XNgpCAqTsqIZMUL5NxPMyT2pHp2WAgKtEmWkKGd-k9U2os9rSIKvNYbzJ47SNyiCqxOTxn5Pp69YJz_ueEdzrbWSTGKEaB7kY412kWmhDnOIbOkoObx-Nx023-YBKAuCvYwrGTeFXeTQMllJSPhNYlfHgIW4byOwvMh21c2zarhMp9WPUofOCTV8ah3ocJN_zQEnTb7fVN2ZuqGQdB0nCobeBAVrMVHDyCabS5nUQwQslm3BO8g0FfKaj5ReV-eBi7IW9CRGJG55AfJJoh6m4zXJ9P-o9Hjr6gX3xyKm6tIbet0HZdC9mw_7wBN-cyXT0fp_-KcoNQtGTUdhroTpA5bD4ACGWHccsTYN9GV2T5fTCbCxqQur1ZvhbP5EdH5aLtkhquMDZnVMV3XP1-nfoIJ2i9bxs1zQiAHvJPZ4ebVDXbgcJ3If2AenpPaKa1-5ija6JSAKHuWbLfMzV3lhIdA-rFcj1ZneJVxMEO-C_vvXZDD1DcFeKhV7owC4WVwZag2HSMYcethkqUE-krWbIrn6u4Ldw5pHA9liqAxHSZb6Ga2DMFGZl0R_pb29Et6PukpukpREXcpZa1DW3E2jF9Z9PC7HbwT-Jz-_KlSPA-Yk-qp9KE9-Dk4CdbIxOR2vwjbAPEnZLPijsNv_UnysFCTFtPcVORJ2lDjsPGHlkRQfjY6iFye6Tb4NOz5zj6qcZO32OjFgwQde_4Ouxc8Q5tyLYwFgq-B083PZTB3BvaXREMkKiKcT2wt-22RS8aaVuMWO0B8OuuBftAoNDZPrlJ7XWIzc4TQVRAZEp_w2Pe6jGICQDjyvfT_XYOSkA51LuA1DzkbjlEYXOpthPzFgDPR084xxJkc3n7F6wV5J2390p6Eb1-Kj86AvpDShLCTbXydXS4ydQQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting job files**\\n\\nI need to query counts by source kind or manually check something. It seems essential to look into session end ingestion events. I’ll inspect the job files and also review the ingestion_events table since that might include the source reference. I’m planning to use Python for this, but I want to ensure I'm covering all necessary aspects for effective data retrieval and analysis. Let's get into this!\"}]}"},{"type":"toolCall","id":"call_FGH1uiXzrx2PnqGTu6WqoBgE|fc_0d46162620b774590169fa28bf2e9c81958a720b81f4134f20","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json\nconn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\nconn.row_factory=sqlite3.Row\nfor table in ['ingestion_events','memories']:\n    cols=conn.execute(f'PRAGMA table_info({table})').fetchall()\n    print('\\nTABLE',table)\n    for c in cols:\n        print(c['name'], c['type'])\nPY"}},{"type":"toolCall","id":"call_9DtNbjm3q2YQbrp0AJszcE3T|fc_0d46162620b774590169fa28bf2eac8195ad033bcc70808474","name":"read","arguments":{"path":"/home/sebas/pi-config/sessions/session-memory-jobs/20260505T172551604683Z.json","offset":1,"limit":200}},{"type":"toolCall","id":"call_Y3GzXiuIOT6xGWoB5I6ZjI8a|fc_0d46162620b774590169fa28bf2eb481959c4a2953cee4f0ab","name":"read","arguments":{"path":"/home/sebas/pi-config/sessions/session-memory-jobs/20260505T172551604683Z.log","offset":1,"limit":200}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":652,"output":262,"cacheRead":29696,"cacheWrite":0,"totalTokens":30610,"cost":{"input":0.0016300000000000002,"output":0.00393,"cacheRead":0.007424,"cacheWrite":0,"total":0.012984}},"stopReason":"toolUse","timestamp":1778002105126,"responseId":"resp_0d46162620b774590169fa28b97d5481958c0a5eb29f27f1e7"}}
{"type":"message","id":"77bc8e11","parentId":"ff932f87","timestamp":"2026-05-05T17:28:31.527Z","message":{"role":"toolResult","toolCallId":"call_FGH1uiXzrx2PnqGTu6WqoBgE|fc_0d46162620b774590169fa28bf2e9c81958a720b81f4134f20","toolName":"bash","content":[{"type":"text","text":"\nTABLE ingestion_events\nid TEXT\ntrigger_type TEXT\nsource_id TEXT\nrequested_scope TEXT\njob_state TEXT\nprocessor_outcome TEXT\nretry_count INTEGER\nerror_message TEXT\ncreated_at TEXT\nupdated_at TEXT\nprocessed_at TEXT\n\nTABLE memories\nid TEXT\ntype TEXT\nscope TEXT\nstatus TEXT\nproject_id TEXT\nrepo_id TEXT\nagent_id TEXT\nsource_kind TEXT\ntitle TEXT\ncontent TEXT\nsummary TEXT\nconfidence REAL\nfreshness REAL\ncreated_at TEXT\nupdated_at TEXT\nobserved_at TEXT\nsource_ref TEXT\nevidence_ref TEXT\nembedding_json TEXT\nmetadata_json TEXT\nschema_version TEXT\nsubtype TEXT\nrun_id TEXT\ntask_id TEXT\nurl TEXT\ndomain TEXT\norigin_agent TEXT\n"}],"isError":false,"timestamp":1778002111527}}
{"type":"message","id":"40f59578","parentId":"77bc8e11","timestamp":"2026-05-05T17:28:31.527Z","message":{"role":"toolResult","toolCallId":"call_9DtNbjm3q2YQbrp0AJszcE3T|fc_0d46162620b774590169fa28bf2eac8195ad033bcc70808474","toolName":"read","content":[{"type":"text","text":"{\n  \"job_id\": \"20260505T172551604683Z\",\n  \"queued_at\": \"2026-05-05T17:25:51.604736+00:00\",\n  \"status\": \"queued\",\n  \"job_type\": \"session_memory_analysis\",\n  \"payload\": {\n    \"session_id\": \"46c88030\",\n    \"session_file\": \"/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-44-38-590Z_019df862-33be-7759-b796-3d6b48b23493.jsonl\",\n    \"reason\": \"before_switch:resume\",\n    \"cwd\": \"/home/sebas\",\n    \"scope\": \"global\",\n    \"user_messages\": 11,\n    \"assistant_messages\": 56,\n    \"last_user_text\": \"and yeah harden like you suggested\",\n    \"last_assistant_text\": \"Done. Manual trigger, last 10 sessions: - `6` saved memories - `4` analyzed and skipped - `0` failed New saved memory ids: - `mem_812ea22ea06d4368bdb66fe531e64fdc` - `mem_f7f168513be944169a0a7790b25293f0` - `mem_02a8d3bf9ae04a85aa700eb4d5c045e9` - `mem_c636b2dc79794c57aded818ba6fdae2f` - `mem_5be93517d726499fbdf0f614da57ef6e` - `mem_68d91c0d9b6244f384fac0abfff678e7` Hardening done: - analyzer now resolves `pi` via absolute/fallback paths - dispatcher now logs analyzer output to per-job `.log` files - service env includes: - `PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi` - user service restarted successfully - reboot persistence: yes Current service: - `install-session-memory-dispatcher.service` - active and enabled under user systemd Note: - rerunning already-completed jobs created some duplicate-style memories for previously saved sessions - future runs should be fine; this was from manual replay If you want, next I can: 1. dedupe the duplicates created today 2. do one fresh close/switch test and verify auto path end-to-end without manual replay\",\n    \"branch\": [\n      {\n        \"type\": \"model_change\",\n        \"id\": \"0ac359e9\",\n        \"parentId\": null,\n        \"timestamp\": \"2026-05-05T13:44:39.932Z\",\n        \"provider\": \"openai-codex\",\n        \"modelId\": \"gpt-5.4\"\n      },\n      {\n        \"type\": \"thinking_level_change\",\n        \"id\": \"ce055adc\",\n        \"parentId\": \"0ac359e9\",\n        \"timestamp\": \"2026-05-05T13:44:39.932Z\",\n        \"thinkingLevel\": \"low\"\n      },\n      {\n        \"type\": \"message\",\n        \"id\": \"78bd71eb\",\n        \"parentId\": \"ce055adc\",\n        \"timestamp\": \"2026-05-05T13:44:54.745Z\",\n        \"message\": {\n          \"role\": \"user\",\n          \"content\": [\n            {\n              \"type\": \"text\",\n              \"text\": \"how is the memory service going? latest runs?\"\n            }\n          ],\n          \"timestamp\": 1777988694740\n        }\n      },\n      {\n        \"type\": \"message\",\n        \"id\": \"ce4f751d\",\n        \"parentId\": \"78bd71eb\",\n        \"timestamp\": \"2026-05-05T13:45:02.104Z\",\n        \"message\": {\n          \"role\": \"assistant\",\n          \"content\": [\n            {\n              \"type\": \"thinking\",\n              \"thinking\": \"**Checking memory service status**\\n\\nI need to find out about the memory service's current status and its latest runs. I think checking the agents database and possibly the service logs could help. It might make sense to use a memory-read skill since the user is looking for current information. I could inspect local services or search memories; the term \\\"memory service\\\" likely refers to the agents database. So, gathering status from the API or checking service status endpoints seems essential!\",\n              \"thinkingSignature\": \"{\\\"id\\\":\\\"rs_0d852cbc8d6115830169f9f45809dc8194ac8c50eae10cd6fb\\\",\\\"type\\\":\\\"reasoning\\\",\\\"encrypted_content\\\":\\\"gAAAAABp-fRdZfsQMmKiQr7IuqZaGQJxPyF7qwM13ZMJvYbIm637odUWw6iZaKyQmWshCgal7G9dXSxX-EUn6SgN1ckPNEnDzSzXurlLRqjx6QQWWY2vcjiPWVWgKCVfgazlANV8wD71DLSfjTH75Coj2z3X2iHpiO8x-7tW4r4w4Q6vav7bSjQq5MC7b2wVxUHKzO4lMiMmbbxg0ZK-gSJZmYworvKCr3N2RMuon02zOYHtiPzIaAW62s8wAzYhXOqN8jLSCsd-HhJqEXracIhnWrmsVviCps5y1aa_gznU227vx2EbypcdjpSq07uhDxtGDA2IUb4BBrhEu9m-9cDVdWCpXUjoXvH3KwBd2xXKHlOJgxDDn3EFml-3Y09O8-pljlk2gE9164AX_W3wMznPH0914npLUNR2ekIhyjYHJAr7IEejkU21jNIg4ZS0T_gnn7zJV0gJ8xTKc0ZiAoLq6jpJvIFV55oeXwIz8oC_NDeRg3fD51ctwWeTZd8Qf8biqBNvYM-3IDSPMXu2xt0uVAiev0Dkey_k-HWWZb25VAmd4rmZk68aDcNnbERc72gKaZsVH-NCQtMnxpf_3p2IGsfqZnXjoAWaPC2_VruPEKvgli20sJFZLCGP8_9wiFbjtbDjnQBQdMMV7t5aswPfDSs-pm_Zn4G2gH0M2oQiGyuH5_GBY14j4RQ32UWV8vEqkx_E3Vwz2kb8gbs5RdD0N4Yr9Z4G6empmWMTHJSIWVcQa1dWKIgkOaw8XG5vtrO8KA3AOwuK0JvcS8wnLYSnaL2ngQzzxIZRGXy7QnuWjOlTci8xKuqv9eNxiogSfxNfuX-34KnfRTnuZweuw3kuIypomc0axNe9h2UjRaPlYkmZcuuavP1fvN_EAKdBD9eIYgGTpAjdNd4vfAqYmtcZGKvwqD1FEtzibED9DQqKkNJtDC0rAVIKjdj_vajq5oWCTz_gMzKOoUxtJ-B89bcZtkSyIvwwFdVn0J9WUa3HPzBpy0OPOcMTHAeO8qjnBBBIoVRmGIeLr37MzWzNVmp5xc3d_HOOPH42SgdrKtNyILa3jwai_IKS9JWPmh2pAktQMCPyPkN83yfPdobIaNBbE_idbJZ_Zbod31KYTPenbgsvraEeTgS4RPjhmhL4Awk27VFBNdtgSoKFeK9e98KqU1EzdysH9-njjgqUx0QbUn7AN0JAdNdwieWpC_MJ0waMwZD-SxNA-okpPhDmdBKlv4Vwl9tB_KcFNLp7Ri_HQ8D9Q0uybVrAE61y8RQh5baN9M1ajH5Wo4SYj2oL9E6aGjLtqi8sIBVrfLJQyEe7dZQbze26xOQZvYMAlV4EWGchQqVqdZgWhiUKsKN6dsfjPM5bFAUCV-xCGvRGyG9P9KxkeTQeZZUWV4lQYHx54e-EA59y0QY6mjRNN8JD4tK50V5VbocTfRlJZPZbbHX3YYL64ydTtWmfrNLdUDg7SYwqhOgA4TMFnP5yytaYxyIJ-RMFczTsqKZXYQR9RNtCZq6_i5OKxomTt7K4lQIvKNeDFMwk1q6g\\\",\\\"summary\\\":[{\\\"type\\\":\\\"summary_text\\\",\\\"text\\\":\\\"**Checking memory service status**\\\\n\\\\nI need to find out about the memory service's current status and its latest runs. I think checking the agents database and possibly the service logs could help. It might make sense to use a memory-read skill since the user is looking for current information. I could inspect local services or search memories; the term \\\\\\\"memory service\\\\\\\" likely refers to the agents database. So, gathering status from the API or checking service status endpoints seems essential!\\\"}]}\"\n            },\n            {\n              \"type\": \"toolCall\",\n              \"id\": \"call_Rhjt6X3LrnHl6ttHliPK1uFU|fc_0d852cbc8d6115830169f9f45de4488194ad0be0d63b9f9ea7\",\n              \"name\": \"bash\",\n              \"arguments\": {\n                \"command\": \"set -e\\nprintf 'STATUS\\\\n'\\ncurl -sf http://100.116.176.16:8091/api/status || true\\nprintf '\\\\n\\\\nROOT\\\\n'\\ncurl -sf http://100.116.176.16:8091/ || true\\nprintf '\\\\n\\\\nOPENAPI?\\\\n'\\ncurl -sf http://100.116.176.16:8091/openapi.json || true\",\n                \"timeout\": 15\n              }\n            },\n            {\n              \"type\": \"toolCall\",\n              \"id\": \"call_bTQg7QCVQOl7qteQl50hO0yL|fc_0d852cbc8d6115830169f9f45de4588194ab579f888979c45d\",\n              \"name\": \"bash\",\n              \"arguments\": {\n                \"command\": \"set -e\\nps -ef | grep -i '[a]gents-database' || true\\nsystemctl --no-pager --full status agents-database 2>/dev/null || true\\njournalctl -u agents-database -n 40 --no-pager 2>/dev/null || true\",\n                \"timeout\": 15\n              }\n            }\n          ],\n          \"api\": \"openai-codex-responses\",\n          \"provider\": \"openai-codex\",\n          \"model\": \"gpt-5.4\",\n          \"usage\": {\n            \"input\": 3968,\n            \"output\": 299,\n            \"cacheRead\": 0,\n            \"cacheWrite\": 0,\n            \"totalTokens\": 4267,\n            \"cost\": {\n              \"input\": 0.00992,\n              \"output\": 0.004485,\n              \"cacheRead\": 0,\n              \"cacheWrite\": 0,\n              \"total\": 0.014405000000000001\n            }\n          },\n          \"stopReason\": \"toolUse\",\n          \"timestamp\": 1777988694742,\n          \"responseId\": \"resp_0d852cbc8d6115830169f9f45716588194a436c10ff146e102\"\n        }\n      },\n      {\n        \"type\": \"message\",\n        \"id\": \"1a272a01\",\n        \"parentId\": \"ce4f751d\",\n        \"timestamp\": \"2026-05-05T13:45:02.253Z\",\n        \"message\": {\n          \"role\": \"toolResult\",\n          \"toolCallId\": \"call_Rhjt6X3LrnHl6ttHliPK1uFU|fc_0d852cbc8d6115830169f9f45de4488194ad0be0d63b9f9ea7\",\n          \"toolName\": \"bash\",\n          \"content\": [\n            {\n              \"type\": \"text\",\n              \"text\": \"STATUS\\n{\\\"ok\\\": true, \\\"db_path\\\": \\\"/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\\", \\\"dashboard\\\": {\\\"active_tasks\\\": [{\\\"id\\\": \\\"task_33d854e72d094660a2fb293a8723f5c5\\\", \\\"title\\\": \\\"Fix GitHub credential automation so repo pushes and PR creation use the intended account reliably\\\", \\\"intent\\\": \\\"The current credential helper flow can select the wrong GitHub identity during multi-repo push and PR automation. Investigate the root cause across gh auth git credential helper and repo remote handling, then design a safer simpler script or convention.\\\", \\\"kind\\\": \\\"subtask\\\", \\\"status\\\": \\\"open\\\", \\\"priority\\\": 9, \\\"project_id\\\": \\\"proj_personal_agent\\\", \\\"repo_id\\\": \\\"repo_personal_agent\\\", \\\"parent_task_id\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"origin\\\": \\\"implementation\\\", \\\"owner_agent\\\": \\\"personal-agent\\\", \\\"blocked_reason\\\": null, \\\"requires_human_input\\\": false, \\\"due_at\\\": null, \\\"created_at\\\": \\\"2026-03-14T22:07:02.464013+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:13:45.052882+00:00\\\", \\\"schema_version\\\": \\\"shared-agent-memory-v2\\\", \\\"run_id\\\": null, \\\"metadata\\\": {\\\"acceptance\\\": [\\\"push uses intended GitHub account without token workarounds\\\", \\\"PR creation works consistently across public and private repos\\\", \\\"documented operator flow is simpler than the current state\\\"], \\\"area\\\": \\\"git_credentials\\\", \\\"classification_note\\\": \\\"User confirmed this credential automation task is post-V1 and not a blocker for V1 closeout.\\\", \\\"kind\\\": \\\"low_priority_followup\\\", \\\"milestone\\\": \\\"post-V1\\\", \\\"relevant_repos\\\": [\\\"/Users/sebas/personal-agent\\\", \\\"/Users/sebas/agents-database\\\", \\\"/Users/sebas/ai-dev-workflow\\\", \\\"/Users/sebas/ballbox-company-agent\\\"], \\\"requested_on\\\": \\\"2026-03-14\\\", \\\"v1_closeout_epic_id\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"v1_excluded\\\": true, \\\"why_now\\\": \\\"PR creation required direct credential workarounds; automation should simplify this later\\\"}}], \\\"blocked_tasks\\\": [], \\\"recent_runs\\\": [{\\\"id\\\": \\\"run_d37c8577c9674333b7157a945274385a\\\", \\\"task_id\\\": \\\"task_3d7271f7d19e456791b5796695fbe2fa\\\", \\\"agent_id\\\": \\\"personal-agent\\\", \\\"status\\\": \\\"completed\\\", \\\"result_summary\\\": \\\"Local readiness is partial: repo docs and runtime surface exist, current status API works, but full verification is not yet proven in this sandbox.\\\", \\\"error_message\\\": null, \\\"started_at\\\": \\\"2026-03-15T14:21:34.691945+00:00\\\", \\\"completed_at\\\": \\\"2026-03-15T14:22:58.675711+00:00\\\", \\\"created_at\\\": \\\"2026-03-15T14:21:34.691945+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:22:58.675711+00:00\\\", \\\"input_payload\\\": {\\\"mode\\\": \\\"codex-agentic\\\"}, \\\"metadata\\\": {}}, {\\\"id\\\": \\\"run_ce032d1c6fbf4e3aa23a26df10d8d91f\\\", \\\"task_id\\\": \\\"task_46bb6d81471e47e39340198a889252a1\\\", \\\"agent_id\\\": \\\"personal-agent\\\", \\\"status\\\": \\\"failed\\\", \\\"result_summary\\\": null, \\\"error_message\\\": \\\"error: unexpected argument '--ask-for-approval' found\\\\n\\\\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\\\\n\\\\nUsage: codex exec --sandbox <SANDBOX_MODE> [PROMPT]\\\\n\\\\nFor more information, try '--help'.\\\", \\\"started_at\\\": \\\"2026-03-15T14:19:23.634939+00:00\\\", \\\"completed_at\\\": \\\"2026-03-15T14:19:23.690219+00:00\\\", \\\"created_at\\\": \\\"2026-03-15T14:19:23.634939+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:19:23.690219+00:00\\\", \\\"input_payload\\\": {\\\"mode\\\": \\\"codex-agentic\\\"}, \\\"metadata\\\": {}}, {\\\"id\\\": \\\"run_d7ea2a60bbd74e9b8fdaa628115d7cbd\\\", \\\"task_id\\\": \\\"task_4a80a42f5d424f7191b151c3640db934\\\", \\\"agent_id\\\": \\\"personal-agent\\\", \\\"status\\\": \\\"failed\\\", \\\"result_summary\\\": null, \\\"error_message\\\": \\\"error: unexpected argument '--ask-for-approval' found\\\\n\\\\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\\\\n\\\\nUsage: codex exec --sandbox <SANDBOX_MODE> [PROMPT]\\\\n\\\\nFor more information, try '--help'.\\\", \\\"started_at\\\": \\\"2026-03-15T14:16:58.205690+00:00\\\", \\\"completed_at\\\": \\\"2026-03-15T14:16:58.257806+00:00\\\", \\\"created_at\\\": \\\"2026-03-15T14:16:58.205690+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:16:58.257806+00:00\\\", \\\"input_payload\\\": {\\\"mode\\\": \\\"codex-agentic\\\"}, \\\"metadata\\\": {}}], \\\"recent_artifacts\\\": [{\\\"id\\\": \\\"art_d9ed019809c24381919c280f28481652\\\", \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\", \\\"artifact_type\\\": \\\"delegation_note\\\", \\\"title\\\": \\\"Handoff personal-agent -> ai-dev-workflow\\\", \\\"content\\\": \\\"{\\\\\\\"completed_at\\\\\\\": \\\\\\\"2026-03-15T14:18:48.588043+00:00\\\\\\\", \\\\\\\"error_message\\\\\\\": null, \\\\\\\"from_agent\\\\\\\": \\\\\\\"personal-agent\\\\\\\", \\\\\\\"payload\\\\\\\": {\\\\\\\"expected_artifacts\\\\\\\": [\\\\\\\"report\\\\\\\"], \\\\\\\"idempotency_key\\\\\\\": \\\\\\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\\\\\", \\\\\\\"intent\\\\\\\": \\\\\\\"Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\\\\\\\", \\\\\\\"route\\\\\\\": {\\\\\\\"codex_instruction\\\\\\\": \\\\\\\"\\\\\\\", \\\\\\\"delegation_target\\\\\\\": \\\\\\\"ai-dev-workflow\\\\\\\", \\\\\\\"planning_source\\\\\\\": \\\\\\\"fallback\\\\\\\", \\\\\\\"primary_agent\\\\\\\": \\\\\\\"code\\\\\\\", \\\\\\\"reason\\\\\\\": \\\\\\\"code work (repo)\\\\\\\", \\\\\\\"secondary_agent\\\\\\\": null}, \\\\\\\"task_id\\\\\\\": \\\\\\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\\\\\"}, \\\\\\\"reason\\\\\\\": \\\\\\\"code work (repo)\\\\\\\", \\\\\\\"result_summary\\\\\\\": \\\\\\\"ai-dev-workflow accepted the handoff and persisted intake context.\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"accepted\\\\\\\", \\\\\\\"to_agent\\\\\\\": \\\\\\\"ai-dev-workflow\\\\\\\"}\\\", \\\"format\\\": \\\"json\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"migration:v2\\\", \\\"created_at\\\": \\\"2026-04-30T17:32:27.377428+00:00\\\", \\\"updated_at\\\": \\\"2026-04-30T17:32:27.377432+00:00\\\", \\\"metadata\\\": {}}, {\\\"id\\\": \\\"art_4e83c1c6fdf94b3ca70f6e0dbb9be2d7\\\", \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\", \\\"artifact_type\\\": \\\"delegation_note\\\", \\\"title\\\": \\\"Handoff personal-agent -> ai-dev-workflow\\\", \\\"content\\\": \\\"{\\\\\\\"completed_at\\\\\\\": \\\\\\\"2026-03-15T14:18:33.506326+00:00\\\\\\\", \\\\\\\"error_message\\\\\\\": null, \\\\\\\"from_agent\\\\\\\": \\\\\\\"personal-agent\\\\\\\", \\\\\\\"payload\\\\\\\": {\\\\\\\"expected_artifacts\\\\\\\": [\\\\\\\"report\\\\\\\"], \\\\\\\"idempotency_key\\\\\\\": \\\\\\\"task_ed6a38c1b49f4e829e067d49d206148b\\\\\\\", \\\\\\\"intent\\\\\\\": \\\\\\\"Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\\\\\\\", \\\\\\\"route\\\\\\\": {\\\\\\\"codex_instruction\\\\\\\": \\\\\\\"\\\\\\\", \\\\\\\"delegation_target\\\\\\\": \\\\\\\"ai-dev-workflow\\\\\\\", \\\\\\\"planning_source\\\\\\\": \\\\\\\"fallback\\\\\\\", \\\\\\\"primary_agent\\\\\\\": \\\\\\\"code\\\\\\\", \\\\\\\"reason\\\\\\\": \\\\\\\"code work (fix)\\\\\\\", \\\\\\\"secondary_agent\\\\\\\": null}, \\\\\\\"task_id\\\\\\\": \\\\\\\"task_ed6a38c1b49f4e829e067d49d206148b\\\\\\\"}, \\\\\\\"reason\\\\\\\": \\\\\\\"code work (fix)\\\\\\\", \\\\\\\"result_summary\\\\\\\": \\\\\\\"ai-dev-workflow accepted the handoff and persisted intake context.\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"accepted\\\\\\\", \\\\\\\"to_agent\\\\\\\": \\\\\\\"ai-dev-workflow\\\\\\\"}\\\", \\\"format\\\": \\\"json\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"migration:v2\\\", \\\"created_at\\\": \\\"2026-04-30T17:32:27.377374+00:00\\\", \\\"updated_at\\\": \\\"2026-04-30T17:32:27.377379+00:00\\\", \\\"metadata\\\": {}}, {\\\"id\\\": \\\"art_7ed9c6867a814f7fb9ad4f45fc9a578e\\\", \\\"task_id\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"artifact_type\\\": \\\"delegation_note\\\", \\\"title\\\": \\\"Handoff personal-agent -> implementation-agent\\\", \\\"content\\\": \\\"{\\\\\\\"completed_at\\\\\\\": \\\\\\\"2026-03-14T21:29:18.517341+00:00\\\\\\\", \\\\\\\"error_message\\\\\\\": null, \\\\\\\"from_agent\\\\\\\": \\\\\\\"personal-agent\\\\\\\", \\\\\\\"payload\\\\\\\": {\\\\\\\"artifact_id\\\\\\\": \\\\\\\"art_598b5da707dd47709fc0fe9bb3e79e28\\\\\\\", \\\\\\\"epic_id\\\\\\\": \\\\\\\"task_afed9eaf504f42b99348a815d56148cc\\\\\\\", \\\\\\\"expected_artifacts\\\\\\\": [\\\\\\\"migration_report\\\\\\\", \\\\\\\"v1_cross_reference_report\\\\\\\"], \\\\\\\"idempotency_key\\\\\\\": \\\\\\\"task_afed9eaf504f42b99348a815d56148cc\\\\\\\", \\\\\\\"remaining_task_ids\\\\\\\": [\\\\\\\"task_a240986d7c8a4474ba5246536769deed\\\\\\\", \\\\\\\"task_c0b65186af7d4013b2c1d2906ea6a28e\\\\\\\"]}, \\\\\\\"reason\\\\\\\": \\\\\\\"Finish remaining V1 migration and GitHub cross-reference work\\\\\\\", \\\\\\\"result_summary\\\\\\\": \\\\\\\"Finished legacy bridge and V1 multi-repo cross-reference work; shared DB now contains the resume anchors and completion artifacts.\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\", \\\\\\\"to_agent\\\\\\\": \\\\\\\"implementation-agent\\\\\\\"}\\\", \\\"format\\\": \\\"json\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"migration:v2\\\", \\\"created_at\\\": \\\"2026-04-30T17:32:27.377239+00:00\\\", \\\"updated_at\\\": \\\"2026-04-30T17:32:27.377247+00:00\\\", \\\"metadata\\\": {}}, {\\\"id\\\": \\\"art_eb70bf77f5524240a4782e1e6e5eddb3\\\", \\\"task_id\\\": \\\"task_3d7271f7d19e456791b5796695fbe2fa\\\", \\\"artifact_type\\\": \\\"execution_state\\\", \\\"title\\\": \\\"Execution state snapshot\\\", \\\"content\\\": \\\"{\\\\n  \\\\\\\"action_results\\\\\\\": [\\\\n    {\\\\n      \\\\\\\"task_id\\\\\\\": \\\\\\\"task_561a1444260c4c23b716ab65ad2ebe3d\\\\\\\",\\\\n      \\\\\\\"type\\\\\\\": \\\\\\\"create_followup_task\\\\\\\"\\\\n    }\\\\n  ],\\\\n  \\\\\\\"outcome\\\\\\\": \\\\\\\"complete\\\\\\\",\\\\n  \\\\\\\"run_id\\\\\\\": \\\\\\\"run_d37c8577c9674333b7157a945274385a\\\\\\\",\\\\n  \\\\\\\"summary\\\\\\\": \\\\\\\"Local readiness is partial: repo docs and runtime surface exist, current status API works, but full verification is not yet proven in this sandbox.\\\\\\\"\\\\n}\\\", \\\"format\\\": \\\"json\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:22:58.674979+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:22:58.674979+00:00\\\", \\\"metadata\\\": {\\\"outcome\\\": \\\"complete\\\", \\\"task_run_id\\\": \\\"run_d37c8577c9674333b7157a945274385a\\\"}}, {\\\"id\\\": \\\"art_f2e3c950a9b8427e89e083457683077e\\\", \\\"task_id\\\": \\\"task_3d7271f7d19e456791b5796695fbe2fa\\\", \\\"artifact_type\\\": \\\"report\\\", \\\"title\\\": \\\"CLI Smoke Readiness Summary\\\", \\\"content\\\": \\\"## Findings\\\\n- Confirmed locally on 2026-03-15: `python3 scripts/personal.py --json status` works and returns live task/run state. Current snapshot shows `active_task_count=19`, `blocked_task_count=2`, `pending_approval_count=0`.\\\\n- Confirmed locally on 2026-03-15: `codex exec --help` matches the newer CLI shape and supports `-C/--cd`, `--add-dir`, `--output-last-message`; it does not advertise the older `--ask-for-approval` flow.\\\\n- Confirmed from repo state: local edits in `personal_agent/planner.py`, `personal_agent/runtime.py`, and `tests/test_personal.py` replace the obsolete `--ask-for-approval` and `--search` flags with `-C`, and add test coverage for that command shape.\\\\n- Confirmed from runtime evidence: recent stored runs failed with exact error `unexpected argument '--ask-for-approval' found`, so the earlier smoke failures were real and explainable.\\\\n- Confirmed from docs/scripts: the only declared repo gate is `./scripts/run-checks.sh`, which runs `python3 -m unittest discover -s tests`.\\\\n- Not confirmed: full repo test pass. In this read-only sandbox, the test suite fails before exercising app behavior because Python cannot create temp dirs: `FileNotFoundError: No usable temporary directory found`.\\\\n- Not confirmed: shared-memory operational readiness under this sandbox. Targeted memory search failed with `sqlite3.OperationalError: attempt to write a readonly database`, so shared DB interaction is not proven here.\\\\n- Not confirmed: production readiness. Docs still describe dashboard, daemon, worker, approvals, and multi-repo handoff topology, but this run did not validate dashboard serving, worker completion, or end-to-end shared-memory writes.\\\\n\\\\n## Risks\\\\n- Main near-term risk is false confidence from static docs plus partial command success while writable-path and temp-dir assumptions remain unverified.\\\\n- Working tree is dirty, so the codex CLI fix appears in progress but not yet fully validated by a successful full gate.\\\\n\\\\n## Recommended Next Action\\\\n- Queue one internal follow-up task: rerun the full local smoke gate in a writable environment after the current codex CLI flag fix lands, with focus on two proofs only: tests pass and shared-memory-backed worker flow completes without the old CLI error.\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:22:58.671693+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:22:58.671693+00:00\\\", \\\"metadata\\\": {\\\"outcome\\\": \\\"complete\\\", \\\"task_run_id\\\": \\\"run_d37c8577c9674333b7157a945274385a\\\"}}, {\\\"id\\\": \\\"art_504acf83d6fe481f82082c3fa518b6cb\\\", \\\"task_id\\\": \\\"task_3d7271f7d19e456791b5796695fbe2fa\\\", \\\"artifact_type\\\": \\\"plan\\\", \\\"title\\\": \\\"Execution Plan\\\", \\\"content\\\": \\\"# Plan for Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordinati...\\\\n\\\\n- Route: personal\\\\n- Secondary route: code\\\\n- Planning source: codex\\\\n- Codex instruction: Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\\\\n\\\\n## Subtasks\\\\n- Collect readiness signals\\\\n- Write smoke summary\\\\n- Propose one coordination step\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:21:32.622366+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:21:32.622366+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\\\", \\\"delegation_target\\\": null, \\\"planning_source\\\": \\\"codex\\\", \\\"primary_agent\\\": \\\"personal\\\", \\\"reason\\\": \\\"Planning/synthesis task with light repo-readiness context; no external action.\\\", \\\"secondary_agent\\\": \\\"code\\\"}}}, {\\\"id\\\": \\\"art_52e95ab045ea45df8cd59f44707194ef\\\", \\\"task_id\\\": \\\"task_3d7271f7d19e456791b5796695fbe2fa\\\", \\\"artifact_type\\\": \\\"normalized_intent\\\", \\\"title\\\": \\\"Normalized Intent\\\", \\\"content\\\": \\\"Request: Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordination step only. No external actions.\\\\nPrimary route: personal\\\\nSecondary route: code\\\\nReason: Planning/synthesis task with light repo-readiness context; no external action.\\\\nPlanning source: codex\\\\nCodex instruction: Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\\\\nRelevant memory:\\\\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]\\\\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\\\\n- Sebas desired relationship with money [mem_sebas_money_relationship]\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:21:32.621453+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:21:32.621453+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\\\", \\\"delegation_target\\\": null, \\\"planning_source\\\": \\\"codex\\\", \\\"primary_agent\\\": \\\"personal\\\", \\\"reason\\\": \\\"Planning/synthesis task with light repo-readiness context; no external action.\\\", \\\"secondary_agent\\\": \\\"code\\\"}}}, {\\\"id\\\": \\\"art_64eb875c9be041c2bf56ef7825e7b49d\\\", \\\"task_id\\\": \\\"task_46bb6d81471e47e39340198a889252a1\\\", \\\"artifact_type\\\": \\\"plan\\\", \\\"title\\\": \\\"Execution Plan\\\", \\\"content\\\": \\\"# Plan for Smoke task: summarize current system readiness and suggest one safe internal coordination step on...\\\\n\\\\n- Route: personal\\\\n- Planning source: fallback\\\\n\\\\n## Subtasks\\\\n- Clarify objective and constraints\\\\n- Gather context and evidence\\\\n- Produce final recommendation\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:19:21.818641+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:19:21.818641+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": null, \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"personal\\\", \\\"reason\\\": \\\"default personal route\\\", \\\"secondary_agent\\\": null}}}, {\\\"id\\\": \\\"art_0f68333747c744728f5291e4e64d563a\\\", \\\"task_id\\\": \\\"task_46bb6d81471e47e39340198a889252a1\\\", \\\"artifact_type\\\": \\\"normalized_intent\\\", \\\"title\\\": \\\"Normalized Intent\\\", \\\"content\\\": \\\"Request: Smoke task: summarize current system readiness and suggest one safe internal coordination step only. No external actions.\\\\nPrimary route: personal\\\\nSecondary route: none\\\\nReason: default personal route\\\\nPlanning source: fallback\\\\nRelevant memory:\\\\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]\\\\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\\\\n- Sebas desired relationship with money [mem_sebas_money_relationship]\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:19:21.818049+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:19:21.818049+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": null, \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"personal\\\", \\\"reason\\\": \\\"default personal route\\\", \\\"secondary_agent\\\": null}}}, {\\\"id\\\": \\\"art_a60c0d4bffb14d67be795c5f45124318\\\", \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\", \\\"artifact_type\\\": \\\"handoff_result\\\", \\\"title\\\": \\\"Handoff result from ai-dev-workflow\\\", \\\"content\\\": \\\"{\\\\n  \\\\\\\"artifacts\\\\\\\": [],\\\\n  \\\\\\\"memory_id\\\\\\\": \\\\\\\"mem_17eea5c0658f4cd3beece90a93d35e36\\\\\\\",\\\\n  \\\\\\\"status\\\\\\\": \\\\\\\"accepted\\\\\\\",\\\\n  \\\\\\\"summary\\\\\\\": \\\\\\\"ai-dev-workflow accepted the handoff and persisted intake context.\\\\\\\",\\\\n  \\\\\\\"task_id\\\\\\\": \\\\\\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\\\\\"\\\\n}\\\", \\\"format\\\": \\\"json\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"ai-dev-workflow\\\", \\\"created_at\\\": \\\"2026-03-15T14:18:48.589136+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:18:48.589136+00:00\\\", \\\"metadata\\\": {\\\"handoff_id\\\": \\\"hof_bdc2e7b08a7c4cdb803c986fc6ec84fc\\\"}}, {\\\"id\\\": \\\"art_ecadce16c6ce44e9b8b534630a2aea26\\\", \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\", \\\"artifact_type\\\": \\\"plan\\\", \\\"title\\\": \\\"Execution Plan\\\", \\\"content\\\": \\\"# Plan for Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and...\\\\n\\\\n- Route: code\\\\n- Planning source: fallback\\\\n- Delegation target: ai-dev-workflow\\\\n\\\\n## Subtasks\\\\n- Inspect technical context\\\\n- Delegate code execution\\\\n- Summarize technical outcome\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:18:47.342014+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:18:47.342014+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (repo)\\\", \\\"secondary_agent\\\": null}}}, {\\\"id\\\": \\\"art_73f79012e7f0471085d2749541f5748b\\\", \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\", \\\"artifact_type\\\": \\\"normalized_intent\\\", \\\"title\\\": \\\"Normalized Intent\\\", \\\"content\\\": \\\"Request: Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\\\\nPrimary route: code\\\\nSecondary route: none\\\\nReason: code work (repo)\\\\nPlanning source: fallback\\\\nRelevant memory:\\\\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\\\\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]\\\\n- Research run: Production-readiness plan for ballbox-payment-integration [legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54]\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:18:47.341454+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:18:47.341454+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (repo)\\\", \\\"secondary_agent\\\": null}}}, {\\\"id\\\": \\\"art_1accabbcec71437d8592292c9e412153\\\", \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\", \\\"artifact_type\\\": \\\"handoff_result\\\", \\\"title\\\": \\\"Handoff result from ai-dev-workflow\\\", \\\"content\\\": \\\"{\\\\n  \\\\\\\"artifacts\\\\\\\": [],\\\\n  \\\\\\\"memory_id\\\\\\\": \\\\\\\"mem_04dc02a90cad40cc826ed1e8f93aff36\\\\\\\",\\\\n  \\\\\\\"status\\\\\\\": \\\\\\\"accepted\\\\\\\",\\\\n  \\\\\\\"summary\\\\\\\": \\\\\\\"ai-dev-workflow accepted the handoff and persisted intake context.\\\\\\\",\\\\n  \\\\\\\"task_id\\\\\\\": \\\\\\\"task_ed6a38c1b49f4e829e067d49d206148b\\\\\\\"\\\\n}\\\", \\\"format\\\": \\\"json\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"ai-dev-workflow\\\", \\\"created_at\\\": \\\"2026-03-15T14:18:33.507460+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:18:33.507460+00:00\\\", \\\"metadata\\\": {\\\"handoff_id\\\": \\\"hof_d7a1028b907341fa93a5cb8cb460e2c7\\\"}}, {\\\"id\\\": \\\"art_39c706c5d09f421a9198fc938020c79f\\\", \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\", \\\"artifact_type\\\": \\\"plan\\\", \\\"title\\\": \\\"Execution Plan\\\", \\\"content\\\": \\\"# Plan for Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal ne...\\\\n\\\\n- Route: code\\\\n- Planning source: fallback\\\\n- Delegation target: ai-dev-workflow\\\\n\\\\n## Subtasks\\\\n- Inspect technical context\\\\n- Delegate code execution\\\\n- Summarize technical outcome\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:18:28.454377+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:18:28.454377+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (fix)\\\", \\\"secondary_agent\\\": null}}}, {\\\"id\\\": \\\"art_8225d4c69b224564ba55a14f20519717\\\", \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\", \\\"artifact_type\\\": \\\"normalized_intent\\\", \\\"title\\\": \\\"Normalized Intent\\\", \\\"content\\\": \\\"Request: Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\\\\nPrimary route: code\\\\nSecondary route: none\\\\nReason: code work (fix)\\\\nPlanning source: fallback\\\\nRelevant memory:\\\\n- Refactor runtime toward Codex-agentic orchestration [mem_runtime_agentic_refactor_priority]\\\\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]\\\\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:18:28.453697+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:18:28.453697+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (fix)\\\", \\\"secondary_agent\\\": null}}}, {\\\"id\\\": \\\"art_4b872101defe41188e12e81c2c5e0ffc\\\", \\\"task_id\\\": \\\"task_4a80a42f5d424f7191b151c3640db934\\\", \\\"artifact_type\\\": \\\"plan\\\", \\\"title\\\": \\\"Execution Plan\\\", \\\"content\\\": \\\"# Plan for Smoke test: summarize current V1 readiness and recommend the next safe internal step without exte...\\\\n\\\\n- Route: personal\\\\n- Planning source: fallback\\\\n\\\\n## Subtasks\\\\n- Clarify objective and constraints\\\\n- Gather context and evidence\\\\n- Produce final recommendation\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:16:55.509883+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:16:55.509883+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": null, \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"personal\\\", \\\"reason\\\": \\\"default personal route\\\", \\\"secondary_agent\\\": null}}}, {\\\"id\\\": \\\"art_0f65fad23c6e4f698fa606feead854e1\\\", \\\"task_id\\\": \\\"task_4a80a42f5d424f7191b151c3640db934\\\", \\\"artifact_type\\\": \\\"normalized_intent\\\", \\\"title\\\": \\\"Normalized Intent\\\", \\\"content\\\": \\\"Request: Smoke test: summarize current V1 readiness and recommend the next safe internal step without external side effects.\\\\nPrimary route: personal\\\\nSecondary route: none\\\\nReason: default personal route\\\\nPlanning source: fallback\\\\nRelevant memory:\\\\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\\\\n- Refactor runtime toward Codex-agentic orchestration [mem_runtime_agentic_refactor_priority]\\\\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:16:55.509067+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:16:55.509067+00:00\\\", \\\"metadata\\\": {\\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": null, \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"personal\\\", \\\"reason\\\": \\\"default personal route\\\", \\\"secondary_agent\\\": null}}}, {\\\"id\\\": \\\"art_f7fad73c89674ef1b9f9248c7b829ca1\\\", \\\"task_id\\\": \\\"task_afed9eaf504f42b99348a815d56148cc\\\", \\\"artifact_type\\\": \\\"closeout\\\", \\\"title\\\": \\\"V1 final closeout\\\", \\\"content\\\": \\\"# V1 Closeout\\\\n\\\\n## Outcome\\\\nV1 is complete.\\\\n\\\\n`personal-agent` now operates as the shared-memory front door with:\\\\n- shared operational state in `agents-database`\\\\n- intake planning and runtime orchestration backed by Codex outputs\\\\n- daemon/dashboard surfaces for status, blockers, approvals, and next actions\\\\n- approval resolution with automatic resume\\\\n- stable subagent handoff contract for `ai-dev-workflow` and `ballbox-company-agent`\\\\n- legacy bridge for old personal-agent research/task inspection\\\\n- documented sandbox write convention for canonical shared DB access\\\\n\\\\n## Verification\\\\n- local repo gate: `./scripts/run-checks.sh` passed with 26 tests\\\\n- direct suite: `python3 -m pytest tests/test_personal.py` passed with 26 tests\\\\n- PR CI: run `23112079488` passed on 2026-03-15 (`unit-tests`)\\\\n\\\\n## Runtime use cases covered by tests\\\\n- intake creates operational task, subtasks, and specialist handoff\\\\n- Codex planning path and fallback path\\\\n- worker terminal outcomes: `complete`, `needs_approval`, `blocked`\\\\n- approval resolution paths: `approved` resumes and completes; `rejected` stays blocked\\\\n- structured runtime actions: follow-up task, handoff, artifact\\\\n- dashboard snapshot enrichment: summary, next action, latest run, pending approval, route summary\\\\n- end-to-end flow: intake -> approval -> resume -> complete\\\\n- Codex command wiring includes `--add-dir /Users/sebas/agents-database`\\\\n- handoff dispatch completion persists result artifact\\\\n- preference blocker can resolve from memory without human input\\\\n- daemon API surface covers status, intake, blocker response, and approval resolution\\\\n- CLI accepts `--json` before or after subcommands\\\\n\\\\n## Scope boundary\\\\n- task `task_33d854e72d094660a2fb293a8723f5c5` is explicitly post-V1 follow-up, not a V1 blocker\\\\n- remaining work after V1 should continue as follow-up capabilities and operator hardening, not as part of this milestone\\\\n\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-15T14:13:45.050563+00:00\\\", \\\"updated_at\\\": \\\"2026-03-15T14:13:45.050563+00:00\\\", \\\"metadata\\\": {\\\"completed_on\\\": \\\"2026-03-15\\\", \\\"kind\\\": \\\"v1_closeout\\\", \\\"post_v1_followup_task_id\\\": \\\"task_33d854e72d094660a2fb293a8723f5c5\\\", \\\"verification\\\": {\\\"pr_ci_run_id\\\": \\\"23112079488\\\", \\\"pr_ci_status\\\": \\\"success\\\", \\\"pytest\\\": \\\"26 passed\\\", \\\"run_checks\\\": \\\"passed\\\"}}}, {\\\"id\\\": \\\"art_4151fdb5c75c48449199130022f7a909\\\", \\\"task_id\\\": \\\"task_eaf005df03fb441ab2b84df7ea05178d\\\", \\\"artifact_type\\\": \\\"continuation_brief\\\", \\\"title\\\": \\\"Phase 2 closeout brief\\\", \\\"content\\\": \\\"Phase 2 closeout:\\\\n- structured runtime actions implemented\\\\n- approval loop resolves and resumes\\\\n- execution_state snapshots persisted\\\\n- dashboard/status surfaces expose next actions and latest run state\\\\n- end-to-end runtime tests cover intake -> approval -> resume -> complete\\\\n\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-14T22:18:49.489775+00:00\\\", \\\"updated_at\\\": \\\"2026-03-14T22:18:49.489775+00:00\\\", \\\"metadata\\\": {\\\"kind\\\": \\\"phase2_closeout\\\"}}, {\\\"id\\\": \\\"art_b4b8c8b4bcc344ae81c206fa3ca57724\\\", \\\"task_id\\\": \\\"task_33d854e72d094660a2fb293a8723f5c5\\\", \\\"artifact_type\\\": \\\"task_note\\\", \\\"title\\\": \\\"Credential automation follow-up note\\\", \\\"content\\\": \\\"Low-priority follow-up. During multi-repo PR prep, git push and gh PR creation hit mixed-account behavior. Temporary workaround used direct credential injection. Future work should simplify and harden this flow instead of relying on manual recovery steps.\\\", \\\"format\\\": \\\"md\\\", \\\"status\\\": \\\"active\\\", \\\"source_ref\\\": \\\"personal-agent\\\", \\\"created_at\\\": \\\"2026-03-14T22:07:02.464680+00:00\\\", \\\"updated_at\\\": \\\"2026-03-14T22:07:02.464680+00:00\\\", \\\"metadata\\\": {\\\"kind\\\": \\\"followup_context\\\"}}]}, \\\"audit\\\": {\\\"schema_version\\\": \\\"shared-agent-memory-v2\\\", \\\"memories\\\": 223, \\\"tasks\\\": 39, \\\"approvals\\\": 0, \\\"handoffs\\\": 0, \\\"maintenance_jobs\\\": 3, \\\"legacy_memory_rows\\\": 0, \\\"legacy_task_rows\\\": 0}}\\n\\nROOT\\n\\n\\nOPENAPI?\\n\"\n            }\n          ],\n          \"isError\": false,\n          \"timestamp\": 1777988702250\n        }\n      },\n      {\n        \"type\": \"message\",\n        \"id\": \"a7b555b1\",\n        \"parentId\": \"1a272a01\",\n        \"timestamp\": \"2026-05-05T13:45:02.253Z\",\n        \"message\": {\n          \"role\": \"toolResult\",\n          \"toolCallId\": \"call_bTQg7QCVQOl7qteQl50hO0yL|fc_0d852cbc8d6115830169f9f45de4588194ab579f888979c45d\",\n          \"toolName\": \"bash\",\n          \"content\": [\n            {\n              \"type\": \"text\",\n              \"text\": \"sebas       1366    1289  0 10:07 ?        00:00:00 python3 -m shared_agent_memory.cli --db /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3 serve-http --host 0.0.0.0 --port 8091\\nsebas       1369    1289  0 10:07 ?        00:00:00 /mnt/rpi/agents-database/.venv/bin/python -m shared_agent_memory.cli --db /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3 maintenance-daemon --interval-seconds 900 --jitter-seconds 60\\nroot        1505    1273  0 10:07 ?        00:00:00 sudo -n -u sebas /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data\\nsebas       1511    1505  0 10:07 ?        00:00:00 /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data\\nsebas      12842   10916  0 10:45 ?        00:00:00 /bin/bash -c set -e ps -ef | grep -i '[a]gents-database' || true systemctl --no-pager --full status agents-database 2>/dev/null || true journalctl -u agents-database -n 40 --no-pager 2>/dev/null || true\\n-- No entries --\\n\"\n            }\n          ],\n          \"isError\": false,\n          \"timestamp\": 1777988702253\n        }\n      },\n      {\n        \"type\": \"message\",\n        \"id\": \"7b364d5d\",\n        \"parentId\": \"a7b555b1\",\n        \"timestamp\": \"2026-05-05T13:45:08.427Z\",\n        \"message\": {\n          \"role\": \"assistant\",\n          \"content\": [\n            {\n              \"type\": \"thinking\",\n              \"thinking\": \"**Evaluating recent runs**\\n\\nI need to check if I can summarize recent runs, maybe focusing on more than three of them. I could mention there aren't any systemd unit logs, but the processes are running well. Additionally, I'm considering looking into maintenance jobs or their last execution times. It might be ideal to utilize an API's recent runs endpoint or even inspect the repo. I think querying the SQLite database directly for the top runs and maintenance jobs could be simpler.\",\n              \"thinkingSignature\": \"{\\\"id\\\":\\\"rs_0d852cbc8d6115830169f9f45f29388194ac8dd67c1f99348e\\\",\\\"type\\\":\\\"reasoning\\\",\\\"encrypted_content\\\":\\\"gAAAAABp-fRi5h77qPAbnvrkXgWjtcQcRk7xSHO7G6i0BiCWoe7ZHvgECfjuf2sAYN-QtJll41I_kVkCojI1YZc_bPpO8Tnb_NVqHKO32Cc_Zjz4jhzXl77demnf0osULwnuGLzxFJiY7pgaSFBxxKGDGBWgDKtFV9U1EsdkkAF6jSX-5lo6ZOwyie5jx5LsVFYjjZAjyUYlaSk8y6_oya2Cyh3dYoISECNDZbYBxrZisAfQ4kdsjCuIGShYOYn1DOkSTtqr8BopdXRmbEfBoeA9U6CVPM82aGpfDUUfWVctahCW4urILELLcbTvVVAHyLpZIC0qHAHBZ1H9VapxO1dQ5ao8JIhN1kOrgcxyKyXdqPQpE24REpEpWxwTAwpNA5Y2ztviObf8bslgKQg8xnuNii09CH1CpIG3ek2-RfMHumS-XqA4viN9RvwamdE4f5g5FuGA25cCyaPyHrcz0C4NNuMZNcoJ3cS2r-gUOv2ZZI5wK32PpS8VGe5xzSnTb3gpBeqOWQPZp_dbbs_vrme2NQcgW3C57R-MeYr83sdRxZBmLORJM0ZH7ob5gxQjDYG-jKyIwyx0jc8NqSYd5JJpPfES4FiQ0HeqljW-hGqC6ZFb2TqhbWLkqNjy-GSSPhLc_VMVWT7kd5E_sXRlaxWVV-KXxBzHmcXP1zX9wAjBM9s02GyQlr40Qm-tQ4t3qo2x8n34Dpflcff9-ePMDTdxtMn52za8moS3boKXunD20Zoiap1tvvod9n5DWBcRq_4LTEsPcyHqMgedUNTytnYWmcaVbgAmud0KiipLdokj30zppI2LsbDzuWAJ4jO8q6JAg2xlHE5v1QHFDmjSydkA6el7rFzK9Yc4SRX-GdSuOfgkzoOw1Z0JmKcJ04AX5KgqZFDEb6i-FVwcdrlCaGdd3L8MTf3tJfouEusgYNTUfmWhq-Vl5dKN2MNBKFnFVGPJHEf46PvgW66_7_ZbbqiuxsOW1bzMHqnrH6RNHtTu766GX4SF1-DfhP_Ullwl47OO7yhJDT-ouEBZEZGRVHm6Z9WEVVCyqr5hq2plJihel4w8s8KZscV9FK2kGQK9zEtlLMqPPS9z4QSetGAVdEpjVvcn9RgJk9erC6a_Wm33D0KNPpeSdJ5JyRk5yVNoMwdbMhXCbYK3JZQToQjGGtsTy5k57x8zT0DGyL8CcVpBOERYhtFruJkhLQtXSo4syr9h8h1vHT37ui-PfAIZ7vIGtafG77028r3MJsetIlt5esueziRBmmrL9Q8_q35dQEWyvZm0Pso7Hxqa4c_aVMOGkn9CHNnLAsTDUqsRfvkM3zjE1zeNpVpSS332YCTo5nkOwEYwFPfcs9GTsMEGDzDz1wpcLbheDqNvXlwYqrgRl4yLVC6nHbY3BMo1Zl-RXd5-X--PfVyGwA9aiHuTPnf9Mt1xHw_vkg==\\\",\\\"summary\\\":[{\\\"type\\\":\\\"summary_text\\\",\\\"text\\\":\\\"**Evaluating recent runs**\\\\n\\\\nI need to check if I can summarize recent runs, maybe focusing on more than three of them. I could mention there aren't any systemd unit logs, but the processes are running well. Additionally, I'm considering looking into maintenance jobs or their last execution times. It might be ideal to utilize an API's recent runs endpoint or even inspect the repo. I think querying the SQLite database directly for the top runs and maintenance jobs could be simpler.\\\"}]}\"\n            },\n            {\n              \"type\": \"toolCall\",\n              \"id\": \"call_zXnKI5qXft0536IAhNudgvdB|fc_0d852cbc8d6115830169f9f462e620819491276b2c7a575213\",\n              \"name\": \"bash\",\n              \"arguments\": {\n                \"command\": \"python3 - <<'PY'\\nimport sqlite3, json\\npath='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\nconn=sqlite3.connect(path)\\nconn.row_factory=sqlite3.Row\\nprint('RUNS')\\nfor row in conn.execute(\\\"select id, task_id, agent_id, status, started_at, completed_at, substr(coalesce(result_summary,error_message,''),1,160) as summary from task_runs order by datetime(created_at) desc limit 8\\\"):\\n    print(json.dumps(dict(row), ensure_ascii=False))\\nprint('JOBS')\\nfor row in conn.execute(\\\"select job_name, status, started_at, completed_at, items_processed, error_message from maintenance_jobs order by datetime(created_at) desc limit 5\\\"):\\n    print(json.dumps(dict(row), ensure_ascii=False))\\nPY\",\n                \"timeout\": 15\n              }\n            }\n          ],\n          \"api\": \"openai-codex-responses\",\n          \"provider\": \"openai-codex\",\n          \"model\": \"gpt-5.4\",\n          \"usage\": {\n            \"input\": 9368,\n            \"output\": 283,\n            \"cacheRead\": 3584,\n            \"cacheWrite\": 0,\n            \"totalTokens\": 13235,\n            \"cost\": {\n              \"input\": 0.023420000000000003,\n              \"output\": 0.0042450000000000005,\n              \"cacheRead\": 0.000896,\n              \"cacheWrite\": 0,\n              \"total\": 0.028561000000000003\n            }\n          },\n          \"stopReason\": \"toolUse\",\n          \"timestamp\": 1777988702254,\n          \"responseId\": \"resp_0d852cbc8d6115830169f9f45e864c81948533736ec94fe8bc\"\n        }\n      },\n      {\n        \"type\": \"message\",\n        \"id\": \"b54400f6\",\n        \"parentId\": \"7b364d5d\",\n        \"timestamp\": \"2026-05-05T13:45:08.483Z\",\n        \"message\": {\n          \"role\": \"toolResult\",\n          \"toolCallId\": \"call_zXnKI5qXft0536IAhNudgvdB|fc_0d852cbc8d6115830169f9f462e620819491276b2c7a575213\",\n          \"toolName\": \"bash\",\n          \"content\": [\n            {\n              \"type\": \"text\",\n              \"text\": \"RUNS\\n{\\\"id\\\": \\\"run_d37c8577c9674333b7157a945274385a\\\", \\\"task_id\\\": \\\"task_3d7271f7d19e456791b5796695fbe2fa\\\", \\\"agent_id\\\": \\\"personal-agent\\\", \\\"status\\\": \\\"completed\\\", \\\"started_at\\\": \\\"2026-03-15T14:21:34.691945+00:00\\\", \\\"completed_at\\\": \\\"2026-03-15T14:22:58.675711+00:00\\\", \\\"summary\\\": \\\"Local readiness is partial: repo docs and runtime surface exist, current status API works, but full verification is not yet proven in this sandbox.\\\"}\\n{\\\"id\\\": \\\"run_ce032d1c6fbf4e3aa23a26df10d8d91f\\\", \\\"task_id\\\": \\\"task_46bb6d81471e47e39340198a889252a1\\\", \\\"agent_id\\\": \\\"personal-agent\\\", \\\"status\\\": \\\"failed\\\", \\\"started_at\\\": \\\"2026-03-15T14:19:23.634939+00:00\\\", \\\"completed_at\\\": \\\"2026-03-15T14:19:23.690219+00:00\\\", \\\"summary\\\": \\\"error: unexpected argument '--ask-for-approval' found\\\\n\\\\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\\\\n\\\\nUsage: codex exec --sandbox \\\"}\\n{\\\"id\\\": \\\"run_d7ea2a60bbd74e9b8fdaa628115d7cbd\\\", \\\"task_id\\\": \\\"task_4a80a42f5d424f7191b151c3640db934\\\", \\\"agent_id\\\": \\\"personal-agent\\\", \\\"status\\\": \\\"failed\\\", \\\"started_at\\\": \\\"2026-03-15T14:16:58.205690+00:00\\\", \\\"completed_at\\\": \\\"2026-03-15T14:16:58.257806+00:00\\\", \\\"summary\\\": \\\"error: unexpected argument '--ask-for-approval' found\\\\n\\\\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\\\\n\\\\nUsage: codex exec --sandbox \\\"}\\nJOBS\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 9, in <module>\\nsqlite3.OperationalError: no such column: job_name\\n\\n\\nCommand exited with code 1\"\n            }\n          ],\n          \"details\": {},\n\n[3977 more lines in file. Use offset=201 to continue.]"}],"isError":false,"timestamp":1778002111527}}
{"type":"message","id":"fe8afa29","parentId":"40f59578","timestamp":"2026-05-05T17:28:31.532Z","message":{"role":"toolResult","toolCallId":"call_Y3GzXiuIOT6xGWoB5I6ZjI8a|fc_0d46162620b774590169fa28bf2eb481959c4a2953cee4f0ab","toolName":"read","content":[{"type":"text","text":"{\"status\": \"failed\", \"save\": null, \"memory_id\": null, \"error\": \"Warning: No models match pattern \\\"gemini-3-flash-preview:minimal\\\"\\nWarning: No models match pattern \\\"gemini-3-flash-preview:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:low\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:high\\\"\\nWarning: No models match pattern \\\"o3-mini:low\\\"\\nWarning: No models match pattern \\\"o3-mini:medium\\\"\\nWarning: No models match pattern \\\"o3-mini:high\\\"\\nWarning: No models match pattern \\\"minimax-m2.5-free:minimal\\\"\\nWarning: No models match pattern \\\"qwen/qwen3-coder:free:minimal\\\"\\nWarning: No models match pattern \\\"google/gemma-3-27b-it:free:minimal\\\"\\n[openai-codex] Token refresh failed: 401 {\\n  \\\"error\\\": {\\n    \\\"message\\\": \\\"Your refresh token has already been used to generate a new access token. Please try signing in again.\\\",\\n    \\\"type\\\": \\\"invalid_request_error\\\",\\n    \\\"param\\\": null,\\n    \\\"code\\\": \\\"refresh_token_reused\\\"\\n  }\\n}\\nNo API key for provider: openai-codex\"}\n"}],"isError":false,"timestamp":1778002111527}}
{"type":"message","id":"6836e90c","parentId":"fe8afa29","timestamp":"2026-05-05T17:28:39.431Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Analyzing database jobs**\n\nI need to conduct our own database analysis using Python. It looks like we need job logs since we've had recent failures due to a missing API key. I wonder if 5 or 6 hooks would have yielded many memories, especially from recent jobs. I need to count session memory analysis jobs, their outcomes, and saved memories by source reference and creation date. Let's inspect the logs for any successes and build a script to query the database.","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28c18e0881959ae410759d5cca6a\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ijH5QYbF3RkqoqFIL0t9ju3hNPbmYBmrMg90Fey2hDCY-4j5tkZVdrRLHHYGybVK8g-YacWjZPBDELyDwq5r_Ed1rbfU04Xpei1GCbhFK4ceEb3FC5O1jV0IdiMO0836-1K3cV-_lX1JT5tzLZT2fsw5yGsJVaZ4E7XnHZ__Js63VS_Nnbgme2mv7mKbcjybjqA6ZhQTGUxBzcFEiX9GwbovwKCzsnylHvvE5nLy7KP083M2jzAZao44Wa14q71JkYzUZyGn3jUUI91DE2YKU3ZgvkaSG4cmuBAK_7ay9KynXvkWN-B0MrMuUWJti4EV1wVhXaOumrI-fkCWDnqK7KtQfrw8EPWk2bkxkOqxUrjuHlbHd6Gi8LoFP-Dt9gUT60uDWYt2CMqgiDfjtQTFEiWbJsF4P924pGDcPopnZ0ulZUAbMJMWkWZpOhI7-eF4jz1NVJgtRNbPH65sM7dKoaCoGf4cfHMxQ0HPrUwlYHbd45KT1xUkzMU--xCvkwHgOMVjWJX7-39zuSuKuUGlKRXdz2xQBDGwkJ-ZBVuNju4c54ze-qDlVsJkWUklsKkxbbRvvF5gCISpsNRLTWrC2hOIE4lpq0BYeheG0DPNh8g-BZhu9-tZSZ4B643D1Wdm2CHUYjYnE5ptZLKK8Nxed-IQFsgziDh8yJOOYagQAlx7f250USgnnCbVsDI6nQc8x__fU_8_Y4HjBvUcyWVH2NFOYpVRT2VnxaYDRKGMMDWhItHr_uBjO0Rhe6vJ0Nsixww4rcTPEuxIFyGpM9ijE0IxkpjTCiXxssWVTVKmgQeLKxR6rX0XbLY9Kr-XlXkdjDqQzyrzs4gCAPOZBaslNG14QWeK6Bck-IIdOkSWjD1VYu_yJrfzV-Cwr7r1K-hEWmmxkvnVrhesg3MRzC6uXTKdF9njrJtpKfmxt5p94S3fWES9IgKrL78Wpa2E7UyNM5TiPwlFl7TBe1oW4BXOpX9ttnhPl0uzWzJD80g3mhu8XgngUDY3Pc0pTtk8ng1RzmWzoaskHYgBaGkQq4G1-MyxjsXInXunPDf185hDKbdn7S0C1cA_hBQCjYOpBpgUvK_628CUDmofqQS5DKehDjN7FGIbVdKKck18oxN_y3ME_aefxOM8nS9k2GgjaGAm_hWjX63YzNdBaW9FMnwxp3wJfgZCyu62YiNUmzxu5ojFw859rAQXHVulN80w3q8fKgXYqSvnTF9SZPs-tgwGo7zOmjWVIpAlG7U5iJde64TGFL9OCuaSrKV1yewQvNmQf9m6x6qu8lt7eCI0FrSeRNg_eVDL40U0b2C3xSHz5O7yihJcHjv-pXuoOVoQmOHkCFbmN7YtnU3PAJaaB0S3QPLxOsM3SL0qomnhdF2pwczGv3RxiINOBgWvaAIvuaFxrv8Z9ePzWg2Ypp8Qg2YF0msZtm8NSb7dSky3gtbEjb64Gqq1_rg9DYHoqMtpCdSZr7ZXWj94kbD7U1w93Lg1kod9zGBYEBz2d8LDWUKjOKM_wsxPHkpOl6aNlBep8pIscbTOHYX64AleB6ugoPKmjrbQ_K26WZKjzAlqD0YejISrro=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Analyzing database jobs**\\n\\nI need to conduct our own database analysis using Python. It looks like we need job logs since we've had recent failures due to a missing API key. I wonder if 5 or 6 hooks would have yielded many memories, especially from recent jobs. I need to count session memory analysis jobs, their outcomes, and saved memories by source reference and creation date. Let's inspect the logs for any successes and build a script to query the database.\"}]}"},{"type":"toolCall","id":"call_ZSc0tVlrSzUCJxAeqSUax7Cx|fc_0d46162620b774590169fa28c736708195af6fb11a026b4e1e","name":"bash","arguments":{"command":"cd /home/sebas/agents-database && rg -n \"create_memory\\(|source_ref.*conversation|session_memory|last_user_text|memory_id|save\": src tests scripts /home/sebas/pi-config -S | head -200","timeout":20}},{"type":"toolCall","id":"call_SqgayE0T2JTKMwW5LXUlwZLt|fc_0d46162620b774590169fa28c73684819595907d8c540a5f33","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json\nconn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\nconn.row_factory=sqlite3.Row\nprint('maintenance_jobs cols:')\nfor r in conn.execute('pragma table_info(maintenance_jobs)'): print(r['name'])\nprint('maintenance_runs cols:')\nfor r in conn.execute('pragma table_info(maintenance_runs)'): print(r['name'])\nPY"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":15675,"output":308,"cacheRead":30208,"cacheWrite":0,"totalTokens":46191,"cost":{"input":0.0391875,"output":0.00462,"cacheRead":0.007552,"cacheWrite":0,"total":0.0513595}},"stopReason":"toolUse","timestamp":1778002111528,"responseId":"resp_0d46162620b774590169fa28c041b08195900d99dedc22007f"}}
{"type":"message","id":"8ffaae03","parentId":"6836e90c","timestamp":"2026-05-05T17:28:39.498Z","message":{"role":"toolResult","toolCallId":"call_ZSc0tVlrSzUCJxAeqSUax7Cx|fc_0d46162620b774590169fa28c736708195af6fb11a026b4e1e","toolName":"bash","content":[{"type":"text","text":"src/shared_agent_memory/schema.py:83:    from_memory_id TEXT NOT NULL,\nsrc/shared_agent_memory/schema.py:84:    to_memory_id TEXT NOT NULL,\nsrc/shared_agent_memory/schema.py:88:    FOREIGN KEY(from_memory_id) REFERENCES memories(id),\nsrc/shared_agent_memory/schema.py:89:    FOREIGN KEY(to_memory_id) REFERENCES memories(id)\nsrc/shared_agent_memory/schema.py:131:    memory_id TEXT NOT NULL,\nsrc/shared_agent_memory/schema.py:132:    conflicting_memory_id TEXT NOT NULL,\nsrc/shared_agent_memory/schema.py:136:    FOREIGN KEY(memory_id) REFERENCES memories(id),\nsrc/shared_agent_memory/schema.py:137:    FOREIGN KEY(conflicting_memory_id) REFERENCES memories(id)\nsrc/shared_agent_memory/schema.py:224:CREATE INDEX IF NOT EXISTS idx_memory_conflicts_mem ON memory_conflicts(memory_id);\nsrc/shared_agent_memory/schema.py:227:    memory_id UNINDEXED,\nsrc/shared_agent_memory/engine.py:112:        return {\"event_id\": event[\"id\"], \"memory_id\": memory[\"id\"]}\nsrc/shared_agent_memory/cli.py:100:    consolidate.add_argument(\"memory_ids\", nargs=\"+\")\nsrc/shared_agent_memory/cli.py:239:        print(json.dumps(service.consolidate(args.memory_ids), indent=2))\ntests/test_memory_service.py:57:    memory_id = processed[0][\"memory_id\"]\ntests/test_memory_service.py:58:    memory = service.get_memory(memory_id)\ntests/test_memory_service.py:64:    promoted = service.consolidate([memory_id])\ntests/test_memory_service.py:65:    assert promoted[\"promoted\"] == [memory_id]\ntests/test_memory_service.py:68:    assert search_with_active[\"results\"][0][\"memory\"][\"id\"] == memory_id\ntests/test_memory_service.py:76:    service.create_memory(\ntests/test_memory_service.py:86:    service.create_memory(\ntests/test_memory_service.py:96:    service.create_memory(\ntests/test_memory_service.py:154:    service.create_memory(\ntests/test_memory_service.py:166:    service.create_memory(\ntests/test_memory_service.py:188:    service.create_memory(\ntests/test_memory_service.py:283:    service.create_memory(\ntests/test_memory_service.py:319:    service.create_memory(\ntests/test_memory_service.py:399:    memory = service.create_memory(\ntests/test_memory_service.py:417:    service.create_memory(\ntests/test_memory_service.py:427:    service.create_memory(\nsrc/shared_agent_memory/service.py:71:    def create_memory(self, memory_input: dict[str, Any]) -> dict[str, Any]:\nsrc/shared_agent_memory/service.py:129:        return self.create_memory(memory_input)\nsrc/shared_agent_memory/service.py:278:            for memory_id in inbox_items:\nsrc/shared_agent_memory/service.py:279:                row = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (memory_id,)).fetchone()\nsrc/shared_agent_memory/service.py:293:                    (memory_id, memory[\"type\"], memory[\"scope\"], memory[\"content\"]),\nsrc/shared_agent_memory/service.py:299:                        INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)\nsrc/shared_agent_memory/service.py:304:                            memory_id,\nsrc/shared_agent_memory/service.py:311:                    conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"archived\", utcnow(), memory_id))\nsrc/shared_agent_memory/service.py:312:                    linked.append({\"from\": memory_id, \"to\": duplicate[\"id\"], \"relation\": \"related_to\"})\nsrc/shared_agent_memory/service.py:314:                conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"active\", utcnow(), memory_id))\nsrc/shared_agent_memory/service.py:315:                promoted.append(memory_id)\nsrc/shared_agent_memory/service.py:453:        return self.create_memory(\nsrc/shared_agent_memory/service.py:540:    def get_memory(self, memory_id: str) -> dict[str, Any]:\nsrc/shared_agent_memory/service.py:542:            row = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (memory_id,)).fetchone()\nsrc/shared_agent_memory/service.py:544:                raise KeyError(memory_id)\nsrc/shared_agent_memory/service.py:1534:        conn.execute(\"DELETE FROM memories_fts WHERE memory_id = ?\", (record[\"id\"],))\nsrc/shared_agent_memory/service.py:1536:            \"INSERT INTO memories_fts (memory_id, title, summary, content) VALUES (?, ?, ?, ?)\",\nsrc/shared_agent_memory/service.py:1653:            INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)\nsrc/shared_agent_memory/service.py:1659:    def _record_conflict(self, conn: Any, memory_id: str, conflicting_id: str, reason: str) -> None:\nsrc/shared_agent_memory/service.py:1662:            INSERT INTO memory_conflicts (id, memory_id, conflicting_memory_id, reason, created_at, metadata_json)\nsrc/shared_agent_memory/service.py:1665:            (make_id(\"mcf\"), memory_id, conflicting_id, reason, utcnow(), self.store.dumps({})),\n/home/sebas/pi-config/extensions/session-memory-dispatcher.ts:4:const DISPATCH_URL = process.env.PI_SESSION_MEMORY_DISPATCH_URL || \"http://127.0.0.1:17882/api/session-memory\";\n/home/sebas/pi-config/extensions/session-memory-dispatcher.ts:72:\t\tlast_user_text: truncate(stats.lastUserText, 2000),\n/home/sebas/pi-config/SYSTEM.md:33:Especially save: stable user preferences, recurring constraints, important project facts, decisions, reusable sources/artifacts, and notable blocked state or handoff context.\n/home/sebas/pi-config/scripts/session-memory-dispatcher.py:13:JOB_DIR = Path(os.environ.get(\"PI_SESSION_MEMORY_JOB_DIR\", BASE_DIR / \"sessions\" / \"session-memory-jobs\"))\n/home/sebas/pi-config/scripts/session-memory-dispatcher.py:14:ANALYZER = Path(os.environ.get(\"PI_SESSION_MEMORY_ANALYZER\", BASE_DIR / \"scripts\" / \"session-memory-analyzer.py\"))\n/home/sebas/pi-config/scripts/session-memory-dispatcher.py:25:        \"job_type\": \"session_memory_analysis\",\n/home/sebas/pi-config/scripts/session-memory-dispatcher.py:30:            \"memory_id\": None,\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:15:PI_MODEL = os.environ.get(\"PI_SESSION_MEMORY_MODEL\", \"gpt-5.4-mini\")\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:16:PI_THINKING = os.environ.get(\"PI_SESSION_MEMORY_THINKING\", \"off\")\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:17:PI_BIN = os.environ.get(\"PI_SESSION_MEMORY_PI_BIN\")\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:137:- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:157:        \"memory_id\": None,\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:199:            job[\"analysis\"][\"memory_id\"] = saved.get(\"id\")\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:203:            job[\"analysis\"][\"memory_id\"] = None\n/home/sebas/pi-config/scripts/install-session-memory-dispatcher.service:12:Environment=PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi\n"}],"isError":false,"timestamp":1778002119498}}
{"type":"message","id":"b6b40578","parentId":"8ffaae03","timestamp":"2026-05-05T17:28:39.499Z","message":{"role":"toolResult","toolCallId":"call_SqgayE0T2JTKMwW5LXUlwZLt|fc_0d46162620b774590169fa28c73684819595907d8c540a5f33","toolName":"bash","content":[{"type":"text","text":"maintenance_jobs cols:\nid\njob_type\ncadence\ninterval_minutes\nwindow_start\nwindow_end\nnext_due_at\nlast_run_at\nlast_status\nlast_summary\nenabled\nmetadata_json\ncreated_at\nupdated_at\nmaintenance_runs cols:\nid\njob_id\nstatus\nstarted_at\ncompleted_at\nsummary\nstats_json\nerror_message\n"}],"isError":false,"timestamp":1778002119498}}
{"type":"message","id":"bc65eff4","parentId":"b6b40578","timestamp":"2026-05-05T17:28:44.274Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28ca472c8195bc49c62f81cd47eb\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ijMA20XZdmrbAmOV4l3IxGE7Y_05P90FTAg1bCnWjXvHUF4aO8oZfQXRLJOvNxv_NT684I06vFRjZNDALiZ0WYmk0KNwEMRZ0722CYR_KD3r7bRN0Raq3tKqE2pE1AhvOOEBzAMJ3Tx82wbYzw8--BSSxr3jym12P3m6DPQW2sENXcFid-BO6KRHOJbCtRnGcrArLUbRZ9OB-JeTKQ3nshz8XI6ySjHedDGh0COU7KoPsgoFr8IVZTO-_QTk-BO70O1jCC0ZikUtWxnKhG6qeYw8_fhpjFvH4d18GzjX4rA-dMg74Zv9hUW3eimjqlfEN2iR2kZwVI5QEISn02_R_VWg0Bf52irAJHsGV53EHM6rC7rOpTdliHmNTbH08krOgmzUiZCMNCE3eOLIk_zPdJnovgwsbVO8UUPj4fyGqyJTD4XHH51RhQPXxydRbT1FYAsy2q8J_gPPxDdXgGyqplaHkWYLz_26W_OySW9yhgNN-wRF3mAh9P3bDIljWlwMP1oNKk6036O_HDt7L_Tujn3pyHYQpfNwX3RVeF9vV7SXwqHpPjr9iaeBy-g0ZsU-Sk7zu0EnjPOtVmHlGGKt8bFoWN5PseVd4aejKUkcQAk6-LvEgciS-N8UOzTtzx6R_--pKsWsgjANz1dOhKSutlH7kXWUA6CEsQa_kqDw4evqWtvDIKN2oWlmaeGxA4KiPW0uj9OIJTpAYFpMxU_bcH9rl_fUSr4015xeZx-_CYfpE9sylZs_AID_qTAFOqFF4_5hJtbYmRjcDWQBzf3mRi2eSpz5esymcZTxq90HtR6r5Mbz_3FR8x_s_9Wkju7Qel8YnOuWZq7dC5WHH0Bi_5mWdMWDJCVUoRdanpBxya8dQ4kt2xi351cAbBpOL3LpW-Vuzcj8PMU7a2QrsKeKCBjbOk8rRs0aQ_UMTd-GhkfYJppQArKJVcEaSDyVaRH3XMV\",\"summary\":[]}"},{"type":"toolCall","id":"call_WGsLP8IKnreQkm6T64BDI8ak|fc_0d46162620b774590169fa28cc176c8195ac33b12e9c229006","name":"read","arguments":{"path":"/home/sebas/pi-config/scripts/session-memory-analyzer.py","offset":1,"limit":280}},{"type":"toolCall","id":"call_hnzZ3UNqiReYppyRo5ztiYqi|fc_0d46162620b774590169fa28cc178481959486b953e9325c61","name":"read","arguments":{"path":"/home/sebas/pi-config/scripts/session-memory-dispatcher.py","offset":1,"limit":220}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":2300,"output":96,"cacheRead":45568,"cacheWrite":0,"totalTokens":47964,"cost":{"input":0.005750000000000001,"output":0.00144,"cacheRead":0.011392,"cacheWrite":0,"total":0.018582}},"stopReason":"toolUse","timestamp":1778002119499,"responseId":"resp_0d46162620b774590169fa28c840e88195958b0e0a0b11dba7"}}
{"type":"message","id":"9a2e822a","parentId":"bc65eff4","timestamp":"2026-05-05T17:28:44.306Z","message":{"role":"toolResult","toolCallId":"call_WGsLP8IKnreQkm6T64BDI8ak|fc_0d46162620b774590169fa28cc176c8195ac33b12e9c229006","toolName":"read","content":[{"type":"text","text":"#!/usr/bin/env python3\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport shutil\nimport subprocess\nimport sys\nfrom pathlib import Path\nfrom typing import Any\nfrom urllib import request\n\nAGENTS_DB_API = os.environ.get(\"AGENTS_DB_API\", \"http://100.116.176.16:8091\")\nPI_MODEL = os.environ.get(\"PI_SESSION_MEMORY_MODEL\", \"gpt-5.4-mini\")\nPI_THINKING = os.environ.get(\"PI_SESSION_MEMORY_THINKING\", \"off\")\nPI_BIN = os.environ.get(\"PI_SESSION_MEMORY_PI_BIN\")\n\n\ndef resolve_pi_bin() -> str:\n    candidates = [\n        PI_BIN,\n        shutil.which(\"pi\"),\n        str(Path.home() / \".npm-global\" / \"bin\" / \"pi\"),\n        \"/usr/local/bin/pi\",\n        \"/usr/bin/pi\",\n    ]\n    for candidate in candidates:\n        if not candidate:\n            continue\n        if Path(candidate).exists():\n            return candidate\n    raise FileNotFoundError(\"pi\")\n\n\ndef load_job(job_file: Path) -> dict[str, Any]:\n    return json.loads(job_file.read_text(encoding=\"utf-8\"))\n\n\ndef save_job(job_file: Path, payload: dict[str, Any]) -> None:\n    job_file.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + \"\\n\", encoding=\"utf-8\")\n\n\ndef call_pi(prompt: str) -> str:\n    cmd = [\n        resolve_pi_bin(),\n        \"-p\",\n        \"--provider\",\n        \"openai-codex\",\n        \"--model\",\n        PI_MODEL,\n        \"--thinking\",\n        PI_THINKING,\n        \"--no-tools\",\n        \"--no-session\",\n        prompt,\n    ]\n    proc = subprocess.run(cmd, capture_output=True, text=True)\n    if proc.returncode != 0:\n        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f\"pi failed ({proc.returncode})\")\n    return proc.stdout.strip()\n\n\ndef ingest(memory: dict[str, Any]) -> dict[str, Any]:\n    req = request.Request(\n        f\"{AGENTS_DB_API}/api/ingest\",\n        method=\"POST\",\n        data=json.dumps(memory).encode(),\n        headers={\"Content-Type\": \"application/json\"},\n    )\n    with request.urlopen(req, timeout=30) as resp:\n        return json.loads(resp.read().decode(\"utf-8\"))\n\n\ndef extract_json(text: str) -> dict[str, Any]:\n    raw = text.strip()\n    if raw.startswith(\"```\"):\n        raw = raw.strip(\"`\")\n        raw = raw.split(\"\\n\", 1)[-1] if \"\\n\" in raw else raw\n    start = raw.find(\"{\")\n    end = raw.rfind(\"}\")\n    if start >= 0 and end > start:\n        raw = raw[start : end + 1]\n    return json.loads(raw)\n\n\ndef session_meta(job: dict[str, Any]) -> dict[str, Any]:\n    payload = job.get(\"payload\")\n    if isinstance(payload, dict):\n        return payload\n    return job\n\n\ndef build_prompt(job: dict[str, Any]) -> str:\n    meta = session_meta(job)\n    return f\"\"\"You are deciding whether a Pi session has durable value.\n\nRules:\n- Prefer NOT saving anything unless it is durable and reusable.\n- Session metadata alone is not enough.\n- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.\n- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.\n- If nothing durable exists, return save=false.\n- If the memory is likely durable, assign the best type and include evidence.\n- Output JSON only. No markdown. No extra text.\n\nReturn this schema:\n{{\n  \"save\": boolean,\n  \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n  \"subtype\": string | null,\n  \"scope\": \"global\" | \"project\",\n  \"title\": string,\n  \"content\": string,\n  \"source_ref\": string,\n  \"evidence_ref\": string | null,\n  \"confidence\": number,\n  \"freshness\": number,\n  \"importance\": number,\n  \"reason\": string\n}}\n\nScoring guidance:\n- confidence: 0 to 1, how well supported the memory is.\n- freshness: 0 to 1, how current it is.\n- importance: 0 to 1, how much future value it has.\n- evidence_ref: cite the strongest source in the session or branch excerpt when possible.\n\nSession metadata:\n- session_id: {meta.get('session_id')}\n- session_file: {meta.get('session_file')}\n- cwd: {meta.get('cwd')}\n- scope: {meta.get('scope')}\n- reason: {meta.get('reason')}\n- user_messages: {meta.get('user_messages')}\n- assistant_messages: {meta.get('assistant_messages')}\n- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}\n- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}\n- trigger: {meta.get('trigger')}\n\nBranch excerpt JSON:\n{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}\n\"\"\"\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--job-file\", required=True)\n    args = parser.parse_args()\n\n    job_file = Path(args.job_file)\n    job = load_job(job_file)\n    meta = session_meta(job)\n    job[\"analysis\"] = {\n        \"status\": \"running\",\n        \"save\": None,\n        \"memory_id\": None,\n    }\n    save_job(job_file, job)\n\n    try:\n        raw = call_pi(build_prompt(job))\n        analysis = extract_json(raw)\n        job[\"analysis\"][\"raw\"] = raw\n        job[\"analysis\"][\"result\"] = analysis\n        job[\"analysis\"][\"status\"] = \"completed\"\n\n        if bool(analysis.get(\"save\")):\n            memory_type = analysis.get(\"type\") or \"episode\"\n            is_candidate = memory_type == \"episode\" or bool(float(analysis.get(\"importance\") or 0.5) < 0.75)\n            memory = {\n                \"type\": memory_type,\n                \"subtype\": analysis.get(\"subtype\"),\n                \"scope\": analysis.get(\"scope\") or meta.get(\"scope\") or \"global\",\n                \"status\": \"inbox\" if is_candidate else \"active\",\n                \"source_kind\": \"manual\",\n                \"title\": analysis.get(\"title\") or \"Session insight\",\n                \"content\": analysis.get(\"content\") or \"\",\n                \"confidence\": float(analysis.get(\"confidence\") or 0.75),\n                \"freshness\": float(analysis.get(\"freshness\") or 0.9),\n                \"importance\": float(analysis.get(\"importance\") or 0.5),\n                \"source_ref\": analysis.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n                \"evidence_ref\": analysis.get(\"evidence_ref\"),\n                \"origin_agent\": \"pi-session-memory-analyzer\",\n                \"metadata\": {\n                    \"job_file\": str(job_file),\n                    \"session_id\": meta.get(\"session_id\"),\n                    \"session_file\": meta.get(\"session_file\"),\n                    \"cwd\": meta.get(\"cwd\"),\n                    \"reason\": meta.get(\"reason\"),\n                    \"user_messages\": meta.get(\"user_messages\"),\n                    \"assistant_messages\": meta.get(\"assistant_messages\"),\n                    \"analysis_reason\": analysis.get(\"reason\"),\n                    \"trigger\": meta.get(\"trigger\"),\n                    \"promotion_candidate\": not is_candidate,\n                },\n            }\n            saved = ingest(memory)\n            job[\"analysis\"][\"memory_id\"] = saved.get(\"id\")\n            job[\"analysis\"][\"saved\"] = True\n        else:\n            job[\"analysis\"][\"saved\"] = False\n            job[\"analysis\"][\"memory_id\"] = None\n\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False))\n        return 0\n    except Exception as exc:\n        job[\"analysis\"][\"status\"] = \"failed\"\n        job[\"analysis\"][\"error\"] = str(exc)\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False), file=sys.stderr)\n        return 1\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n"}],"isError":false,"timestamp":1778002124301}}
{"type":"message","id":"a82e2ea3","parentId":"9a2e822a","timestamp":"2026-05-05T17:28:44.308Z","message":{"role":"toolResult","toolCallId":"call_hnzZ3UNqiReYppyRo5ztiYqi|fc_0d46162620b774590169fa28cc178481959486b953e9325c61","toolName":"read","content":[{"type":"text","text":"#!/usr/bin/env python3\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport subprocess\nfrom datetime import datetime, timezone\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\n\nBASE_DIR = Path(__file__).resolve().parent.parent\nJOB_DIR = Path(os.environ.get(\"PI_SESSION_MEMORY_JOB_DIR\", BASE_DIR / \"sessions\" / \"session-memory-jobs\"))\nANALYZER = Path(os.environ.get(\"PI_SESSION_MEMORY_ANALYZER\", BASE_DIR / \"scripts\" / \"session-memory-analyzer.py\"))\n\n\ndef write_job(payload: dict[str, object]) -> Path:\n    JOB_DIR.mkdir(parents=True, exist_ok=True)\n    job_id = datetime.now(timezone.utc).strftime(\"%Y%m%dT%H%M%S%fZ\")\n    job_file = JOB_DIR / f\"{job_id}.json\"\n    job = {\n        \"job_id\": job_id,\n        \"queued_at\": datetime.now(timezone.utc).isoformat(),\n        \"status\": \"queued\",\n        \"job_type\": \"session_memory_analysis\",\n        \"payload\": payload,\n        \"analysis\": {\n            \"status\": \"queued\",\n            \"save\": None,\n            \"memory_id\": None,\n        },\n    }\n    job_file.write_text(json.dumps(job, indent=2, ensure_ascii=False) + \"\\n\", encoding=\"utf-8\")\n    return job_file\n\n\ndef launch_analyzer(job_file: Path) -> None:\n    log_file = job_file.with_suffix(\".log\")\n    log_handle = log_file.open(\"ab\")\n    subprocess.Popen(\n        [\"python3\", str(ANALYZER), \"--job-file\", str(job_file)],\n        stdout=log_handle,\n        stderr=subprocess.STDOUT,\n        start_new_session=True,\n        close_fds=True,\n    )\n\n\nclass Handler(BaseHTTPRequestHandler):\n    def do_POST(self):\n        if self.path != \"/api/session-memory\":\n            self.send_response(404)\n            self.end_headers()\n            return\n\n        length = int(self.headers.get(\"Content-Length\", \"0\") or \"0\")\n        raw = self.rfile.read(length)\n        try:\n            payload = json.loads(raw.decode(\"utf-8\"))\n        except Exception as exc:\n            self.send_response(400)\n            self.send_header(\"Content-Type\", \"application/json\")\n            self.end_headers()\n            self.wfile.write(json.dumps({\"error\": f\"invalid json: {exc}\"}).encode())\n            return\n\n        try:\n            job_file = write_job(payload)\n            launch_analyzer(job_file)\n            self.send_response(202)\n            self.send_header(\"Content-Type\", \"application/json\")\n            self.end_headers()\n            self.wfile.write(json.dumps({\"status\": \"queued\", \"job_file\": str(job_file)}).encode())\n        except Exception as exc:\n            self.send_response(502)\n            self.send_header(\"Content-Type\", \"application/json\")\n            self.end_headers()\n            self.wfile.write(json.dumps({\"error\": str(exc)}).encode())\n\n    def log_message(self, fmt, *args):\n        return\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--host\", default=\"127.0.0.1\")\n    parser.add_argument(\"--port\", type=int, default=17882)\n    args = parser.parse_args()\n\n    server = ThreadingHTTPServer((args.host, args.port), Handler)\n    print(f\"session-memory-dispatcher listening on http://{args.host}:{args.port}/api/session-memory\", flush=True)\n    try:\n        server.serve_forever()\n    except KeyboardInterrupt:\n        return 0\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n"}],"isError":false,"timestamp":1778002124306}}
{"type":"message","id":"5b09d65d","parentId":"a82e2ea3","timestamp":"2026-05-05T17:28:55.468Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Planning job queries**\n\nI'm thinking I need to extrapolate data, which involves using job files and their statuses — saved, not saved, and failed. I'll also consider events from the fix onward. The next step is to query the job JSON directory along with memories from the origin agent. Alright, I’ll need to script this out to get everything I’m looking for in a structured way. Let's see how it all comes together!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28cde6b48195ad0f024f456faba0\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ijRB-YUW9JuJETYTj9NZhMVkGeWRleTUrYiQrmZrjWi50Ph7SW_ShPFTeV4pSn5RseZNCCgIsIu8ZCklJYDjwAYpOhkwOLo8fhTOlbGV9PbX6VsGvyajGIZjm7_a8ZN85HC3lP10p96_DpRfe9bzQ9Hh2Qwp8MuWhSszkgsAw7SRdvjUL6gLBkAVyY608JvM8uABdwdLdIZUY_1mobRttfZI6GhA4XwC4xF6ufNu68CDeAVJLfBzbaRTk8t6LxvHuzUYR1KNUo1WROEa1kxSWpRQTbgskFBWj_s_8EvtRvdjCu0ImRARzULuEfVF1_wg9RP3YyAF4DRcdzdULoeqMrPWZA9QF9Yn4HRutgw3GbqBB_wTJcenq_ZTj2K8Jwex-SvN_6-z_a-e9_gDY4BrOBXv_ICtEWwnPI-BTS-a7lMgaddm607rfVc5s53XgKbfq2yl_TbL8wlU0fomZ26ZBX5QUvQjZw8CXEW9-z5YoDXHg0JJBFDI5sBiPrGz6Yx_8jelVXDM3jNjPjO7zSDMqOxlP0DtDkMo4HASR2ehpXmhFRGy-iAEVVZ4AlvG6ZHQxrvLJEEAUuVWdEcRLmVZxyVG6dzVu638arTM_662b3CEvaVhVbtB4q9sstGDVTYRsAkiZnSSDOSFrSG5KBAUT3wTObXfj-9SEh-qGE9QR7KTlpHR_UP91L-ClQw7zfJ-sFK6A8A7OqcCHwMk8pM2lCp2IyK1cRa2efZrFhQBD3r_Yhz-vIjG4E1jHlDeBllBirgp8LAIl7BtdenBB_y22wC0nX9fT5lqCmqL_30g5cJxYn9EBD9nuvkvFJSaLp8bXxNsKzE3diApzktp0Ufz4WHAyE9UuZpwKHsNhOoAdJYuBoAckYtcTcD8mFNDJsRv_tn-QpO7LT_r34cuxk4vEqFTkwo61hBgX7e0K1rqHVEbJP8J1oBHv9OUw4bKrlvudnfNqZWnFiUSJcB76o_9nxC8aqyZ_mRarQAb090F6OjH_oJU6Fn--xPBvvQVa7VwxP793R1jrUGcOtt1ZyDMPzE__KRQgVrfVrxUpNUcItFDxBk4WTk-xN20Haxrc4qPmOrjAQaJXuPiAY5aNouIUtmBpHo7uHAVQQpAFzP6OF4KR9OmHuZDxSAw6jmahCwR6V-jvlS9oGFWCQ_GeueP977Bw==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Planning job queries**\\n\\nI'm thinking I need to extrapolate data, which involves using job files and their statuses — saved, not saved, and failed. I'll also consider events from the fix onward. The next step is to query the job JSON directory along with memories from the origin agent. Alright, I’ll need to script this out to get everything I’m looking for in a structured way. Let's see how it all comes together!\"}]}"},{"type":"toolCall","id":"call_gncbNvJTVVC0hYOUZPKRzh0p|fc_0d46162620b774590169fa28d18e4c81959acc2c9699f9b1b0","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json, glob, os, sqlite3\nfrom pathlib import Path\njob_dir=Path('/home/sebas/pi-config/sessions/session-memory-jobs')\nfiles=sorted(job_dir.glob('*.json'))\nrows=[]\nfor f in files:\n    try:\n        j=json.loads(f.read_text())\n    except Exception:\n        continue\n    a=j.get('analysis',{})\n    p=j.get('payload',{})\n    rows.append({\n        'job':f.name,\n        'queued':j.get('queued_at'),\n        'status':a.get('status'),\n        'saved':a.get('saved'),\n        'memory_id':a.get('memory_id'),\n        'reason':p.get('reason'),\n        'session_file':p.get('session_file'),\n        'last_user_text':p.get('last_user_text'),\n        'error':a.get('error'),\n    })\nprint('jobs',len(rows))\nfrom collections import Counter\nprint('status_counts', Counter(r['status'] for r in rows))\nprint('saved_counts', Counter(r['saved'] for r in rows))\nprint('recent 12:')\nfor r in rows[-12:]:\n    print(json.dumps(r, ensure_ascii=False))\n\nconn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\nconn.row_factory=sqlite3.Row\nprint('\\nmemories by origin_agent:')\nfor r in conn.execute(\"select origin_agent, count(*) c from memories group by origin_agent order by c desc\"):\n    print(dict(r))\nprint('\\nrecent session analyzer memories:')\nfor r in conn.execute(\"select id,type,status,confidence,created_at,title,source_ref,substr(content,1,120) content from memories where origin_agent='pi-session-memory-analyzer' order by datetime(created_at) desc limit 20\"):\n    print(json.dumps(dict(r), ensure_ascii=False))\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":2927,"output":518,"cacheRead":47616,"cacheWrite":0,"totalTokens":51061,"cost":{"input":0.0073175,"output":0.00777,"cacheRead":0.011904,"cacheWrite":0,"total":0.0269915}},"stopReason":"toolUse","timestamp":1778002124309,"responseId":"resp_0d46162620b774590169fa28ccc20c81959a22d81623d3ca7b"}}
{"type":"message","id":"29692149","parentId":"5b09d65d","timestamp":"2026-05-05T17:28:55.903Z","message":{"role":"toolResult","toolCallId":"call_gncbNvJTVVC0hYOUZPKRzh0p|fc_0d46162620b774590169fa28d18e4c81959acc2c9699f9b1b0","toolName":"bash","content":[{"type":"text","text":"jobs 30\nstatus_counts Counter({'completed': 24, 'failed': 3, None: 3})\nsaved_counts Counter({True: 14, False: 10, None: 6})\nrecent 12:\n{\"job\": \"20260501T035247390123Z.json\", \"queued\": \"2026-05-01T03:52:47.390187+00:00\", \"status\": \"completed\", \"saved\": false, \"memory_id\": null, \"reason\": \"before_switch:resume\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-01T03-36-54-752Z_019de19c-5edf-7752-929a-a73bd4bd5ad5.jsonl\", \"last_user_text\": \"yes. revert the other thing because i dont even know what it was\", \"error\": null}\n{\"job\": \"20260501T044435783880Z.json\", \"queued\": \"2026-05-01T04:44:35.783932+00:00\", \"status\": \"completed\", \"saved\": true, \"memory_id\": \"mem_02a8d3bf9ae04a85aa700eb4d5c045e9\", \"reason\": \"before_switch:new\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-30T23-18-37-574Z_019de0af-e705-76bd-814c-bde81adc0657.jsonl\", \"last_user_text\": \"remove de ~/.agents/skills/pi-extension-search/SKILL.md - Prefer packages with explicit `pi-package`, `pi-extension`, or `pi-coding-agent` keywords. - For multi-agent workflows, compare native tmux jobs and delegation tools against `pi-subagents`, `pi-crew`, `pi-messenger-swarm`, and `taskplane` when relevant.\", \"error\": null}\n{\"job\": \"20260501T044724939741Z.json\", \"queued\": \"2026-05-01T04:47:24.939811+00:00\", \"status\": \"completed\", \"saved\": true, \"memory_id\": \"mem_f7f168513be944169a0a7790b25293f0\", \"reason\": \"before_switch:new\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-01T04-44-59-379Z_019de1da-b272-75a2-8b82-9e6f52ce8073.jsonl\", \"last_user_text\": \"this error throws running /new here and saying yes to analyzing for memory file:///home/sebas/.npm-global/lib/node_modules/@mariozechner/pi-coding-agent/dist/core/extensions/runner.js:297 throw new Error(this.staleMessage);───────────────────────────────────────────────────────────────────────────────────────────────────────────── ^ $0.000 (sub) 0.0%/272k (auto) (openai-codex) gpt-5.4 • low Error: This extension ctx is stale after session replacement or reload. Do not use a captured pi or command ctx after ctx.newSession(), ctx.fork(), ctx.switchSession(), or ctx.reload(). For newSession, fork, and switchSession, move post-replacement work into withSession and use the ctx passed to withSession. For reload, do not use the old ctx after await ctx.reload(). at ExtensionRunner.assertActive (file:///home/sebas/.npm-global/lib/node_modules/@mariozechner/pi-coding-agent/dist/core/extensions/runner.js:297:19) at get sessionManager (file:///home/sebas/.npm-global/lib/node_modules/@mariozechner/pi-coding-agent/dist/core/extensions/runner.js:394:24) at WebSocket.<anonymous> (/home/sebas/.npm-global/lib/node_modules/pi-tau-mux/extensions/tau-client.ts:41:23) at WebSocket.emit (node:events:519:28) at WebSocket.setSocket (/home/sebas/.npm-global/lib/node_modules/pi-tau-mux/node_modules/ws/lib/websocket.js:252:10) at ClientRequest.<anonymous> (/home/sebas/.npm-global/lib/node_modules/pi-tau-mux/node_modules/ws/lib/websocket.js:1017:15) at ClientRequest.emit (node:events:519:28) at Socket.socketOnData (node:_http_client:654:11) at Socket.emit (node:events:519:28) at addChunk (node:internal/streams/readable:561:12)\", \"error\": null}\n{\"job\": \"20260501T194747970340Z.json\", \"queued\": \"2026-05-01T19:47:47.970454+00:00\", \"status\": \"completed\", \"saved\": false, \"memory_id\": null, \"reason\": \"before_switch:new\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-01T19-05-01-653Z_019de4ee-15d4-773b-acfe-3b755d8f8625.jsonl\", \"last_user_text\": \"since now we have a session analysis at the session end, change agents wording so we dont store memories mid conversation\", \"error\": null}\n{\"job\": \"20260505T134438583726Z.json\", \"queued\": \"2026-05-05T13:44:38.583785+00:00\", \"status\": \"completed\", \"saved\": true, \"memory_id\": \"mem_812ea22ea06d4368bdb66fe531e64fdc\", \"reason\": \"before_switch:new\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-05T13-38-23-930Z_019df85c-7c39-73e8-aa53-3b548239898e.jsonl\", \"last_user_text\": \"apply yourself, do outside this session as it will shut down when running restart\", \"error\": null}\n{\"job\": \"20260505T143201664755Z.json\", \"queued\": \"2026-05-05T14:32:01.664814+00:00\", \"status\": \"completed\", \"saved\": false, \"memory_id\": null, \"reason\": \"before_switch:resume\", \"session_file\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-04T21-17-14-403Z_019df4da-3522-776c-986b-78e057101a55.jsonl\", \"last_user_text\": \"we were updating linux, can you move some things to /mnt/rpi to make space?\", \"error\": null}\n{\"job\": \"20260505T172506522944Z.json\", \"queued\": \"2026-05-05T17:25:06.522989+00:00\", \"status\": \"failed\", \"saved\": null, \"memory_id\": null, \"reason\": \"before_switch:resume\", \"session_file\": \"/home/sebas/pi-config/sessions/--home-sebas--/2026-05-01T20-20-59-838Z_019de533-a33e-7348-809e-ac73ede080fb.jsonl\", \"last_user_text\": \"matala por si acaso. aca ya hice el connect al nuevo y tomo el mensaje ya mandado por telegram bien. incluso cerre la ssh session y corrio por detras y respondio\", \"error\": \"Warning: No models match pattern \\\"gemini-3-flash-preview:minimal\\\"\\nWarning: No models match pattern \\\"gemini-3-flash-preview:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:low\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:high\\\"\\nWarning: No models match pattern \\\"o3-mini:low\\\"\\nWarning: No models match pattern \\\"o3-mini:medium\\\"\\nWarning: No models match pattern \\\"o3-mini:high\\\"\\nWarning: No models match pattern \\\"minimax-m2.5-free:minimal\\\"\\nWarning: No models match pattern \\\"qwen/qwen3-coder:free:minimal\\\"\\nWarning: No models match pattern \\\"google/gemma-3-27b-it:free:minimal\\\"\\n[openai-codex] Token refresh failed: 401 {\\n  \\\"error\\\": {\\n    \\\"message\\\": \\\"Your refresh token has already been used to generate a new access token. Please try signing in again.\\\",\\n    \\\"type\\\": \\\"invalid_request_error\\\",\\n    \\\"param\\\": null,\\n    \\\"code\\\": \\\"refresh_token_reused\\\"\\n  }\\n}\\nNo API key for provider: openai-codex\"}\n{\"job\": \"20260505T172551604683Z.json\", \"queued\": \"2026-05-05T17:25:51.604736+00:00\", \"status\": \"failed\", \"saved\": null, \"memory_id\": null, \"reason\": \"before_switch:resume\", \"session_file\": \"/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-44-38-590Z_019df862-33be-7759-b796-3d6b48b23493.jsonl\", \"last_user_text\": \"and yeah harden like you suggested\", \"error\": \"Warning: No models match pattern \\\"gemini-3-flash-preview:minimal\\\"\\nWarning: No models match pattern \\\"gemini-3-flash-preview:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:low\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:high\\\"\\nWarning: No models match pattern \\\"o3-mini:low\\\"\\nWarning: No models match pattern \\\"o3-mini:medium\\\"\\nWarning: No models match pattern \\\"o3-mini:high\\\"\\nWarning: No models match pattern \\\"minimax-m2.5-free:minimal\\\"\\nWarning: No models match pattern \\\"qwen/qwen3-coder:free:minimal\\\"\\nWarning: No models match pattern \\\"google/gemma-3-27b-it:free:minimal\\\"\\n[openai-codex] Token refresh failed: 401 {\\n  \\\"error\\\": {\\n    \\\"message\\\": \\\"Your refresh token has already been used to generate a new access token. Please try signing in again.\\\",\\n    \\\"type\\\": \\\"invalid_request_error\\\",\\n    \\\"param\\\": null,\\n    \\\"code\\\": \\\"refresh_token_reused\\\"\\n  }\\n}\\nNo API key for provider: openai-codex\"}\n{\"job\": \"20260505T172601128581Z.json\", \"queued\": \"2026-05-05T17:26:01.128642+00:00\", \"status\": \"failed\", \"saved\": null, \"memory_id\": null, \"reason\": \"before_switch:new\", \"session_file\": \"/home/sebas/pi-config/sessions/--home-sebas--/2026-05-01T20-17-40-015Z_019de530-96ae-7339-a1e7-d5e4e6a7e377.jsonl\", \"last_user_text\": \"y\", \"error\": \"Warning: No models match pattern \\\"gemini-3-flash-preview:minimal\\\"\\nWarning: No models match pattern \\\"gemini-3-flash-preview:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:low\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:high\\\"\\nWarning: No models match pattern \\\"o3-mini:low\\\"\\nWarning: No models match pattern \\\"o3-mini:medium\\\"\\nWarning: No models match pattern \\\"o3-mini:high\\\"\\nWarning: No models match pattern \\\"minimax-m2.5-free:minimal\\\"\\nWarning: No models match pattern \\\"qwen/qwen3-coder:free:minimal\\\"\\nWarning: No models match pattern \\\"google/gemma-3-27b-it:free:minimal\\\"\\n[openai-codex] Token refresh failed: 401 {\\n  \\\"error\\\": {\\n    \\\"message\\\": \\\"Your refresh token has already been used to generate a new access token. Please try signing in again.\\\",\\n    \\\"type\\\": \\\"invalid_request_error\\\",\\n    \\\"param\\\": null,\\n    \\\"code\\\": \\\"refresh_token_reused\\\"\\n  }\\n}\\nNo API key for provider: openai-codex\"}\n{\"job\": \"consolidation-20260422T142817021443Z.json\", \"queued\": null, \"status\": null, \"saved\": null, \"memory_id\": null, \"reason\": null, \"session_file\": null, \"last_user_text\": null, \"error\": null}\n{\"job\": \"consolidation-20260422T142832064769Z.json\", \"queued\": null, \"status\": null, \"saved\": null, \"memory_id\": null, \"reason\": null, \"session_file\": null, \"last_user_text\": null, \"error\": null}\n{\"job\": \"consolidation-20260422T164146241829Z.json\", \"queued\": null, \"status\": null, \"saved\": null, \"memory_id\": null, \"reason\": null, \"session_file\": null, \"last_user_text\": null, \"error\": null}\n\nmemories by origin_agent:\n{'origin_agent': 'personal-agent', 'c': 159}\n{'origin_agent': None, 'c': 65}\n{'origin_agent': 'pi-session-memory-analyzer', 'c': 8}\n\nrecent session analyzer memories:\n{\"id\": \"mem_68d91c0d9b6244f384fac0abfff678e7\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.94, \"created_at\": \"2026-05-05T15:09:45.606010+00:00\", \"title\": \"Mercado Pago split payments need club revenue share option\", \"source_ref\": \"session:31d27042:/mnt/rpi/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-38-27-079Z_019db5a0-cb07-701e-b971-284bf7a60070.jsonl\", \"content\": \"The ballbox payment flow now needs support for a customizable percentage of each sale paid to the club hosting the vendi\"}\n{\"id\": \"mem_5be93517d726499fbdf0f614da57ef6e\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.98, \"created_at\": \"2026-05-05T15:09:39.327960+00:00\", \"title\": \"Memory layer RFC approved; hierarchical promotion is the key design\", \"source_ref\": \"conversation:2026-04-22\", \"content\": \"RFC aprobado. Resultado durable: la capa de memoria debe ser jerárquica con promoción explícita, no solo almacenamiento.\"}\n{\"id\": \"mem_c636b2dc79794c57aded818ba6fdae2f\", \"type\": \"decision\", \"status\": \"active\", \"confidence\": 0.93, \"created_at\": \"2026-05-05T15:09:33.872267+00:00\", \"title\": \"Replace agent-start extension with native tmux+pi workflow\", \"source_ref\": \"session:5a93c9f9\", \"content\": \"Use native tmux sessions to run `pi` in a chosen cwd, with command output logged to files, and let the parent decide syn\"}\n{\"id\": \"mem_02a8d3bf9ae04a85aa700eb4d5c045e9\", \"type\": \"decision\", \"status\": \"active\", \"confidence\": 0.96, \"created_at\": \"2026-05-05T15:09:24.051633+00:00\", \"title\": \"Prefer native tmux + pi sessions over the agent-start extension for side-agent workflows\", \"source_ref\": \"session:2df681e4\", \"content\": \"Replace the extension-driven agent-start flow with native tmux sessions that run pi in the target cwd, use log files for\"}\n{\"id\": \"mem_f7f168513be944169a0a7790b25293f0\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.97, \"created_at\": \"2026-05-05T15:09:18.919824+00:00\", \"title\": \"pi-tau-mux stale ctx crash on session replacement\", \"source_ref\": \"session:e0dc43af\", \"content\": \"`pi-tau-mux` can crash after `/new` because websocket callbacks in `extensions/tau-client.ts` keep using a captured exte\"}\n{\"id\": \"mem_812ea22ea06d4368bdb66fe531e64fdc\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.99, \"created_at\": \"2026-05-05T15:08:53.052616+00:00\", \"title\": \"Server SSH idle timeout raised to 4h\", \"source_ref\": \"session bf3e179b / /mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-05T13-38-23-930Z_019df85c-7c39-73e8-aa53-3b548239898e.jsonl\", \"content\": \"Set server-side OpenSSH keepalives on ballbox-first via /etc/ssh/sshd_config.d/99-idle-4h.conf: ClientAliveInterval 60, \"}\n{\"id\": \"mem_b161e2e3072a49099cabb17193a4fb15\", \"type\": \"decision\", \"status\": \"active\", \"confidence\": 0.99, \"created_at\": \"2026-05-05T14:49:55.380603+00:00\", \"title\": \"Server-side SSH idle timeout set to 4h\", \"source_ref\": \"session bf3e179b / /mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-05T13-38-23-930Z_019df85c-7c39-73e8-aa53-3b548239898e.jsonl\", \"content\": \"Set server SSH keepalive on ballbox-first via /etc/ssh/sshd_config.d/99-idle-4h.conf: ClientAliveInterval 60, ClientAliv\"}\n{\"id\": \"mem_08b4efed55144d78af56bdd15821b61d\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.98, \"created_at\": \"2026-05-05T14:49:24.000542+00:00\", \"title\": \"Root disk nearly full; /mnt/rpi has room and safe caches were moved there\", \"source_ref\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-04T21-17-14-403Z_019df4da-3522-776c-986b-78e057101a55.jsonl\", \"content\": \"Root filesystem on ballbox-first was at 97% used with ~494M free. Safe large paths were moved off root to /mnt/rpi and s\"}\n"}],"isError":false,"timestamp":1778002135902}}
{"type":"message","id":"7f69b40c","parentId":"29692149","timestamp":"2026-05-05T17:29:01.077Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Investigating memories**\n\nI'm thinking about the 8 memories and wondering if my jobs have true duplicates among the 14 I noted. Maybe some have been archived or deleted? I should query all 14 again, but I'm not sure if they all come from the same origin agent. It feels like I need to inspect the recent memories in my analyzer to get a clearer picture. Let's take a deeper look into this!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28d9857c8195b529c2062cc95ad1\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ijcb8CHX6NJXijU5QOAY0ZhiQe8qip-RsQTnhQcbEYBnenH79KaaLfrbOOWqzgBLU2xZsALOBdDfVX_RoUapSSB95xIeasf14VBCrP7w86xTF-76If5aREhqhGGHZdC_2GdDD-aUskTF8Ly0bEY5glyIDu82HNrsAVn30qP0X9gpUXCCt-me5cUCV-SBHc30edIvAxHcjOjpcD-81VPpmyB26fksPM048Za8B9snkiBnCAttuspwOYzA2DV2XQYExtu2k2aQXT3PIJJ_fy7qgDua73tt8PgMuaHKrm75W7xJqGXpfN3ovLn7LlOtOpZT3AiTI7JmOD1LrywcCCFz9Vq0gu-BNAV5xgMknq3Ax5nE3hMbdxDJfKHbkEe1FMytDYKKsGTnUrOD1XXJDwv2Vu-pXAP7hrsknanwS8wGelzFIc9wJPm-i2a5uCWNZ-r3N7UEluE955grpHutJbFqwLPqpyAdPc80gOiStlS9bEXbR-Bd9vWauoavxPM5ZT4jhbmiyRQfmxuyZaN4vWMRedzPZ-_IotzMp1PjHlRo8TDvkk0deY0tXaCZmPxalTOCPLAT68Gi68rbEReeElb0CsDr2ZUPi2bQA0oMNdUUhtjehRjnnDEnFcL9v-RIidNNZ0jdyhlO9RpedXsmwpJxUPY-O0L9N2Gm7IGM51d2ia_D7RKTgVfbBNDh4g28Ug39DkIilcsAMSdqa7xLQurYb_Hgb6G5-16wA12K7V3NoDidW88706EimA-VzXB1cMQ8O_OGJZxXQ3z4vq91KKOsdV6JG4DSmpKwhdXD6_9ptaR9vgJu59CbG_TVNakHDgwl4p9Z8IDV5aMWt_mAECDkXai_v_KhhzV9-dvVNxgzZLjue0he_QUyrl-CDxkiUqhz6k93tLgmJ8rA7TZc3U19x7NfQqSZWvNNNDgOM1jqDV7D3qfevgJQCl-FhHDDYqDsPAvrcGtLFMs64VfhFlqbx4vlSQrlXbls3-6PshTwY_9cdUnIF0ElR11K2tBiZAXDgBUkE6_Chkp453T3ki7scg3Ih7C5gbhnjdknD21oC5BT1GtMtb9h_f3sFUCC7D1UUdTuCa8SXDcv8V1vTIx5uYhEg1wzlQbwM1CE714F-t_CCe6BAsr5E-oyj0LO0Wt7eFi9u3dHP4bvKXneykXs4HxTjs9d_kF8cr0J60Pe-C8SIs43e4WR76fVCetfRz5JQer1B7gAOKqx6Q4Erjzaen6G-WmT1FTmiAhq018mMF4n7I=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Investigating memories**\\n\\nI'm thinking about the 8 memories and wondering if my jobs have true duplicates among the 14 I noted. Maybe some have been archived or deleted? I should query all 14 again, but I'm not sure if they all come from the same origin agent. It feels like I need to inspect the recent memories in my analyzer to get a clearer picture. Let's take a deeper look into this!\"}]}"},{"type":"toolCall","id":"call_LFoRgR17NHiBWtM6ZdyIJLfP|fc_0d46162620b774590169fa28dc38908195b8c11d92639f92a2","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json\nconn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\nconn.row_factory=sqlite3.Row\nrows=conn.execute(\"select id,type,status,confidence,created_at,title,source_ref,origin_agent from memories where origin_agent='pi-session-memory-analyzer' order by datetime(created_at)\").fetchall()\nprint('count', len(rows))\nfor r in rows:\n    print(json.dumps(dict(r), ensure_ascii=False))\nPY"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":5209,"output":186,"cacheRead":50176,"cacheWrite":0,"totalTokens":55571,"cost":{"input":0.013022500000000001,"output":0.00279,"cacheRead":0.012544,"cacheWrite":0,"total":0.0283565}},"stopReason":"toolUse","timestamp":1778002135904,"responseId":"resp_0d46162620b774590169fa28d895448195a97ce069c65fb34f"}}
{"type":"message","id":"2d2d616d","parentId":"7f69b40c","timestamp":"2026-05-05T17:29:01.131Z","message":{"role":"toolResult","toolCallId":"call_LFoRgR17NHiBWtM6ZdyIJLfP|fc_0d46162620b774590169fa28dc38908195b8c11d92639f92a2","toolName":"bash","content":[{"type":"text","text":"count 8\n{\"id\": \"mem_08b4efed55144d78af56bdd15821b61d\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.98, \"created_at\": \"2026-05-05T14:49:24.000542+00:00\", \"title\": \"Root disk nearly full; /mnt/rpi has room and safe caches were moved there\", \"source_ref\": \"/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-04T21-17-14-403Z_019df4da-3522-776c-986b-78e057101a55.jsonl\", \"origin_agent\": \"pi-session-memory-analyzer\"}\n{\"id\": \"mem_b161e2e3072a49099cabb17193a4fb15\", \"type\": \"decision\", \"status\": \"active\", \"confidence\": 0.99, \"created_at\": \"2026-05-05T14:49:55.380603+00:00\", \"title\": \"Server-side SSH idle timeout set to 4h\", \"source_ref\": \"session bf3e179b / /mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-05T13-38-23-930Z_019df85c-7c39-73e8-aa53-3b548239898e.jsonl\", \"origin_agent\": \"pi-session-memory-analyzer\"}\n{\"id\": \"mem_812ea22ea06d4368bdb66fe531e64fdc\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.99, \"created_at\": \"2026-05-05T15:08:53.052616+00:00\", \"title\": \"Server SSH idle timeout raised to 4h\", \"source_ref\": \"session bf3e179b / /mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-05T13-38-23-930Z_019df85c-7c39-73e8-aa53-3b548239898e.jsonl\", \"origin_agent\": \"pi-session-memory-analyzer\"}\n{\"id\": \"mem_f7f168513be944169a0a7790b25293f0\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.97, \"created_at\": \"2026-05-05T15:09:18.919824+00:00\", \"title\": \"pi-tau-mux stale ctx crash on session replacement\", \"source_ref\": \"session:e0dc43af\", \"origin_agent\": \"pi-session-memory-analyzer\"}\n{\"id\": \"mem_02a8d3bf9ae04a85aa700eb4d5c045e9\", \"type\": \"decision\", \"status\": \"active\", \"confidence\": 0.96, \"created_at\": \"2026-05-05T15:09:24.051633+00:00\", \"title\": \"Prefer native tmux + pi sessions over the agent-start extension for side-agent workflows\", \"source_ref\": \"session:2df681e4\", \"origin_agent\": \"pi-session-memory-analyzer\"}\n{\"id\": \"mem_c636b2dc79794c57aded818ba6fdae2f\", \"type\": \"decision\", \"status\": \"active\", \"confidence\": 0.93, \"created_at\": \"2026-05-05T15:09:33.872267+00:00\", \"title\": \"Replace agent-start extension with native tmux+pi workflow\", \"source_ref\": \"session:5a93c9f9\", \"origin_agent\": \"pi-session-memory-analyzer\"}\n{\"id\": \"mem_5be93517d726499fbdf0f614da57ef6e\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.98, \"created_at\": \"2026-05-05T15:09:39.327960+00:00\", \"title\": \"Memory layer RFC approved; hierarchical promotion is the key design\", \"source_ref\": \"conversation:2026-04-22\", \"origin_agent\": \"pi-session-memory-analyzer\"}\n{\"id\": \"mem_68d91c0d9b6244f384fac0abfff678e7\", \"type\": \"project\", \"status\": \"active\", \"confidence\": 0.94, \"created_at\": \"2026-05-05T15:09:45.606010+00:00\", \"title\": \"Mercado Pago split payments need club revenue share option\", \"source_ref\": \"session:31d27042:/mnt/rpi/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-38-27-079Z_019db5a0-cb07-701e-b971-284bf7a60070.jsonl\", \"origin_agent\": \"pi-session-memory-analyzer\"}\n"}],"isError":false,"timestamp":1778002141131}}
{"type":"message","id":"1b005a20","parentId":"2d2d616d","timestamp":"2026-05-05T17:29:09.801Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28df1fc08195a19bac3e21b2f183\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ijkFYPii-6vHbPSt5DoCHu8DVuZEWYUZOwLTGflQ2IFEji0LvrpTpqpqscjXaTHDJ2UTLjAOgdtrfTrDdP_UVkcIAsmqqB6PJFjpl1R3EhRmAeYw62ZnWYRTTgn6fMByJ2d937XKxeOUXoc_Nrm-aAq9A5DaYkQEtiprpzpGBRQqQtTtZR8W269H9dRXLLs_X0g7TwQhEFMrrU4Hl6grbxObwu2TC6JjzP246GCIPjsKUuOYUeM53JfEodpmqrbrc5_sW_B3UQXtTCr6xzCbh0Ci-wCHT0vUi1n0EfOnazHow-PJ09hw867FRVSgkLc2r-7sEWdBpfbXjnO1LulBRgCHPJbSQIe49us4Q6yCYDw2xdW0d4f-CKOgU5sZtft295eJ4DSMGL-ZwzbgKsACNg3ZmWO-03fWTVT4_lhFZppbM6P6TnUxweK554yKe9faWp_OvVpGHBnY1tUFyIBdGJwUNo9c1kF5gzL-4EX1Okza3rj9ak-HOAGU-g22DduKSMMYy2vSSu4DCOd--TDLKxpIZrL_Kvi1L7CXvkJ6G0dSq5bu7Bij5oaQFYpYWNetAKDGGLc_8ZUUW0ueqGojHxKBWrFB4Fj3pA_EhE2sGuQvcg7elmJjvdl3GNslvFszVQ2tDj6qSuHOcnCO9tTBx0CJVP803l0m2Ynj1oDzDy9R9ZHl9RCz3SwILfL1cIG8FSLS0UugJ1_JnaZKRIIU9LnE7GHo9VssoFXDvle9t5zbXwlWJ9OlkIuP7R9wt_G_RelW1PhZ7qjlNjZI_nJFNvQZ0a2VQ871tg2V0F84L3sLlwpWdjQYd6BWyJNG_4VSrxXGcVxIesf5_h3GSi12IvQbHWx4Kyatrc9qTKi4c1u09Ot34eiIPxJ7UhAwwoYbeAHItmQ8R1n1gaAUT-eswiqiMxxiLWX1DdorU0td5QvD34fhX5WGQ-HDaiAMkmBLXxQ-4lbX18o7NrKRR6JOiXSObHnp6LTKbevyH09w7gi1MwcDFz8MGqoFnt4yJ9PMedTn8rsAD7qquymOJBJJrhNXbmgfNdIqFX4xurmCWB9389PHQxDgLkA_fgjvxGbMulwPoGW8uy8UWLzg0gDnNfiecqp4SX24OFAb_J24TGNMDeYIeQE2p_WB_cAJ69NRdJSUo1xeHWyPGAv12Jcfqn41SZ86lPCJU-EMWUUmpZWSjSxzvBp6uCcvPI89RXpIFgpa8Y8WNQzQeYhmXY8cxLLHBD1HxaKzGJOJ8w3YVphLn-yoc6ClSARp7-UXod-Qz4Vp1k-iwID3ahS7_E0N392T6RE-nFBwo3a7XXAGyjekyuYtjNaDBfg95xLc9gyGEvOL-FdgQL_6FdWb636AMMWrxlx0KB9F_zdPkoGwoBV2tVEN3n9JelGVO7rildE_hLRCr-LWccI2RcELHTvBnf-Eg==\",\"summary\":[]}"},{"type":"toolCall","id":"call_rUa6OJZu3wqvuUgRSqj0obWl|fc_0d46162620b774590169fa28e4a2c08195b4508fcf09681a6d","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json, sqlite3\nfrom pathlib import Path\njob_dir=Path('/home/sebas/pi-config/sessions/session-memory-jobs')\nconn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\nconn.row_factory=sqlite3.Row\nfor f in sorted(job_dir.glob('*.json')):\n    try:j=json.loads(f.read_text())\n    except: continue\n    a=j.get('analysis',{})\n    if a.get('saved'):\n        mid=a.get('memory_id')\n        exists=conn.execute('select count(*) from memories where id=?',(mid,)).fetchone()[0]\n        print(f.name, mid, 'exists' if exists else 'MISSING', j.get('payload',{}).get('session_file'))\nPY"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1385,"output":289,"cacheRead":55296,"cacheWrite":0,"totalTokens":56970,"cost":{"input":0.0034625000000000003,"output":0.004335,"cacheRead":0.013824,"cacheWrite":0,"total":0.021621500000000002}},"stopReason":"toolUse","timestamp":1778002141132,"responseId":"resp_0d46162620b774590169fa28ddb868819583121cf5ad6fd78e"}}
{"type":"message","id":"00c0420c","parentId":"1b005a20","timestamp":"2026-05-05T17:29:09.984Z","message":{"role":"toolResult","toolCallId":"call_rUa6OJZu3wqvuUgRSqj0obWl|fc_0d46162620b774590169fa28e4a2c08195b4508fcf09681a6d","toolName":"bash","content":[{"type":"text","text":"20260422T032521567405Z.json mem_e8986c94a8444f79825cbe2d06a16aa7 MISSING /mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\n20260422T032957877477Z.json mem_1f52dece877a424c89bc6e84a252609c MISSING /mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T03-05-29-888Z_019db326-6020-77ae-b0bb-ce6a502f54f1.jsonl\n20260422T123506691388Z.json mem_7e571be6dcee4a0b8d53c3cf27898c50 MISSING /mnt/rpi/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T00-40-43-848Z_019da855-1e48-73f6-9fb3-3e7af0384b1a.jsonl\n20260422T124200568464Z.json mem_7f28b7d39d064165a80ca6c5fe534ccd MISSING /mnt/rpi/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-32-15-820Z_019db52d-43cb-7714-b600-a8e0da2d5b72.jsonl\n20260422T134209878594Z.json mem_5de09001821c49cdb1b93f0766585215 MISSING /mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T12-58-12-075Z_019db545-02eb-75f4-99f6-4333c7edcc11.jsonl\n20260422T142938948545Z.json mem_809845d47a694176b64b5bb9466bb74c MISSING /mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T14-27-54-097Z_019db597-2271-709a-8fd0-26d42cc048ea.jsonl\n20260422T143827064890Z.json mem_8dce0d0ebd844ddeb82b3df16e39d4bd MISSING /mnt/rpi/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-31-46-594Z_019db59a-aea2-71dd-aca1-e14a4a32409b.jsonl\n20260422T152622743888Z.json mem_43cfc467de814cbf9e7fd2526df867ad MISSING /mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T14-29-38-952Z_019db598-bc08-75f9-a6f5-0346369b6ced.jsonl\n20260423T133840452732Z.json mem_68d91c0d9b6244f384fac0abfff678e7 exists /mnt/rpi/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-38-27-079Z_019db5a0-cb07-701e-b971-284bf7a60070.jsonl\n20260423T191132397823Z.json mem_5be93517d726499fbdf0f614da57ef6e exists /mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T13-42-09-890Z_019db56d-42e2-71c2-9480-6d2d78403521.jsonl\n20260501T033642290422Z.json mem_c636b2dc79794c57aded818ba6fdae2f exists /mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-30T23-18-37-574Z_019de0af-e705-76bd-814c-bde81adc0657.jsonl\n20260501T044435783880Z.json mem_02a8d3bf9ae04a85aa700eb4d5c045e9 exists /mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-30T23-18-37-574Z_019de0af-e705-76bd-814c-bde81adc0657.jsonl\n20260501T044724939741Z.json mem_f7f168513be944169a0a7790b25293f0 exists /mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-01T04-44-59-379Z_019de1da-b272-75a2-8b82-9e6f52ce8073.jsonl\n20260505T134438583726Z.json mem_812ea22ea06d4368bdb66fe531e64fdc exists /mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-05T13-38-23-930Z_019df85c-7c39-73e8-aa53-3b548239898e.jsonl\n"}],"isError":false,"timestamp":1778002149984}}
{"type":"message","id":"cca55139","parentId":"00c0420c","timestamp":"2026-05-05T17:29:23.318Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating memory save rates**\n\nI’m noticing there might be 8 old memories missing, possibly from a migration. I need to analyze the last 5-6 events since the hook was fixed, likely around May 5, 2025. The user mentioned running those events, so I’ll compute the save rate for recent jobs, excluding failures. There could be 2 successful saves with duplicates, and for genuine auto events after the fix, maybe 2 out of 6 saved. If the hook worked properly, I could expect around 2-4 more memories, not dozens.\n\n**Calculating success rates**\n\nI need to compute the success rate over completed sessions in the recent window. It might make sense to exclude manual replays and any duplicates from the same session file. So, I’ll focus on unique sessions to analyze and determine the save rate. By doing that, I can get a clearer picture of how well everything is functioning right now, ensuring I’m accounting for only the relevant data. Let's see what I find!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28e8e6388195b7ac3d9b0c7cf544\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ijx0rltk8LGNcLTQBdD6WlcJIpx4fj9GJTzg2PAuQDd50NhUA1TGS0DRWuD3qisEUy7MWByRsDFrZpzNBf7mm7ysfJ9j0Fp2RBSS38-ihgGtxSs9TJb74xTKhqLGurzMWZAQYV6YbVFT2MYYcLfZP-Ipj3BRbLop1xuOxjaVcdZzDmUseUvO33s9_NGOABsVeKiWqufusWmCwa1guM8au3OU4ZjpDuWtRHPDyuqFrhgF1CUyD5_erVzVSfvKtBeP1h8Dv8tRxQ0KIJWICeTsXpGBOJSfcs0OJpTiTWFmk41bcLadBgAEEYdAizDRyJVY0oD_lwRu8txSYcfvjayetBb679qx_Bb6JD_IjGQeFMkHOX4KKNeCONsob9zuh9h6Qqmt_ORzgkF0sc8Kmr_GWL83tibsbWVx5V9YYUHuTg4xKG1tSmv98pd7fe1b9rR-I4dn-QovLLKvIx48LUZGdKvtROLpIl_iCrYPeDLfxXdAgW69rJOXlIR672Oqrxfc1LtY7nvZUANO6CbpocQkdlTcvNkWWJm2JfQLWSopwbtLRR0sKchxvrKXNJ1nSb3t1GQvr-XkFrLNK6ofH8gvLQ9O2xi8JaaEb_HDcdq3_f7p38hfIajUgiopN4uk8o6QVkrIv-Qk9ZpjqCKgK0zG6DsmvKKoBnnVOpbniD8BBJhKmZSbvhBzCT0P2mk2cEqVTayUpAnzYO9cHhiF-g2LPau-YQlE9KyEURxSY0S07XHuWX83WfLKWPxcZrjBPl7RbkmkkuuMoHk8fnXzlWt3gPBW6QbkPzsTKGbXGki5e5GC3t2KhFp0rol3IN0SakMx-9gIzc5plg0NwC1q6zeeHzT5UHbDsANBmQZV7cSlawkdDd3xZKXxxKQVSARS-cPGAyQ9PUbE_xsBU5S_gWswlb3c4kCBh_KT2xZhpb_Hp6NA7eVdGwQr7kqNzWHK5BH2qlcFgOPx4p8gDzXGtPxmvV0iLBp8tfJFWyweHXHNjMoQvsASiUloYpoAWOfYVFrx5eC9V-dqnJVcJM1TD8_gOy7Z_LTUk0Sv-ddtwb-FE9JAV6kKSWizEtNzhr0fClX2WiqZ6onTvr20i-WHJB94rW9ZM3CRi3lDPGAGn1yn1WxtUQonrCpt7dtUfOuKiQVsW-Ilh19jWWfU8iw_hFDNz5F_Pukb6-x1kq7QFrP26ad0oaCOYdc5SGD0Otiyl4uq_AFfEOqKN_oml0mX8HvQaookDhyKX6reAyXVpqRuhsW5utC2-W1eGxjfj4Wq6UCg0ugw2Ni61tus6d4dHlS_ErGL-aPw_XDiYuSl8Am8gZZ1vH00Cz2WGGj0mfPLMOz4eAsYRw4UVSJVykD4qXOqlJ23o0ArBj0Tj8ZCokvk_AD_yUsBd-bMF77rC3of2VOpGTFkbiPJk20briU5BAEfEq9VdsWi-P2zFONhw2VRpxNRpe1Wp8Rfx5whadsR3JeRD6AtD4HvgkNLs7FU4Y0jG7slLGw61egO_VfMOe6OTKfkIBQAy-YO9wKlNaRuQ_foKmc_Rpbh1l4LGYFAEiusiDago5KEwmr3o-dfymxYrnsKSPi2mxYAEJuUFewdrGc1W5nlMZILinmBwYJYIbJgaqxoC6eO1fx3f3MDh973IrS-H20Dc6BJ-KUKhbRk23o6XaZr01gzxzW3aI0OPwIzUauOEFqIeBDRHKY2Os88Oa2wAgGFq1EGIV2411RnV2jWybP8sL7jcQGzVnGt9DppTTTdtbcVC1zOIs1v_MzmzVybACMsKt8Pbx0RBSXStfw6MRyDh5kdCAlZzHCdcxPdFkRuJ3QUFRt1UdMp1RNKIjCV7IR6PUH0Lfmy3POR8LL6C84mFaRLyp4ZiN8Wc99j7rovrNhUcQwV6IK_gHOH2SV2zdCZVCp-0wxOhBazmfsuiJyKkVht5IaBw5YDHA1YlaHdNS-P_oFg-IN0hA5Ji4BcJDE1voeETEtyHy3ygDiIGWQW9El4iJESo6ku0KBBESR9biUgGeZFD27yZo6FsRUBhEk9oG8OlH_uQ_9s8AaM7JZD8_9XoCYV1N2jgOPBdDNgq-EvSir4ZaEcLTv4eWMtxlV0fEe6uSJ2DfkBPd_4f7pL-yDz6z5NjdmQ2kmb567cKogJTtZuC7OdASmYHW36_cKVBpMi2VOEbWZkNYLyzDVM7ABTbSxwfjRPK29seundIM5BimYE2VRZJFAsM2Iw5m_3OmB-LVDOfu6uyAtpY2Dd-Oi5J0TCqUvMmDcLUfzmRlq2b4wmgtHJvan-KZdBkuAihuxjiLkh0bqmkuaTtAPFVjC6lGz6iDrls-tdyK63eiI7DlnO3MWDgcJfZJJ6RA=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Evaluating memory save rates**\\n\\nI’m noticing there might be 8 old memories missing, possibly from a migration. I need to analyze the last 5-6 events since the hook was fixed, likely around May 5, 2025. The user mentioned running those events, so I’ll compute the save rate for recent jobs, excluding failures. There could be 2 successful saves with duplicates, and for genuine auto events after the fix, maybe 2 out of 6 saved. If the hook worked properly, I could expect around 2-4 more memories, not dozens.\"},{\"type\":\"summary_text\",\"text\":\"**Calculating success rates**\\n\\nI need to compute the success rate over completed sessions in the recent window. It might make sense to exclude manual replays and any duplicates from the same session file. So, I’ll focus on unique sessions to analyze and determine the save rate. By doing that, I can get a clearer picture of how well everything is functioning right now, ensuring I’m accounting for only the relevant data. Let's see what I find!\"}]}"},{"type":"toolCall","id":"call_AyZCQYDFkIa2ZiiyUmJ5d3NM|fc_0d46162620b774590169fa28f193d08195b6efdfa37aa30604","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json\nfrom pathlib import Path\nrows=[]\nfor f in sorted(Path('/home/sebas/pi-config/sessions/session-memory-jobs').glob('*.json')):\n    if f.name.startswith('consolidation-'): continue\n    j=json.loads(f.read_text())\n    p=j.get('payload',{})\n    a=j.get('analysis',{})\n    rows.append({'job':f.name,'queued':j.get('queued_at'),'status':a.get('status'),'saved':a.get('saved'),'session_file':p.get('session_file'),'reason':p.get('reason')})\n# unique by session file, latest job wins\nlatest={}\nfor r in rows:\n    latest[r['session_file']]=r\nprint('total jobs',len(rows),'unique sessions',len(latest))\ncompleted=[r for r in latest.values() if r['status']=='completed']\nprint('unique completed',len(completed),'saved',sum(1 for r in completed if r['saved'] is True),'not_saved',sum(1 for r in completed if r['saved'] is False))\n# recent unique sessions sorted by queued\nrecent=sorted(latest.values(), key=lambda r:r['queued'] or '')[-10:]\nprint('recent unique 10')\nfor r in recent:\n    print(r)\nPY"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1893,"output":564,"cacheRead":56320,"cacheWrite":0,"totalTokens":58777,"cost":{"input":0.004732500000000001,"output":0.00846,"cacheRead":0.014079999999999999,"cacheWrite":0,"total":0.027272499999999998}},"stopReason":"toolUse","timestamp":1778002149985,"responseId":"resp_0d46162620b774590169fa28e6c144819591e9162fc6b4fcf4"}}
{"type":"message","id":"6f3e05a6","parentId":"cca55139","timestamp":"2026-05-05T17:29:23.491Z","message":{"role":"toolResult","toolCallId":"call_AyZCQYDFkIa2ZiiyUmJ5d3NM|fc_0d46162620b774590169fa28f193d08195b6efdfa37aa30604","toolName":"bash","content":[{"type":"text","text":"total jobs 27 unique sessions 24\nunique completed 21 saved 12 not_saved 9\nrecent unique 10\n{'job': '20260423T191132397823Z.json', 'queued': '2026-04-23T19:11:32.397875+00:00', 'status': 'completed', 'saved': True, 'session_file': '/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T13-42-09-890Z_019db56d-42e2-71c2-9480-6d2d78403521.jsonl', 'reason': 'before_switch:new'}\n{'job': '20260501T035247390123Z.json', 'queued': '2026-05-01T03:52:47.390187+00:00', 'status': 'completed', 'saved': False, 'session_file': '/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-01T03-36-54-752Z_019de19c-5edf-7752-929a-a73bd4bd5ad5.jsonl', 'reason': 'before_switch:resume'}\n{'job': '20260501T044435783880Z.json', 'queued': '2026-05-01T04:44:35.783932+00:00', 'status': 'completed', 'saved': True, 'session_file': '/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-30T23-18-37-574Z_019de0af-e705-76bd-814c-bde81adc0657.jsonl', 'reason': 'before_switch:new'}\n{'job': '20260501T044724939741Z.json', 'queued': '2026-05-01T04:47:24.939811+00:00', 'status': 'completed', 'saved': True, 'session_file': '/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-01T04-44-59-379Z_019de1da-b272-75a2-8b82-9e6f52ce8073.jsonl', 'reason': 'before_switch:new'}\n{'job': '20260501T194747970340Z.json', 'queued': '2026-05-01T19:47:47.970454+00:00', 'status': 'completed', 'saved': False, 'session_file': '/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-01T19-05-01-653Z_019de4ee-15d4-773b-acfe-3b755d8f8625.jsonl', 'reason': 'before_switch:new'}\n{'job': '20260505T134438583726Z.json', 'queued': '2026-05-05T13:44:38.583785+00:00', 'status': 'completed', 'saved': True, 'session_file': '/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-05T13-38-23-930Z_019df85c-7c39-73e8-aa53-3b548239898e.jsonl', 'reason': 'before_switch:new'}\n{'job': '20260505T143201664755Z.json', 'queued': '2026-05-05T14:32:01.664814+00:00', 'status': 'completed', 'saved': False, 'session_file': '/mnt/rpi/pi-config/sessions/--home-sebas--/2026-05-04T21-17-14-403Z_019df4da-3522-776c-986b-78e057101a55.jsonl', 'reason': 'before_switch:resume'}\n{'job': '20260505T172506522944Z.json', 'queued': '2026-05-05T17:25:06.522989+00:00', 'status': 'failed', 'saved': None, 'session_file': '/home/sebas/pi-config/sessions/--home-sebas--/2026-05-01T20-20-59-838Z_019de533-a33e-7348-809e-ac73ede080fb.jsonl', 'reason': 'before_switch:resume'}\n{'job': '20260505T172551604683Z.json', 'queued': '2026-05-05T17:25:51.604736+00:00', 'status': 'failed', 'saved': None, 'session_file': '/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-44-38-590Z_019df862-33be-7759-b796-3d6b48b23493.jsonl', 'reason': 'before_switch:resume'}\n{'job': '20260505T172601128581Z.json', 'queued': '2026-05-05T17:26:01.128642+00:00', 'status': 'failed', 'saved': None, 'session_file': '/home/sebas/pi-config/sessions/--home-sebas--/2026-05-01T20-17-40-015Z_019de530-96ae-7339-a1e7-d5e4e6a7e377.jsonl', 'reason': 'before_switch:new'}\n"}],"isError":false,"timestamp":1778002163491}}
{"type":"message","id":"c109f68c","parentId":"6f3e05a6","timestamp":"2026-05-05T17:29:44.039Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Summarizing session outcomes**\n\nI've been thinking about the session memory. Since there's only one memory, quantifying the most recent completed sessions could help. Out of the last six sessions, I saved true for three. If I include the failed ones, I might expect to add about one and a half more. The user mentioned 5 to 6 events post-fix. Analyzing the last unique sessions since May 1, I completed 5 and saved 3, which is about 60%. I’ll focus on finalizing my summary and next steps.","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa28f5f95c8195b31e85fc4330060f\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ij_CVKsJGYmQxT4wP_XwalHPeLlPnYBb02x6H9TzpOkWuSSv_TLjwobke6Cn-vxhDxCgecGnO3HZxx0Dud_DX9748Gso40OIxrapL833ulax9rEAS6skEHhR6GS9t7RIvNb2QGH_eqocYN0_OkzKg9vRfan2zdS8j6StWljjVHiDppOKAVLRJlW-w7IkceTHsIXvvIawzuMxBUR2KoaoRTQ5BPsNe7Gwd4osxzJxBvoNJb8b0de9VbTV-2X407EkJRtbeTwxDHv_sOzIc-MtjEK2JfzizgVhyeyeLq8KzVgqJ_iwjZ6RlblPIStZXAsOTBiQ2AiocEox4MQ0BobWk0gy9uWtarOhGORZLWCnK-k3uC55M5u1xECnHsciS1mrghFxf99pugwDBbRuwkFy6Yrs4eNUrLOKfPbQIHcqHufSgwAxnlZHXy8NQ6sH8ZXwCL1JB9mmP5zUMcwE85ZQAkayW7yxoViAfKzS1U_mcwC6zVzPKpdjaplQwd4H4QBmBz0s-58Z-uq4R6Bp-QwzGa3NbEDHohvMFNcpLCHNdZdKNqttSjhCEUIC0TGeOd8CCps0mwzBssFfL-7DKrbhGB4qrLywteNIrhCjbwU43ejpNqXn-aN1iQF6XYt2mwYNwS9m8WO8ypjMJh-REn2Hg3B4cKGmscnHZOcJXS4EmRUKcGNKoIapi08UvW4PsYsHf0XEhCwtscTb6wiIZC5vqqgzSKmqemDmG4EGV1GdJHhu_pAEszVH6h-zZFsS3-bFj6iuTCDwYIsYUxS3oSVhZDWWpxDWq9lu9MZsyKTKce6oCrl9mqEEgEWhWa1RmwlBYaK1h5F2qHkUrUWtM1NXaICbS_trCVeoYuteQ1R4SlLjroW-tTSOgiOVGf0IOFu5Di1HejVboeXzHbZVDzZAHO4DdPnVc9XjIYWnpWKk7fMO8vvaGDvOkjKZ53j0IhBoErlOUzl90Uu9bUaJMxbOAjf0zBKJYI1JNrW9pan2_GufHRuJ1Esh9k-d0X7WnzMl1jnIiIm9Zg_x4L23pXjBMJbo2LwMQhFLl1wLPNzJWWn9AfJQOA09MDq66kBjm-HBuq_aTp72vTy1bUvPflT-NEv4qiY9L7rYB6B-k5fUJLWYBVjJzMiwZ3uMli3wQ8eReA9r0JoFX0abWt_iLaNceKbo9HRBocwsdyJt6fCcsh0xEflTFzQcLeyKVHgSVEtg-R3KWRjKq6Oi0v-xecjEIaYTQ2HyNZviSuGLczaPjvbba1UMyCvD0eHanx7RZliF3bUYU8D7uyq2CaKIeKEMOGtzZz_c-Y2TN2cpsA7mQhsmbWYdWrAFxsRdy4WitrIr8izzo4nInEo8t5wR0HKETUnIJVWGlfz1C3-ve_AcQfPBrhP3EndK_pmcPVcy_9UdDQNsjbkbHG4WvzO4bmdvkSDAJrT-sNr8YKpIj1ZopjUnNzEN-qrXlO1FrnhA7E1Eotrt1VvgO8HVGcoAMh0YXbqVb3ihipZZ5EctVP15J6kvgclJktwS4Ovt6Yuwx62QlTIOAZX0Gb4rj-MjSbujiA-84U17rx6K2ppdu5_9DaCUcN41xGhc6SvrkFwFXdQlPBeNTysMjtBgXT3whjzJ3OvkTaY4cLFz0I1NCpAeRbxnY_HP2wpq9iXPCpKCMPUnOzi_rI5YlYGvx9oXvRaHW46LAiaP7NCT_rsjtsri06jkPi-KQuSr8UjkpwMA7nThCk2DaS43O2J381DsSoNaOMP29HV52Jq4XMu6IAMoz1YgdCSsb7NgZH5ZjavVv1WMCaPzn-14KSQtIOO-oYR7Z7DcusjZd33lAnHCMFrHKfGDgtEgLwxFT9x-DnRU4WZO8AK0G_5Xr7D4Sm1qVOLWS0-9vROaa86oGCc4QIyE04qikxiNDjSk4MfNKNrIqFEx-bxoCt0q0gRTXIHqtZ2SV9daaWmHjcT2gB5wO31AsItfSbbTbhvWaElE86g5euo8gLhoAGU9MmZNu3bTzHEvK-cefwhRmEpRtzGNhpKMHOv22Ls-79Ynh_yEiHdbnjWuDxgi4wUCMmyZml2s79DwMufxD-WJ3XvZeaJHor3OXAwzm-xD9nuwJWF3U5gqhX3MbRJpnuHJ99TniARQhPx8cEnq-1o2lOujPYDU8PtIr8LI87VnlAl3Y1M1IpHR2xah-SBkrj5ws-naMCikg5LPgFBtcHNBG56iXpeDt0WcDUi7rrva0MsPNuhP0kkPEQ0hwK6TwyVweC1z0LGKNhCe_VV6Anklua0_XgKAWc98BH4Pz-k0LHsjymlBlIasIbWAZuSBW4t2xQ458HMtn4rGjQZkGQXFzoLLxM6ZT9mmqU6BcFwv1kno3DsXpWf-8_MDHslNO_X3-dLH4am9-QDwn7cq5d1DXa5hKubeIHQPKUGFawdKixhOqiuRimAm7eXCUJRlT326EjhzcDdtnz20GYyn0_Kj2VbbOcjLRSDu1n-SRE9sYS7-wKcoV1m0g-Uxx1tbIzI3_RaSpavsmjfh-A6CXJgDgEt32U45am5L37920Kf1xaRtoBwxyCYm1uzDwN6KF9bzNoYWddAAQntM2LjI1MnztqJW_plvQmdSL0GL2fX5Yn3CcKwhiv2eQGTHCGuqjUDCeeFvZbrRLAnW6wUNeXEKmHPiPVz7hh4wZnjWGPNDe2H8YV8ul0EA6ri7xu68nUwI5sPwqvuNCtb13inZDm4ISCc25YLktamQ3NdRk5rNtIjGRa1jbRrMfzFfZXJnPIxuCKwwfmzk_bSIDNJsGcL5Np96htJZwy-iq8Z6rn8viAXaY3tWxeS0XRRp-h6kWouWwj6KyU_qm9js7FfG6EdhiDlQ-rm8nwl5unPRC3HL08cf2nwRgaccpgEk9SLdl_KOzkUYRaQv7Y-z86Z_nYJ3AxjdnPq78ayLdRY0ZWuWmHKwB1RkirNnxxAnMObBFOx4gqRtNFn6u6AtFltduNnnQjUFpSll9YaOcNecHC_bWnypQO36UmSG2eyE5wehX58nod-2aiRKQeI0I0j_bpJzinhEMTpCLG3svuqCB_aqH1bxnnhP3jWwZp1rVM5NvhSmKfIwcPbrLgUXGbrjw==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Summarizing session outcomes**\\n\\nI've been thinking about the session memory. Since there's only one memory, quantifying the most recent completed sessions could help. Out of the last six sessions, I saved true for three. If I include the failed ones, I might expect to add about one and a half more. The user mentioned 5 to 6 events post-fix. Analyzing the last unique sessions since May 1, I completed 5 and saved 3, which is about 60%. I’ll focus on finalizing my summary and next steps.\"}]}"},{"type":"text","text":"Sí. Hice la extrapolación.\n\n## Qué dicen los datos\n\nSession-end analyzer:\n\n- `27` jobs de análisis\n- `24` terminaron\n- `14` dijeron `saved=true`\n- pero solo `8` memorias de ese analyzer existen hoy en DB\n  - varias de las viejas quedaron perdidas/no migradas\n  - algunas fueron replays/duplicados del mismo session file\n\nSi lo mirás por **sesiones únicas**:\n\n- `24` sesiones únicas analizadas\n- `21` completadas\n- `12` guardadas\n- tasa real de guardado: **~57%**\n\n## Sobre esos “5 o 6 eventos” recientes\n\nLos más recientes dan esto:\n\n- completadas: `2`\n  - `1` guardó memoria\n  - `1` no guardó\n- fallidas por auth/token: `3`\n\nSi esas 3 fallidas hubieran corrido bien, usando la tasa reciente/histórica, la expectativa razonable era:\n\n- **~1 a 2 memorias más** con tasa reciente conservadora\n- **~2 a 3 memorias más** usando la tasa histórica ~57%\n\n## Conclusión\n\nNo parece que el hook arreglado hubiera producido “muchísimas más” memorias.\n\nMás probable:\n\n- **unas pocas más**\n- orden de magnitud: **2–3 memorias extra**, no decenas\n\n## Hallazgo más importante\n\nEl cuello de botella no es inbox vs promotion.\n\nEs esto:\n\n1. el analyzer hace decisión binaria:\n   - `save=false`, o\n   - `save=true` con **una sola memoria**\n2. además el prompt dice:\n   - “Prefer NOT saving anything unless durable and reusable”\n3. y cuando guarda, casi siempre entra ya como `active`\n   - o sea, **no está usando inbox como red de captura amplia**\n\nEntonces hoy el sistema está optimizado para:\n\n- **pocas memorias**\n- **alta precisión**\n- **casi nula captura de candidatos débiles**\n\n## Mi lectura\n\nTu claim original está bastante bien:\n\n- sí, había ruido por el hook roto\n- pero **aunque hubiera andado**, el sistema igual seguiría generando pocas memorias\n- porque el diseño actual **limita el throughput de extracción**, no solo la promoción\n\n## Qué repensaría después\n\nSi querés más insights futuros, el cambio grande sería:\n\n- pasar de **“0 o 1 memoria durable por sesión”**\n- a **“N candidatos por sesión”**\n  - 1 durable fuerte\n  - 2–5 candidatos inbox de menor calidad\n  - consolidación/dedupe/promoción después\n\nO sea:\n- capture ancho en ingest\n- filtro duro en consolidate\n- no filtro duro en el primer paso\n\nSi querés, siguiente paso hago un diseño concreto de ingest nuevo:\n- esquema de salida\n- límites por sesión\n- scoring\n- qué va a inbox vs active\n- cómo evitar que explote el ruido.","textSignature":"{\"v\":1,\"id\":\"msg_0d46162620b774590169fa28ff3e0c8195ab4bd8f1d47c6e32\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":2130,"output":996,"cacheRead":57856,"cacheWrite":0,"totalTokens":60982,"cost":{"input":0.005325000000000001,"output":0.01494,"cacheRead":0.014464,"cacheWrite":0,"total":0.034729}},"stopReason":"stop","timestamp":1778002163492,"responseId":"resp_0d46162620b774590169fa28f3eef48195b08fbb0fef28ef78"}}
{"type":"message","id":"0a6f5b94","parentId":"c109f68c","timestamp":"2026-05-05T17:32:20.723Z","message":{"role":"user","content":[{"type":"text","text":"). Dale, cambiemos a que se permitan n candidatos por sesión y promovamos el uso de inbox y de guardado de memorias de menor confianza. Después el sistema debería con sus procesos de consolidación periódicos promover la señal y archivar el ruido."}],"timestamp":1778002340717}}
{"type":"message","id":"82e8ed45","parentId":"0a6f5b94","timestamp":"2026-05-05T17:32:24.747Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa29a660bc81959165913ae9197ad9\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-imoNaVFqKlIOvPvlYWY1z31BXKWY46LMVXmIogPGtU4M4FGLkL-Hr5hSsqPciGuoYnizbV1ArKz3yIw9UMFu4oZb_B4Q01zL4fZT9m3R56ZbAtb_GrBOSIg_dFVI9EH1HMll44RfPhQMFkdeghXbhxsPVu_BzvxBOdb_1JTtghS80qKI9rMxW1S4mHyH3SX-qEVSoQV4L_7-7iP4nvJYXBHrE_uSj63BBq1dm41t3-HoykenyfgJWaMqNgFm4NdxCr-5NM8ksSWOI2MmmkNA7Ga-b6U3IOePU-mNkZkLSr8SaIuj3gQyaOxigtPFfnksA052vYwAd3XPEIheG3UVtA3JQQF1GO0XLZZqx04tg-yjdtvIszXJZdeCDvmmNgpfwFN7mXC7ftYMzqeILL1NG2rcXAqQVAVtLL2nq6M0pn1mQYzCXPAApqz49EcZURAezwL-wqNyE6OtNGhcOntb3NKDbRNPsEx5-9KHvk5BiDiMzElxE6EpOJSrwZ05kfg1cuu0CthahyAN69ZbUo7joRJniduxivngK6gAysECin_nEoHtKz_0jfnHPDwr-UqVp_pQCXV0JMgzqTExbHbmB3BRNbb-RqC--xAxD33xmFxoIgbtXrC2KiNcvGTr83EcUOgzPPNBqpplvjfm5fwMxqyXgHxnF5xZH3SZpcrfLfBZdUJAQ6A8TXv-rqtK9foYwZBSqvQCZ8Pm4JmBw40Sfn_cEKF3u0SZo2Xd2X54LrVF2b7u0Z2RncbbAkcw-enSt5ciqF_cGbiKpTn75M1klyeL3tnpZGpWJ6n2Oz4Qa6Gcbstftz_Aov_ku1alTXEFHtfkwNnxVI566anMgoslSaqbv_furAmlwUkS4xK4Hy_8uOu9wvXF59H7DtAq0NVrwi0BKWV5-trOwB4Kbibv6PmMuOwk2UVklD3wZNbA4PezdpwAIEkErbUdE0u4WecNh_k4PEX8OHrp3HqBfM8SDdHCv7SdavPsnh5om-C6On2FBBNLhOCfoNTnFEFv4eZZoBuTRpOaQztZO0eYkFnxDXhoQ==\",\"summary\":[]}"},{"type":"toolCall","id":"call_F5IzRIlt7UuNWXgvFkpiMUAh|fc_0d46162620b774590169fa29a836e88195b1de6d2fca78ee92","name":"todo","arguments":{"action":"create","subject":"Rework session-end memory ingestion for multi-candidate inbox capture","description":"Change session-end analyzer from single save/no-save output to multiple candidates per session, bias toward inbox capture for lower-confidence items, and rely on consolidation for later promotion/archive.","activeForm":"rewriting session memory ingestion","status":"in_progress"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":59901,"output":105,"cacheRead":0,"cacheWrite":0,"totalTokens":60006,"cost":{"input":0.1497525,"output":0.001575,"cacheRead":0,"cacheWrite":0,"total":0.1513275}},"stopReason":"toolUse","timestamp":1778002340718,"responseId":"resp_0d46162620b774590169fa29a50e088195b9c8e9b6645df5ea"}}
{"type":"message","id":"0225cd39","parentId":"82e8ed45","timestamp":"2026-05-05T17:32:24.752Z","message":{"role":"toolResult","toolCallId":"call_F5IzRIlt7UuNWXgvFkpiMUAh|fc_0d46162620b774590169fa29a836e88195b1de6d2fca78ee92","toolName":"todo","content":[{"type":"text","text":"Created #1: Rework session-end memory ingestion for multi-candidate inbox capture (pending)"}],"details":{"action":"create","params":{"action":"create","subject":"Rework session-end memory ingestion for multi-candidate inbox capture","description":"Change session-end analyzer from single save/no-save output to multiple candidates per session, bias toward inbox capture for lower-confidence items, and rely on consolidation for later promotion/archive.","activeForm":"rewriting session memory ingestion","status":"in_progress"},"tasks":[{"id":1,"subject":"Rework session-end memory ingestion for multi-candidate inbox capture","status":"pending","description":"Change session-end analyzer from single save/no-save output to multiple candidates per session, bias toward inbox capture for lower-confidence items, and rely on consolidation for later promotion/archive.","activeForm":"rewriting session memory ingestion"}],"nextId":2},"isError":false,"timestamp":1778002344752}}
{"type":"message","id":"16794b62","parentId":"0225cd39","timestamp":"2026-05-05T17:32:29.912Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa29acb538819589fcb363cd6866b6\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-imtzHkmA6_OONwDzxBh6Wq5loUjUCP_hXOoT0SPaL4SeykhW64mU9nHkIYkpvaydEx40RTuLhF3WNsjK3qtphGtP4MeBJJuUKU7HLzklGByAYTQqk6c5VGJ3ssrZKqKE3MBmm_CfsR4_U0UhTrJa_W8Kjq3ybpUHl8epWOXZaOzaQFIEK-J-U2xXlI-47sAcKBBMb92r_EB38FvPT-mYwXHUExEe2fS2BomNJoE01XDft1epvC14P6U2WDELYc0moY42IOTk18ZIM25o2Ks53DidOCvl-OML7D_EAH4BOgpG9Li0OOmzVE5fdpXLZ4l7QK3Wgpiqh1q4FAOGjAegO7T-5-jxTxYedkcUwgbTbg1oIl6x7qmGygTWtUvXIfzoarSvI9shI4pDTI3Kk4xJiVTMNnDOHBPxMneYylFG4fLSILqUOeXWKh0lv99JL5dSk67FqDP_Lp5B8_YAiAmwY1u302XdpAo1EqfsaiydDYCPtBirYrlt2LKLGSJVWgF__saE2kUTQg2VM9Kcj8glkqWeIyiZ3Cph425rfwbl_O0L1wn-A1TYyUuZ0nsCxnXu4obftVxL3h3Y7ehMmU8RX4VdsHT5FpQk8owfGAAiGyXgnv5DweJ6W8-3GIG9-2Th9Sqt1kpUaJPpCKl1iy-nHKYtrc6G6dwBZgkXakHIaf4oYrHDFuhOxALNlbYC1FE3cX9BfM0bCobfOQPl1bEBIvfDfHyuXba-beSdckgQ7azvUs6KBQ3zpH__Ykq524k5pz1nxAR7449MjRw4SJfTb3S-zZABYAO32XrmtEiwgQUUWkJlMvjhCCSO7zEY5zI29aKSK4aulopuXUx8CBuwncW6-PTx9wUi1vERSzRMufPpPjZ-66gBSfLTKB5E6l4mviI0iMAIsIM5cQuvu45l-dDmfBoiJTCfyP_Us5p4-YmpouVjPX-h1WNjl_kutslWnNC\",\"summary\":[]}"},{"type":"toolCall","id":"call_kD4b3M4Q2nswqgZ0kXWTSsKB|fc_0d46162620b774590169fa29ad79e48195b192cf202df00ad8","name":"todo","arguments":{"action":"update","id":1,"status":"in_progress"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":643,"output":37,"cacheRead":59392,"cacheWrite":0,"totalTokens":60072,"cost":{"input":0.0016075000000000002,"output":0.000555,"cacheRead":0.014848,"cacheWrite":0,"total":0.0170105}},"stopReason":"toolUse","timestamp":1778002344753,"responseId":"resp_0d46162620b774590169fa29aa59308195bbb063523baac9ab"}}
{"type":"message","id":"f052a5de","parentId":"16794b62","timestamp":"2026-05-05T17:32:29.913Z","message":{"role":"toolResult","toolCallId":"call_kD4b3M4Q2nswqgZ0kXWTSsKB|fc_0d46162620b774590169fa29ad79e48195b192cf202df00ad8","toolName":"todo","content":[{"type":"text","text":"Updated #1 (pending → in_progress)"}],"details":{"action":"update","params":{"action":"update","id":1,"status":"in_progress"},"tasks":[{"id":1,"subject":"Rework session-end memory ingestion for multi-candidate inbox capture","status":"in_progress","description":"Change session-end analyzer from single save/no-save output to multiple candidates per session, bias toward inbox capture for lower-confidence items, and rely on consolidation for later promotion/archive.","activeForm":"rewriting session memory ingestion"}],"nextId":2},"isError":false,"timestamp":1778002349913}}
{"type":"message","id":"8a514bb1","parentId":"f052a5de","timestamp":"2026-05-05T17:32:34.406Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa29afc1448195a8d96441a40616eb\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-imyHFsdJqbHnb1piboRdiD6DZwrc_J4vRYNRUERcqXuakeuXMVNGEoT1BehrvkAj6G0BmEXzMiKqygprnoZ8xQOxV_DSsNXtjIWfKIZap53bc0qeY0ssMA3XMga5TOeP61APNtWtrL0Qh_rx1aKWzH8ByUwUd1Pd0upsi4TUTo-DsMFm7befcE6ObcljLyNg2Wrlfd9aMaCB1qByq9vfZ3tWIx9pValtym0S0PZM6gCUXNq44F6NgNiYlKlgbgodw0EejrWROJ2dYA0cfM-y0EBjsLxSKEMdXCW0-O5GE5IgPjXkGtTLncX8Eu1ac_PsJ2rmV_K_KY841Ntx8D84UxvPeoDNdm8mqaAmVmh1gDdPYUTzTV1Z7p_MBESLh3nrpGHFST48m0mjRC_KO-sOZI8TMYXHI7Rm41KSZds1GYGjekD-FHl1BzJ1dyovJ0eLIdXsBC3NXXSF5NJMu4OoZ-Qqmzb0kbvtmvALMSuWu0CVlcuJfrAHreYYse0Ugygk-9uK_IUXHL8tEZzadDUnt86JeORit-NZLwGiGOmqEb8OoECx0_p6AFdZ7z048s43Wkv8FuSZVs34bduYNAHbMFAHiB9qv7bubtSQfGBTwjx-BxhFIYt0eSMEAJcGfF8RRGYGP6ahQipj8u_hQpPVRdQD2rYwxM5FqX0SDNFsub3JKmysC0tDiCajvCrC1vdg8hjaJYE9SAb-5ji6BlQGtut_upb4IQ0StrsSJtdR5lZli2E8cTXO71-lD_XkDIERauDKkjmmfsirWBpb-h_KLJSudX7sf0VvJTfjVU8wpNd_OmvfiZuEuBhse9-3tZDKExaQnO8rZMOQKOoOGnv3XdMQ8_6Zgz8ntLRTC2DT_Yoh0AkgBGiQJ1a3jzu50pCL7EmjdTUyxExVYe1CIHINDFJdOuOGRQSZlSQt03OlPdv6EPhKHXob3tc4I4isuxyu8X2IM_gkd2Ojekgn7E1SlO9Tg==\",\"summary\":[]}"},{"type":"toolCall","id":"call_1xY2O93AR8RVOCba9ssVr3jS|fc_0d46162620b774590169fa29b23f308195b2391220dc5ea62b","name":"read","arguments":{"path":"/home/sebas/pi-config/scripts/session-memory-analyzer.py","offset":1,"limit":260}},{"type":"toolCall","id":"call_CpbjACg1EyRneybTi6h9hTf4|fc_0d46162620b774590169fa29b23f408195b43ac61289456687","name":"read","arguments":{"path":"/home/sebas/agents-database/src/shared_agent_memory/service.py","offset":71,"limit":90}},{"type":"toolCall","id":"call_jVNAFsmyJfx2703rjJuGGwJ9|fc_0d46162620b774590169fa29b23f4c8195b15ca45a3684f748","name":"read","arguments":{"path":"/home/sebas/agents-database/src/shared_agent_memory/http_api.py","offset":1,"limit":180}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":187,"output":134,"cacheRead":59904,"cacheWrite":0,"totalTokens":60225,"cost":{"input":0.00046750000000000003,"output":0.00201,"cacheRead":0.014976,"cacheWrite":0,"total":0.0174535}},"stopReason":"toolUse","timestamp":1778002349914,"responseId":"resp_0d46162620b774590169fa29ae6910819584251f6bd7b3fd96"}}
{"type":"message","id":"a3bb7be0","parentId":"8a514bb1","timestamp":"2026-05-05T17:32:34.456Z","message":{"role":"toolResult","toolCallId":"call_1xY2O93AR8RVOCba9ssVr3jS|fc_0d46162620b774590169fa29b23f308195b2391220dc5ea62b","toolName":"read","content":[{"type":"text","text":"#!/usr/bin/env python3\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport shutil\nimport subprocess\nimport sys\nfrom pathlib import Path\nfrom typing import Any\nfrom urllib import request\n\nAGENTS_DB_API = os.environ.get(\"AGENTS_DB_API\", \"http://100.116.176.16:8091\")\nPI_MODEL = os.environ.get(\"PI_SESSION_MEMORY_MODEL\", \"gpt-5.4-mini\")\nPI_THINKING = os.environ.get(\"PI_SESSION_MEMORY_THINKING\", \"off\")\nPI_BIN = os.environ.get(\"PI_SESSION_MEMORY_PI_BIN\")\n\n\ndef resolve_pi_bin() -> str:\n    candidates = [\n        PI_BIN,\n        shutil.which(\"pi\"),\n        str(Path.home() / \".npm-global\" / \"bin\" / \"pi\"),\n        \"/usr/local/bin/pi\",\n        \"/usr/bin/pi\",\n    ]\n    for candidate in candidates:\n        if not candidate:\n            continue\n        if Path(candidate).exists():\n            return candidate\n    raise FileNotFoundError(\"pi\")\n\n\ndef load_job(job_file: Path) -> dict[str, Any]:\n    return json.loads(job_file.read_text(encoding=\"utf-8\"))\n\n\ndef save_job(job_file: Path, payload: dict[str, Any]) -> None:\n    job_file.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + \"\\n\", encoding=\"utf-8\")\n\n\ndef call_pi(prompt: str) -> str:\n    cmd = [\n        resolve_pi_bin(),\n        \"-p\",\n        \"--provider\",\n        \"openai-codex\",\n        \"--model\",\n        PI_MODEL,\n        \"--thinking\",\n        PI_THINKING,\n        \"--no-tools\",\n        \"--no-session\",\n        prompt,\n    ]\n    proc = subprocess.run(cmd, capture_output=True, text=True)\n    if proc.returncode != 0:\n        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f\"pi failed ({proc.returncode})\")\n    return proc.stdout.strip()\n\n\ndef ingest(memory: dict[str, Any]) -> dict[str, Any]:\n    req = request.Request(\n        f\"{AGENTS_DB_API}/api/ingest\",\n        method=\"POST\",\n        data=json.dumps(memory).encode(),\n        headers={\"Content-Type\": \"application/json\"},\n    )\n    with request.urlopen(req, timeout=30) as resp:\n        return json.loads(resp.read().decode(\"utf-8\"))\n\n\ndef extract_json(text: str) -> dict[str, Any]:\n    raw = text.strip()\n    if raw.startswith(\"```\"):\n        raw = raw.strip(\"`\")\n        raw = raw.split(\"\\n\", 1)[-1] if \"\\n\" in raw else raw\n    start = raw.find(\"{\")\n    end = raw.rfind(\"}\")\n    if start >= 0 and end > start:\n        raw = raw[start : end + 1]\n    return json.loads(raw)\n\n\ndef session_meta(job: dict[str, Any]) -> dict[str, Any]:\n    payload = job.get(\"payload\")\n    if isinstance(payload, dict):\n        return payload\n    return job\n\n\ndef build_prompt(job: dict[str, Any]) -> str:\n    meta = session_meta(job)\n    return f\"\"\"You are deciding whether a Pi session has durable value.\n\nRules:\n- Prefer NOT saving anything unless it is durable and reusable.\n- Session metadata alone is not enough.\n- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.\n- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.\n- If nothing durable exists, return save=false.\n- If the memory is likely durable, assign the best type and include evidence.\n- Output JSON only. No markdown. No extra text.\n\nReturn this schema:\n{{\n  \"save\": boolean,\n  \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n  \"subtype\": string | null,\n  \"scope\": \"global\" | \"project\",\n  \"title\": string,\n  \"content\": string,\n  \"source_ref\": string,\n  \"evidence_ref\": string | null,\n  \"confidence\": number,\n  \"freshness\": number,\n  \"importance\": number,\n  \"reason\": string\n}}\n\nScoring guidance:\n- confidence: 0 to 1, how well supported the memory is.\n- freshness: 0 to 1, how current it is.\n- importance: 0 to 1, how much future value it has.\n- evidence_ref: cite the strongest source in the session or branch excerpt when possible.\n\nSession metadata:\n- session_id: {meta.get('session_id')}\n- session_file: {meta.get('session_file')}\n- cwd: {meta.get('cwd')}\n- scope: {meta.get('scope')}\n- reason: {meta.get('reason')}\n- user_messages: {meta.get('user_messages')}\n- assistant_messages: {meta.get('assistant_messages')}\n- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}\n- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}\n- trigger: {meta.get('trigger')}\n\nBranch excerpt JSON:\n{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}\n\"\"\"\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--job-file\", required=True)\n    args = parser.parse_args()\n\n    job_file = Path(args.job_file)\n    job = load_job(job_file)\n    meta = session_meta(job)\n    job[\"analysis\"] = {\n        \"status\": \"running\",\n        \"save\": None,\n        \"memory_id\": None,\n    }\n    save_job(job_file, job)\n\n    try:\n        raw = call_pi(build_prompt(job))\n        analysis = extract_json(raw)\n        job[\"analysis\"][\"raw\"] = raw\n        job[\"analysis\"][\"result\"] = analysis\n        job[\"analysis\"][\"status\"] = \"completed\"\n\n        if bool(analysis.get(\"save\")):\n            memory_type = analysis.get(\"type\") or \"episode\"\n            is_candidate = memory_type == \"episode\" or bool(float(analysis.get(\"importance\") or 0.5) < 0.75)\n            memory = {\n                \"type\": memory_type,\n                \"subtype\": analysis.get(\"subtype\"),\n                \"scope\": analysis.get(\"scope\") or meta.get(\"scope\") or \"global\",\n                \"status\": \"inbox\" if is_candidate else \"active\",\n                \"source_kind\": \"manual\",\n                \"title\": analysis.get(\"title\") or \"Session insight\",\n                \"content\": analysis.get(\"content\") or \"\",\n                \"confidence\": float(analysis.get(\"confidence\") or 0.75),\n                \"freshness\": float(analysis.get(\"freshness\") or 0.9),\n                \"importance\": float(analysis.get(\"importance\") or 0.5),\n                \"source_ref\": analysis.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n                \"evidence_ref\": analysis.get(\"evidence_ref\"),\n                \"origin_agent\": \"pi-session-memory-analyzer\",\n                \"metadata\": {\n                    \"job_file\": str(job_file),\n                    \"session_id\": meta.get(\"session_id\"),\n                    \"session_file\": meta.get(\"session_file\"),\n                    \"cwd\": meta.get(\"cwd\"),\n                    \"reason\": meta.get(\"reason\"),\n                    \"user_messages\": meta.get(\"user_messages\"),\n                    \"assistant_messages\": meta.get(\"assistant_messages\"),\n                    \"analysis_reason\": analysis.get(\"reason\"),\n                    \"trigger\": meta.get(\"trigger\"),\n                    \"promotion_candidate\": not is_candidate,\n                },\n            }\n            saved = ingest(memory)\n            job[\"analysis\"][\"memory_id\"] = saved.get(\"id\")\n            job[\"analysis\"][\"saved\"] = True\n        else:\n            job[\"analysis\"][\"saved\"] = False\n            job[\"analysis\"][\"memory_id\"] = None\n\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False))\n        return 0\n    except Exception as exc:\n        job[\"analysis\"][\"status\"] = \"failed\"\n        job[\"analysis\"][\"error\"] = str(exc)\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False), file=sys.stderr)\n        return 1\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n"}],"isError":false,"timestamp":1778002354447}}
{"type":"message","id":"433a2553","parentId":"a3bb7be0","timestamp":"2026-05-05T17:32:34.457Z","message":{"role":"toolResult","toolCallId":"call_CpbjACg1EyRneybTi6h9hTf4|fc_0d46162620b774590169fa29b23f408195b43ac61289456687","toolName":"read","content":[{"type":"text","text":"    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\n[1549 more lines in file. Use offset=161 to continue.]"}],"isError":false,"timestamp":1778002354456}}
{"type":"message","id":"82125928","parentId":"433a2553","timestamp":"2026-05-05T17:32:34.463Z","message":{"role":"toolResult","toolCallId":"call_jVNAFsmyJfx2703rjJuGGwJ9|fc_0d46162620b774590169fa29b23f4c8195b15ca45a3684f748","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\n[185 more lines in file. Use offset=181 to continue.]"}],"isError":false,"timestamp":1778002354456}}
{"type":"message","id":"e79f1e03","parentId":"82125928","timestamp":"2026-05-05T17:32:40.399Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Checking API support**\n\nI need to check if the ingest API supports only one memory at a time. It seems like I could just loop the ingest calls if that's the case. Also, should I create tests? I don't think there are any existing tests for the analyzer itself, so it might require some searching to see if I can come up with something useful. It’s a bit of a puzzle, but I'm sure I can figure it out!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa29b3e77081958c5cf99bb0bb60b1\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-im4Hr4GC7wemHEghi9BXtlFK50feNy6hRVM2RubjKCos3Ks8hbzxzPqyNL5oA44ZhnLVDcVg14uzYdwvAePXjI-Y6obsCKkkdKlVEjTRKN5CSWWqx_NzqGsC4hgT6T6Yo1VaZyg4rUdwXnHO-y3b9YlJlwjw187_lt60XjT7cTVQ7NYMFyueOxiukxH9RzZ7Tgtn6JkCOv6a_GeVZ5WtQvYJy7Ohu03sP0VAp2velyM_U5U_wTt-W9PCsqXzG18UWfaWcw19YE_OPcStG1Up5jTLz2Vv0oIoAA3dxzoiho3JD2N7b6CBs8Rcpdm8pESa329zZcFggHBODFnoydtJ6sls1kGA-jHyqdvzrgS58pQV8jTEkCCQAgsEX0caAhTz3Kk0nnIW6QAWZXurX569COF8s5vGfa83h2-3aBC-crsHBZaxmuKgooUzYVd41kl-iHPMPp_PDmfOXZr5rR45bK05fFQPQ0b7OBJE_SS-kJdIB5w13_3LhbDf0vWuurJIUelsmaanUSEfi6kMeZU8Zk-QJx4BjxuzVWs-iD034M-TPMo11c12-05CD7l_2e8CMv5FYntPnXlvRrXi0J0agvHwclQEC8Lectm7RQMBFvC0wAcBu9z6i9KsDgoE0naJCTXidtsT8cxSHzclU6TxUNqF4P6qa5H6NQbnj39NMgRpca99lp13XDQTcgIBFrinhn8PcbLG5teNHyalEx0brpdqqwGAEyw-y5lRXfXSuPm9u0fT_pc4tVGXRD8atJRvAWXdzi36dLINUadiwPO3QzGiCmWVDfb4xiCFDUStKjG42fxuiAMgs70MT16kH-UVaPDlszy1Tq_gkWUp1fCY-IHLj1YXQR8z2OogkRMvo6Ow5zfhqrfc_Vnfh6pe4ojjCFdhkp5iT7A_QhkZGyDZlc6K6VPG7lomEmvPoQNr0RUxtbep-_7qB2XJSICrmgrj703_NLePNnxaRjgP90JwekZ-a692tRYF4Wcq9ec6H2at-b0hjHUQL1F9kEtOkjcqt84-7UfNQ4lirUPCDFwO-4Bspbv7xhSCs4cFhmU03MJod_9k23mPlAQERn3kvi6gwPDn2JZMEeqQZHdr2wFZTBKwcXVRPmLdZMzHjXjsIFgD59Ehy8vai6_jX_shosXmdDF\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking API support**\\n\\nI need to check if the ingest API supports only one memory at a time. It seems like I could just loop the ingest calls if that's the case. Also, should I create tests? I don't think there are any existing tests for the analyzer itself, so it might require some searching to see if I can come up with something useful. It’s a bit of a puzzle, but I'm sure I can figure it out!\"}]}"},{"type":"toolCall","id":"call_wne0RQwbKAhrwCDQeQNDhgeM|fc_0d46162620b774590169fa29b832c48195b0486cdcf592d283","name":"bash","arguments":{"command":"cd /home/sebas/pi-config && rg -n \"session-memory-analyzer|session_memory_analyzer|session-memory\" -S tests scripts | head -100"}},{"type":"toolCall","id":"call_jcCzGmJX5b2PiBp6h2Yxz6sE|fc_0d46162620b774590169fa29b832e08195b519f88ed0ea1c4d","name":"bash","arguments":{"command":"cd /home/sebas/pi-config && python3 -m py_compile scripts/session-memory-analyzer.py scripts/session-memory-dispatcher.py","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":4517,"output":149,"cacheRead":59904,"cacheWrite":0,"totalTokens":64570,"cost":{"input":0.0112925,"output":0.002235,"cacheRead":0.014976,"cacheWrite":0,"total":0.0285035}},"stopReason":"toolUse","timestamp":1778002354457,"responseId":"resp_0d46162620b774590169fa29b2f6cc81959e689994494b7670"}}
{"type":"message","id":"d9bf4d80","parentId":"e79f1e03","timestamp":"2026-05-05T17:32:40.468Z","message":{"role":"toolResult","toolCallId":"call_wne0RQwbKAhrwCDQeQNDhgeM|fc_0d46162620b774590169fa29b832c48195b0486cdcf592d283","toolName":"bash","content":[{"type":"text","text":"scripts/session-memory-dispatcher.py:13:JOB_DIR = Path(os.environ.get(\"PI_SESSION_MEMORY_JOB_DIR\", BASE_DIR / \"sessions\" / \"session-memory-jobs\"))\nscripts/session-memory-dispatcher.py:14:ANALYZER = Path(os.environ.get(\"PI_SESSION_MEMORY_ANALYZER\", BASE_DIR / \"scripts\" / \"session-memory-analyzer.py\"))\nscripts/session-memory-dispatcher.py:51:        if self.path != \"/api/session-memory\":\nscripts/session-memory-dispatcher.py:91:    print(f\"session-memory-dispatcher listening on http://{args.host}:{args.port}/api/session-memory\", flush=True)\nscripts/session-memory-analyzer.py:101:- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.\nscripts/session-memory-analyzer.py:184:                \"origin_agent\": \"pi-session-memory-analyzer\",\nscripts/install-session-memory-dispatcher.service:8:ExecStart=/usr/bin/env python3 /home/sebas/pi-config/scripts/session-memory-dispatcher.py --host 127.0.0.1 --port 17882\nrg: tests: No such file or directory (os error 2)\n"}],"isError":false,"timestamp":1778002360468}}
{"type":"message","id":"83863310","parentId":"d9bf4d80","timestamp":"2026-05-05T17:32:40.469Z","message":{"role":"toolResult","toolCallId":"call_jcCzGmJX5b2PiBp6h2Yxz6sE|fc_0d46162620b774590169fa29b832e08195b519f88ed0ea1c4d","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1778002360468}}
{"type":"message","id":"20c902d3","parentId":"83863310","timestamp":"2026-05-05T17:33:45.075Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering editing parameters**\n\nI need to think about editing the analyzer. Maybe I should include a maximum candidates environment and design an output schema that has a memories array and a session summary or reason. It could also be useful to keep some backward analysis fields like saved count and memory IDs. I guess I should modify the write job fields, but it seems like that shouldn't be an issue. Let's see how we can edit this effectively!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa29b9f6808195b52faff97d8a30d5\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-im9eBLiH7rltcdYTKFzvrOYr7-8WQrgGZCbkKf5w2tTkJdXH-a6ag3I_ktP1lz2AhYw9ZPEOktAzfPjUZofKBNklVbkqofY3xlIwHjIj_2IYjV3DGFiagCVMyrojKFFK2QTUrXGbeEMcYfJJReu0dih9mXQjzo6uUyqTX1NdNJ3zjybCSJGeZpQ5RHtih2Gnj4N2Ov1uUXheZBqVuC2QJBmemJDfBGyb2oSoBq4gY4KxAEsrWsjh8ZQW3trfZx3dlBhFC02FwU-7ZihzeAW92uDt_YeeKjwUfbG9frtpX3nwirOrp8Bu_S8Rj0cO0csxjUn7OJCzL6C1NTDOPZyIeiNuN6oHStK0JXgprc99N2oy7f3osnh3KrLdsH1nHRyFvVZYvkTxRpk2sNXTtFExN4NiNOpER9qu0UnKNwn_PmiBY8tA8uYydBRWh8hX6JQuYhj6IIbc2xLdWM7uqwLVSAADXFgJGTXJtJzHgLowSZ0F7_iiPFN3maUOkQx501HB2vgs18w4PQ0wKSOWVQvh7982BWl2BiIlLGy4JFtVR3FSoLpjjtDwPndTHUnUJfw79Wjsq273PBLtpA5t9UwE8jBZ9yom1wL0jTzNV-t3GC6nTBdSCzDBFSGmRDlYqJMQagbySi2NOHNfzwUyOmtrhQIUweUp1HNiYtXNyLf0eSQZHhnj-6HgrUKoIOzYPnI9sIARYXT3D7E-AZV6gj5EEcRyXWNCh__4WPMVB1SH8LQD9MgScC17jFYzDd9n2bVzzgWROFCx0k1IjRE51eZgpD2zLM19IvnwoEGSqfQNQBk-95gPQwW-KGXOg0Ssc0rkYIszYEbmVtcoC86KRIUyBlsSd7LbmaTWwAoncHu4t7HhfdAjLw76iJJ96grEFQri0wKvVvZzirKzY3CBY9pEkx1PsPdqK0nR2SrX7ap4Y07mNCQ4CtzdZ9Jn9oL_l59bIdL507JHRHgAI2cmQuvgYZWAKRFLRzwhsP8AjjEvQ1S6fs1fiDcbvQX6rw4G1zRP_a1V81jxbWZImxzSzjL0rW90nWXtr4es4mNBqG-YI3FsTPXFW3DhjvQ-nAyKyO1n5KVEi-wba5FVw1BXAgfCErg3JN-WM3Hr6I7JOnXCJV1ZV776D3KvVh3sV8K28CT6TbrgVHhtLaX58pkr-_PLAaOhloxvanhrjBgqKEo1M5wb-c0JsvXapvSQ0kF4HdkYIDzE05Sqm6w0fDyadOhISajsuCqDNN6tgOE1Lhs_9e2Nz0=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering editing parameters**\\n\\nI need to think about editing the analyzer. Maybe I should include a maximum candidates environment and design an output schema that has a memories array and a session summary or reason. It could also be useful to keep some backward analysis fields like saved count and memory IDs. I guess I should modify the write job fields, but it seems like that shouldn't be an issue. Let's see how we can edit this effectively!\"}]}"},{"type":"toolCall","id":"call_tYrh6796KZDcSEBLKdJEihVQ|fc_0d46162620b774590169fa29bd89b48195b8084a4d6cc1a6aa","name":"edit","arguments":{"path":"/home/sebas/pi-config/scripts/session-memory-analyzer.py","edits":[{"oldText":"AGENTS_DB_API = os.environ.get(\"AGENTS_DB_API\", \"http://100.116.176.16:8091\")\nPI_MODEL = os.environ.get(\"PI_SESSION_MEMORY_MODEL\", \"gpt-5.4-mini\")\nPI_THINKING = os.environ.get(\"PI_SESSION_MEMORY_THINKING\", \"off\")\nPI_BIN = os.environ.get(\"PI_SESSION_MEMORY_PI_BIN\")\n","newText":"AGENTS_DB_API = os.environ.get(\"AGENTS_DB_API\", \"http://100.116.176.16:8091\")\nPI_MODEL = os.environ.get(\"PI_SESSION_MEMORY_MODEL\", \"gpt-5.4-mini\")\nPI_THINKING = os.environ.get(\"PI_SESSION_MEMORY_THINKING\", \"off\")\nPI_BIN = os.environ.get(\"PI_SESSION_MEMORY_PI_BIN\")\nMAX_CANDIDATES = int(os.environ.get(\"PI_SESSION_MEMORY_MAX_CANDIDATES\", \"6\"))\nACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD\", \"0.92\"))\nACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD\", \"0.9\"))\nDURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\n"},{"oldText":"def build_prompt(job: dict[str, Any]) -> str:\n    meta = session_meta(job)\n    return f\"\"\"You are deciding whether a Pi session has durable value.\n\nRules:\n- Prefer NOT saving anything unless it is durable and reusable.\n- Session metadata alone is not enough.\n- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.\n- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.\n- If nothing durable exists, return save=false.\n- If the memory is likely durable, assign the best type and include evidence.\n- Output JSON only. No markdown. No extra text.\n\nReturn this schema:\n{{\n  \"save\": boolean,\n  \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n  \"subtype\": string | null,\n  \"scope\": \"global\" | \"project\",\n  \"title\": string,\n  \"content\": string,\n  \"source_ref\": string,\n  \"evidence_ref\": string | null,\n  \"confidence\": number,\n  \"freshness\": number,\n  \"importance\": number,\n  \"reason\": string\n}}\n\nScoring guidance:\n- confidence: 0 to 1, how well supported the memory is.\n- freshness: 0 to 1, how current it is.\n- importance: 0 to 1, how much future value it has.\n- evidence_ref: cite the strongest source in the session or branch excerpt when possible.\n\nSession metadata:\n- session_id: {meta.get('session_id')}\n- session_file: {meta.get('session_file')}\n- cwd: {meta.get('cwd')}\n- scope: {meta.get('scope')}\n- reason: {meta.get('reason')}\n- user_messages: {meta.get('user_messages')}\n- assistant_messages: {meta.get('assistant_messages')}\n- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}\n- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}\n- trigger: {meta.get('trigger')}\n\nBranch excerpt JSON:\n{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}\n\"\"\"\n","newText":"def build_prompt(job: dict[str, Any]) -> str:\n    meta = session_meta(job)\n    return f\"\"\"You are extracting reusable memory candidates from a Pi session.\n\nGoal:\n- Prefer broad candidate capture over premature rejection.\n- Return 0 to {MAX_CANDIDATES} memories from this session.\n- Include strong durable memories and weaker candidate memories that may be worth later consolidation.\n\nRules:\n- Session metadata alone is not enough.\n- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.\n- Favor stable user preferences, enduring project facts, reusable external artifacts, decisions, procedures, blockers, and compact episode summaries with future reuse value.\n- It is OK to include lower-confidence candidates if they could become useful after consolidation.\n- Avoid trivial chatter, one-off execution noise, and duplicates of the same point unless they capture materially different memories.\n- Prefer at most one memory per distinct insight.\n- Output JSON only. No markdown. No extra text.\n\nReturn this schema:\n{{\n  \"memories\": [\n    {{\n      \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n      \"subtype\": string | null,\n      \"scope\": \"global\" | \"project\",\n      \"title\": string,\n      \"content\": string,\n      \"source_ref\": string,\n      \"evidence_ref\": string | null,\n      \"confidence\": number,\n      \"freshness\": number,\n      \"importance\": number,\n      \"reason\": string\n    }}\n  ],\n  \"reason\": string\n}}\n\nScoring guidance:\n- confidence: 0 to 1, how well supported the memory is.\n- freshness: 0 to 1, how current it is.\n- importance: 0 to 1, how much future value it has.\n- evidence_ref: cite the strongest source in the session or branch excerpt when possible.\n- Use lower confidence/importance for tentative candidates rather than omitting them.\n\nSession metadata:\n- session_id: {meta.get('session_id')}\n- session_file: {meta.get('session_file')}\n- cwd: {meta.get('cwd')}\n- scope: {meta.get('scope')}\n- reason: {meta.get('reason')}\n- user_messages: {meta.get('user_messages')}\n- assistant_messages: {meta.get('assistant_messages')}\n- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}\n- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}\n- trigger: {meta.get('trigger')}\n\nBranch excerpt JSON:\n{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}\n\"\"\"\n"},{"oldText":"def main() -> int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--job-file\", required=True)\n    args = parser.parse_args()\n\n    job_file = Path(args.job_file)\n    job = load_job(job_file)\n    meta = session_meta(job)\n    job[\"analysis\"] = {\n        \"status\": \"running\",\n        \"save\": None,\n        \"memory_id\": None,\n    }\n    save_job(job_file, job)\n\n    try:\n        raw = call_pi(build_prompt(job))\n        analysis = extract_json(raw)\n        job[\"analysis\"][\"raw\"] = raw\n        job[\"analysis\"][\"result\"] = analysis\n        job[\"analysis\"][\"status\"] = \"completed\"\n\n        if bool(analysis.get(\"save\")):\n            memory_type = analysis.get(\"type\") or \"episode\"\n            is_candidate = memory_type == \"episode\" or bool(float(analysis.get(\"importance\") or 0.5) < 0.75)\n            memory = {\n                \"type\": memory_type,\n                \"subtype\": analysis.get(\"subtype\"),\n                \"scope\": analysis.get(\"scope\") or meta.get(\"scope\") or \"global\",\n                \"status\": \"inbox\" if is_candidate else \"active\",\n                \"source_kind\": \"manual\",\n                \"title\": analysis.get(\"title\") or \"Session insight\",\n                \"content\": analysis.get(\"content\") or \"\",\n                \"confidence\": float(analysis.get(\"confidence\") or 0.75),\n                \"freshness\": float(analysis.get(\"freshness\") or 0.9),\n                \"importance\": float(analysis.get(\"importance\") or 0.5),\n                \"source_ref\": analysis.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n                \"evidence_ref\": analysis.get(\"evidence_ref\"),\n                \"origin_agent\": \"pi-session-memory-analyzer\",\n                \"metadata\": {\n                    \"job_file\": str(job_file),\n                    \"session_id\": meta.get(\"session_id\"),\n                    \"session_file\": meta.get(\"session_file\"),\n                    \"cwd\": meta.get(\"cwd\"),\n                    \"reason\": meta.get(\"reason\"),\n                    \"user_messages\": meta.get(\"user_messages\"),\n                    \"assistant_messages\": meta.get(\"assistant_messages\"),\n                    \"analysis_reason\": analysis.get(\"reason\"),\n                    \"trigger\": meta.get(\"trigger\"),\n                    \"promotion_candidate\": not is_candidate,\n                },\n            }\n            saved = ingest(memory)\n            job[\"analysis\"][\"memory_id\"] = saved.get(\"id\")\n            job[\"analysis\"][\"saved\"] = True\n        else:\n            job[\"analysis\"][\"saved\"] = False\n            job[\"analysis\"][\"memory_id\"] = None\n\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False))\n        return 0\n    except Exception as exc:\n        job[\"analysis\"][\"status\"] = \"failed\"\n        job[\"analysis\"][\"error\"] = str(exc)\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False), file=sys.stderr)\n        return 1\n","newText":"def normalize_memories(analysis: dict[str, Any]) -> list[dict[str, Any]]:\n    memories = analysis.get(\"memories\")\n    if isinstance(memories, list):\n        return [item for item in memories if isinstance(item, dict)]\n    if bool(analysis.get(\"save\")):\n        return [analysis]\n    return []\n\n\ndef choose_status(memory_type: str, confidence: float, importance: float) -> str:\n    if memory_type in DURABLE_TYPES and confidence >= ACTIVE_CONFIDENCE_THRESHOLD and importance >= ACTIVE_IMPORTANCE_THRESHOLD:\n        return \"active\"\n    return \"inbox\"\n\n\ndef build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n    memory_type = candidate.get(\"type\") or \"episode\"\n    confidence = float(candidate.get(\"confidence\") or 0.6)\n    importance = float(candidate.get(\"importance\") or 0.5)\n    status = choose_status(memory_type, confidence, importance)\n    return {\n        \"type\": memory_type,\n        \"subtype\": candidate.get(\"subtype\"),\n        \"scope\": candidate.get(\"scope\") or meta.get(\"scope\") or \"global\",\n        \"status\": status,\n        \"source_kind\": \"manual\",\n        \"title\": candidate.get(\"title\") or f\"Session insight {index}\",\n        \"content\": candidate.get(\"content\") or \"\",\n        \"confidence\": confidence,\n        \"freshness\": float(candidate.get(\"freshness\") or 0.9),\n        \"importance\": importance,\n        \"source_ref\": candidate.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n        \"evidence_ref\": candidate.get(\"evidence_ref\"),\n        \"origin_agent\": \"pi-session-memory-analyzer\",\n        \"metadata\": {\n            \"job_file\": str(job_file),\n            \"session_id\": meta.get(\"session_id\"),\n            \"session_file\": meta.get(\"session_file\"),\n            \"cwd\": meta.get(\"cwd\"),\n            \"reason\": meta.get(\"reason\"),\n            \"user_messages\": meta.get(\"user_messages\"),\n            \"assistant_messages\": meta.get(\"assistant_messages\"),\n            \"analysis_reason\": analysis_reason,\n            \"candidate_reason\": candidate.get(\"reason\"),\n            \"trigger\": meta.get(\"trigger\"),\n            \"promotion_candidate\": status == \"inbox\",\n            \"session_candidate_index\": index,\n        },\n    }\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--job-file\", required=True)\n    args = parser.parse_args()\n\n    job_file = Path(args.job_file)\n    job = load_job(job_file)\n    meta = session_meta(job)\n    job[\"analysis\"] = {\n        \"status\": \"running\",\n        \"save\": None,\n        \"saved\": None,\n        \"memory_id\": None,\n        \"memory_ids\": [],\n        \"saved_count\": 0,\n    }\n    save_job(job_file, job)\n\n    try:\n        raw = call_pi(build_prompt(job))\n        analysis = extract_json(raw)\n        candidates = normalize_memories(analysis)[:MAX_CANDIDATES]\n        saved_items: list[dict[str, Any]] = []\n\n        for index, candidate in enumerate(candidates, start=1):\n            memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n            if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n                continue\n            saved_items.append(ingest(memory))\n\n        job[\"analysis\"][\"raw\"] = raw\n        job[\"analysis\"][\"result\"] = analysis\n        job[\"analysis\"][\"status\"] = \"completed\"\n        job[\"analysis\"][\"save\"] = bool(saved_items)\n        job[\"analysis\"][\"saved\"] = bool(saved_items)\n        job[\"analysis\"][\"saved_count\"] = len(saved_items)\n        job[\"analysis\"][\"memory_ids\"] = [item.get(\"id\") for item in saved_items if item.get(\"id\")]\n        job[\"analysis\"][\"memory_id\"] = job[\"analysis\"][\"memory_ids\"][0] if job[\"analysis\"][\"memory_ids\"] else None\n\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False))\n        return 0\n    except Exception as exc:\n        job[\"analysis\"][\"status\"] = \"failed\"\n        job[\"analysis\"][\"error\"] = str(exc)\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False), file=sys.stderr)\n        return 1\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":848,"output":3488,"cacheRead":64000,"cacheWrite":0,"totalTokens":68336,"cost":{"input":0.0021200000000000004,"output":0.05232,"cacheRead":0.016,"cacheWrite":0,"total":0.07044}},"stopReason":"toolUse","timestamp":1778002360470,"responseId":"resp_0d46162620b774590169fa29b90ab8819580d1d880d78ff5ee"}}
{"type":"message","id":"e286d894","parentId":"20c902d3","timestamp":"2026-05-05T17:33:45.282Z","message":{"role":"toolResult","toolCallId":"call_tYrh6796KZDcSEBLKdJEihVQ|fc_0d46162620b774590169fa29bd89b48195b8084a4d6cc1a6aa","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 3 block(s) in /home/sebas/pi-config/scripts/session-memory-analyzer.py."}],"details":{"diff":"     ...\n  14 AGENTS_DB_API = os.environ.get(\"AGENTS_DB_API\", \"http://100.116.176.16:8091\")\n  15 PI_MODEL = os.environ.get(\"PI_SESSION_MEMORY_MODEL\", \"gpt-5.4-mini\")\n  16 PI_THINKING = os.environ.get(\"PI_SESSION_MEMORY_THINKING\", \"off\")\n  17 PI_BIN = os.environ.get(\"PI_SESSION_MEMORY_PI_BIN\")\n+ 18 MAX_CANDIDATES = int(os.environ.get(\"PI_SESSION_MEMORY_MAX_CANDIDATES\", \"6\"))\n+ 19 ACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD\", \"0.92\"))\n+ 20 ACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD\", \"0.9\"))\n+ 21 DURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\n  18 \n  19 \n  20 def resolve_pi_bin() -> str:\n  21     candidates = [\n     ...\n  92 \n  93 \n  94 def build_prompt(job: dict[str, Any]) -> str:\n  95     meta = session_meta(job)\n- 96     return f\"\"\"You are deciding whether a Pi session has durable value.\n+100     return f\"\"\"You are extracting reusable memory candidates from a Pi session.\n  97 \n+102 Goal:\n+103 - Prefer broad candidate capture over premature rejection.\n+104 - Return 0 to {MAX_CANDIDATES} memories from this session.\n+105 - Include strong durable memories and weaker candidate memories that may be worth later consolidation.\n+106 \n  98 Rules:\n- 99 - Prefer NOT saving anything unless it is durable and reusable.\n 100 - Session metadata alone is not enough.\n 101 - Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.\n-102 - Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.\n-103 - If nothing durable exists, return save=false.\n-104 - If the memory is likely durable, assign the best type and include evidence.\n+110 - Favor stable user preferences, enduring project facts, reusable external artifacts, decisions, procedures, blockers, and compact episode summaries with future reuse value.\n+111 - It is OK to include lower-confidence candidates if they could become useful after consolidation.\n+112 - Avoid trivial chatter, one-off execution noise, and duplicates of the same point unless they capture materially different memories.\n+113 - Prefer at most one memory per distinct insight.\n 105 - Output JSON only. No markdown. No extra text.\n 106 \n 107 Return this schema:\n 108 {{\n-109   \"save\": boolean,\n-110   \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n-111   \"subtype\": string | null,\n-112   \"scope\": \"global\" | \"project\",\n-113   \"title\": string,\n-114   \"content\": string,\n-115   \"source_ref\": string,\n-116   \"evidence_ref\": string | null,\n-117   \"confidence\": number,\n-118   \"freshness\": number,\n-119   \"importance\": number,\n+118   \"memories\": [\n+119     {{\n+120       \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n+121       \"subtype\": string | null,\n+122       \"scope\": \"global\" | \"project\",\n+123       \"title\": string,\n+124       \"content\": string,\n+125       \"source_ref\": string,\n+126       \"evidence_ref\": string | null,\n+127       \"confidence\": number,\n+128       \"freshness\": number,\n+129       \"importance\": number,\n+130       \"reason\": string\n+131     }}\n+132   ],\n 120   \"reason\": string\n 121 }}\n 122 \n 123 Scoring guidance:\n 124 - confidence: 0 to 1, how well supported the memory is.\n 125 - freshness: 0 to 1, how current it is.\n 126 - importance: 0 to 1, how much future value it has.\n 127 - evidence_ref: cite the strongest source in the session or branch excerpt when possible.\n+141 - Use lower confidence/importance for tentative candidates rather than omitting them.\n 128 \n 129 Session metadata:\n 130 - session_id: {meta.get('session_id')}\n 131 - session_file: {meta.get('session_file')}\n     ...\n 142 {json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}\n 143 \"\"\"\n 144 \n 145 \n+160 def normalize_memories(analysis: dict[str, Any]) -> list[dict[str, Any]]:\n+161     memories = analysis.get(\"memories\")\n+162     if isinstance(memories, list):\n+163         return [item for item in memories if isinstance(item, dict)]\n+164     if bool(analysis.get(\"save\")):\n+165         return [analysis]\n+166     return []\n+167 \n+168 \n+169 def choose_status(memory_type: str, confidence: float, importance: float) -> str:\n+170     if memory_type in DURABLE_TYPES and confidence >= ACTIVE_CONFIDENCE_THRESHOLD and importance >= ACTIVE_IMPORTANCE_THRESHOLD:\n+171         return \"active\"\n+172     return \"inbox\"\n+173 \n+174 \n+175 def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n+176     memory_type = candidate.get(\"type\") or \"episode\"\n+177     confidence = float(candidate.get(\"confidence\") or 0.6)\n+178     importance = float(candidate.get(\"importance\") or 0.5)\n+179     status = choose_status(memory_type, confidence, importance)\n+180     return {\n+181         \"type\": memory_type,\n+182         \"subtype\": candidate.get(\"subtype\"),\n+183         \"scope\": candidate.get(\"scope\") or meta.get(\"scope\") or \"global\",\n+184         \"status\": status,\n+185         \"source_kind\": \"manual\",\n+186         \"title\": candidate.get(\"title\") or f\"Session insight {index}\",\n+187         \"content\": candidate.get(\"content\") or \"\",\n+188         \"confidence\": confidence,\n+189         \"freshness\": float(candidate.get(\"freshness\") or 0.9),\n+190         \"importance\": importance,\n+191         \"source_ref\": candidate.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n+192         \"evidence_ref\": candidate.get(\"evidence_ref\"),\n+193         \"origin_agent\": \"pi-session-memory-analyzer\",\n+194         \"metadata\": {\n+195             \"job_file\": str(job_file),\n+196             \"session_id\": meta.get(\"session_id\"),\n+197             \"session_file\": meta.get(\"session_file\"),\n+198             \"cwd\": meta.get(\"cwd\"),\n+199             \"reason\": meta.get(\"reason\"),\n+200             \"user_messages\": meta.get(\"user_messages\"),\n+201             \"assistant_messages\": meta.get(\"assistant_messages\"),\n+202             \"analysis_reason\": analysis_reason,\n+203             \"candidate_reason\": candidate.get(\"reason\"),\n+204             \"trigger\": meta.get(\"trigger\"),\n+205             \"promotion_candidate\": status == \"inbox\",\n+206             \"session_candidate_index\": index,\n+207         },\n+208     }\n+209 \n+210 \n 146 def main() -> int:\n 147     parser = argparse.ArgumentParser()\n 148     parser.add_argument(\"--job-file\", required=True)\n 149     args = parser.parse_args()\n     ...\n 153     meta = session_meta(job)\n 154     job[\"analysis\"] = {\n 155         \"status\": \"running\",\n 156         \"save\": None,\n+222         \"saved\": None,\n 157         \"memory_id\": None,\n+224         \"memory_ids\": [],\n+225         \"saved_count\": 0,\n 158     }\n 159     save_job(job_file, job)\n 160 \n 161     try:\n 162         raw = call_pi(build_prompt(job))\n 163         analysis = extract_json(raw)\n+232         candidates = normalize_memories(analysis)[:MAX_CANDIDATES]\n+233         saved_items: list[dict[str, Any]] = []\n+234 \n+235         for index, candidate in enumerate(candidates, start=1):\n+236             memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n+237             if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n+238                 continue\n+239             saved_items.append(ingest(memory))\n+240 \n 164         job[\"analysis\"][\"raw\"] = raw\n 165         job[\"analysis\"][\"result\"] = analysis\n 166         job[\"analysis\"][\"status\"] = \"completed\"\n+244         job[\"analysis\"][\"save\"] = bool(saved_items)\n+245         job[\"analysis\"][\"saved\"] = bool(saved_items)\n+246         job[\"analysis\"][\"saved_count\"] = len(saved_items)\n+247         job[\"analysis\"][\"memory_ids\"] = [item.get(\"id\") for item in saved_items if item.get(\"id\")]\n+248         job[\"analysis\"][\"memory_id\"] = job[\"analysis\"][\"memory_ids\"][0] if job[\"analysis\"][\"memory_ids\"] else None\n 167 \n-168         if bool(analysis.get(\"save\")):\n-169             memory_type = analysis.get(\"type\") or \"episode\"\n-170             is_candidate = memory_type == \"episode\" or bool(float(analysis.get(\"importance\") or 0.5) < 0.75)\n-171             memory = {\n-172                 \"type\": memory_type,\n-173                 \"subtype\": analysis.get(\"subtype\"),\n-174                 \"scope\": analysis.get(\"scope\") or meta.get(\"scope\") or \"global\",\n-175                 \"status\": \"inbox\" if is_candidate else \"active\",\n-176                 \"source_kind\": \"manual\",\n-177                 \"title\": analysis.get(\"title\") or \"Session insight\",\n-178                 \"content\": analysis.get(\"content\") or \"\",\n-179                 \"confidence\": float(analysis.get(\"confidence\") or 0.75),\n-180                 \"freshness\": float(analysis.get(\"freshness\") or 0.9),\n-181                 \"importance\": float(analysis.get(\"importance\") or 0.5),\n-182                 \"source_ref\": analysis.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n-183                 \"evidence_ref\": analysis.get(\"evidence_ref\"),\n-184                 \"origin_agent\": \"pi-session-memory-analyzer\",\n-185                 \"metadata\": {\n-186                     \"job_file\": str(job_file),\n-187                     \"session_id\": meta.get(\"session_id\"),\n-188                     \"session_file\": meta.get(\"session_file\"),\n-189                     \"cwd\": meta.get(\"cwd\"),\n-190                     \"reason\": meta.get(\"reason\"),\n-191                     \"user_messages\": meta.get(\"user_messages\"),\n-192                     \"assistant_messages\": meta.get(\"assistant_messages\"),\n-193                     \"analysis_reason\": analysis.get(\"reason\"),\n-194                     \"trigger\": meta.get(\"trigger\"),\n-195                     \"promotion_candidate\": not is_candidate,\n-196                 },\n-197             }\n-198             saved = ingest(memory)\n-199             job[\"analysis\"][\"memory_id\"] = saved.get(\"id\")\n-200             job[\"analysis\"][\"saved\"] = True\n-201         else:\n-202             job[\"analysis\"][\"saved\"] = False\n-203             job[\"analysis\"][\"memory_id\"] = None\n-204 \n 205         save_job(job_file, job)\n 206         print(json.dumps(job[\"analysis\"], ensure_ascii=False))\n 207         return 0\n 208     except Exception as exc:\n     ...","firstChangedLine":18},"isError":false,"timestamp":1778002425281}}
{"type":"message","id":"a7b28cda","parentId":"e286d894","timestamp":"2026-05-05T17:33:51.052Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering compilation options**\n\nI need to think about using `py_compile`, which seems necessary. It might be good to analyze how the `dispatcher` and `write_job` functions can be structured to incorporate optional `memory_ids`. While it’s possible to add this feature, I'm considering whether it’s truly necessary or just an enhancement. My priority is getting things right and making sure the overall structure remains efficient and clear.","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa29fc63a081958a2e7a6028886d57\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-in-wcIqxMs4LWGSgir_QWcnwf2nMhkESNcR-GhUZY7DbhiNy1-yUBU5m9uAP8HAr67eXsWGN_1w3kX_Y06jiFAw8jz6sOcSFxmE9-O_L40_M8IwzDNeS2IceuD4W_hALKcndjOunJUUYfgCbXCKAKJQxcw_eVkWR193uMhLUNh5vWR-vkq2GR9U8JJfYPIscRBoTy4KoroS4VyrLt71qRZY2TVfIeqbWG4W_Ws2Bn9V53FOx5I5RgLU7FdTahf5YyeJzpnFYmMq9k_54E7CGVd2iqpyGDZx2m_DFtaNI4d_ccJcmr_gCKkX7mGORv4A2C7J02iEP5zYaCn__oKIuwUB1v5VURjMJ4YJWVOQghCDyktttZCQjLT4JUep5dncvTJPIsq0xOjtJYpLXDH3q6cy-D4HhqJMsuPHbRrBL-4ot-4tgiUeeWqpHzwgh0ReoN644NzJDz3ixAsD4SAsfR7FzrHaTPTZU6czdekDryXTd_SWF_rKK0BhHk9_7y7L1llEBPaSSNvLX5MCypghc34Nz76ipeQ77rj5R1cQvJ-XNnaki7v0dltJPtX0SSkZwRiK46UX0CdejTArMEa2z6pXU6iG753_W6Qby2gAsGKb4WtHWosH41N3FzbdyFo0pWxKn7bIi6M847MCcjhPliMqv3LyunskV-TgkfYLLyF_xIk1wVBR1cIdibuEhFAagt525TUegq4tu50THgBJgmUjm72m0iQXnfiS5RShnBhY07g1Mn2HcOWFDlujcH6-dElBWbmTLFTgGDK2VrXbEtVgKJjoL53xhne3URA6VL_Kksui65B2qkQGlY0uJzY26PvZF7rCY0kdnS42EHnKZoxJs7bxDeYJiDmyj8xk_NSOtFjnZAOU25wY1xrFqgsGOAo280QRhzocp9Wn_SBH7lsbPnvohbzpdiiE7zq4Ba-p-itEMAshPCq6lJj6UHoh6OjBtRpKDaKjiYp_W8X8fABucCMUnxd-smGqAc3KwsOkLAm4hzzEX_tYNe2p0yro1B8AtAT8A1uIjnKGCktQxtNhl_YsV4gg_mK6-1c9ua1qUvomug8zKPXgWVIDgnrhHXTj\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering compilation options**\\n\\nI need to think about using `py_compile`, which seems necessary. It might be good to analyze how the `dispatcher` and `write_job` functions can be structured to incorporate optional `memory_ids`. While it’s possible to add this feature, I'm considering whether it’s truly necessary or just an enhancement. My priority is getting things right and making sure the overall structure remains efficient and clear.\"}]}"},{"type":"toolCall","id":"call_Kb0tJLHbGVebGGEN412PmboF|fc_0d46162620b774590169fa29fea8b88195b6466f2fabcaa440","name":"bash","arguments":{"command":"cd /home/sebas/pi-config && python3 -m py_compile scripts/session-memory-analyzer.py scripts/session-memory-dispatcher.py","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":3855,"output":76,"cacheRead":64512,"cacheWrite":0,"totalTokens":68443,"cost":{"input":0.0096375,"output":0.00114,"cacheRead":0.016128,"cacheWrite":0,"total":0.0269055}},"stopReason":"toolUse","timestamp":1778002425283,"responseId":"resp_0d46162620b774590169fa29fa62d08195b8f93b19a6c15732"}}
{"type":"message","id":"72b59029","parentId":"a7b28cda","timestamp":"2026-05-05T17:33:51.118Z","message":{"role":"toolResult","toolCallId":"call_Kb0tJLHbGVebGGEN412PmboF|fc_0d46162620b774590169fa29fea8b88195b6466f2fabcaa440","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1778002431118}}
{"type":"message","id":"0952d1f0","parentId":"72b59029","timestamp":"2026-05-05T17:33:54.822Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa2a01a3c481959b8c9a9c3f79c1aa\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ioC4ueu6FRlKfiv6ooNkmZx02NCqZ45dGUQAXq3Ow3sTR4GR5aqvAvnqc970m-VJOd55oa-Q0cbavP_fgP6MWSMGK_QIphotnct8iY2pzYyDlWGpGmtr3G4V6NL42f4OAAREBN3rqBRQktUSEoUnrQVIkew8f_2hZHDwWu_wBXAIpKHTInKQq6E9Je-INJeM0uDgxEwtxqeqVkIG3lZrMFSJ3oJ4zWNE-byMKQNkp2yVBkuTzQGTNC09gJgl3Qeb-NbTHirBmh_sg3W6dR02LxtBUXJKSvA6ULeqbTd2COTAD-HFavnOAbjWHsovQhB6PA4vC4rqtivp09_oWZ8h1ie6AXbwt0l3rFrTwFidSAD2xHV9yzwr70uUw71ZfwNNAHDj1NnBnfl6bce0fnZ726Hd6PU4NrmekoYXWRrZX4GIkniDubjajMz35th7dtpP86rsewnRu97ZW_TP_GyTK90dr6IH_jtVapl7SHQ-UuH496Xzb5kutY0z2YQCYTy7Zb2O56gAEJbpujME_z-GIvvhYhoQAK6KPSXpPVjuUZSLUQRHw8rCxFIcHwtFTWg_yubYpzsmItptPMCCf7lg4JzP9a6F9ppkRBKf6Z20BOKq4GaP5L75-ZHHiAgBbeeiLNdDw6JaN_Bw9AWqXM2G8MGP7mgT1PCy0mfri9iZX1cV3Bg4oep_nkZb7N6lXkhfN-nF38vPxB8N3eXk-0U3PDyB4ihfr5eMaCrUTOaCkFFuwvyfHmfwucJYNRAnn3p5RHmAsg_lLgtvdEpZ7fthr4fcufM4b8qAjKrrPKy5zAqydYC0Kn5fuYyGKWw-beNUjs93RaP2b_XS5QhdGBqN3U1g3BOcFI0G9wWkKVzclqW2dvNSON6xVBgnxTkHgbpGYILcgwNkcwc0Bq1nFStRwQhSNSQFdp-jcmgzbcDmpbikyBX-DO6HJjfnjYvLEznfRLiFbadbxCOle9WZ3J-GBse-w==\",\"summary\":[]}"},{"type":"toolCall","id":"call_a6nhO8zCClwANN58h7TvpzB4|fc_0d46162620b774590169fa2a02718c81959593713dc28420b8","name":"bash","arguments":{"command":"cd /home/sebas/pi-config && git diff -- scripts/session-memory-analyzer.py","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":361,"output":53,"cacheRead":68096,"cacheWrite":0,"totalTokens":68510,"cost":{"input":0.0009025000000000001,"output":0.000795,"cacheRead":0.017024,"cacheWrite":0,"total":0.018721500000000002}},"stopReason":"toolUse","timestamp":1778002431119,"responseId":"resp_0d46162620b774590169fa29ffdb5481958a12d7a0a7b840d1"}}
{"type":"message","id":"9a4c8635","parentId":"0952d1f0","timestamp":"2026-05-05T17:33:54.839Z","message":{"role":"toolResult","toolCallId":"call_a6nhO8zCClwANN58h7TvpzB4|fc_0d46162620b774590169fa2a02718c81959593713dc28420b8","toolName":"bash","content":[{"type":"text","text":"diff --git a/scripts/session-memory-analyzer.py b/scripts/session-memory-analyzer.py\nindex aafda6b..94a981a 100644\n--- a/scripts/session-memory-analyzer.py\n+++ b/scripts/session-memory-analyzer.py\n@@ -4,6 +4,7 @@ from __future__ import annotations\n import argparse\n import json\n import os\n+import shutil\n import subprocess\n import sys\n from pathlib import Path\n@@ -13,6 +14,27 @@ from urllib import request\n AGENTS_DB_API = os.environ.get(\"AGENTS_DB_API\", \"http://100.116.176.16:8091\")\n PI_MODEL = os.environ.get(\"PI_SESSION_MEMORY_MODEL\", \"gpt-5.4-mini\")\n PI_THINKING = os.environ.get(\"PI_SESSION_MEMORY_THINKING\", \"off\")\n+PI_BIN = os.environ.get(\"PI_SESSION_MEMORY_PI_BIN\")\n+MAX_CANDIDATES = int(os.environ.get(\"PI_SESSION_MEMORY_MAX_CANDIDATES\", \"6\"))\n+ACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD\", \"0.92\"))\n+ACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD\", \"0.9\"))\n+DURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\n+\n+\n+def resolve_pi_bin() -> str:\n+    candidates = [\n+        PI_BIN,\n+        shutil.which(\"pi\"),\n+        str(Path.home() / \".npm-global\" / \"bin\" / \"pi\"),\n+        \"/usr/local/bin/pi\",\n+        \"/usr/bin/pi\",\n+    ]\n+    for candidate in candidates:\n+        if not candidate:\n+            continue\n+        if Path(candidate).exists():\n+            return candidate\n+    raise FileNotFoundError(\"pi\")\n \n \n def load_job(job_file: Path) -> dict[str, Any]:\n@@ -25,7 +47,7 @@ def save_job(job_file: Path, payload: dict[str, Any]) -> None:\n \n def call_pi(prompt: str) -> str:\n     cmd = [\n-        \"pi\",\n+        resolve_pi_bin(),\n         \"-p\",\n         \"--provider\",\n         \"openai-codex\",\n@@ -75,30 +97,39 @@ def session_meta(job: dict[str, Any]) -> dict[str, Any]:\n \n def build_prompt(job: dict[str, Any]) -> str:\n     meta = session_meta(job)\n-    return f\"\"\"You are deciding whether a Pi session has durable value.\n+    return f\"\"\"You are extracting reusable memory candidates from a Pi session.\n+\n+Goal:\n+- Prefer broad candidate capture over premature rejection.\n+- Return 0 to {MAX_CANDIDATES} memories from this session.\n+- Include strong durable memories and weaker candidate memories that may be worth later consolidation.\n \n Rules:\n-- Prefer NOT saving anything unless it is durable and reusable.\n - Session metadata alone is not enough.\n - Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.\n-- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.\n-- If nothing durable exists, return save=false.\n-- If the memory is likely durable, assign the best type and include evidence.\n+- Favor stable user preferences, enduring project facts, reusable external artifacts, decisions, procedures, blockers, and compact episode summaries with future reuse value.\n+- It is OK to include lower-confidence candidates if they could become useful after consolidation.\n+- Avoid trivial chatter, one-off execution noise, and duplicates of the same point unless they capture materially different memories.\n+- Prefer at most one memory per distinct insight.\n - Output JSON only. No markdown. No extra text.\n \n Return this schema:\n {{\n-  \"save\": boolean,\n-  \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n-  \"subtype\": string | null,\n-  \"scope\": \"global\" | \"project\",\n-  \"title\": string,\n-  \"content\": string,\n-  \"source_ref\": string,\n-  \"evidence_ref\": string | null,\n-  \"confidence\": number,\n-  \"freshness\": number,\n-  \"importance\": number,\n+  \"memories\": [\n+    {{\n+      \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n+      \"subtype\": string | null,\n+      \"scope\": \"global\" | \"project\",\n+      \"title\": string,\n+      \"content\": string,\n+      \"source_ref\": string,\n+      \"evidence_ref\": string | null,\n+      \"confidence\": number,\n+      \"freshness\": number,\n+      \"importance\": number,\n+      \"reason\": string\n+    }}\n+  ],\n   \"reason\": string\n }}\n \n@@ -107,6 +138,7 @@ Scoring guidance:\n - freshness: 0 to 1, how current it is.\n - importance: 0 to 1, how much future value it has.\n - evidence_ref: cite the strongest source in the session or branch excerpt when possible.\n+- Use lower confidence/importance for tentative candidates rather than omitting them.\n \n Session metadata:\n - session_id: {meta.get('session_id')}\n@@ -125,6 +157,57 @@ Branch excerpt JSON:\n \"\"\"\n \n \n+def normalize_memories(analysis: dict[str, Any]) -> list[dict[str, Any]]:\n+    memories = analysis.get(\"memories\")\n+    if isinstance(memories, list):\n+        return [item for item in memories if isinstance(item, dict)]\n+    if bool(analysis.get(\"save\")):\n+        return [analysis]\n+    return []\n+\n+\n+def choose_status(memory_type: str, confidence: float, importance: float) -> str:\n+    if memory_type in DURABLE_TYPES and confidence >= ACTIVE_CONFIDENCE_THRESHOLD and importance >= ACTIVE_IMPORTANCE_THRESHOLD:\n+        return \"active\"\n+    return \"inbox\"\n+\n+\n+def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n+    memory_type = candidate.get(\"type\") or \"episode\"\n+    confidence = float(candidate.get(\"confidence\") or 0.6)\n+    importance = float(candidate.get(\"importance\") or 0.5)\n+    status = choose_status(memory_type, confidence, importance)\n+    return {\n+        \"type\": memory_type,\n+        \"subtype\": candidate.get(\"subtype\"),\n+        \"scope\": candidate.get(\"scope\") or meta.get(\"scope\") or \"global\",\n+        \"status\": status,\n+        \"source_kind\": \"manual\",\n+        \"title\": candidate.get(\"title\") or f\"Session insight {index}\",\n+        \"content\": candidate.get(\"content\") or \"\",\n+        \"confidence\": confidence,\n+        \"freshness\": float(candidate.get(\"freshness\") or 0.9),\n+        \"importance\": importance,\n+        \"source_ref\": candidate.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n+        \"evidence_ref\": candidate.get(\"evidence_ref\"),\n+        \"origin_agent\": \"pi-session-memory-analyzer\",\n+        \"metadata\": {\n+            \"job_file\": str(job_file),\n+            \"session_id\": meta.get(\"session_id\"),\n+            \"session_file\": meta.get(\"session_file\"),\n+            \"cwd\": meta.get(\"cwd\"),\n+            \"reason\": meta.get(\"reason\"),\n+            \"user_messages\": meta.get(\"user_messages\"),\n+            \"assistant_messages\": meta.get(\"assistant_messages\"),\n+            \"analysis_reason\": analysis_reason,\n+            \"candidate_reason\": candidate.get(\"reason\"),\n+            \"trigger\": meta.get(\"trigger\"),\n+            \"promotion_candidate\": status == \"inbox\",\n+            \"session_candidate_index\": index,\n+        },\n+    }\n+\n+\n def main() -> int:\n     parser = argparse.ArgumentParser()\n     parser.add_argument(\"--job-file\", required=True)\n@@ -133,53 +216,36 @@ def main() -> int:\n     job_file = Path(args.job_file)\n     job = load_job(job_file)\n     meta = session_meta(job)\n-    job.setdefault(\"analysis\", {})\n-    job[\"analysis\"][\"status\"] = \"running\"\n+    job[\"analysis\"] = {\n+        \"status\": \"running\",\n+        \"save\": None,\n+        \"saved\": None,\n+        \"memory_id\": None,\n+        \"memory_ids\": [],\n+        \"saved_count\": 0,\n+    }\n     save_job(job_file, job)\n \n     try:\n         raw = call_pi(build_prompt(job))\n         analysis = extract_json(raw)\n+        candidates = normalize_memories(analysis)[:MAX_CANDIDATES]\n+        saved_items: list[dict[str, Any]] = []\n+\n+        for index, candidate in enumerate(candidates, start=1):\n+            memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n+            if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n+                continue\n+            saved_items.append(ingest(memory))\n+\n         job[\"analysis\"][\"raw\"] = raw\n         job[\"analysis\"][\"result\"] = analysis\n         job[\"analysis\"][\"status\"] = \"completed\"\n-\n-        if bool(analysis.get(\"save\")):\n-            memory_type = analysis.get(\"type\") or \"episode\"\n-            is_candidate = memory_type == \"episode\" or bool(float(analysis.get(\"importance\") or 0.5) < 0.75)\n-            memory = {\n-                \"type\": memory_type,\n-                \"subtype\": analysis.get(\"subtype\"),\n-                \"scope\": analysis.get(\"scope\") or meta.get(\"scope\") or \"global\",\n-                \"status\": \"inbox\" if is_candidate else \"active\",\n-                \"source_kind\": \"manual\",\n-                \"title\": analysis.get(\"title\") or \"Session insight\",\n-                \"content\": analysis.get(\"content\") or \"\",\n-                \"confidence\": float(analysis.get(\"confidence\") or 0.75),\n-                \"freshness\": float(analysis.get(\"freshness\") or 0.9),\n-                \"importance\": float(analysis.get(\"importance\") or 0.5),\n-                \"source_ref\": analysis.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n-                \"evidence_ref\": analysis.get(\"evidence_ref\"),\n-                \"origin_agent\": \"pi-session-memory-analyzer\",\n-                \"metadata\": {\n-                    \"job_file\": str(job_file),\n-                    \"session_id\": meta.get(\"session_id\"),\n-                    \"session_file\": meta.get(\"session_file\"),\n-                    \"cwd\": meta.get(\"cwd\"),\n-                    \"reason\": meta.get(\"reason\"),\n-                    \"user_messages\": meta.get(\"user_messages\"),\n-                    \"assistant_messages\": meta.get(\"assistant_messages\"),\n-                    \"analysis_reason\": analysis.get(\"reason\"),\n-                    \"trigger\": meta.get(\"trigger\"),\n-                    \"promotion_candidate\": not is_candidate,\n-                },\n-            }\n-            saved = ingest(memory)\n-            job[\"analysis\"][\"memory_id\"] = saved.get(\"id\")\n-            job[\"analysis\"][\"saved\"] = True\n-        else:\n-            job[\"analysis\"][\"saved\"] = False\n-            job[\"analysis\"][\"memory_id\"] = None\n+        job[\"analysis\"][\"save\"] = bool(saved_items)\n+        job[\"analysis\"][\"saved\"] = bool(saved_items)\n+        job[\"analysis\"][\"saved_count\"] = len(saved_items)\n+        job[\"analysis\"][\"memory_ids\"] = [item.get(\"id\") for item in saved_items if item.get(\"id\")]\n+        job[\"analysis\"][\"memory_id\"] = job[\"analysis\"][\"memory_ids\"][0] if job[\"analysis\"][\"memory_ids\"] else None\n \n         save_job(job_file, job)\n         print(json.dumps(job[\"analysis\"], ensure_ascii=False))\n"}],"isError":false,"timestamp":1778002434837}}
{"type":"message","id":"92bb65b9","parentId":"9a4c8635","timestamp":"2026-05-05T17:34:02.248Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Noting updates needed**\n\nI need to consider if I'm adding `shutil` and resolving `pi_bin` in an existing context; it feels a bit weird. I should make sure I've noted everything down properly. It seems fine for now, but I do need to update my to-do list based on what's completed. Maybe I'll also double-check to see if there's anything else I should address regarding those weird shows.","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa2a0537a48195889560a1ae6399af\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-ioJ2o8BZhvstbXh1O7Y_llA6bqkZc8XlU4tMuAvS4EsCZcJl7mXSeWw-5kxJfwVVU0CrDYk41TzXuzsr18zS-eT1LvpNNKnjJ8onyfH-hl6ENv5KLCSuthGnvSEw5kcib4Zq2HtsgU-H8hOkuyrFE5e2NSWUvhBKqF51P7Y-4Pi_gkXSJCkzmmzexAVtLyxtZOdB30mVLADPHYmpYBE0m7MJtLi4hD3FZlHToE46Vs4Ufw06y-5SBjPpnp9SRPuU9YbhwOsuWYnFQg7E35bZVFXC9a-zGr3rX5bv4YMPReDwAVfXzDv_qNf8OZFA6OkDweaRtxs4D2fYm2SILfacqx_gNNUQCr7cSPanYuVEHV9wu0VljrGhLisrbWXyQcCRpZvNXoiLDr54oLFw0aFlWxpdx_OTV8saJc3BfOcbhmey8DQn8WzuSXzl5GjSXgl-u_euC-FdjXC_lSeCw1DQeKi270GKmk5lt7TKGmJ0q35sfBYuVprjp4w_XXuIbhg7xUzuyYHsB0HHQHWzpqLCfDpbFIFth-tk-d3iz19HDoBCig3rTFEQzfJVncrLi8xIW4AbOApXl-ihXE2MtDsn6BEFZA-bWijq2EoUTK2G7BZAIxqBkVqQ6SXZz5CuMNVcYPW38R7a0efgT0xEre4KsuiZBuWtBR6p3rr8wPZQ2uKh5d08yOpIXOc5hszbfeEmcPy4M2lI3ofnBHd-sh5WsxVKoTSGq785HZmrWjxq58Czv90kq0FHAOruwe6TggP1XgAoMzjXLLSWHBUgftEJhRqFlqP4ZNl3DOEpCHcXCLWFI_08c74zPNqHvC8i4gx7c7cbmQLFGQIeTmAL1qNlaxdhQ-_ZncklyF9ZFwHSFU-_fOdxiXXwIq0S89O7pO-7NgQf8bZ3PDH2dEZgpjrjGcNYVt0DZq4kmbyduv1zCDU2usIFaHoU2w3AvNj2oPcLKO9R7rJcgD0uVKKT7xYFdlqPYzGlrhFhgPBu8TsMH3nvmwXvI6q0FKKh-xtygYXZZrme7b3Ri02uj5jDRQICPKczMzUQRMAmmaDzcAoztYfYgI=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Noting updates needed**\\n\\nI need to consider if I'm adding `shutil` and resolving `pi_bin` in an existing context; it feels a bit weird. I should make sure I've noted everything down properly. It seems fine for now, but I do need to update my to-do list based on what's completed. Maybe I'll also double-check to see if there's anything else I should address regarding those weird shows.\"}]}"},{"type":"toolCall","id":"call_pmir1YbvGRdE0lux9yyh9Gvf|fc_0d46162620b774590169fa2a09e24c81958670f0b6f65cdbb5","name":"todo","arguments":{"action":"update","id":1,"status":"completed"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":3040,"output":51,"cacheRead":68096,"cacheWrite":0,"totalTokens":71187,"cost":{"input":0.007600000000000001,"output":0.0007650000000000001,"cacheRead":0.017024,"cacheWrite":0,"total":0.025389000000000002}},"stopReason":"toolUse","timestamp":1778002434840,"responseId":"resp_0d46162620b774590169fa2a0327488195ab0f718d452eca89"}}
{"type":"message","id":"4343d90e","parentId":"92bb65b9","timestamp":"2026-05-05T17:34:02.249Z","message":{"role":"toolResult","toolCallId":"call_pmir1YbvGRdE0lux9yyh9Gvf|fc_0d46162620b774590169fa2a09e24c81958670f0b6f65cdbb5","toolName":"todo","content":[{"type":"text","text":"Updated #1 (in_progress → completed)"}],"details":{"action":"update","params":{"action":"update","id":1,"status":"completed"},"tasks":[{"id":1,"subject":"Rework session-end memory ingestion for multi-candidate inbox capture","status":"completed","description":"Change session-end analyzer from single save/no-save output to multiple candidates per session, bias toward inbox capture for lower-confidence items, and rely on consolidation for later promotion/archive.","activeForm":"rewriting session memory ingestion"}],"nextId":2},"isError":false,"timestamp":1778002442249}}
{"type":"message","id":"32547334","parentId":"4343d90e","timestamp":"2026-05-05T17:34:10.931Z","message":{"role":"assistant","content":[{"type":"text","text":"Hecho.\n\nCambios:\n\n- session-end analyzer ahora pide **0..N memorias** por sesión\n- sesgo nuevo: **capturar más candidatos**, no solo 0/1\n- memorias flojas o tentativas van a **`inbox`**\n- solo memorias muy fuertes pasan directo a **`active`**\n- guarda:\n  - `memory_ids`\n  - `saved_count`\n  - razón general + razón por candidato\n\nReglas nuevas:\n\n- máximo por sesión: `6` candidatos\n  - configurable con `PI_SESSION_MEMORY_MAX_CANDIDATES`\n- directo a `active` solo si:\n  - tipo durable (`profile|decision|project|artifact|procedural|semantic`)\n  - `confidence >= 0.9`\n  - `importance >= 0.92`\n- todo lo demás: `inbox`\n\nCompat:\n\n- si el modelo devuelve formato viejo `save=true`, igual lo adapta\n- compile check OK\n\nArchivo tocado:\n\n- `/home/sebas/pi-config/scripts/session-memory-analyzer.py`\n\nSiguiente recomendable:\n\n1. reiniciar el dispatcher/service\n2. correr 2–3 sesiones nuevas\n3. medir:\n   - cuántos candidatos por sesión\n   - % inbox vs active\n   - qué promueve consolidación después\n\nSi querés, sigo con eso y además te dejo una pasada de hardening para que:\n- no duplique fácil por replay\n- agregue una marca de “session candidate batch”\n- y podamos consultar métricas de inbox por origen.","textSignature":"{\"v\":1,\"id\":\"msg_0d46162620b774590169fa2a0ce9a88195af8a89af48c9fb1a\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":550,"output":322,"cacheRead":70656,"cacheWrite":0,"totalTokens":71528,"cost":{"input":0.0013750000000000001,"output":0.00483,"cacheRead":0.017664,"cacheWrite":0,"total":0.023869}},"stopReason":"stop","timestamp":1778002442250,"responseId":"resp_0d46162620b774590169fa2a0afa5481959cd19f7198338eef"}}
{"type":"message","id":"102aa48f","parentId":"32547334","timestamp":"2026-05-05T20:14:51.303Z","message":{"role":"user","content":[{"type":"text","text":"dejalo configurado y persistente. despues pasamos a testing"}],"timestamp":1778012091290}}
{"type":"message","id":"661032ad","parentId":"102aa48f","timestamp":"2026-05-05T20:14:59.390Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting service restart**\n\nI’m considering whether the service restart I need to do is likely reversible. I think it’s not irreversible, which is a relief. It seems like I should inspect the service file to ensure everything is in order before making any changes. I want to be careful and thorough with this to avoid any issues. Let’s go ahead and check the details in the service file to make sure everything’s set right!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa4fbf4ecc81958d2846fcc3f30818\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-k_DtpiAF2zXRnA7LcXYn_flh7HmnfjpqKCkvIMe2xS2QmdaWRfhQ3Yqg-flvmftvNuhGY2BTXcrOv5ErPkn3cD8oLm92F2WQHqfVenBS5CTnbCAzA8uyB8yGMTdZSdFcqa3NaaGJ54hjhRLUEN70pcEPxzb0qxI491Zrdou_JzbCVfpOiDIdi4bVGrSQfh9l9l0YheXNVNOWiS5qkXeQouiBOd1xpBvWvjEH7R_MCVAIkrWkWX0EJUCrr_op2Y4HvX2fSFLjPRuElyzJKzDAh_xYrac0kUSPVP1t2qWHbtda3FWdsCf5vOgM33bK6J1DQxEr4gTJlyYvZKfjUpLEvXXRjz584Hs44HjSGPTy5q8_3El6XDnxqfQ0dAm55eVCybMrJl7gOppeiML0663MpMcXZyfaShA55ym-VpvRi8Q9P99OgiFm1dATmBcb_sWL9tToqNtZFtCTq7rGCHHT8jLIGwzrGAERUQ396rBmwyUeLsrHlvDmpl6cPZnemPmvdynrbYbotx3jHjX6Kft8sVd3cuZTc45x2PkheKOYqqw890e5_4Px2afUe8zrNMW33BCrk1XLjuh26wRjjF_lVOLApxqxWEYgtsKmLm0xhmBGJMwkaVCA6hWGGd8WWnktHYAps8f3IkMcwyavLUGmpAiwDblORsIRXvX8xzvug5XNDuqEd83doH6ZXBko9881BO_abm3r9_i2CIKjOuyQo8_tgg09Fbw7cZEAxvWRrTxjzxYEdZr8Tz_do75ZXo27Y8SpbTNFfb4VOF5fdo8h8aViJlcX1phg9OngzU7crkZOp_xKLmBsnaItYTtZGiw44gCnnOe_imI4eHeGD5CVfy4_KG4mGrKlp7X6WYLxgwjwoY9RoTtv9BbnxaBg9VEAem5ap1tE5K5hJ0a4b-0tZlGFbuNJC0M2OZ1PTSjXa13JR9Bjb6lCK5d-Li-JSlMyVxSZEbqfYwgXfpYixoXuelOjgZ1jsxwLlawi_Te1J2IvycMxL5f5z4Ypg9Sk9lilWGjbWJRbeTMghYWL2f3qyGXYQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting service restart**\\n\\nI’m considering whether the service restart I need to do is likely reversible. I think it’s not irreversible, which is a relief. It seems like I should inspect the service file to ensure everything is in order before making any changes. I want to be careful and thorough with this to avoid any issues. Let’s go ahead and check the details in the service file to make sure everything’s set right!\"}]}"},{"type":"toolCall","id":"call_G9n5GQwProqk1F1oPQb6O2DP|fc_0d46162620b774590169fa4fc320c08195993a431b0cc83870","name":"bash","arguments":{"command":"systemctl --user list-unit-files | rg 'session-memory|dispatcher' || true"}},{"type":"toolCall","id":"call_3v4BJBmHUDf3HZk8ROHl6lYo|fc_0d46162620b774590169fa4fc320d8819596249314594ae21f","name":"bash","arguments":{"command":"systemctl --user status install-session-memory-dispatcher.service --no-pager || true","timeout":20}},{"type":"toolCall","id":"call_YdtTahHV6KUQtX0jz9C5U6nT|fc_0d46162620b774590169fa4fc320e48195976360baa3d1fc17","name":"read","arguments":{"path":"/home/sebas/pi-config/scripts/install-session-memory-dispatcher.service","offset":1,"limit":200}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":11947,"output":141,"cacheRead":59392,"cacheWrite":0,"totalTokens":71480,"cost":{"input":0.0298675,"output":0.002115,"cacheRead":0.014848,"cacheWrite":0,"total":0.046830500000000004}},"stopReason":"toolUse","timestamp":1778012091294,"responseId":"resp_0d46162620b774590169fa4fbdc3488195b64766ed5b626cd8"}}
{"type":"message","id":"43462e2f","parentId":"661032ad","timestamp":"2026-05-05T20:14:59.492Z","message":{"role":"toolResult","toolCallId":"call_G9n5GQwProqk1F1oPQb6O2DP|fc_0d46162620b774590169fa4fc320c08195993a431b0cc83870","toolName":"bash","content":[{"type":"text","text":"install-session-memory-dispatcher.service                            enabled   enabled\n"}],"isError":false,"timestamp":1778012099491}}
{"type":"message","id":"45bfa549","parentId":"43462e2f","timestamp":"2026-05-05T20:14:59.493Z","message":{"role":"toolResult","toolCallId":"call_3v4BJBmHUDf3HZk8ROHl6lYo|fc_0d46162620b774590169fa4fc320d8819596249314594ae21f","toolName":"bash","content":[{"type":"text","text":"● install-session-memory-dispatcher.service - Pi session memory dispatcher\n     Loaded: loaded (/home/sebas/.config/systemd/user/install-session-memory-dispatcher.service; enabled; preset: enabled)\n     Active: active (running) since Tue 2026-05-05 12:28:52 -03; 4h 46min ago\n Invocation: 5a1e9ed6fb5b479394e48baab5680610\n   Main PID: 48009 (python3)\n      Tasks: 2 (limit: 9575)\n        CPU: 9.660s\n     CGroup: /user.slice/user-1000.slice/user@1000.service/app.slice/install-session-memory-dispatcher.service\n             └─48009 python3 /home/sebas/pi-config/scripts/session-memory-dispatcher.py --host 127.0.0.1 --port 17882\n\nMay 05 12:28:52 ballbox-first systemd[1289]: Started install-session-memory-dispatcher.service - Pi session memory dispatcher.\nMay 05 12:28:52 ballbox-first env[48009]: session-memory-dispatcher listening on http://127.0.0.1:17882/api/session-memory\n"}],"isError":false,"timestamp":1778012099492}}
{"type":"message","id":"9777e940","parentId":"45bfa549","timestamp":"2026-05-05T20:14:59.499Z","message":{"role":"toolResult","toolCallId":"call_YdtTahHV6KUQtX0jz9C5U6nT|fc_0d46162620b774590169fa4fc320e48195976360baa3d1fc17","toolName":"read","content":[{"type":"text","text":"[Unit]\nDescription=Pi session memory dispatcher\nAfter=network-online.target\nWants=network-online.target\n\n[Service]\nType=simple\nExecStart=/usr/bin/env python3 /home/sebas/pi-config/scripts/session-memory-dispatcher.py --host 127.0.0.1 --port 17882\nRestart=always\nRestartSec=2\nEnvironment=AGENTS_DB_API=http://100.116.176.16:8091\nEnvironment=PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi\nWorkingDirectory=/home/sebas/pi-config\n\n[Install]\nWantedBy=default.target\n"}],"isError":false,"timestamp":1778012099492}}
{"type":"message","id":"d45396b5","parentId":"9777e940","timestamp":"2026-05-05T20:15:04.105Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering environment variables**\n\nI think I need to set some environment variables persistently in the service. Maybe I can edit a file and copy it to the user unit, which seems like the likely source. I'll check the install script to see if I can create a symlink, but first, it might be good to inspect the loaded file and consider copying it too. There’s a bit to sort through, but I’m getting there!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa4fc4f2f881958e2a44817b8b798b\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-k_HFyC1dYp2SS21NeW0yk672Ju_fv_21ELKBKEjsuN2tObYhkkg0aU2Dyc1p8RzsF1G2omvrJOKlt3ktymf4F3VLEqp3DBMDe2_LD9GjbaSm3tKR4RDbHP-AAqVVTJvNOS0AcYVtFT70mLyUdXmhPdRX1-lQ0HXHBvj4SGct5pvkxDK6YFOm_n_GYQ4my24smuiBrbtYeKURuwnzHYXGQuiNz5vNtm-SKoawNyItKiAwLEEuHVSx0UB601aucOlRoc5mxjTI-ZE7Q0GGIYEb4gvq-0qy66_XZyv7cCYUmNPjuMvtPtm4sZNsLaU2c8Hm4hivUQjDjhy2KzYclMpTxHj1PYgYoS9p62I03q-PMv9mGQNuFFbF9KByyY43FFUEWIkjuwirKyG33zuBi5q4ScoK5sEKLQ_3oE5eC3XBj9U8td3xvW4L4LCdrmckHtRm9FmzTrmOP_-NplVGCWqHdp1KCTVyXWJDAqfQoKwVUTGriSEuwl4aXQHXf7S4Lnmwd9wa0PDCDdAzTTpbpI03ooQCvlzpMbOpuAYGLtv3yhAhsrOdYpi-atlCVY2fk2lkqiajPljAxG-j4C8qhQ35dJFxvhQZOZ9BDcVHjKlWeiWRYv0UWXIycr8dOb6uAS7NFj8Y1UOjmJbeRtA9OiZuWBkLJ9SvCMwjuNIwMTMFDsg8KgxXGDlG4uF2DUdF_Km9hykio1YRfGXoWdXhTOjPha7MkfFKXYWVUXCqlbQ16dDbT8wmqMBjVHXoFFgOP7VZnD8P5cMxEMnwtsjpt7_zmveq7njLsxeLot91Qqn0DjOg2Ce1zeqYCDpqhwfQaY4Kgh-227z8eXs_uPv_j8OiZ7r_6vEZmFDobkkvOKZQY-Wr_WxXveulQPOa7K3VAKKS4R_sNqFLghPlg06aQx3P6zDF0IQcww22zzPNoXKs00yJuL182vyE_mPG0I8XZL3k1UsSFrZW8-gPO4EhpqjnwG5o5VNgp5Felsr89retS0GI7EHR9GtN-wtRhzsedwQgQgIi-mLflYQiDlSEfVZajOBzDOzc04zO4wIN_4dY6ReHZj5ntw_oQ2lXSr2Nc7ub1T_uzaCwVgunPYjmUKKu4abYTo6ffcdxnGP5pxDvMTLB5-3MFqODToOxv_dT9lBMkvT\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering environment variables**\\n\\nI think I need to set some environment variables persistently in the service. Maybe I can edit a file and copy it to the user unit, which seems like the likely source. I'll check the install script to see if I can create a symlink, but first, it might be good to inspect the loaded file and consider copying it too. There’s a bit to sort through, but I’m getting there!\"}]}"},{"type":"toolCall","id":"call_PZbEPHA9k7FfjYR5SwARALGt|fc_0d46162620b774590169fa4fc7f2448195a3a627e551c95efb","name":"bash","arguments":{"command":"readlink -f /home/sebas/.config/systemd/user/install-session-memory-dispatcher.service && ls -l /home/sebas/.config/systemd/user/install-session-memory-dispatcher.service /home/sebas/pi-config/scripts/install-session-memory-dispatcher.service"}},{"type":"toolCall","id":"call_J5vayrGclkOXCCKiQfUb3Lpb|fc_0d46162620b774590169fa4fc7f2548195ab32064981ba77dc","name":"read","arguments":{"path":"/home/sebas/pi-config/scripts/install-session-memory-dispatcher.sh","offset":1,"limit":220}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":747,"output":162,"cacheRead":71168,"cacheWrite":0,"totalTokens":72077,"cost":{"input":0.0018675000000000002,"output":0.00243,"cacheRead":0.017792,"cacheWrite":0,"total":0.022089499999999998}},"stopReason":"toolUse","timestamp":1778012099493,"responseId":"resp_0d46162620b774590169fa4fc406e881958292c8413df93eed"}}
{"type":"message","id":"0eceaa58","parentId":"d45396b5","timestamp":"2026-05-05T20:15:04.118Z","message":{"role":"toolResult","toolCallId":"call_PZbEPHA9k7FfjYR5SwARALGt|fc_0d46162620b774590169fa4fc7f2448195a3a627e551c95efb","toolName":"bash","content":[{"type":"text","text":"/home/sebas/.config/systemd/user/install-session-memory-dispatcher.service\n-rw-r--r-- 1 sebas sebas 470 May  5 12:28 /home/sebas/.config/systemd/user/install-session-memory-dispatcher.service\n-rw-r--r-- 1 sebas sebas 470 May  5 12:33 /home/sebas/pi-config/scripts/install-session-memory-dispatcher.service\n"}],"isError":false,"timestamp":1778012104118}}
{"type":"message","id":"90daa1b4","parentId":"0eceaa58","timestamp":"2026-05-05T20:15:04.120Z","message":{"role":"toolResult","toolCallId":"call_J5vayrGclkOXCCKiQfUb3Lpb|fc_0d46162620b774590169fa4fc7f2548195ab32064981ba77dc","toolName":"read","content":[{"type":"text","text":"ENOENT: no such file or directory, access '/home/sebas/pi-config/scripts/install-session-memory-dispatcher.sh'"}],"details":{},"isError":true,"timestamp":1778012104118}}
{"type":"message","id":"10327e91","parentId":"90daa1b4","timestamp":"2026-05-05T20:15:10.696Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Updating files and restarting services**\n\nI need to update both files, which probably means copying the repository file to the unit file. After that, I'll need to run a daemon-reload and restart the service. It sounds straightforward, but I want to make sure I’m covering all my bases and not missing any vital step. Keeping everything in sync is essential for everything to run smoothly. Let’s proceed carefully!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa4fcacfa08195825a887ca5916e96\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-k_MbZmJKxNha4ZUjk7qLrzL0TAFWm7HyuTNK3sSnJr7yK4hMZ-DhxqounvlgbQOvlY_9aMNEzXpwFIKswcY6ggOOU4-Rx6QqIu1gFHWT9ETmXyKdTm0jDOaI8WdSQurA5FssMAFfKwmY0DVVygVkzMfwxitbLUkshCSEYHyHFK5JamIyHwDMRskImX312_RymShZTWNQmutaMWQla2CMbLDoGqfC6GtkPjlNCPFyfCStZSLHqvFmds4dMNfcMX4jVHFiUpZG2RZIzocRdDQvwww_mKoHqJ_NbyI38lcYXOL7grARuTMvwQVqOqCZU4Kr80m-mVy1HsJCMRQDP3iKUd2CvPzCl8Al46AbnK5EdPF1M3hsJCbZmOTBix0kpBI6P7meDsIlm3BLioix1q0tSJTakcvliwzRg6VbZyT9Nbnf9p0C_abSent2x04p6qIIItf9tBiW2-NpxxyV7hKM5PDM5nfmb5dSUCKlv1HEnm3itnlRCqe4AMjuEfSOZKE0jnhPGmDt2OAO7UBqWQxNBlKX6R56_BTpVP4khuBElpnLja8jUVV55WVYqnTIrKLx5E04WBw8VFDu0fh1LIdSOckm24WEQ3YcPsKzxPBoZOHRSGe2eHWfzwKm__VtDXD1jbyH64aozBx4v0R8LKfIV-ERNPTIb7WXOgKHYp9HTbRbfNnYLizkvSpOWsEg_cae1clupER39N4OTuQyiinxAAdta7YmwdedGNrvyp4drT8_qQpHD96QNAh1x37_Tym8dT-kysyOfITo01R36-lXGxjhuAr3h-zhzNrANn4Y2zJXOGBUtOk3taoXjZysQugCF8CMkVrn68mdlz7hlacofwdPV2AWmZ8QDyR4HIP2mLLD1QUj4F2tcgBBFl-wO-0aMVuB0hnE2F4m997dF1rv-15XB4Y2-0YFCHLMIss2oUBFIzAcXHzRxnMxsaXn5shBk4QvEcwEwIyJ_aS0xEVbR3R9XCogL6bsJzrszPOgH0rR3XG-EcXllbsKQaLgd39nuKy\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Updating files and restarting services**\\n\\nI need to update both files, which probably means copying the repository file to the unit file. After that, I'll need to run a daemon-reload and restart the service. It sounds straightforward, but I want to make sure I’m covering all my bases and not missing any vital step. Keeping everything in sync is essential for everything to run smoothly. Let’s proceed carefully!\"}]}"},{"type":"toolCall","id":"call_KAqW6LtMEnlFj6zKehd9SR7Y|fc_0d46162620b774590169fa4fcc92948195ada1e9331774e780","name":"edit","arguments":{"path":"/home/sebas/pi-config/scripts/install-session-memory-dispatcher.service","edits":[{"oldText":"Environment=AGENTS_DB_API=http://100.116.176.16:8091\nEnvironment=PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi\nWorkingDirectory=/home/sebas/pi-config\n","newText":"Environment=AGENTS_DB_API=http://100.116.176.16:8091\nEnvironment=PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi\nEnvironment=PI_SESSION_MEMORY_MAX_CANDIDATES=6\nEnvironment=PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD=0.92\nEnvironment=PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD=0.9\nWorkingDirectory=/home/sebas/pi-config\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":533,"output":198,"cacheRead":71680,"cacheWrite":0,"totalTokens":72411,"cost":{"input":0.0013325000000000001,"output":0.00297,"cacheRead":0.01792,"cacheWrite":0,"total":0.0222225}},"stopReason":"toolUse","timestamp":1778012104120,"responseId":"resp_0d46162620b774590169fa4fc8a2a0819582ed74007143521d"}}
{"type":"message","id":"a5c594ba","parentId":"10327e91","timestamp":"2026-05-05T20:15:10.705Z","message":{"role":"toolResult","toolCallId":"call_KAqW6LtMEnlFj6zKehd9SR7Y|fc_0d46162620b774590169fa4fcc92948195ada1e9331774e780","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in /home/sebas/pi-config/scripts/install-session-memory-dispatcher.service."}],"details":{"diff":"    ...\n  9 Restart=always\n 10 RestartSec=2\n 11 Environment=AGENTS_DB_API=http://100.116.176.16:8091\n 12 Environment=PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi\n+13 Environment=PI_SESSION_MEMORY_MAX_CANDIDATES=6\n+14 Environment=PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD=0.92\n+15 Environment=PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD=0.9\n 13 WorkingDirectory=/home/sebas/pi-config\n 14 \n 15 [Install]\n 16 WantedBy=default.target","firstChangedLine":13},"isError":false,"timestamp":1778012110705}}
{"type":"message","id":"faff8a7c","parentId":"a5c594ba","timestamp":"2026-05-05T20:15:14.584Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_jFmDfoPP2esAAt36p7NzCk61|fc_0d46162620b774590169fa4fd0c9808195a25ea6fdcde901d1","name":"bash","arguments":{"command":"cp /home/sebas/pi-config/scripts/install-session-memory-dispatcher.service /home/sebas/.config/systemd/user/install-session-memory-dispatcher.service && systemctl --user daemon-reload && systemctl --user restart install-session-memory-dispatcher.service && systemctl --user status install-session-memory-dispatcher.service --no-pager","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":764,"output":90,"cacheRead":71680,"cacheWrite":0,"totalTokens":72534,"cost":{"input":0.0019100000000000002,"output":0.00135,"cacheRead":0.01792,"cacheWrite":0,"total":0.021179999999999997}},"stopReason":"toolUse","timestamp":1778012110706,"responseId":"resp_0d46162620b774590169fa4fcfbf2881959578a94b48590c63"}}
{"type":"message","id":"aa1e4089","parentId":"faff8a7c","timestamp":"2026-05-05T20:15:14.835Z","message":{"role":"toolResult","toolCallId":"call_jFmDfoPP2esAAt36p7NzCk61|fc_0d46162620b774590169fa4fd0c9808195a25ea6fdcde901d1","toolName":"bash","content":[{"type":"text","text":"● install-session-memory-dispatcher.service - Pi session memory dispatcher\n     Loaded: loaded (/home/sebas/.config/systemd/user/install-session-memory-dispatcher.service; enabled; preset: enabled)\n     Active: active (running) since Tue 2026-05-05 17:15:14 -03; 14ms ago\n Invocation: 451bb782f036480f89f33ec4084e0112\n   Main PID: 183490 ((env))\n      Tasks: 1 (limit: 9575)\n        CPU: 7ms\n     CGroup: /user.slice/user-1000.slice/user@1000.service/app.slice/install-session-memory-dispatcher.service\n             └─183490 \"(env)\"\n\nMay 05 17:15:14 ballbox-first systemd[1289]: Started install-session-memory-dispatcher.service - Pi session memory dispatcher.\n"}],"isError":false,"timestamp":1778012114835}}
{"type":"message","id":"fac4d8a1","parentId":"aa1e4089","timestamp":"2026-05-05T20:15:19.348Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa4fd4f1ec819592102863f63f5951\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-k_WIcsbuf_PMxXT9ZZthdzc0QjAhJzUhOssZ9MwxWWrEkt0aXCYPEWVbDxagcwPMm5umA1HYhWw5ERBpaboJ1ZlzDD27dFMrvQvT5OG5Q_wyeXgsRBeOm8aiHtbJ3eq_giURLWqCWt8Qov3YILClL5WwuFc9IQHRUH8imZ0O9BMHHobH4sUltrSUt9ufJPoBKPqJ2JfPMII58maYoZ4izojF-pqSnQrlZLUrlhq31EVdoMwBeN8nceydqP_AHyWooqK1PUwgYF6y6PY5C4T4jX17waP5rtV8mJIeZr1aPdnnM2UQyANjsQcBymXyvzaWqPGopfore7CwFkZMyvQ-HN2lqR12ffBg8eVyI2za-dyR8im5Fob5_Rum6S4_-DVtB7gnAmu6mJxTG-CSlmXHjjDvLgSLxLTvac2gJbh1ZZQCKEDWHwPU4w7FGmAUUbfPUYZp-NovUGStQp8LOLHBtyjbLSjt6k2K_HARJhuHqGYhviBxIO2OcGaY74R4p_zu4y3zlnq1Pheemc92uJVMdRTYD71U0rs2htk_1WktHHl_3IbVIJ1GxoM903j3z7fekUSxMdKB9tUwSs51s4_EaNgAmyli0E75eBLLqLUnOHTdPcgElvHBJyQ4Ot2wp30TsaS3G3jw7wNBYcvzq_QIamuYliUEg1kbYnzK3FX5GDyIv7k2sYW4Byg0N5M5jHvY0dO94boq6w8riNYI2GwFS-vtpdQAzv7H3JK1yFfgDvepXW_5gL8L2x-mt3pYl7vX0N0pVRYD2y57FQ-9J7lfwT2HeqYeq2c7sCcmywrFYX5ktgFBL7VzJRUn4HkF9uBaKGIsoplEPPETjUmRU0uPQ54V5QYzAAnDqjfGSugqmvqdQOjeMnETctq84F35IygVMtsMuRu8_rk79Eir3D_qwiNTtQ3XkLDEDLCAMwHVXlOw11KsAkqCwiRsT_zgTCwqTGhjHLm7N7Z4Q7kiZmnIz_URQ4LCGTD2s6PrUgu1cuh8QqURWx71wZqeztCrN7vOwU5yJBL3SHZpaooSGZpaI4iZw==\",\"summary\":[]}"},{"type":"toolCall","id":"call_zVAduzUwL2cloWGDiohJJ7RT|fc_0d46162620b774590169fa4fd6f7108195a5b0e7096c328957","name":"bash","arguments":{"command":"sleep 1; systemctl --user status install-session-memory-dispatcher.service --no-pager; echo '---'; systemctl --user show install-session-memory-dispatcher.service -p Environment --value","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":541,"output":87,"cacheRead":72192,"cacheWrite":0,"totalTokens":72820,"cost":{"input":0.0013525000000000002,"output":0.001305,"cacheRead":0.018047999999999998,"cacheWrite":0,"total":0.020705499999999998}},"stopReason":"toolUse","timestamp":1778012114836,"responseId":"resp_0d46162620b774590169fa4fd3a78881959945ee5b642405e8"}}
{"type":"message","id":"c4349998","parentId":"fac4d8a1","timestamp":"2026-05-05T20:15:20.389Z","message":{"role":"toolResult","toolCallId":"call_zVAduzUwL2cloWGDiohJJ7RT|fc_0d46162620b774590169fa4fd6f7108195a5b0e7096c328957","toolName":"bash","content":[{"type":"text","text":"● install-session-memory-dispatcher.service - Pi session memory dispatcher\n     Loaded: loaded (/home/sebas/.config/systemd/user/install-session-memory-dispatcher.service; enabled; preset: enabled)\n     Active: active (running) since Tue 2026-05-05 17:15:14 -03; 5s ago\n Invocation: 451bb782f036480f89f33ec4084e0112\n   Main PID: 183490 (python3)\n      Tasks: 1 (limit: 9575)\n        CPU: 80ms\n     CGroup: /user.slice/user-1000.slice/user@1000.service/app.slice/install-session-memory-dispatcher.service\n             └─183490 python3 /home/sebas/pi-config/scripts/session-memory-dispatcher.py --host 127.0.0.1 --port 17882\n\nMay 05 17:15:14 ballbox-first systemd[1289]: Started install-session-memory-dispatcher.service - Pi session memory dispatcher.\nMay 05 17:15:14 ballbox-first env[183490]: session-memory-dispatcher listening on http://127.0.0.1:17882/api/session-memory\n---\nAGENTS_DB_API=http://100.116.176.16:8091 PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi PI_SESSION_MEMORY_MAX_CANDIDATES=6 PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD=0.92 PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD=0.9\n"}],"isError":false,"timestamp":1778012120388}}
{"type":"message","id":"694c2fd9","parentId":"c4349998","timestamp":"2026-05-05T20:15:25.356Z","message":{"role":"assistant","content":[{"type":"text","text":"Listo. Persistente.\n\nHecho:\n\n- actualicé el unit file:\n  - `/home/sebas/pi-config/scripts/install-session-memory-dispatcher.service`\n- copié config activa a:\n  - `/home/sebas/.config/systemd/user/install-session-memory-dispatcher.service`\n- `daemon-reload`\n- restart del servicio\n- verificado activo y enabled\n\nEnv persistidos:\n\n- `PI_SESSION_MEMORY_MAX_CANDIDATES=6`\n- `PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD=0.92`\n- `PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD=0.9`\n\nEstado actual:\n\n- servicio: `install-session-memory-dispatcher.service`\n- activo\n- persistente en user systemd\n\nSiguiente: testing.","textSignature":"{\"v\":1,\"id\":\"msg_0d46162620b774590169fa4fda4cf48195b773e4bbad6fe506\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":961,"output":159,"cacheRead":72192,"cacheWrite":0,"totalTokens":73312,"cost":{"input":0.0024025,"output":0.002385,"cacheRead":0.018047999999999998,"cacheWrite":0,"total":0.022835499999999998}},"stopReason":"stop","timestamp":1778012120391,"responseId":"resp_0d46162620b774590169fa4fd958e08195a5916dc28a06b3f5"}}
{"type":"message","id":"eed4787f","parentId":"694c2fd9","timestamp":"2026-05-05T20:25:35.201Z","message":{"role":"user","content":[{"type":"text","text":"¿Podéis simular el cerrado de una sesión previa o crees que habrá yo una y lo cierre para que se trigue el hook?"}],"timestamp":1778012735190}}
{"type":"message","id":"008f68de","parentId":"eed4787f","timestamp":"2026-05-05T20:25:42.420Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Exploring simulation options**\n\nI need to keep my answer concise. It sounds like I can simulate by using a POST to the dispatcher with an existing session file, which might be easier than asking the user directly. But since the user is asking for a preference, it makes sense to mention that we can safely simulate using prior sessions or replays. However, for a true end-to-end hook, it would be better to create and close one. I might recommend both options to the user.","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa52411c0081958db37f29b8b3e0e7\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-lJFv6dfNqr2yw8C_X2LwzjPXja_wdFCopH91J7nTCsLWFZUxwPfp6nDJwbSpH9CVUNZOs3nZjWZ_-2WMUCUc__c9mqBWRYlYAhwJW6_1JZ6XX2Ekh5z2fixLzcUeoqiHttsXzJH_5Quh9A-8vxgi86z1oaqcv3mItSbXoYIXeXQaaGjdvBjpSlhXZRPOSNxROOX9DNNx2YB_kUxWXwwVfsvAZgi0UjJ3ns4ts5Wld1fVQbbnj27lxaY5kocVnMqBv4GPGLv1kYkSdP_RQZ3J0NQYGzKg61In3kTcQf54Xz12rAOeGKiba3T-YZ6laPaj71amYCG5IFTI23M8slTY0ulcXjldvRGMeh1bnErkuCTQD1rh2Y4s4wQwMovhwB2__bFaP0IDh2Y4ZKnDfgbmTYD4ZK5lhlIf2T24tDFjopTxOm1cHSMD6k-tSUVq9tgNwjLWzCLFM1gy2JM19BfNSVxC-NWgdUMMyVFUTE3PQHFSZGyFIh2Y49RyyGftHhnpkZkewFseuwPJa-psc49V1hoxTtRRstw1dUcmm_lVYwrZqLv9SEpmbKeByWZKrPPbC2bqZ28Eie51KV5PqXGZiJKs37ox5UTb0a4TTVUVoE851hkPCO4rFGbDjG_2I5-OQMPpjvXCJpFscSo1JaFv9BUd1wzzsEleE6IZyD-TfDLImjZO1Mu_rE3bLynqI47ZihxW6C5Ez1woWMj5eSXcCdg1rxqyNbxmaETl2dFZDPtN83HBaDs19--kuPt_WDvNunpnzXYAjfnVdkXk26iUkTSAA4gfa_TzHpTHLixGVP6qWG4O2GdCd5DqVZry_nnFMUts7GxC9M5RciAm4qsXZPtgX5fiv2mRbXXeZ5IUo97woxmn7xg-4HtzGO0OYq6t329JLSAl_E6GEuvBRBUZ9r-nIRZnPQCCP1X64qJOagJiZWb3oez8kxhI3tIZoT5jnSECTyrMmUqxlB9fiG030nF2UE-zy-WfhBDozGzo2t1tc4LP7eJqnJ3uiwZz7UrhmVTDFNsN0QQdNVgN7yfQJBA_DHRUoWjfEQp-MSXtI_6mwk3ouxh8OndunKq72izqdKdNO6lTp3sAEJzUbdocbhtBBiDZ9MGSf01KCeK1S8iWDQt1UjyVphdBnjwF5VEiOwrzQmcQmCBXEQQkscbyv0jzpqSR93rlKYE3lo_CPIboCq1TtyLb0BlCkeOJwkGM4u9Xu5RTa1X1LGfG_MHeAxgB-AThymKjeNCS7-t1ZWrHTG40fC31vpX20sjDfZaZD2RRWVeiMgy0fsXPWpUjslkRFVDNIT2R-ySNkWlk1V5ApqQo39ABkonqUmSqeeqXd9TQkgfHl5ynAxZh0JJ8X6tnw==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Exploring simulation options**\\n\\nI need to keep my answer concise. It sounds like I can simulate by using a POST to the dispatcher with an existing session file, which might be easier than asking the user directly. But since the user is asking for a preference, it makes sense to mention that we can safely simulate using prior sessions or replays. However, for a true end-to-end hook, it would be better to create and close one. I might recommend both options to the user.\"}]}"},{"type":"text","text":"Sí. Puedo simularlo.\n\nOpciones:\n\n1. **Simulación rápida**\n   - reinyecto un `session_file` viejo al dispatcher\n   - valida analyzer + ingest + persistencia\n   - no valida el hook real de cierre\n\n2. **Prueba real end-to-end**\n   - abrís una sesión nueva\n   - la cerrás o hacés switch\n   - valida hook + dispatcher + analyzer + ingest\n\nRecomiendo:\n\n- primero **simulación rápida**\n- si pasa, después **una prueba real**\n\nAsí aislamos:\n- si falla la sim: problema en analyzer/dispatcher\n- si pasa la sim pero falla la real: problema en hook\n\nSi querés, arranco ahora con la simulación.","textSignature":"{\"v\":1,\"id\":\"msg_0d46162620b774590169fa5245ac688195954add0c78c12269\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":2069,"output":228,"cacheRead":71168,"cacheWrite":0,"totalTokens":73465,"cost":{"input":0.0051725,"output":0.0034200000000000003,"cacheRead":0.017792,"cacheWrite":0,"total":0.026384499999999998}},"stopReason":"stop","timestamp":1778012735193,"responseId":"resp_0d46162620b774590169fa523fc0cc8195b25ad17568c68a16"}}
{"type":"message","id":"6035d420","parentId":"008f68de","timestamp":"2026-05-05T22:03:01.740Z","message":{"role":"user","content":[{"type":"text","text":"dale"}],"timestamp":1778018581731}}
{"type":"message","id":"e1d8d9e5","parentId":"6035d420","timestamp":"2026-05-05T22:03:09.904Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Simulating task process**\n\nI need to simulate something, and I'm thinking about using a to-do list. Should I follow up inside the active task or maybe create a new one? I suppose updating the to-do would be fine since the task is done already. I’ll probably need to use an old session file or something recent. Then, I’ll post to the dispatcher with the right payload and inspect the created job and memories afterward.","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa6917c2c081958964eb0724e7b096\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-mka0b94nQhcXxhwzOOouNi9VrCdc7QMS-ZrB4WZ8mFb9PEnBqQ3onx4k1R02TURT5ZMhHyn27vptrQjqtXUxCFOZ9pwPoc21BHyZR74T_8Dg7h56yn_Cj_kekyGlBucXorLMPtgQTmGrJZaUYnAVCQR43-9p9bFXXufIc-O1iSClIqBKDUCYDjALiOm0WaQomNhfqA_faBz8RP2Mvwiz08E1C3AhMLKotn4Kq5xd9qOZaJEKDTokW_xzXkaumpyQS42FmT22Z_bDliq1I8HNYtqtygZJwZBnhneaHDdkHKZfxDbJRNPAeLsDAIhPdtED17s9iU4A6u-DFvl0HouY6qokDF-wFJbmaC3MFIWLUVCxUyOLPhAjx5ZDhQkJQJeJ_AulRkkvlBcCu3_0qdPSklD_zryhbXE_cnWnzRA-8COyMvGOZEkW2KgrPHuP3Ca6v1d-OAe4i-se2yheIzjWnh1h_6Gc-jT677QQEF0X-wdw_8fHA_7jXpJPGJSNzQk5xFwhneeRkW9T0oy03xR4hTqTXYK6aUVxXELecu1LQWA0G_mBCBUKKSh2dyuvAMV-YClgDE75It2-LUCyAIN-UjDlh7awzmQfWz0fUuFohDSUV3CvG-UCklluHY8tEUlmVre2FhuGSwL24Ijn9HqbOUusaaXlbuvgcDmWDuDTcr43-gRmNd0Xog0iBPTV92lu7JQZlg36grgF8d_b-vB48W-NfO2WF9SVgayHCtTuYlETis4ekYnu-Hv4tofnEDJaG1uDCQXRPANA7vegsh_pNtcrzZs4-J_d4Xc9Kd48KCGDCPO4BgUr1Ofodi-U_fDpaED8NUHTl-bJdJ11iuR9WbsB9i4GNk6oS-u2Hswj9_4iosJaPCvZQ0b-HLbjYJnuFnQc2xp_BdUuZicEW7nyNt8jfU_UzbyiKK80T6Hr9aaxbRby2HDvD7HoRunLWxNhGivJCAlR9X3sP0pym7bVwtXpgQaASBM8aFMOhkxUnnHcWplCxT76Fa5vz5lT7uMPZs-7sWeeWCxMfvjdo4pKlj-9MZskXicgwtS3QFf70TsrZz0mt5gLP66fGzdS74r0s6mAGXDzhVt7CUE4eSwDZgXUxxAtK1q9lYYyJF-LbKOVWJWXiI3cOoR8Lr9oT9G5m_a33aruoo8RFZjYGXOZy7NGrIBzlaBznyPJDv8BoMihJ-rhGfQNB8rhRpLp2ljoMGtTdsBMyw3qUdQx6rJCrQL027LPDDE7-C9Q2QSM88f9sw=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Simulating task process**\\n\\nI need to simulate something, and I'm thinking about using a to-do list. Should I follow up inside the active task or maybe create a new one? I suppose updating the to-do would be fine since the task is done already. I’ll probably need to use an old session file or something recent. Then, I’ll post to the dispatcher with the right payload and inspect the created job and memories afterward.\"}]}"},{"type":"toolCall","id":"call_LPDFzjgqFXkkOunqNTn4jKlL|fc_0d46162620b774590169fa691aee2081958d07649d16b65e26","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json, urllib.request\npayload = {\n  'session_id': 'sim-test-20260505',\n  'session_file': '/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-44-38-590Z_019df862-33be-7759-b796-3d6b48b23493.jsonl',\n  'reason': 'manual-simulation',\n  'cwd': '/home/sebas',\n  'scope': 'global',\n  'user_messages': 11,\n  'assistant_messages': 56,\n  'last_user_text': 'and yeah harden like you suggested',\n  'last_assistant_text': 'simulation trigger',\n  'trigger': 'manual-test',\n}\nreq = urllib.request.Request('http://127.0.0.1:17882/api/session-memory', data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'})\nwith urllib.request.urlopen(req, timeout=10) as r:\n    print(r.read().decode())\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":698,"output":325,"cacheRead":72704,"cacheWrite":0,"totalTokens":73727,"cost":{"input":0.0017450000000000002,"output":0.004875,"cacheRead":0.018175999999999998,"cacheWrite":0,"total":0.024796}},"stopReason":"toolUse","timestamp":1778018581733,"responseId":"resp_0d46162620b774590169fa6916735081958f9509f9812d1a9e"}}
{"type":"message","id":"db41a0a7","parentId":"e1d8d9e5","timestamp":"2026-05-05T22:03:10.000Z","message":{"role":"toolResult","toolCallId":"call_LPDFzjgqFXkkOunqNTn4jKlL|fc_0d46162620b774590169fa691aee2081958d07649d16b65e26","toolName":"bash","content":[{"type":"text","text":"{\"status\": \"queued\", \"job_file\": \"/home/sebas/pi-config/sessions/session-memory-jobs/20260505T220309977099Z.json\"}\n"}],"isError":false,"timestamp":1778018589999}}
{"type":"message","id":"f19fad73","parentId":"db41a0a7","timestamp":"2026-05-05T22:03:14.822Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_hTrrEaOLRyA4hPifmdEoKYJY|fc_0d46162620b774590169fa691fd8bc8195a9da4aaac97b0a1d","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json, time\nfrom pathlib import Path\njob=Path('/home/sebas/pi-config/sessions/session-memory-jobs/20260505T220309977099Z.json')\nfor i in range(30):\n    data=json.loads(job.read_text())\n    status=data.get('analysis',{}).get('status')\n    if status in {'completed','failed'}:\n        print(json.dumps(data, ensure_ascii=False, indent=2))\n        break\n    time.sleep(1)\nelse:\n    print(json.dumps(data, ensure_ascii=False, indent=2))\nPY","timeout":40}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":556,"output":159,"cacheRead":73216,"cacheWrite":0,"totalTokens":73931,"cost":{"input":0.0013900000000000002,"output":0.002385,"cacheRead":0.018304,"cacheWrite":0,"total":0.022079}},"stopReason":"toolUse","timestamp":1778018590001,"responseId":"resp_0d46162620b774590169fa691e8df48195b129a859a681d543"}}
{"type":"message","id":"77821c15","parentId":"f19fad73","timestamp":"2026-05-05T22:03:14.881Z","message":{"role":"toolResult","toolCallId":"call_hTrrEaOLRyA4hPifmdEoKYJY|fc_0d46162620b774590169fa691fd8bc8195a9da4aaac97b0a1d","toolName":"bash","content":[{"type":"text","text":"{\n  \"job_id\": \"20260505T220309977099Z\",\n  \"queued_at\": \"2026-05-05T22:03:09.977139+00:00\",\n  \"status\": \"queued\",\n  \"job_type\": \"session_memory_analysis\",\n  \"payload\": {\n    \"session_id\": \"sim-test-20260505\",\n    \"session_file\": \"/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-44-38-590Z_019df862-33be-7759-b796-3d6b48b23493.jsonl\",\n    \"reason\": \"manual-simulation\",\n    \"cwd\": \"/home/sebas\",\n    \"scope\": \"global\",\n    \"user_messages\": 11,\n    \"assistant_messages\": 56,\n    \"last_user_text\": \"and yeah harden like you suggested\",\n    \"last_assistant_text\": \"simulation trigger\",\n    \"trigger\": \"manual-test\"\n  },\n  \"analysis\": {\n    \"status\": \"failed\",\n    \"save\": null,\n    \"saved\": null,\n    \"memory_id\": null,\n    \"memory_ids\": [],\n    \"saved_count\": 0,\n    \"error\": \"Warning: No models match pattern \\\"gemini-3-flash-preview:minimal\\\"\\nWarning: No models match pattern \\\"gemini-3-flash-preview:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:low\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:high\\\"\\nWarning: No models match pattern \\\"o3-mini:low\\\"\\nWarning: No models match pattern \\\"o3-mini:medium\\\"\\nWarning: No models match pattern \\\"o3-mini:high\\\"\\nWarning: No models match pattern \\\"minimax-m2.5-free:minimal\\\"\\nWarning: No models match pattern \\\"qwen/qwen3-coder:free:minimal\\\"\\nWarning: No models match pattern \\\"google/gemma-3-27b-it:free:minimal\\\"\\n[openai-codex] Token refresh failed: 401 {\\n  \\\"error\\\": {\\n    \\\"message\\\": \\\"Your refresh token has already been used to generate a new access token. Please try signing in again.\\\",\\n    \\\"type\\\": \\\"invalid_request_error\\\",\\n    \\\"param\\\": null,\\n    \\\"code\\\": \\\"refresh_token_reused\\\"\\n  }\\n}\\nNo API key for provider: openai-codex\"\n  }\n}\n"}],"isError":false,"timestamp":1778018594881}}
{"type":"message","id":"abdcfa85","parentId":"77821c15","timestamp":"2026-05-05T22:03:20.617Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Creating a concise report**\n\nI'm aiming to prepare a report that's straightforward and to the point while making sure to highlight any blockers. It’s important to present the information clearly so the user understands what’s at stake and where any obstacles are. I wonder if I’ll need to include any details that might help clarify the situation. Okay, I’ll focus on maintaining clarity and brevity throughout the report. Let’s get to it!","thinkingSignature":"{\"id\":\"rs_0d46162620b774590169fa692473fc8195a96e79f2482a62f2\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-mkmZ8A9DgR9Pr20f-8k7zb0z6QBeQ9zxMNPq772oBsU8X8M34lidCitc4Mt5ddzwB0BLWGsjU8zkoYv1ZWhgG42NAv7uNRLAZfqCIGiY5uRCuFzY_s-vYnOiuxztadCMzVw-sgdz37807gW7An2LxFOk4mRvlYfCBv-ZymuAAJNHy8GiptURRRD20ZapWB_dUYwsU4lNEKGCsZ8q9xAy_9smxc-eXu5wuW9jEn-sKeXJCHH-eIpQsas04767ctr3FNYo40q6rqqlTfzVrbkQYxjL2iFCLiuxUnEMpneR8UymUTan0fS9NxCyI2X9CCDCj7YdqUOnwcEMJtuRpoUHQh6jXKt7VjKkHsiTKJQPFU2MnRVgA5F1cnsm14_8FEuIOc6sXu0CvPs0nZbC2EkOOg069T1UPrtuhiNzny5FMWiFigr2PhGOfCamm85zU-DMdFR6E4gNgkFNoVpe9jfaDtRjvZRMXlZE4Q12eusHsJza2Eos1W--mhsxAr_AnOdNJj78cnaOe3YCEPLxrreuS7U2PLnY5A4DxUFvQAHogwLoQzYhtjQXIJ_iT3d7lgqJxrHft9qMDsn3h3liM1RPIx-hDwYE4XWLccanaSK2pmtiX_5PmGGrvUCpTIH5geS-uuelUwRU-EfE4SjhVq0YyTC0q5vseqJG0OTSoWE_NRQ76eveEt9ltN95sWuQEr6lkVSQmmSCHkkCFaVeip6zHMqYowkrDlWR_UB99FlbIWziQcKGjurUMJyvT6dyg3DMeQbnx5ol5_thfIFOBVVEgjZCSIcX8E7V7xGqgPdy9WmJ-5G6jFnTThdhhfyj_oC9Jin8oKKPOBTj0yQRMYv_ZphchO7wpZyMZNQ-fyU4vH2-gqviCtd1cg9DLdphPPjghzBdwXAopiLoFPcIGYfr9Oh3a3Q5TzicoSG344ra35LPv-TnO1ZgPUn-8qumS3ngWS5dFGVqx07nwuTAbSlFxVzdA==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Creating a concise report**\\n\\nI'm aiming to prepare a report that's straightforward and to the point while making sure to highlight any blockers. It’s important to present the information clearly so the user understands what’s at stake and where any obstacles are. I wonder if I’ll need to include any details that might help clarify the situation. Okay, I’ll focus on maintaining clarity and brevity throughout the report. Let’s get to it!\"}]}"},{"type":"text","text":"Simulé. Falló.\n\nResultado:\n\n- dispatcher: OK\n- job file: creado\n- analyzer: arrancó\n- persistencia del job: OK\n- fallo real: **auth del modelo**, no del hook\n\nError:\n\n- `openai-codex` token refresh `401`\n- `refresh_token_reused`\n- `No API key for provider: openai-codex`\n\nConclusión:\n\n- el pipeline nuevo quedó conectado\n- **ahora mismo no podemos validar ingest real** hasta arreglar auth del provider usado por `pi`\n\nO sea:\n- no hace falta cerrar una sesión real todavía\n- primero conviene arreglar este blocker\n- si no, la prueba real va a fallar igual\n\nSiguiente recomendado:\n\n1. arreglar login/auth de `pi` / `openai-codex`\n2. rerun simulación\n3. si pasa, hacer cierre real end-to-end\n\nSi querés, sigo directo con el arreglo de auth.","textSignature":"{\"v\":1,\"id\":\"msg_0d46162620b774590169fa6926743081959ea7bf72d569e502\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1282,"output":219,"cacheRead":73216,"cacheWrite":0,"totalTokens":74717,"cost":{"input":0.0032050000000000004,"output":0.003285,"cacheRead":0.018304,"cacheWrite":0,"total":0.024794000000000004}},"stopReason":"stop","timestamp":1778018594882,"responseId":"resp_0d46162620b774590169fa69236d388195985d42b53b045295"}}