{ "job_id": "20260524T091449627951Z", "queued_at": "2026-05-24T09:14:49Z", "status": "completed", "job_type": "session_memory_analysis", "payload": { "session_id": "019df92c-e0ad-77b9-8651-27b7657bd979", "session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T17-26-01-134Z_019df92c-e0ad-77b9-8651-27b7657bd979.jsonl", "cwd": "/home/sebas", "scope": "global", "reason": "periodic_sweep", "trigger": "periodic_sweeper", "branch": [ { "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": "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": "2c171437", "parentId": "ce1b6f42", "timestamp": "2026-05-05T17:28:14.193Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Inspecting database schema**\n\nI need to gather better data from the database. It seems like I should inspect the schema and look into the session end jobs. I think getting counts of memories by date or source reference would help, especially for session ends. I'll need to query SQLite to get the relevant information. Let's take a closer look at the tables that are connected to this query.", "thinkingSignature": "{\"id\":\"rs_0d46162620b774590169fa28a923dc8195844e4296fe678a3b\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-iitnzcOFpDxxK3LhRGLNgZhkt5pTMrIY5dJk2oBBXtO5ndIFxQFmMDoPUxrBSVg96dEJYdAuvyx4dK6QodIZ-zI0rXugzqp0KIDW3OJilwCVDqWC6Kxc1jQnhu0RUhvKSVbiUBUpHlURO_lc2RaQ2x95k_iACIo1bBuGAIXbRVMFjwgdT0vipop7OnY8VNuHxBVsCYZko7tLWeGIQCB9_-0bK3il_fJ1Blnxtp2LGjaDHzp1LDniV8lAM51uw9c9YWP7Oz3XCr7eSxVuNZc2zU_E1NmGj3AuSv-EMsZhY9aqundzYvryqER-s1-0cIA9IzyP72AU_jrwHZUBRX-JxVjabNmB5uSK6Vc-DOcL2tUfIxJ9-bPkp8kFP6eH9z_Ot6A5Px-jVIK--u2qACt4api9oAH1kLkorerDCN4yMH7gU9kyDr6qOObuXOFSaLPdIInPysvYnX5LlOHa0Yg7vSUBlVSOay6dygpFibEIgWigFd-a3RTM0VSCvX91W83cQXLqoDYSFmW4ppxL1eI5Ag7a9C2_V05ycJLhqe5b0Un2Oh6gDby4zA75FXZd9rJFTbD_LCL_WUlaA5G46BcrVx1RNC8mMyh9a-GXN5ULE9B3i-zp4fEyTeNLceo8owpjYXjDfBJGbpRiN5y0p1oRuSn9CpkNB98sRNN1WIOkE-eaxXDLcAzBkkQEEw0btNc5oNuZqyTJ2Wgupkynq_l2WhJbGLHfuK_rYprvwP97YLoRqwCKNzcRsRZYlLr1UALznPJpfrZzL5mKgDJ2fWx_lZmRlOGUpu5DX67jEWJAckrN3nYL7BLUjDzeztAboPNFhZMqYfJByybhplcUpE8sxB8VHeO5WDUiKJV35oIo3rTBWZSAdk5nEuDhwMMxFlJuDaGivc2XSEvJIgrX2BIC7Wh1LWHYZ3MYgtsgZcaybwtJnFCsqLEnFRNpjQAm6C2h2u3vpGUQ4ZMVdbX95T3zGFak8V-WJQFyXJEQielWIYzmebGx5H9l2K3W3zYQnp-a30LbGod5-Z2RhbzK6avUXj9lrpWiG1DIHbhcbXEaQ_fIAbD1I6PVRHJwDIZwX_JTGl__5dOYAWka9hQpfWx056JPXwgxzh_W4wuDq4DTCzbfxSPjUI-4yyA5SVn-Z3DSmgXlr2YjAYqXoYqi-tZCC0Brg==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting database schema**\\n\\nI need to gather better data from the database. It seems like I should inspect the schema and look into the session end jobs. I think getting counts of memories by date or source reference would help, especially for session ends. I'll need to query SQLite to get the relevant information. Let's take a closer look at the tables that are connected to this query.\"}]}" }, { "type": "toolCall", "id": "call_zAbHGM0JE5uNB8ijjZvbf7Ob|fc_0d46162620b774590169fa28adfe888195808b6e3edd5263bb", "name": "bash", "arguments": { "command": "sqlite3 /home/sebas/agents-database/data/shared-agent-memory.sqlite3 \".tables\"" } }, { "type": "toolCall", "id": "call_FuA9Gu7o4TGr3JHr11bUILkv|fc_0d46162620b774590169fa28adfe9c8195b77bfcb6255b70e3", "name": "read", "arguments": { "path": "/home/sebas/pi-config/sessions/session-memory-jobs", "offset": 1, "limit": 200 } }, { "type": "toolCall", "id": "call_VvwYEyWZX24BXKhpRV51wEeC|fc_0d46162620b774590169fa28adfea48195ad92618c88e0a6ac", "name": "bash", "arguments": { "command": "cd /home/sebas/agents-database && rg -n \"session.*end|session-end|memory dispatch|inbox|promot\" -S . | head -200", "timeout": 20 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 14962, "output": 180, "cacheRead": 4096, "cacheWrite": 0, "totalTokens": 19238, "cost": { "input": 0.037405, "output": 0.0027, "cacheRead": 0.001024, "cacheWrite": 0, "total": 0.041129 } }, "stopReason": "toolUse", "timestamp": 1778002084179, "responseId": "resp_0d46162620b774590169fa28a768a4819596c5d282ef93abc5" } }, { "type": "message", "id": "5ec85d29", "parentId": "d2a51900", "timestamp": "2026-05-05T17:28:19.489Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Inspecting service behavior**\n\nI'm thinking about inspecting the service's behavior, particularly focusing on the scripts related to session endings. It seems crucial to find the relevant files for this task. I'll dive into that to ensure I have a clear understanding of how it operates. This could help identify any issues or enhance performance. It's essential to keep everything running smoothly and efficiently, especially if there are potential improvements to make!", "thinkingSignature": "{\"id\":\"rs_0d46162620b774590169fa28afee408195b22de7ac17125969\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-iiz0LvYlpRAmBBrIGoub4CAxwwOqwUsuZAQ8s2ezsiCi_CU0dI0vmOsVANW11PpiImrw9gd-hNRWVTHqE4nQ_de0w9pFnNm6KsQUoKAm-m329iLENehTqFPRQ-zkWFLcrQPMjxVqjxBqKA2xQUygqdZUez3iGBi70-6bKnMjFZAnJeJuFYMvekqMDoV1Loed96unBGWi3VYcxbsySrWHCOpZzw1Mh8JquZCms6zUScuzofiZeJ1ouEW11pQgXsQ0xo1z1TTk8eEp7tQ0laF64X_srMjEMfFVZDbJd7wiAl99I64UuhTKkWU4qhSSV7R_NMZBkHcyxeAZXW9my3JusfiKUdi1sKom0pfgK5aGa9F3b_SfXuqrl8Dl4EoYF6oZQDzNrKDbPKv0Gz0hpvLh2KM_e-oj5Jd2nrXHd2AqWgeddD_aTXKyrDalyrqo_4H5dn6mlXAIhubaNGWcenOhLunNNRK27MimGefLcamTm97aQZCIrBjZWJe42Dw-mE954-pC5XJnuYli0autNTdaF3-t97HOqemaVHnyMJNhH1RBijW9uOl499WFs_H07gJTO4UXtrKZoukxkCJ0Xq3svO_VAQIDmh4rsZWSgXJ4FJcQQhPYdlpPyLeJ2u995ImKYNGxSzwKUZOUs3qcH-2ZVva7LTZYQI7fVQlS3c1G0C_QP1qCWLxcVDBBQVOxKsUgyuFWYmlmG-715XHPKms8htkYeM_hZzwvV0h4qhthuLwdWpxEZbNrpVh-zYX82epOEF1LdsAYdeuVxTNGKcw7mjQOXhGK7rzZqeSenAVcoWD7oi8wctXbN0AdOAOsP6h7x29XjT_UWFLldEP6RaRbalWv9si_TR1ldsg5e8D_1aZezze1YDWOUmeM8wiv8piWoveNV_MnAJ6OzC18h7YG1lFNwqapq_1j6dYAjosasCvyY_GND8nt31T8c6apBpTvdJlfyCAO1fgnRRC8zXy2fz6SC_FXDoD5eqp50eQVKcxHH17PR0a7BAvQ15_pUhMaF-T\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting service behavior**\\n\\nI'm thinking about inspecting the service's behavior, particularly focusing on the scripts related to session endings. It seems crucial to find the relevant files for this task. I'll dive into that to ensure I have a clear understanding of how it operates. This could help identify any issues or enhance performance. It's essential to keep everything running smoothly and efficiently, especially if there are potential improvements to make!\"}]}" }, { "type": "toolCall", "id": "call_mnqcfNz1CJ3M058OV9UKjYXg|fc_0d46162620b774590169fa28b352b08195a60859c47e5c4132", "name": "bash", "arguments": { "command": "find /home/sebas/pi-config/sessions -maxdepth 2 -type f | sort" } }, { "type": "toolCall", "id": "call_xVmebiSl8P4ENLZDhgN6zJfo|fc_0d46162620b774590169fa28b352c4819595d66838d215fbce", "name": "read", "arguments": { "path": "/home/sebas/agents-database/src/shared_agent_memory/service.py", "offset": 330, "limit": 130 } }, { "type": "toolCall", "id": "call_FK1HIsbXemKSrlT1UOMqU0si|fc_0d46162620b774590169fa28b352cc8195b93276f1d9b9ae03", "name": "read", "arguments": { "path": "/home/sebas/agents-database/tests/test_memory_service.py", "offset": 388, "limit": 80 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 1872, "output": 139, "cacheRead": 18944, "cacheWrite": 0, "totalTokens": 20955, "cost": { "input": 0.00468, "output": 0.002085, "cacheRead": 0.004736, "cacheWrite": 0, "total": 0.011501 } }, "stopReason": "toolUse", "timestamp": 1778002094236, "responseId": "resp_0d46162620b774590169fa28aedfd88195870687633c128f8f" } }, { "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": "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": "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": "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": "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": "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": "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": "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": "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": 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"cacheRead": 0.014976, "cacheWrite": 0, "total": 0.0174535 } }, "stopReason": "toolUse", "timestamp": 1778002349914, "responseId": "resp_0d46162620b774590169fa29ae6910819584251f6bd7b3fd96" } }, { "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": "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": "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": "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": "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": "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": "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": "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": "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": "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": "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": "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": "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" } } ], "user_messages": 5, "assistant_messages": 32, "last_user_text": "dale", "last_assistant_text": "Simulé. Falló. Resultado: - dispatcher: OK - job file: creado - analyzer: arrancó - persistencia del job: OK - fallo real: **auth del modelo**, no del hook Error: - `openai-codex` token refresh `401` - `refresh_token_reused` - `No API key for provider: openai-codex` Conclusión: - el pipeline nuevo quedó conectado - **ahora mismo no podemos validar ingest real** hasta arreglar auth del provider usado por `pi` O sea: - no hace falta cerrar una sesión real todavía - primero conviene arreglar este blocker - si no, la prueba real va a fallar igual Siguiente recomendado: 1. arreglar login/auth de `pi` / `openai-codex` 2. rerun simulación 3. si pasa, hacer cierre real end-to-end Si querés, sigo directo con el arreglo de auth.", "first_user_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 memoria…" }, "analysis": { "status": "completed", "save": true, "saved": true, "memory_id": "mem_3b089c54e76d4a79a5bb8836551e9571", "memory_ids": [ "mem_3b089c54e76d4a79a5bb8836551e9571", "mem_d8269f8eb0104dc3961fd2fb9762a0f0", "mem_b7c29c94c9aa44448e1a043e2740ab87", "mem_7241420239534c22be6b71d3588ca81f", "mem_7bd9f85afc174694af2ab2653786ca8e" ], "saved_count": 5, "raw": "{\"memories\":[{\"type\":\"episode\",\"subtype\":\"analysis\",\"scope\":\"global\",\"title\":\"Memory ingestion likely undercounts low-quality candidates\",\"content\":\"The memory service promotes ingested memories automatically and may be losing useful lower-quality candidates because too few high-quality memories are being ingested up front. Session-end hook failures may also have reduced observed volume; fixing the hook should be checked before changing ingestion policy.\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"branch excerpt: user message id 1602bef2 and assistant analysis on session-end hook behavior\",\"confidence\":0.78,\"freshness\":0.78,\"importance\":0.83,\"reason\":\"Durable product insight about ingestion strategy and observed undercount risk.\"},{\"type\":\"project\",\"subtype\":\"system_behavior\",\"scope\":\"project\",\"title\":\"Agents-database memory service auto-promotes ingested memories\",\"content\":\"The shared agent-memory service does not appear to rely on an inbox flow; memories entering the service are promoted automatically. Any quality-control strategy must happen before or at ingestion time, not via deferred inbox triage.\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"branch excerpt: user message id 1602bef2\",\"confidence\":0.7,\"freshness\":0.72,\"importance\":0.79,\"reason\":\"Useful durable project fact for future memory pipeline work.\"},{\"type\":\"decision\",\"subtype\":\"debugging_blocker\",\"scope\":\"project\",\"title\":\"Fix provider auth before validating memory ingest end-to-end\",\"content\":\"The current blocker is provider auth for `openai-codex` (`401 refresh_token_reused` / no API key), not the dispatcher hook itself. Validate auth first, then rerun the simulated and real ingest path.\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"last assistant text\",\"confidence\":0.95,\"freshness\":0.97,\"importance\":0.88,\"reason\":\"Concrete blocker and next step likely to matter in follow-up sessions.\"},{\"type\":\"artifact\",\"subtype\":\"service_url\",\"scope\":\"global\",\"title\":\"agents-database status endpoint\",\"content\":\"agents-database status endpoint: http://100.116.176.16:8091/api/status\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"branch excerpt / server details\",\"confidence\":0.99,\"freshness\":0.99,\"importance\":0.62,\"reason\":\"Reusable operational reference for future checks.\"},{\"type\":\"procedural\",\"subtype\":\"analysis_method\",\"scope\":\"project\",\"title\":\"Session-memory analysis should inspect job logs plus SQLite tables\",\"content\":\"For memory-ingest audits, inspect the session-memory-jobs files, the SQLite schema (`ingestion_events`, `memories`, job tables), and code paths in `session-memory-analyzer.py` / `session-memory-dispatcher.py` to correlate observed memory counts with actual pipeline behavior.\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"branch excerpt: assistant tool calls reading session-memory-analyzer.py, dispatcher, and SQLite schema\",\"confidence\":0.81,\"freshness\":0.8,\"importance\":0.7,\"reason\":\"Reusable procedure for future audits of the memory pipeline.\"}],\"reason\":\"Session mostly contained investigation of memory ingestion volume and a blocker in provider auth. Kept the durable product insight, one concrete decision/blocker, one reusable procedure, and one operational artifact.\"}", "result": { "memories": [ { "type": "episode", "subtype": "analysis", "scope": "global", "title": "Memory ingestion likely undercounts low-quality candidates", "content": "The memory service promotes ingested memories automatically and may be losing useful lower-quality candidates because too few high-quality memories are being ingested up front. Session-end hook failures may also have reduced observed volume; fixing the hook should be checked before changing ingestion policy.", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "branch excerpt: user message id 1602bef2 and assistant analysis on session-end hook behavior", "confidence": 0.78, "freshness": 0.78, "importance": 0.83, "reason": "Durable product insight about ingestion strategy and observed undercount risk." }, { "type": "project", "subtype": "system_behavior", "scope": "project", "title": "Agents-database memory service auto-promotes ingested memories", "content": "The shared agent-memory service does not appear to rely on an inbox flow; memories entering the service are promoted automatically. Any quality-control strategy must happen before or at ingestion time, not via deferred inbox triage.", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "branch excerpt: user message id 1602bef2", "confidence": 0.7, "freshness": 0.72, "importance": 0.79, "reason": "Useful durable project fact for future memory pipeline work." }, { "type": "decision", "subtype": "debugging_blocker", "scope": "project", "title": "Fix provider auth before validating memory ingest end-to-end", "content": "The current blocker is provider auth for `openai-codex` (`401 refresh_token_reused` / no API key), not the dispatcher hook itself. Validate auth first, then rerun the simulated and real ingest path.", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "last assistant text", "confidence": 0.95, "freshness": 0.97, "importance": 0.88, "reason": "Concrete blocker and next step likely to matter in follow-up sessions." }, { "type": "artifact", "subtype": "service_url", "scope": "global", "title": "agents-database status endpoint", "content": "agents-database status endpoint: http://100.116.176.16:8091/api/status", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "branch excerpt / server details", "confidence": 0.99, "freshness": 0.99, "importance": 0.62, "reason": "Reusable operational reference for future checks." }, { "type": "procedural", "subtype": "analysis_method", "scope": "project", "title": "Session-memory analysis should inspect job logs plus SQLite tables", "content": "For memory-ingest audits, inspect the session-memory-jobs files, the SQLite schema (`ingestion_events`, `memories`, job tables), and code paths in `session-memory-analyzer.py` / `session-memory-dispatcher.py` to correlate observed memory counts with actual pipeline behavior.", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "branch excerpt: assistant tool calls reading session-memory-analyzer.py, dispatcher, and SQLite schema", "confidence": 0.81, "freshness": 0.8, "importance": 0.7, "reason": "Reusable procedure for future audits of the memory pipeline." } ], "reason": "Session mostly contained investigation of memory ingestion volume and a blocker in provider auth. Kept the durable product insight, one concrete decision/blocker, one reusable procedure, and one operational artifact." } } }