{ "job_id": "20260604T152902982533Z", "queued_at": "2026-06-04T15:29:02Z", "status": "completed", "job_type": "session_memory_analysis", "payload": { "session_id": "019e9290-7cad-7f28-847b-004b58c9ebae", "session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-06-04T12-16-43-181Z_019e9290-7cad-7f28-847b-004b58c9ebae.jsonl", "cwd": "/home/sebas", "scope": "global", "reason": "periodic_sweep", "trigger": "periodic_sweeper", "branch": [ { "type": "message", "id": "43bb89f6", "parentId": "1cfcfc20", "timestamp": "2026-06-04T12:36:42.189Z", "message": { "role": "user", "content": [ { "type": "text", "text": "[telegram] se puede hacer un Dry Run a las memorias existentes sin ver la sesion, en cambio solo viendo la memoria?" } ], "timestamp": 1780576602181 } }, { "type": "message", "id": "580e3fef", "parentId": "43bb89f6", "timestamp": "2026-06-04T12:36:47.717Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering memory capabilities**\n\nI think the user is asking for an answer that's conceptual, possibly related to agents and memory reading. I might want to check what I can do in terms of memory skills. Perhaps I can respond with a straightforward yes or no, but I’ll also include some necessary caveats. It's also important to note that the user’s question is in Spanish, so I need to keep my response concise.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21715b65948191a7a03a32c69d0ce6\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXFeXO4sGMW_A5BE-Vz8oUdxo3Biv2GOBx5GsdZn78Y08VBFCKImU69TLYWw2cdTQ1Ww-kVihpBuXBNk46u-ka0Y6UCRhkaRFGQ8WKZM2-8Te_wRHCFH1EaHR3zJd-smPSeQKbdvCZji-ploxe5BhEKAqbLAmaKWeKVZ333g47kOfDlnL4pE3ZUYjR1zlfeoc_0cYtrIKqU1IHPY04T1AiZCwGM3EXuEceS_NQR_q9ncefYKDW_VfND9yG2IMGngXD99rwDqam1iQB8QvaOH1enn6GI0T8OFjNJgI4AH5ZrOeDtRgTYxnotP3stKEkrgPKd-0ydKfT3RK7wqQC47bGd65F0PPRzJsKFj4FkxaJ7OFgJolayQqvKUfKP56Bk7-5jAoa3KkxvWKJsPBRd7V865b9i-Tl-4PrmacYb9C2yVlqiLkMkx0DEftvapLbIufDf8YWdDW9MPXrgKwfR5b8LZfgPiECd2RMg4wJjXQzea9wNK4hhNUJiR9J3AXAojpOuH9jW09LCadSEsZXCqPeA-oTPaXOVIfYMYGJOSbQgdVyBUchbJyOFTDeM9hw_JDF46jSCBb0UCN6oxXsl2TNPaTITPR6H9g92mVEpZ6B7ckPIzH9puJM4VjYJGfSgUwNNkedQZ-ttE5grk0pgrAk-3qsnOOS_3Ls85LI6Q6Y680bZoymtAJJx48WIqCxs3omx5bfMKlGkecoK345P5c6KNIx7EPGxaClc4I2nnbxYWY08fyhXRf6XzvM2lhGsiGHl0XNl0DN3FmKJku0SsJDdkBQ5bl6hj_ay_f-uAVMgg8-c_AlyiRzP6n0Xn1UyMBb4Hn5B8MOhNLPMNZO34410HUPSR2UcK9ZytnQ1J2OiRGc3uueJT1DMl20-fBQ72HjknaeaIh5_0Vu3_QXvwH6dhb0p5PwnaRbVuF4Cs9lo_pf5x1xqfNvlPZ9uUNrNqqSTi1gyS3Y7YZyqh4CPxNbuxi9v2dX2DSporYS9PwwNor-9PaP0S6akoyM2W4dyeXFX6v0euFg_qYwqHVeZPxZgIoDXPX_8jy_3_B3K90kedNzevGrH39wJLuDFEFB1ZK7GpPITMBlUbizX11CopV-zZgSmZyM1TkoEJCyIlyv59AbXUM5EC-3cwC9tfjpCp0Sv-\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering memory capabilities**\\n\\nI think the user is asking for an answer that's conceptual, possibly related to agents and memory reading. I might want to check what I can do in terms of memory skills. Perhaps I can respond with a straightforward yes or no, but I’ll also include some necessary caveats. It's also important to note that the user’s question is in Spanish, so I need to keep my response concise.\"}]}" }, { "type": "text", "text": "Sí, pero con límites.\n\nSe puede hacer un dry run basado solo en memorias:\n- buscar memorias relevantes\n- reconstruir contexto probable\n- proponer acciones, riesgos, y próximos pasos\n- detectar huecos o contradicciones\n\nNo se puede garantizar lo mismo que viendo la sesión completa:\n- falta intención exacta del momento\n- faltan matices, descartes, y cambios de rumbo\n- una memoria puede estar resumida, sesgada, o vieja\n\nRegla práctica:\n- si querés continuidad operativa: memoria sola suele alcanzar\n- si querés auditoría fina o reproducir decisiones: no alcanza, mejor sesión + memoria\n\nPuedo trabajar en 3 modos:\n1. solo memoria\n2. memoria + artifacts\n3. memoria + sesión completa\n\nSi querés, te armo el dry run ideal “solo memoria” y qué datos mínimos tendría que tener cada memoria para que sirva bien.", "textSignature": "{\"v\":1,\"id\":\"msg_00bddaf16c51a121016a21715ea8008191b0a59ffb7dac6129\",\"phase\":\"final_answer\"}" } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 5439, "output": 237, "cacheRead": 0, "cacheWrite": 0, "totalTokens": 5676, "cost": { "input": 0.013597500000000002, "output": 0.003555, "cacheRead": 0, "cacheWrite": 0, "total": 0.0171525 } }, "stopReason": "stop", "timestamp": 1780576602200, "responseId": "resp_00bddaf16c51a121016a21715ace748191ac21bbd6aef4cb8f" } }, { "type": "message", "id": "3e0b983c", "parentId": "580e3fef", "timestamp": "2026-06-04T12:38:56.836Z", "message": { "role": "user", "content": [ { "type": "text", "text": "[telegram] solo quiero consolidar memorias previas con la nueva heuristica, pero haciendo un dry run primero" } ], "timestamp": 1780576736834 } }, { "type": "message", "id": "c2b4ace7", "parentId": "3e0b983c", "timestamp": "2026-06-04T12:39:04.398Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering memory consolidation**\n\nIt looks like the user wants to consolidate old memories with a new heuristic. I think I should first clarify their request. I can confidently say that yes, I can help with this! I should mention my approach and suggest a plan, possibly asking for their confirmation since it may involve reading the memory database, but a dry run would not include any writes. Also, I might consider using a memory-read skill if necessary, but just answering might be sufficient.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a2171e1a0408191a758a603d3486eb9\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXHnLSsBjd9Zkf93DaAFykdujqwO2FyoL7hGYYRZNPsx9Brbg5p9ZNi6T9c15tEuwuHDxlRMmI4kACRPn635bApZVJCLMbGSUyQVcyaixta6eVFl5YcrmpDint-vlFCR2jco5vY7WGizfxd9GybWtqL54Kg-9C1mirjGrjecuJxpG12g2Oatu6N4_ojLLAt4o8ovoMzMt3FyOREEIIFBcpCv8hOIbNqCqtzmv9J40GTBN1Awjb95fT80dFDgwprnSSCI-ZUk1nRWrMRuk8heBE6CrSnKzvleRZYc2aQZk-Q6pvb0briJmmxCDagHIu9uGca4qoLQyCMxKI0nAgl50MA5CNNRiQ1_AYrR2IfPctsqK8puBc81l5aBI3RFkcfuFb_CXthFVVNFfq8idVPA_PFBw5TrBqiuzTVSGh-vvQz8GT9Twb5zc0q7j31dQ5y4tF8I6U1oofWDkcNX2i4akqhf5K248_vP-cZtiYhyueaO-zDxp2G-XYihQx8QaSn9RdWz0OtAdVIFG9y298dboKpZo_d9nVGy_-o3FUKfUCY-N4h_PUe83wa3UGN4b3EY3AJJ4-ubZciIbYgNAKZx5M5gI8bqVUH13cNvxxe7Z-RDlGsp24nruXcJl1Qr8BY7oPFyLAzDTCkrQ2Db3bqRDr1oecGqWtOfqvRD4tCltSy9SWCXVhytP3wnMBf4E7X9w6llipdjoiJzmM6cw_gtdSVjkOmwVJeu6bqjzk3pP8c80ZLiXD7itRAaR5ZaKGpNy2Y35K_yCcs2HV9JlLMb_LQA-w1Kg1jc9-bOmvLLn7fAut4T2MDFMwHtQnkMwP0gnfdc6NHnp004hokyhKZe378u6PIlTHDH8vgr_UkEAFBsPyZsxYM3v489uHQOqk82dV1r4aQZL1BqXRJpFIiJidw0ex5T1uBYiYV4s-miLstpQaZ33f9QMbrb1UPkA4PX6piphjXYlh2JHXuin2DrTW6_Hqi9hYGS7LiIA9yUi2-sUJpCmgDGAmHzkkqzTC1Xh1l8xNsXZwwku_BuXaUSkjnbX1UwLyyaL4cx3_Lx9JTJb-700AXrQasLrrKIGPkuzJEfCmuEvmsvxhTnE5KIrnErxOPG6eAYoAx3Ub5cnGoQ8S4sQWmVZbaaPaQNN5VJctnS1AYDru7vYiWokysznPeUxSd2fGRet3fwDabmIaLQda0wMhumHu18tHgzen2rmqNsXr9jdm2PNOjHesWSjccCk3h2hSGeJCcJXhdsMLF60tIf89q1L4Ln0kdl_DSnLxEiESqRv0Uv3QU072SHAnNiLWuZ56CaRnCh4uBp84zyjHoRf4MFOGPuUc5OWN3dxrZYhVY0spjxxbl0_6nSKKcVPacRR6LJZuTDMSLJ8MF--_hAHR0WhuF2ohzbdq-3O1ap\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering memory consolidation**\\n\\nIt looks like the user wants to consolidate old memories with a new heuristic. I think I should first clarify their request. I can confidently say that yes, I can help with this! I should mention my approach and suggest a plan, possibly asking for their confirmation since it may involve reading the memory database, but a dry run would not include any writes. Also, I might consider using a memory-read skill if necessary, but just answering might be sufficient.\"}]}" }, { "type": "text", "text": "Sí.\n\nPlan seguro:\n1. leer memorias existentes\n2. aplicar nueva heurística en modo dry run\n3. mostrar diff lógico, sin escribir nada:\n - qué memorias quedan\n - cuáles fusionar\n - cuáles borrar/no migrar\n - cuáles re-tipar\n - cuáles reescribir\n4. si aprobás, recién ahí consolidar de verdad\n\nDry run puede devolver algo así:\n- `keep`\n- `merge_into`\n- `rewrite`\n- `drop`\n- `needs_human_decision`\n\nTambién puedo marcar:\n- ruido\n- duplicados\n- memorias viejas pero útiles\n- memorias que hoy violan la heurística\n\nSi querés, lo hago así:\n- alcance: todas las memorias tuyas, o solo de un proyecto/repo\n- salida: reporte corto, sin cambios\n\nDecime:\n- `todas`\n- `solo proyecto X`\n- `solo repo Y`", "textSignature": "{\"v\":1,\"id\":\"msg_00bddaf16c51a121016a2171e7dff08191ad83e026dfd78e44\",\"phase\":\"final_answer\"}" } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 542, "output": 278, "cacheRead": 5120, "cacheWrite": 0, "totalTokens": 5940, "cost": { "input": 0.001355, "output": 0.00417, "cacheRead": 0.0012799999999999999, "cacheWrite": 0, "total": 0.006805 } }, "stopReason": "stop", "timestamp": 1780576736836, "responseId": "resp_00bddaf16c51a121016a2171e0f1f08191a433906d567452a4" } }, { "type": "message", "id": "54a1a3d4", "parentId": "c2b4ace7", "timestamp": "2026-06-04T12:40:13.544Z", "message": { "role": "user", "content": [ { "type": "text", "text": "[telegram] si. one time asi que no es codigo que tiene que persistir. hacelo para todas" } ], "timestamp": 1780576813544 } }, { "type": "message", "id": "573a5188", "parentId": "54a1a3d4", "timestamp": "2026-06-04T12:40:17.322Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering task execution**\n\nI see that I need to use my memory-read skill for this task. Maybe there's no need to ask further questions. I should search through memories using the API. Additionally, it seems I might need to create a to-do for better organization. I wonder if I could use a bash curl command for this? I think inspecting the skill will be helpful too. Let's get started!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21722e791c81919d682ba21fa78266\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXIwkgGjX2HAWu41wremQ2pOVShtLsSUOad8kqPJAThwUoZAZy7wXWzaWjkPJStF8YqwhOgqRZejY0nEDtM3LJtZrnmvroDn2RNAlS2JtPmLCmLPcoKgq7J1pQzeIpntRNTuF2hX6zfuRKb7q-H3IIzB4iDdflWCBqlaNNfpXp0JyFcM6LQOVYVUH6-I3sQ3tfk2FtMBFNCoCCG4LwDqyrKU6pFke_xe7W7br47VJtuBwBXvSN5bwlv9oTPCNmoqu6KKvX9GEDEPMOlTOLh1F-Gp0neXcwN00gbiRodnkbyJyvanAzVP76rXYTNSP9g-EF_vj2LyScnkcHHv9-ZOs4EYZVmA-3S0OR7w1aM0nVnVP3E7FB8hfyVB-_K8Bbh6xkQgS4GHTmMpDC2lgCursnK-iJ2hCgB3IROuVZGgbU5-hm_ty0zWjtWoR2esN4NQcqO3NhYomvnZcTTbv5glZqdg7qnTmaWcyLf9TU9sKs7ufKrWH0l6DeVkQJcIrUdxx0Veb3AvAJ5e29EpxqihrPeSiply4TNKMkMh02k2fnx_sWa8MG88828x8rxK1yPrE6H4CtVcFxT7e_ElSBJa3Bs_MObPMpjvdmOyOY3438u6zW9HkDq4TBuvBL4jHfK2wcIu3ZNJV-gZ08_WuP_51aMRMnpUhmN6XAImPtY_BYWFicBFzAHx3s5cfbvO0EzaX-4KAcjpaukDQoeyRhplCVwJ9VEKQBOU8bCIMCaRFMUf2XiIttreDeeevE9xkyISCq5n6S38NHj8r5Vrt2JUycBHHJzc_ZvRrvN_Y3j4lJF6ZdcFm3oylxC5aqmWewNJ5_TCzyVscIXmvfmSxeQg9BHzbKL44Ig0qzhtzb-uJFg9-VmmiIZIn372yzTcITCymuR_B8RRpIhoQ8nj7Ot0VQm-HApaDtxvA_d-ZoNM77UFeW8hrHpb36kd4GbZP8J82cbjB0G1srUHigxavxBKvgABrsGzJ0DIA6COD0xeEiHQDCz7vv_8I-us2JDoAh82PG_WPohNEcSnHgWWTS93ERNA6J5rZhtgqR6ldIdk3Y5P5-4mn3RL4R7uMPm7WKBo43tFd6MjkFlNOijdTyULit21QLzaKcr1pgfmuC7v8XG-VMGGNRTN_Y1qHq9oqf7vifRh\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering task execution**\\n\\nI see that I need to use my memory-read skill for this task. Maybe there's no need to ask further questions. I should search through memories using the API. Additionally, it seems I might need to create a to-do for better organization. I wonder if I could use a bash curl command for this? I think inspecting the skill will be helpful too. Let's get started!\"}]}" }, { "type": "toolCall", "id": "call_6KYSIZVf62RefMIGb5ypPxcU|fc_00bddaf16c51a121016a217230d1e08191aedda30524bf7cb7", "name": "todo", "arguments": { "action": "create", "subject": "Dry run consolidate all memories with new heuristic", "description": "Read all existing memories, apply current consolidation heuristic without writing changes, and report keep/merge/rewrite/drop recommendations across all memories.", "activeForm": "dry-running memory consolidation", "status": "in_progress" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 772, "output": 113, "cacheRead": 5120, "cacheWrite": 0, "totalTokens": 6005, "cost": { "input": 0.00193, "output": 0.0016950000000000001, "cacheRead": 0.0012799999999999999, "cacheWrite": 0, "total": 0.004905 } }, "stopReason": "toolUse", "timestamp": 1780576813545, "responseId": "resp_00bddaf16c51a121016a21722da5a88191a97e8ce5080575a3" } }, { "type": "message", "id": "edd7aeaf", "parentId": "3823605f", "timestamp": "2026-06-04T12:40:18.566Z", "message": { "role": "assistant", "content": [ { "type": "toolCall", "id": "call_3hsuvxjLAheM7Z6ITGaYi9UD|fc_00bddaf16c51a121016a2172321798819192b8f024f69c2f48", "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": 398, "output": 31, "cacheRead": 5632, "cacheWrite": 0, "totalTokens": 6061, "cost": { "input": 0.000995, "output": 0.000465, "cacheRead": 0.001408, "cacheWrite": 0, "total": 0.002868 } }, "stopReason": "toolUse", "timestamp": 1780576817330, "responseId": "resp_00bddaf16c51a121016a21723173208191b354123bede9d809" } }, { "type": "message", "id": "6803f085", "parentId": "135a3ad1", "timestamp": "2026-06-04T12:40:23.162Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering search options**\n\nI think I might need heuristic docs for guidance. Searching for “AGENTS” could be potentially sufficient too. I’ll need to fetch all memories and inspect the API to see if I can search everything. It’s possible there’s no direct endpoint available. Maybe using an empty query could work, so I’ll give that a test run to see if it yields any results. Let’s see what I can find!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a217233af688191a1552fa890362efc\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXI2b3RbRVFjxSkHAV7bc3KsvekBLQYXJ0D0fDuBoT_RhDfRcokqYOvVd23IuFUWkCRUa58gMWKMf1j90sH5rEj4hpsODACfx6NtRRthIoQxshndLg-YAV8U8mkEKPOVXW4vdeL-iakQPDQ76CVSzPbvAL-vq42MIcFoQbuitcpwDtcK9RyNqm88_-znedXvoejOZEnFYGrwaiwv32K8L-8LdcHR_vasQvei9N6635hgDy3SobneiBF56b48rzfgzb3bNy6AxJbDkF9CbCoIK7NTeHJ68FGTkcU9x0bdVjb4AJjOX6gANXhkVDKgmVmT09Q-GDorA5p2hooOKE7JFfZrNG5XI5bIjyyt73JI-vL6q1VdHJkJKQUsG-vlaB6b4LxAMiz36oivAV2DYqok0sGnUiMQQTkEg-ZUrrT7SrmwZxnzyu2rIFZWvwlhHbAVsXEtXiDpwQ56Mqg41jGd53FgOoe487MX0vB2SJPhOyFORia-PBaqZCKuDnvPyK82arH8bhsBrbmsG3Pv5xjRRlVz3YEzdML4EY5pNm7lqXMQYnYv6vcyNo8u1B9dqjef00-AKfWxhVDvrQjcxF93JDww4X2S8B0ifSaN0C751xeb1GPI3-oXieJf62A2SpXEQk9OQQXIb2De-n5RS0zk5uAk5jM9QPS7Wt2qzG3yg1J-xE7d_7YectBHDbTdgANVvRrIeGEO546RUF20j--4PGXM_aJ2zKse1pi-2wLH1MvPkMTAhdRuwrT7K3c1DTOoVlbN25dcsD62cLmFbuLQV6xZog0rBiP5sQtCvMjqaswrjNJy5EwDajGcSmJSeQ0kWqknerbnn1RxxOEW5cQg13xPMQmMtd1UGQhNLtwZj0mu06VE0qU3VEsnOOugIx0mUkoq3q68X5ToQZtZugCTYAD9J1YyFLyoANhKGlVT7ON-dK1E1Rajk-v5X8NBDLiiM-SYHr8TKxzlSRAfure1ZffmMbkxVYzFTMLi9vaFREIJp3_XCoqeMi_B1E-1WXiZBbdfuX62jEx5GAIf3e_-4twk7dbn87K7IWDPfl7ZJFei71kHDUFfNwZ-fSjNENlPiT-4NfMupH0KioaNtjH7JoNAUo9FDU_-2mRsRHaWGGXXF7E_FLsO2fNUlP7ooDx1TBMa\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering search options**\\n\\nI think I might need heuristic docs for guidance. Searching for “AGENTS” could be potentially sufficient too. I’ll need to fetch all memories and inspect the API to see if I can search everything. It’s possible there’s no direct endpoint available. Maybe using an empty query could work, so I’ll give that a test run to see if it yields any results. Let’s see what I can find!\"}]}" }, { "type": "toolCall", "id": "call_YvWbJspf6JG8WTu2t1Hm9zl3|fc_00bddaf16c51a121016a217236e0a8819195d754c7a7aea652", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport urllib.parse, subprocess\nbase='http://100.116.176.16:8091'\nfor q in ['', '*', 'a']:\n url=f\"{base}/api/search?q={urllib.parse.quote(q)}&scope=global&scope=project&limit=3\"\n print('URL',url)\n r=subprocess.run(['curl','-fsSL',url],capture_output=True,text=True)\n print('code',r.returncode)\n print(r.stdout[:400])\n print(r.stderr)\nPY" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 542, "output": 184, "cacheRead": 5632, "cacheWrite": 0, "totalTokens": 6358, "cost": { "input": 0.001355, "output": 0.00276, "cacheRead": 0.001408, "cacheWrite": 0, "total": 0.005523 } }, "stopReason": "toolUse", "timestamp": 1780576818570, "responseId": "resp_00bddaf16c51a121016a217232ad208191a7e9144183464e03" } }, { "type": "message", "id": "e6fc86b9", "parentId": "5c1519bf", "timestamp": "2026-06-04T12:40:26.955Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Exploring memory retrieval**\n\nI'm considering how to retrieve all memories effectively. It seems like I need to set a query with a limit, maybe around 1000? Pagination could be essential here, depending on how large the dataset is. I should inspect the structure to see how I can count them accurately, which might help in understanding the overall number of memories I can work with. Let's figure this out step by step!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a217237d9d08191b6c8ba781ceb3095\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXI6OcEcL99DYCfDtcBg9H56Sn4fcg9klBhXZMOERJ0zVMA-1rlCkDqTvPL5JGVLna0eSymI_mzRn5B6rZhNncqoMnv3VQodgrlgwJC716ii-G9DNCOWs3RmxfiyueD4qx6FcOcC_iQBm-cA9zetudYO5n93fFY3vludW_RiUZIHb1_IPGET5Qdp_m-2Me1tyRWBJn7y6i1EoVHA-5xHeHpuTBqBHda4681q0ukgFqyVlOLcAhy8boVIoyQoNnB0t0v03HyfXWtWlZX6BI82R0U18lQe5W7sEP-jnrF6q7fBRdkPBEU6Pe1UeSpMCtBsyacRk_j3-Be3--39eZRTrDeRIMyP2ip1RQd3xc4abB9Dfr_NnHmNlQ42R_9oAzJQyFs6A5Xby_B7CfMsZWOgQBODXll6anzqORCa8sFBxMoByWBwKaFvwIOdzstBhGtTX7iwujOTBtAvALMhud25g2DYOzO-gs2trJ_lIkOIqNundKes7OoaZik9eMsqMYGdphuy77bAEjsh9eWu157VtEmvlLrn_JUfen1p1d76a3_ae_1AtcQKzn30oDGIWKTfj2dn4Q6sjm-7On1WO3-nY4Y5u5KyLg-ThjmWvgJapTTCMSokA8AGQFVj-9QTM9e-w_D5ewOWqJaVeljMqRcUT9cGOzxA0ip3GvbkVJh9N98XQm33Hqdug-6l2Vuoh07uDIXzDuM98MLNo32hJwjZGozfpn_QQNAXdesa4l7FQ4z2qQCdZvYoGJlYfNDiZwOG4IgFttyeessQOu79_FFKxyhNfVua6VNglI3ELMO2jeEc_C3hwNvvvYxEZjXeA_QhVFfpmnxpwwaFPk2SHjvIILukfV5sv4Kh0Lcd97miixRqGPgUu8gJwoK8hi0pSBaRnWFBpB_qYRva9KgeM1Ab5lrpVq7xBwEvVlz3uZeUGhGPhtDwbP22MXSUn6iTJ2XZWaBis8JXvt_fhgAIo-Mu37R7c7lROqOnLUY34vLqU_XNr06leooiAKC2gjtKKwa0QBEWCLb-Dh55UG2vvgg95ZD-aFE_Cuwa-L9xRkCkjbvQUGfn0bQO5BEsiYj7YaKDml_T\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Exploring memory retrieval**\\n\\nI'm considering how to retrieve all memories effectively. It seems like I need to set a query with a limit, maybe around 1000? Pagination could be essential here, depending on how large the dataset is. I should inspect the structure to see how I can count them accurately, which might help in understanding the overall number of memories I can work with. Let's figure this out step by step!\"}]}" }, { "type": "toolCall", "id": "call_0ogiXcoeNitTqLgMqjEAXvhB|fc_00bddaf16c51a121016a21723a30688191968b9e7192545d7d", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport requests, json\nurl='http://100.116.176.16:8091/api/search?q=*&scope=global&scope=project&limit=500'\nr=requests.get(url,timeout=30)\nprint(r.status_code)\ndata=r.json()\nprint(data.keys())\nprint('results',len(data.get('results',[])))\nprint('first keys',data['results'][0].keys())\nprint('memory keys',data['results'][0]['memory'].keys())\nPY", "timeout": 60 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 1117, "output": 170, "cacheRead": 5632, "cacheWrite": 0, "totalTokens": 6919, "cost": { "input": 0.0027925000000000003, "output": 0.00255, "cacheRead": 0.001408, "cacheWrite": 0, "total": 0.0067504999999999996 } }, "stopReason": "toolUse", "timestamp": 1780576823305, "responseId": "resp_00bddaf16c51a121016a21723765fc8191b60d607a153269a8" } }, { "type": "message", "id": "cc965a89", "parentId": "520d7941", "timestamp": "2026-06-04T12:40:36.291Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21723d4cf88191b79da0b1d40779f2\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJAOOQtRITwJ9dSkJdgSufIQ21PxPm3i7-WOjkJc8wvEdTdai_phOfL30KjS45cgiuPIcT-9qjm4fT5d5hAIWR3ucaWgt-uoD8qv4cudRPpklQNT3YPi1EDUSYMPKJismg-cxIy6j_piSDI4MCOprTaNhVqFmTzERvAjx_3Ns17FYBo9bUi9GZ85WqQriFB9QzSSO3Vdt4aA0a0L-x_KjO7u_-0ERKg1rCxQ7Cs3b3nrkbzda-GKDlbNOo9ugN0iwesgiEgBsBoTiECGjy5bXO28sk4XiyLIQjputIHDFzbU16akkd4opyjyN33LiLSUf-5Xom-rTWVp3TFIT1a4BaGY7gTI0kOJ4-hGfBMAaUfTPoJ5PZuhFBx9hMK2U70TkGJJPOCvdLdHSQTP7_LF7ZpIF2awHisSp_AnbrI1IKB2-tCr2mMOBkj8CoX70j5NLB84zPyuOBLVVzakZCAb4Sb1vKoa1JklB5uLU6GaSxDw5Gu5LmfQq2hiagVcjBmSub_Oe6AsqR3UEGfyiM_x8TZUDyuwGdoNffxQJ4r17XDxzhZh_uStoLRqFFsCWxS9LLKYq917XRutmFljOeOI19wqpRo3pCRMcDBzBQDyCtd-SwF7imPuRuF9gMBr8twXOW88jfAUTsI4nP4ln8vvxFK036cS2AEWIDIDrIYy1hnf0ikSiHYOGissQa13WH2MDrErzTmzZjUtsKo6i-ouEHR_MzgRqrgJ1FAg6TedLkAJrzP2x9K2LD_bkj2ckGcWTR01UZUmT95FbDPWDvAMFecw-f7R0Ryr1Sy-UB7OvX5y_HbnSihX3Q9Z2Il2tMcuW5DqznJz8CdSB2eDr8qHqJtHVj9tvW5mp5XNy9W3zsG_zb3KHh4O8Pe6woUWV23FKTxL2B4upAEfUwR1xVmcSym8_Mq_xJ84EICsIVupUD_M69oU4f7vdmTBGwIzixZLXkm_HNnb3TT2a1nU-nDZBOlt2ZtjxY3pj3_G6CQm8W1GWfHQZgN-JxGU9urx7T0ITe-sjz8RiLqT8IA8mny7evGiAS94_g-1EdZ_T73HGlVa6-nifoIet0onxIdtI5Ox94iJ_KW5KFNaEcAZh8BOd3UCJZYyQDn9WoSpFBvHPRCVKYvlzmfG8afHZMWHThPSuabJ9Dgya9l2HKg2mhGUh4yncnPkZcHnzP2fxoEOQtfBci2XYGbLmg4biRZIMzp9xQhQ5u48gJEKe8V1SfLhYcCvf_x2SQri4ULsYDA3Kb6j_A_zMhJj1oL4K84H6Q4_ICS5uJfVtG53-PhbD44k2gOGVzxcoL6JfIChOm2yMDPGMX9ekdg-JCu089V1r78tltr0l4BIZANrAAb7UAspTuEa7cDNqyLh1vuuqNtB9CrVSQXl2iWBRaFGX6UQhkyo60JVel3ZICyZLiVlTSn82bAK1PzEVXqaXGpSn8j4n3-Xon5LRdg9BPpAAPRAfy9Hv9CmWAg2_AZZ-IfK4lSgktrs0jmWW5XLYrRlcHekstYy7Ze6BLvWsMXwMgZSt0f7GWDrC6a6mQOq8ewZQ48lD1AV1wwTG7RRRm4SbQGnLSCk4vUXZR_7D1hWe7skC2SvRw8dxKh7T9DPQ9ZLrP_jfTRcd8yZ7lBUvDcxYBGV7W8If8LXZ-ZVPFgZX3S77L-TyScGxgji35eourSRMEfkMfD_njqM7tKDIW0K-oi5a6GDVyrcsqUxZucgbSNblOFZ27i6mXY1LpzZZE7r4Dkn2pddTEYBRKggVwC2V_DYIeKpXIrBkNkwzRJU4gcKrjQ1X4fXATTyz1ToFLv7fVajHZF4h8Vd7de8d6ToGstYDBTD8fXO9PjJlLJHnHpJkUKWlCuLeFVsKnuqXxwxpZZMxmGoGCHBjnlTyKaOdXLgEkFr03xgYD23MgfnQ9r7XVOfU-oixw2EPYUVR0OimYrPs4ZeQLC-Sf3T3obGBcZ5DydEUg=\",\"summary\":[]}" }, { "type": "toolCall", "id": "call_kBfZxc47cLGKaMXoPLVjbHBx|fc_00bddaf16c51a121016a217240e1f48191b416470b373a21f8", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport requests,string\nbase='http://100.116.176.16:8091/api/search?scope=global&scope=project&limit=200&q='\nqueries=['*']+list('etaoinshrdlucmfwypvbgkjqxz')\nseen={}\nfor q in queries:\n url=base+requests.utils.quote(q,safe='')\n r=requests.get(url,timeout=30)\n if r.status_code!=200:\n print('bad',q,r.status_code)\n continue\n data=r.json()['results']\n for item in data:\n seen[item['memory']['id']]=item['memory']\n print(q,len(data),len(seen))\nprint('total unique',len(seen))\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 418, "output": 369, "cacheRead": 6656, "cacheWrite": 0, "totalTokens": 7443, "cost": { "input": 0.0010450000000000001, "output": 0.005535, "cacheRead": 0.001664, "cacheWrite": 0, "total": 0.008244 } }, "stopReason": "toolUse", "timestamp": 1780576827241, "responseId": "resp_00bddaf16c51a121016a21723cba4c8191a1b6287c39caade8" } }, { "type": "message", "id": "3b92c3e1", "parentId": "f717aa0f", "timestamp": "2026-06-04T12:40:44.269Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering memory organization**\n\nI might need to gather more memories, possibly using 2-letter combos, but that could be overkill. It feels like I should do a dry run, likely around 302 unique entries, focusing on active ones. I'll fetch a unique set through multiple queries and classify them based on a new heuristic. It's important to understand the specifics of the heuristic from AGENTS. I want to stick with fewer high-signal types and avoid trivia or secrets while analyzing memory titles and content. Finally, I plan to export the dataset.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a217246b0ac81918d7cc50efc3323cc\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJKrsn0URUxouSaB5GsstKtbTgNmAQeW7Tyqet6CFqNvDv5oT3BYDkix-OeIw-2r1rjAOYABoi7uOZHVJCcsq6diZd7wQs3zUljLRp7GiJY0nXGtSiIM4MX1LiwNKHfxzB9b3kzLHrEOFtNVxh0Dhz9XWSgnd-Fq_2YhrbcqxU7K0lVMLh0fnBA3HJwAkrqpc3pFEukwUBberY5FuofTcC3_oXFhlZsboY6j7EsadoYycvsFHHT3w6GeetIAMQin1rZyhPOpExO7hoRXatLFWBKUF-mkFCzQ-Z4Awlq734G1URMxlfn1Ot-8aKEWoIvWaJev7fRMTviNxgBF4_oMf9XIhijPBoBCff6t-_tPcEh4wOucR7kU-LSbfLNEh4yuGpRT8sd5e2aDlbEWwePZ74u6xQ4nD7ECb12o1YWUpEJE2EUyGDCpXgKZHQjEmG3bATlmkiRJW1BuDMS1t0XpVxuN9LUSEp71IzHOxDwTFr4Vf0l2NSmyNWcwXHUUEqJfSIn-6kO88Ck3Epch7Awbjp3RZppm4lvceu5m24wJv2kvqrju3spGWhh-p79mR_0BN6NUFqw4wN2ZiTgVe2lXeNrmEz7Ix4M6Q-opfMso6UBsjFTBqgZ-PUocQA5Q_3oJJYQhCtvagTC_VBxg3-rYhA9Vuw0dK44IVKnvVtFY30jUFIZjXfVj6yjAf1QZPei49l2dQXsUQ5xwW_UxiXOjmwHPRED_fgWl7fSdyXE6yw_S22INp9WUBwIoK7ugBR-JxPQduQYPMwykUhM2WzfdfKwXcsFcJ7DG7sQHz_Ej17K8-lmPjBa5uVR0NZN81mArEDqIXI0Jgam80F7F_cgONnf5lHJX8wb3MD8SoKRBSbxkSjhiNoHc-MaPle6iYfupBxyDN9Pe_Xu1ZySL2mVj4DIF4Hip40PfZU2N4HyQjCnBLH9XszrezSGQWTWBeNf97uV7riJhQsrdLwd6JgLd1jr8_zLRKCLx3c7vD5emOMTVKqeV9Bmh8jd_6V358ALRYrAYpOkkE4iM65j8AMh88IsfM_n4WwJNerQBvH4GDkJN-9Aea1S3Atouc-27nzdYdIXmIigKs4ONOJWllWc3fg3glBatdv7n0yRaMtZD-e8EfYkdL7_9GY3PMMBFDNSAa0rfzDu_tasN__hpa0uS6tIlG7vuUloDaImEv-kcIuvj4AR2435eQFmjIWLJvdY2_SiXYGxRFhghrjeK0pYhGygwgxNJJH9TUi2pSRDs0bVS50t82HOEvqLlYFxiKLyYtjClz8exaZ7KoNF8MG1XfzM0w0Tp9Kb7gKp7Nv4ZzEot3MhQ2r6nUbPQNdszOjr6zxyWMW7DYeUGSwZ_cL-tkGKZve5eFiZFhYulrbH-_oHKYsmTaFnuaMy1d-EwnovEYxWxvEEvCOpZ09q5TTI0uGDqCq1cIQ4sEfxaSE2GSrlDsmFSsKwwOat6mw1Yy5TtrO0\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering memory organization**\\n\\nI might need to gather more memories, possibly using 2-letter combos, but that could be overkill. It feels like I should do a dry run, likely around 302 unique entries, focusing on active ones. I'll fetch a unique set through multiple queries and classify them based on a new heuristic. It's important to understand the specifics of the heuristic from AGENTS. I want to stick with fewer high-signal types and avoid trivia or secrets while analyzing memory titles and content. Finally, I plan to export the dataset.\"}]}" }, { "type": "toolCall", "id": "call_XMolcfMy9NWX9tF83FVN2Ro7|fc_00bddaf16c51a121016a21724a633c8191b5c582d42f2e899d", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport requests, json, itertools, string, os\nqueries=['*']+list('etaoinshrdlucmfwypvbgkjqxz0123456789_-')\nseen={}\nfor q in queries:\n url='http://100.116.176.16:8091/api/search?scope=global&scope=project&limit=200&q='+requests.utils.quote(q,safe='')\n r=requests.get(url,timeout=30)\n if r.status_code!=200:\n continue\n for item in r.json()['results']:\n m=item['memory']\n seen[m['id']]=m\npath='/tmp/memory_dryrun_all.json'\nwith open(path,'w') as f:\n json.dump(list(seen.values()),f,indent=2)\nprint(path, len(seen))\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 965, "output": 306, "cacheRead": 6656, "cacheWrite": 0, "totalTokens": 7927, "cost": { "input": 0.0024125, "output": 0.00459, "cacheRead": 0.001664, "cacheWrite": 0, "total": 0.0086665 } }, "stopReason": "toolUse", "timestamp": 1780576837999, "responseId": "resp_00bddaf16c51a121016a217246177c8191b69819cd81867170" } }, { "type": "message", "id": "8e6fc781", "parentId": "af2c4858", "timestamp": "2026-06-04T12:40:50.006Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21724f174c8191aa32f29089ff24b4\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJPdIFrNmpbBnVxDiA-nMLGCRdADrwNQShcQsJf_3C2Gfa-x_jBXuh9vsIkW9ZGPdu4AkHeegafokKPdqnE4AlzcOl8E5E355FDZI2btwANx8YqgQSXw3wpEFZQYAk_NdrKnnPPwPO7jP20Nm_4AoOTWNFnu7aoNqpDrgvLfQ-2J4bV1QPWxmJ_XozHH6NBPsYmO8yhOITAYKz_nEFbVr7J6ozT0zibAO_kmh9tL5K7ndgp6hjblr8dRlyavIBpT5_eRHjiUeZGlLT9jXwUOqPkMCBLV4ErZBjS1Fu0CJzcLYei8vhdZsVpqZhyLJwHJd4UlaomvSg5dKyhbGRqb5_fbugLm9FFpfO6uXQeilYrIShANoqLuowmcopnOeCBVcUDX-wUML4sAfX7Byq0AW5jTb2l3bR4_T5jMBeXb2e9Fkk168dSDd5LDETXI9B2WO83OwNVFPmoiifmrMh4bKgLCr7uhg427QiRKlkSvkgOUByDaXDBwjc7Vf6vUpN5gz3hF6O9iDVQ8GBq4io-LslKozvuHqdQrS9FRndofWVrLO1GOil4yRTqv-xeMjWVJj1nVubYIzvMFUEQexOdwrVSAxVWqy7pJSOXi25-GpxkrVWqdGNtFsmc8wvVYTv2KzGbhVUOg5P8Nb8PzJIq21YQsoaXOpMJHclmT0ABQ5gK651Y3KhGnWziejfxP5JIsikVTAgIflUzwWeENU0WP2CSVQsr8rstcdccBDCzrWdQ3YPnFiS-RmOvJ_7EOwgsm2gmOeKOg6j7-6HQsrm5cumRXf-mADY9NOOUG2hOK2QnFmp7RvWWo6VbHhdHaPMQfGzBeAQzAl3HEg4EqVfC4kwv62by_EPrKV4ueubPqX3pVlKWvCgDAuxyFaFt4eMSawflLILkCOqy9jjjZszo5s89jw2rkPSrmn1RNfdljf_86ZiZgAhQxpVett7vwD9nL0mr\",\"summary\":[]}" }, { "type": "toolCall", "id": "call_VorqUYJVxeZtfFhN9NAOLFBE|fc_00bddaf16c51a121016a21724fd4048191b7a05a29b18a3eab", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json, collections\nmems=json.load(open('/tmp/memory_dryrun_all.json'))\nprint('count',len(mems))\nprint('types',collections.Counter(m['type'] for m in mems).most_common())\nprint('scopes',collections.Counter(m['scope'] for m in mems).most_common())\nprint('status',collections.Counter(m['status'] for m in mems).most_common())\nprint('source_kind',collections.Counter(m['source_kind'] for m in mems).most_common(10))\nPY" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 781, "output": 158, "cacheRead": 7168, "cacheWrite": 0, "totalTokens": 8107, "cost": { "input": 0.0019525000000000002, "output": 0.00237, "cacheRead": 0.001792, "cacheWrite": 0, "total": 0.0061145 } }, "stopReason": "toolUse", "timestamp": 1780576846488, "responseId": "resp_00bddaf16c51a121016a21724e93a88191800bf0a2a5fef245" } }, { "type": "message", "id": "d1996648", "parentId": "c95d9903", "timestamp": "2026-06-04T12:40:54.068Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering duplicates and clustering**\n\nI think I need to identify duplicates or clusters among items. To do this, using heuristics based on title similarity seems like a good approach. A concise report would be helpful, I guess. I could utilize an LLM via a subagent or consider Python clustering methods. It might be useful to read through the list first, particularly inspecting the top entries along with their IDs and titles for better clarity.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a217252c6d08191a7d6f742c4e7ea3b\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJVnhYKgF781Va5LCrWGH7ma0Z0xRYHLYc20HgVttu1Or9ITxWhnzrYvHCCowH9XdKSypeUqgBx7me6onZ2XdGrpRs4OBWjZtAIms4XrOujuXZzwEOqvogktjqNP6_-_dguKYX8-WjOrBuenLkucqb4njVK28hQUXcKjRWXxfu0fczTWgc7Zjppw7PlXFX9goT99y91cQq9I2YVoAkqFwWzOqZftuOmhztaV5etgiyW2KM5WDL7DDziIJ2iFkzYh8aap-DGgCtqb0JQ1AeUdXhlI1XapmbL2GjzTQJQnOMGWh0zBFRPzHSIfSUiq0Is-wx3H8uLpxIJf04STRE_9w7sZ7pdGmAEaF768aeO4RT6XwvzdhesalCOppbU_yYDI_vhXwMfcCDTvVL37cjqmSQ-J6XM0as7G6prU9vqpGQnPZseIpiO4MlbDGDtufVTRYzYsgIXvp8DbYUQQfeq7SGsNaMyHqwDDmi43VBRO_EOqlXoBeB3DJIXDw9vSTFTNwxlYBP6OAH9eFZxLZ9LhL3RuVoBhPZzNc1E2UXdzVRLXnfAvMUSTg3VBpjeffqmb86iq1ousUPIq43-fb1j3OrYzk8D6rxzebPeC-79twKFxakYDhPCF5Jc5f09vrITenxDGdh4DaZQqfp7h8XKAftXYjU1m3gAP6ouhWCMzKJ7djx8HyRAjUV35F62GsJPfufhC7YxFyEcU8UTwxHSCo_-BiFtxghEjRDicbYbfZuOVsdy8qN4sl4REDXy7VhsLtJqQoAQwaBEqbGalbSvOhPwZKmzRiRjZxKqgI6WFmvNQ-jE41uDEeD9LY3ZvwI_ojiTAJBZSDDQ3OLxsxGBX86OjMMM6oFc0ze4eVnrz2nEh-wKQxS8BwZ3p488-lPf9KAJCaWmRP7j8NUGkZ1R-tbxWUbWZ0J7fmguFQuIfJSwoTSBb7Lq2e34XahxGkrzuRioVdNmpPxUI3Zltkn9F3RKHwFCh9Y9_7JF3SvvF8WG3jShUYhk-itnXFeEsOURgelZS19a60oAUWpWRIGkCXjE782flZv5J_GhqsQ3zKXlfOWRUq-zzMIejXZJ8RcA2tk32TMNA4JGIhjiScDjsPFz7ceT2KQQn0jW-uECpSsgUHN2mAJqpoq3LfqnT6gsGptE9CNFK_JKEQntQljKCPt1Gy7QKtxdd0YKIHlU64xa_IIDTkQJpKo0mMm7WYIcmD8Jb5TWhmWxCF52FPF0rbEUHg==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering duplicates and clustering**\\n\\nI think I need to identify duplicates or clusters among items. To do this, using heuristics based on title similarity seems like a good approach. A concise report would be helpful, I guess. I could utilize an LLM via a subagent or consider Python clustering methods. It might be useful to read through the list first, particularly inspecting the top entries along with their IDs and titles for better clarity.\"}]}" }, { "type": "toolCall", "id": "call_SYS1GVdgnkDyRsbmbWjjXUrP|fc_00bddaf16c51a121016a217255c7bc81919ead8a821da5df70", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json\nmems=json.load(open('/tmp/memory_dryrun_all.json'))\nfor m in sorted(mems,key=lambda x:(x['type'],x['updated_at'] or ''))[:120]:\n print(f\"{m['id']}\\t{m['type']}\\t{m['scope']}\\t{m['title']}\")\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 534, "output": 163, "cacheRead": 7680, "cacheWrite": 0, "totalTokens": 8377, "cost": { "input": 0.001335, "output": 0.002445, "cacheRead": 0.0019199999999999998, "cacheWrite": 0, "total": 0.0057 } }, "stopReason": "toolUse", "timestamp": 1780576850063, "responseId": "resp_00bddaf16c51a121016a217252275c81918d42ba2f16a922a2" } }, { "type": "message", "id": "79f17663", "parentId": "6ae99c20", "timestamp": "2026-06-04T12:40:58.177Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering analysis needs**\n\nI’m thinking I need to improve my analysis, particularly by identifying any low-quality drops that are obvious. It might help to have more lines, maybe a look at different profiles, projects, or tasks. I could create a report file to organize my findings. Plus, I should inspect the profile titles to see if there’s anything that stands out or requires more attention. Let’s see how best to move forward with that!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a2172571d948191a274dc8b7d14c13a\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJZ-3ZYwHNpX5PqHDE3b0wdrVs0gldNTi7JX-JluwEL_bjwGNldzAfUI4bxigiVm4UZETJvZGSQeN5jolU28g04FoFIjZi9ivjUg-KobsgtEVnhjpmtisWFcLreyY_fjKA83ZEGM4V4zL9TPEochUxKJQyL_cWnWMeElmK0-xmQBJNZVeAVvvebDRQJ_z29LUF5ssEmlr2b4cdwOOXvxTBBMovC27l4sf6CZgS-d2ZokR40se1LguCgTtuJc0r3bDLHLRZ-Kw97orV-_sJf0D7DO0amDpB6LnlBnTO4h9S8fmD1n6Gx0baGeqIfNxXCZ9wKFVnpeyVNBOW9d3UpULhOlUiaxeLIopMXsdCc5EO3fRHYn5TieKdhDHJiqs9qEj52WFDB1FasI-70zwRwIIsOyb_uwoG1QTwDbFen2GHhznOJwpjf9WcJqtYy0xT_0NW6zA6PMYZ38_DQWbRVzF5TE1RXmAxB-yy1Ah2xEtESnd6ywZvJk_jhmt9iZMp4yOz-GILkdqvxQYIeA9jKpLfJv5FWE_iWsbIeaPzLXjZIwNzfjt8eXHK1Vk41EVRiK4lybgA6sFBBc83keVlRUG8iCOAFgecmWnFbZr73W-iJAoQimewv7mI5L0rU8fowTOVpgTwmmh4ProSJTHyR6B0NPm3iASfEPVCyjfPqOQcOdJlVg5nP2ACSuke7hz1_L4eRjrCCjIVxlQDaihNDTkgY91rJIBJJ2xPLQ11r24_IVQPFVLXrEqyujkTniZMyCB5C82EqW2OGpKMB9DlpaK7ZxLRltviQ95lvdg8t70t5r_9r5dyTFp80cmlwx-tM8WwA8VWfu3MuUORxCqjj6QYPkC94iUcTKL535q0woo-pvMbJwbk2m3jOx8HyDShPIEi9jTZAlqLofLx_JJEKRc8rSu-TigZyLiLg6AF-n_NHSxOfAdLg_a9Ul2rS89cKhCx8J-R6G9JjvokIbZ2EYsf0OIRL89G35-iwU22jNsT4vT3mFxLfH3Zc17ETqBv0lyVatezAwf19_nat_QN4gRZwwRvIYEZ8KGF5TwJ_PBkjqe7SpaaNOw1vkc7hFTtBX9AvPiGYKCx8Vm2tzMuWhiXue1EapYRTwwbCv1E77Ut8FmGGk0_1Z6-tiZ3owhuj2mHQ\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering analysis needs**\\n\\nI’m thinking I need to improve my analysis, particularly by identifying any low-quality drops that are obvious. It might help to have more lines, maybe a look at different profiles, projects, or tasks. I could create a report file to organize my findings. Plus, I should inspect the profile titles to see if there’s anything that stands out or requires more attention. Let’s see how best to move forward with that!\"}]}" }, { "type": "toolCall", "id": "call_RLQugXyhYiEA15TgrxxM5Yi6|fc_00bddaf16c51a121016a217259d4a48191ad91d11d5a001ee4", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json\nmems=json.load(open('/tmp/memory_dryrun_all.json'))\nfor typ in ['profile','project','task_hint','procedural','task']:\n print('\\nTYPE',typ)\n for m in sorted([x for x in mems if x['type']==typ], key=lambda x:x['title']):\n print(f\"{m['id']}\\t{m['scope']}\\t{m['title']}\")\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 5695, "output": 166, "cacheRead": 7680, "cacheWrite": 0, "totalTokens": 13541, "cost": { "input": 0.014237500000000002, "output": 0.00249, "cacheRead": 0.0019199999999999998, "cacheWrite": 0, "total": 0.018647500000000004 } }, "stopReason": "toolUse", "timestamp": 1780576854161, "responseId": "resp_00bddaf16c51a121016a2172566adc81919f78acd64bd8b381" } }, { "type": "message", "id": "cb6ffc4b", "parentId": "6dc50846", "timestamp": "2026-06-04T12:41:05.345Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21725ae1d081918b4fbd2cecf24527\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJdq4KMSqU_eralgbk0-2CY4N8zh48pkUUrczxp2oR0YXtET_awlrDoOZevnAjnn5WJxNAURF_LnloWQzagKBM17YWbtp6Hzb6OKOFf84PG0NQ9aEqZFRCdonmLYT0-WX7PtLVK7EIcbdUpGeG0RpKXQevBoLKkNzTjp4VxbC3GResZY4zbDc1chfEtMT8I-lW_xqHN3uyyVuzo6znlJlOTJNetMkYi5Su-j4ieC7T9CrskuLIdcBX2ukAK5ksr-p3nFkteeONIoxtoIb1-ebYZmt7qcuUsP5Pgz-Q7Fx016RMgNGEKX4Bk4e7QAbQS4jebcjMvw0s0jmCm6blqtmkaaouDEHUa5N58WqL30okvU5YU-0TnFrSb6kBiiO7IBTirkETDf_Q6YrN7ozbHNMYzmwir9Dk6TQhNoYF126b-hX1HOuTSv2Lwqc82JPqmRRQkriOKXveqJ-Yk8SwUx2-vUsm-xRPAarrjejQwmDYEwbEuytuO4CIyYs9zVuNXEF1zbFzCQWpgezs85IvhUyYB1VFNPsx5yXI37J5MnfacKlHW0Zu9Tz_YiWVdnbXVER2-FjY-3dsG9u1PNk74mOSD77EmniCX4WbgWGQ_QlEYjzDzQnV7x5P-Nkmwf5kIDUfBzvrbjtO6rdT8UXvf4FDJlA1-qYyDUNARz5XH0PcciXMU-LSEW3tfldbqZwwqELhTs1Dos-VwlLHWULVXlTJs3R092fabCa2tYmEl8Dx2tiGNq4dwKzs6BgRqjfkIMSszNEfH8NxuXW51wRRJIuitPupzDCuOjfIasc8XXtzQrK5vYUBMX96eOBkJy8vL0lJGSJ0LK2z73lHbO4XAgGsN04gnIl3f_NWJ4U9pLKYDqjcIxSKHgbFN8UhFCcFFDtSnvsLzT-MA3vnj1UOd1rYGNuRwHrEZEvgvZ6Ffo2VrQsJXMKBuFLBuJ0Tsu59i3K4yCYXrAkaQDFuSPwQGA3UV_M6KIQeKpbzHtD2CliKw8LymViMco7hNNVfGGzkKush_nb0LrPLsVoa-IbRrXlfW7A6nZEE71UIfs5LsrBFouiOY26Vhh2leJoF0Zswxmtz-IIYC6Oyc8w9gyJqf0usvoUPDsaso38yAzBDfePxOipMOCUT6LviPkNrpw6171u0P-EPtUtiC9B1ekX6ZqS9vzBfvDLL9Op_PO9Mew9pv36nEMNCBFFWz3Mo0kJHPXSU4cCfbssdmLiQKrcG6pgUPdWYRgxACCnBiO_tA7zxX1srqZ9glXuBLBBvbSnw71X9AEbGZjZyeb5H6G3BYikmnDN1EuDD5jcbi-j9K--XqgXk8iu-LPv1S_cWn0ZMscdEVA7Gfz5n9iO2s5b5rIfjkToZyiXziW6-SuDg0YyEZHw_4hySSBytmHkBFrajdloIG1a3B8-WUbQvtijACPaYJS9WTSFuMBPwiZOX_dLfMXbHZ-WCQMgJ5mXl2h7XlRjYRDeMYCXf4vBmtOQw4LoPsR0V1X2EPc_hPWQBr72rDyMx2qVypXNJsKhK4QbC0e1MpHsQfMsTWP65t0NEdfMEATyaB5zpVwPxPeK5lJ8C-Ek1SV680jBBPn8U1dXqK48Jp8qTEElmn2lHfcqSBrnrM2AEZvvnK90X5h9U6KYJ3tN2cWpdcctEi9OqJPTAswykLtT59R1CzhXcLVe2sANJWoBUzINt8BryKlzXwf0FI4wsVFeqJ0M2gfFsHeYP4RDrSuZIbra08xftQjgjzbo9xCaMfO5-womuR7XCLtH0aDsGQJYqZK-gGsq2M3oC4nnNF1DgGRpzJYChyMrzKJP-sxd8M0dA1M0BMzg28VILvnmOB0O5df8iWdKmPbgi5B6uV\",\"summary\":[]}" }, { "type": "toolCall", "id": "call_RoCw8CKIGJzon7ntGhKDczPm|fc_00bddaf16c51a121016a21725dd9848191908a46ffacc15303", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json, collections, re\nmems=json.load(open('/tmp/memory_dryrun_all.json'))\nclasses=collections.Counter()\nfor m in mems:\n id=m['id']\n if id.startswith('legacy_task_'): classes['legacy_task']+=1\n elif id.startswith('legacy_run_'): classes['legacy_run']+=1\n elif id.startswith('legacy_source_'): classes['legacy_source']+=1\n elif id.startswith('legacy_claim_'): classes['legacy_claim']+=1\n elif m['title'].startswith('sebas:'): classes['raw_episode']+=1\n else: classes['normal']+=1\nprint(classes)\nPY" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 4669, "output": 307, "cacheRead": 12800, "cacheWrite": 0, "totalTokens": 17776, "cost": { "input": 0.0116725, "output": 0.004605, "cacheRead": 0.0031999999999999997, "cacheWrite": 0, "total": 0.019477500000000002 } }, "stopReason": "toolUse", "timestamp": 1780576858271, "responseId": "resp_00bddaf16c51a121016a21725a5c7c81919fb9bcf90557d0fc" } }, { "type": "message", "id": "0f14ad8d", "parentId": "379bfad3", "timestamp": "2026-06-04T12:41:08.936Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering episode titles**\n\nI think I need to take a closer look at the raw episode titles to find ones that could be recommended for dropping. There might be some that don’t quite resonate or fit the overall theme. It feels important to ensure my recommendations are well-informed and thoughtful. Let's explore this more deeply, examining the titles to identify any potential candidates for removal that may not be serving their purpose effectively.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21726208c8819180563926e31908ee\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJjZmHeg-l8Dp5cnrDRE83uQoIaVR0SgL6NuXPIYuiHwq7Bt_e2WfOPc-SegclOUMD-x-tFg0uUY0NS32JEp9x3OA5XIeWca5ZFhbaYsgPTkKnPI4AzfgToU51AAybwFtaDsdD4STfb6HSyoVhHtUuda6XxSc106sDaytMWlY1dG2SVzJSZuhJUdUen1N3xjHX9FIvHsVhhqthlktB6ssu7GSqX2zOaC0UcLFBvwNGpkEMIXVSMc6402I_XHrqN1ilQZUIfdoxsqAhS12Ep8-9bjYcjiRaFXl1kzblcdrFWt14V9ehw0RNWdZB6btIIflwVdHw_dHZZxBvQ_m3VF3lT4wy3kDHcJVLypGk6CO7nUUrnMbMiKCq-4g268p88_uoB4GBEj44gLk4ztehau3-Hai48mXuRM6b_MBQUhFxytUr2doedVQhZRoqNId08RFaO-Aop8Sfzvn3EH27VWlfkG1qSFBx3l3db2mVRkcjqO2w3PO3bqPUSpqKuIx2vDoCizp3PKI9PDtDBfVM-YHflq-7765OhcEcjiPbSuPTzOgsCdI14SuRwMDs-eXGqBle9CBgdi6iX4Qpz3Jd2qY8Jbf-XXgraQs3LBxcVyKL0kQYQThxOtlhD8SmoX2tO7e9Jia_6sC4fNLIWqhOH4NZ1P3LG9teCdOjYs8WU8HcsAux9OBy6ld4KnKrJh-tvIjjZHcjnOJZq0LeWdyo2JWhVc2u5301GMOhgJWieduBa9bdg2zSUZivENU0DC84P2jM1zGFhAq2lQuTNgB_hu6w6swHx7ZoQpQrZrF84Mxf8JzWNdI51HyXPbLOVqi7Y7g9q5P-d40g4f0ncZSo3ByugZzf-9pbqzPrgURIUIkLYVYwQFzxsKLpg9g6rZ6JP-EBtEuhaWpKb_y9L4_Ys5XbU8BqyouzB4w3DNk1ggj4sSic4lPrt5acBuPPkUYdbpKM9Sz0Rw1M-y4ZmIuKpiJyC3xvaKWDO09jklmTWnAvJUUY=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering episode titles**\\n\\nI think I need to take a closer look at the raw episode titles to find ones that could be recommended for dropping. There might be some that don’t quite resonate or fit the overall theme. It feels important to ensure my recommendations are well-informed and thoughtful. Let's explore this more deeply, examining the titles to identify any potential candidates for removal that may not be serving their purpose effectively.\"}]}" }, { "type": "toolCall", "id": "call_Auoh45yITG0oo1no0QePApet|fc_00bddaf16c51a121016a217263fea081919ca7628d8aee1ce7", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json\nmems=json.load(open('/tmp/memory_dryrun_all.json'))\nfor m in mems:\n if m['title'].startswith('sebas:') or m['title'].startswith('Research run:') or m['title'].startswith('Legacy task:'):\n continue\nfor m in sorted([x for x in mems if x['title'].startswith('sebas:')], key=lambda x:x['updated_at'] or ''):\n print(m['id'], '\\t', m['title'])\nPY" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 933, "output": 157, "cacheRead": 16896, "cacheWrite": 0, "totalTokens": 17986, "cost": { "input": 0.0023325000000000004, "output": 0.002355, "cacheRead": 0.0042239999999999995, "cacheWrite": 0, "total": 0.0089115 } }, "stopReason": "toolUse", "timestamp": 1780576865391, "responseId": "resp_00bddaf16c51a121016a21726179f081918e63215194621dde" } }, { "type": "message", "id": "d15c54b6", "parentId": "f42507b0", "timestamp": "2026-06-04T12:41:17.041Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Classifying topics for review**\n\nI’m thinking about classifying by topic since the dry run seems to show clear buckets. It might be useful to produce counts for categories like keep, merge, rewrite, drop, and needs human input. I should probably include actual examples too. I wonder if I could programmatically identify duplicates based on title similarity, though it sounds like it might require some manual effort. I should inspect the contents to find good merge candidates!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a217265a55081919c0101e4c5e47e70\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJoJ1AjS1E4aOcTtYlmr99chRbYYoNVUxJdod2LqiPL609dbY96rYv-z2s5TREcO1b39D2XyZMwXUAS-fmgiKFEI3yo8zzEgj4019Q8jBzxq9E2DOSPICBFsgZwIhVd5a8aIvpLFZCgq9ndM6a1zz7-th3jFdm9EHKG-DqkWVuFN_rNG_hqLQbH_zZE4GAqm3r_QYAiOMtojC5aThyZQgen-A-dVMlXLNP3TYJ8HuZq6QzmYtxBPx23I7_a2XIuChb8odj8h3Xcso009gLdTcd96mWvIoISO6wxrYpwtOm1FAGKuZBx9RwSvt99mOXtiB7LhnHohSws1rP62EaHbu3LdiSyb8oawVtOF-0a5F1N7y3ifQ8znq8X25JsaI5CM8kzgq9Cuy0MK7lsmydr9xlo4DDVaza4FI68hBoQAqAsgKeHRxxLIptJl5IZ3QGI713K6Z4LIWyvI7VV2F4XlRUjRCzJI_VsAzkourV9eaABISogH7ZafohI78qN05KoMssX8HtCKiTXItKM8hDfnkczhQ37MaMRdTR8d7tTpK35lGDiLhwpgVJhjJUmpTV78HLfR1vlpzXs715lzauAJFF4i6NWvKCRfNJGzl6HjRcd6DoJNpPaMH5xo40SlRC9lS5E4F74I2Qxb5dX1ut-y2llXDmWauwxAXelddYHjhmmWQhVSK3wHfbzXbjvmBv3452pHihsNYc9V3bwy4MnMIHrL3liauFsyhPhuHNwrwEDFQSPF8h3aSln_tYZJYJxTlvJGhqLWv59NLRqWTE58CCfGYBWXsPXA3zVld7bTJTVgN6a3uDbXEbEzim_1heVhBO3p8-eejzO-xINYYVo8TsthTBRYg1EKk2bZ0PWs2i2azT8LdU0_ZROIcW-gK4GWPNnVUhZsZqZdaTr7RLwX5Hvyi5QRcLQi3MH3APtEhTF7gVScOhdtKfb1i0i46HvIDT384j-d4Qfeu5l5LzsBZOOIt_zQppLX_fLFRG1eXonAa-6yqz1zNxmeIwukr0lIOWFyimFtepblLzgAI_aU9lKzYnKX3HYP3f0vUkTsoFY_kuo-q3T5B9kfHX7cNDKCuXflD_gfEgk3CL2KJ8kpmzBSRXIbqjpRSSiG8X0ZS0c7vyBq4tSLLU3MfV4kKJMApG8xaC4J34ji4vzOsLgshRcUhHuFlrDKWMQaeGX-TShzt4D8amLrukZa_LeAExBbVKdk0tUzPnFAzZ9rn80HJm_karGMeHfhD5VUQ9bY4MbPtWC1Z_IOehSD93dO035D0A6QBFUujnNMipNVt_rs8kj9DZDCOV3DDikLSjdrzhEBgE32NZmp5JJZ3rDmSi6B6Wi\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Classifying topics for review**\\n\\nI’m thinking about classifying by topic since the dry run seems to show clear buckets. It might be useful to produce counts for categories like keep, merge, rewrite, drop, and needs human input. I should probably include actual examples too. I wonder if I could programmatically identify duplicates based on title similarity, though it sounds like it might require some manual effort. I should inspect the contents to find good merge candidates!\"}]}" }, { "type": "toolCall", "id": "call_gBJSmG6j9tp0DmeXtxEKRz8n|fc_00bddaf16c51a121016a2172688fb08191a4e9a2d39f742410", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json\nids=['mem_c24eb94053944d0d8847c565a2a6f65a','mem_bb53229e06914ac4a5aa51fee72455b3','mem_cba4a83f17824b16ac1d2d1949e72c48','mem_cdac83f3c3004eaca99546346d0c90d0','mem_57f96c24431c4d38b5123f37e66bdae8','mem_ce0fd7c72bfd4127b4d5ae206b42da09','mem_812ea22ea06d4368bdb66fe531e64fdc','mem_4c13c21c3c514b669055da643e90049a','mem_c8c7e1c8af7d4665aafe58baca242345','mem_dcaa730515ae48b39eda5dcc5e38edd0','mem_08b4efed55144d78af56bdd15821b61d']\nmems={m['id']:m for m in json.load(open('/tmp/memory_dryrun_all.json'))}\nfor i in ids:\n m=mems[i]\n print('\\nID',i,'\\nTITLE',m['title'],'\\nTYPE',m['type'],'\\nCONTENT',m['content'][:500])\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 1803, "output": 406, "cacheRead": 17408, "cacheWrite": 0, "totalTokens": 19617, "cost": { "input": 0.0045075, "output": 0.00609, "cacheRead": 0.004352, "cacheWrite": 0, "total": 0.0149495 } }, "stopReason": "toolUse", "timestamp": 1780576869001, "responseId": "resp_00bddaf16c51a121016a21726516988191ba3ec4b067aadbd2" } }, { "type": "message", "id": "343c6406", "parentId": "935e1124", "timestamp": "2026-06-04T12:41:24.597Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Identifying search limitations**\n\nI need to address a retrieval blind spot. The API search doesn’t have a canonical list, and I used a wide wildcard sweep. While I have a near-complete result at 304, I can’t guarantee it’s 100%. It’s crucial to note this limitation as it's important for understanding the reliability of the data. So, let’s make sure to create a concise summary that acknowledges this aspect.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21726dc9808191aba7c2d6ebbfc0e1\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXJ08JU0AoxnihMWCYtRrPvizGuz05AEc-4aMwdSxeJWETnpZSFWJuyw6Of_egIYyOTH-GrHo6chYASRawAk-CYLxiXyyfmXvFmbD0ls35VdrApe1aEjd_2rKcNoQ7swwaU4eUpFaGGDzrS3_iFS_JTwUqYbbXVi37fVZr9BYXyQr4Ndf4u6uzvWzBUkJVeTnviK22cm_SJ38LG_PlBb1ZrSQPsOWQBlcKfvcoPQw5hJMbuTHTo6_s2yY8jiWbeLBS5ii-7_39s5anqJ_97R_M1uKR1_AHzgkm3tW5sSV4pcAAQp9b7vFOAZi468-d9pCSr_QhiHy5ZhPjlJwMCmfBUK68UcaOZlNounQgK2ebQJ5pCPtx36kMBvaFXB41e2XudVkd5-QBj9niirdTNLuscBoE8iUMttyddyGLsxOERMfDjgW8CWvoz_JusWaGsqxtZat0hwkEaXD2C5zIiZFVF935D1fMc9PoHmKFsL3gfup8gp5rjaiYOodSmW7yM_j_TCzf3z0aBvleeFMH_qvIyezSVOMTlUyCvp1cz85AMQcOFNsSCdtfPAIMaufCDMHlm45bHuKOrEC9ETqVHPJNLWRLGhtaiogxuFNgF2XiLr3wYgXVg-MCV8n9rTLrItj0t-wOHSqaSbBvGFbhOrgC2VRhpIdXdG657BVUg5dPiAr9n3sWFevWDf9XBNVwHNMchDHQjMz4J4QyIVHCud8FuX2q6FW7JHyPnbzAJACK3jiPaz-v1M1mcfMDLrlGAGLnTTMp8xxO_n-jQd83nNIZLn3fC6sgo_aKv1TOwE5PpETCRvimrlg0KUtaqcfg4NqahKvpIZyjPkZZ1j6W7Saun_oQZmd_75HePZ4P29s4iD-xzcKJ_vdrof_hqHHhGWgxjlpoEQOc2KM3vSchq3pGCCw7tpfl7tyPFAi0qi69BIr0_v6sRjQVELcj6HPV1FPi_6rhqOc13g3HuXFv7k4W4VFE0jSj5Xkrd2pwmAvGnXKeiJqMs1RfZ_fNxyhWOejGRUyw2qcTcGymaLrG2yHU1UT7S7d0ah4b4-CbYZhrk1qlak_MKhG5tSiLmXQGGVxdcYqOXxOZr0_HSjhtMpyIo_PnoNhft5D1dbHglSkMvdnAZkvnddGe3RBM2z1FlX1ydgAiL_8hWj3EISLBBFHoSPiq_LGAejcNHoD2UogZjai2Q77AsfgJfiR2MUwfov6jJtAA2DuU9gS__e1aZB_BOTI8PKXAu4eHNgStOq9POtpT4Skrwtuof_d88gQOEMT-U9AgC0IJcntZMVTMxpQ7pqjYvCIyqR8TmGo1vkrLSgDKb6DZ5hletpqEhPJR9SRiYLPOsn8ft2fiqvZvkfVOVLCEvStRhETla-rigFwYriWfL8GfiBr4cs0pGJFUydUIJPI_4R3UnT8O9KN6NtGJZ2WfRBvzuy-2q0RqdwHYnssv3uEN0Fc8EzfTT4AhufUtgNOpW3wvKsg3PNw4UjkANRJqp6Uob9MJpBY6tUY_80YcuuW9EeyVUP9k69b6QdGZnjl56poiVptAbmMVE_HqcqXGjda9idfLpKgJg2x2vJe5vNazL5fzvBncHrMA1Y2z2fZzg1O8YwWkM23Wy0r_GB9Gu7B8ZgSpGrKpXiBIijA9UclCWURh6Pe4UrPhAj2AmpfHV87ShE6xIk-yYxXdAwkvsKJ0pXqPYhet3pFW4-Ju9IU-CxbFC4ztgicZhGyVi8tyl_8275Wfd4BgnwhQ7fw3zLdIgMOHuqUggf3eRJ41akNphs_80w6LmxmGbi5xpvRaQEaOB0k_y_uFLgLfpNf0SM5tsKqe9pgBymh5Q1dx0WkpKwZ4H7ewh4mbHsGJpGknhPSCfxzHyb8FluqUuMjJubleB6K-OhOR120RIUqLLqYlI77pDEIq3-H7fimKQHujOrI1P8mxvDhpJh0RIF5P5PXAsL6ctJH5HuA-7G6HVFmnJyXRN0kuwu_7_uE-yJT5zZ8vXYpb2HfoxOA4yvYfWRXsplDBsL8NQKDrqKAQ7HBL8egd9FN2l2ymWpxSJpshmmDjlDSMdQIO3zWEtASoT1ZleMdFnOoBuT4bafh6QDAGc502w8xU-9bO_--KSLi8KN0m3hlm88mAzloQZlZ5ESRddbTFpL1Gi79CyXe2aR2-SiLGLRynjDTuvjpgjWn8xIcY2ZAI7HRV66dYfhIpkEtsId13CdmsJ4ra800-5lw3mqqby_dhYRFrhetxOQhnw4kAcWZi-2jbB8ryHA-Vp9cWnkmr8U576C2j2mssBcQc8Xinvbui_R7mJ9A_8l9cv_grIvuIf603ORq9KCs06ee5pksRH27k69px1_JlXD2aaKHaL57or6AUoIa9opDPFa_coLywcGHqsu0IX9xHq6tVVh9NG-KuYFMgpoX0iSHvkU9NpO0r41wVaHuwAnBKWTuelCLql6au4Je0q6rN_m48ABFrwlyWTUj69eevxHqVyqxKl_e81bLShJSx84\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Identifying search limitations**\\n\\nI need to address a retrieval blind spot. The API search doesn’t have a canonical list, and I used a wide wildcard sweep. While I have a near-complete result at 304, I can’t guarantee it’s 100%. It’s crucial to note this limitation as it's important for understanding the reliability of the data. So, let’s make sure to create a concise summary that acknowledges this aspect.\"}]}" }, { "type": "toolCall", "id": "call_XKKiLyEjm1SaNuXLCErc0x0D|fc_00bddaf16c51a121016a21727473f48191bf228582fff362b1", "name": "todo", "arguments": { "action": "update", "id": 1, "status": "completed" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 1659, "output": 290, "cacheRead": 18944, "cacheWrite": 0, "totalTokens": 20893, "cost": { "input": 0.0041475, "output": 0.00435, "cacheRead": 0.004736, "cacheWrite": 0, "total": 0.013233499999999999 } }, "stopReason": "toolUse", "timestamp": 1780576877104, "responseId": "resp_00bddaf16c51a121016a21726d3acc8191aa0e9ccd7bf65f8a" } }, { "type": "message", "id": "b4c6f653", "parentId": "1ceb6d43", "timestamp": "2026-06-04T12:41:44.905Z", "message": { "role": "assistant", "content": [ { "type": "text", "text": "Sí. Hecho. Dry run only.\nNo escribí nada.\n\nLeí memorias con barrido amplio de search.\nResultado: **304 memorias** encontradas.\nNota: como el API expone `search` y no un `list all`,\nesto es **casi completo pero no matemáticamente\ngarantizado**.\n\n### Veredicto\nLa base está **sobrefragmentada**.\nCon la heurística nueva conviene:\n- conservar memorias durables de alto valor\n- fusionar duplicados/correcciones\n- podar ruido de sesión\n- colapsar research legacy en paquetes\n\n### Corte propuesto\n- **keep:** ~110\n- **merge/rewrite:** ~60\n- **drop:** ~134\n\n### Buckets claros\n\n#### 1. Drop casi seguro\n- **83 `legacy_task_*`**\n - muy granulares\n - son historial de runs viejos, no memoria durable\n- **14 `legacy_run_*`**\n - salvo que quieras archivo histórico explícito\n- **31 episodios crudos tipo**\n `sebas: [telegram] ...`\n - ruido de sesión\n - poca reutilidad futura\n- algunos episodios operativos menores\n tipo “alo”, “pudiste?”, etc.\n\n#### 2. Merge / rewrite casi seguro\n- **22 `legacy_claim_*`**\n- **22 `legacy_source_*`**\n\nEso hoy está muy atomizado.\nMejor rehacerlos como pocos `artifact`:\n- un artifact por research topic/run\n- con claims confirmados + sources clave\n- no una memoria por source/claim\n\n#### 3. Keep con limpieza mínima\nGran parte de:\n- `decision`\n- `project`\n- `profile`\n- algunos `artifact`\n- pocos `procedural`\n\nEstos sí ya parecen alineados con la heurística.\n\n---\n\n## Duplicados / conflictos detectados\n\n### Merge claros\n1. **User priorities for agent improvement**\n - `mem_c24eb...`\n - `mem_bb532...`\n - dejar solo la versión corregida\n\n2. **Ambiguous term resolution**\n - `mem_cba4...`\n - `mem_cdac...`\n - fusionar en una sola regla general\n\n3. **Laptop access**\n - `mem_57f9...`\n - `mem_ce0f...`\n - una sola memoria: preferir laptop helpers,\n sin web auth, sin pedir permiso extra\n para acceso no destructivo\n\n### Conflicto real\n4. **SSH timeout**\n - `mem_812e...` = timeout 4h\n - `mem_4c13...` = timeout disabled\n - `mem_c8c7...` episodio de remoción\n - `mem_b161...` decisión 4h\n\nAcá hay contradicción temporal.\nLa consolidación debería dejar:\n- **1 memoria vigente**\n- quizá **1 episodio** si querés historial\n- no ambas políticas activas\n\n### Solapamiento fuerte\n5. **Disk/root storage**\n - varias memorias sobre root casi lleno,\n mover caches a `/mnt/rpi`,\n home-first layout, etc.\n - conviene agrupar en:\n - layout durable actual\n - episodio puntual de space recovery\n\n---\n\n## Heurística aplicada en el dry run\n\n### Keep si:\n- preferencia estable del usuario\n- decisión vigente\n- hecho durable del proyecto/máquina\n- artifact reusable\n- procedural reusable\n\n### Drop si:\n- mensaje crudo de sesión\n- task transient\n- research scratchpad muy granular\n- episodio sin valor futuro claro\n- duplicado corregido por otro\n\n### Merge si:\n- misma política en dos memorias\n- topic único repartido en muchas\n- contradicción por versión temporal\n- source/claim split artificial\n\n---\n\n## Qué haría en la consolidación real\n\n### A. Borrar/podar\n- todos los `legacy_task_*`\n- casi todos los `legacy_run_*`\n- casi todos los episodios `sebas: ...`\n\n### B. Reescribir en bundles\n- payment integration legacy research\n- display sourcing research\n- fullscreen overlay research\n- Trading Mastery / Humand public-reference research\n\n### C. Unificar políticas vivas\n- ambiguous-term lookup\n- laptop access\n- SSH timeout actual\n- memory-service behavior\n- storage/layout facts\n\n---\n\n## Riesgo principal\nSi querés preservar trazabilidad histórica fina,\npodar `legacy_run/source/claim/task` reduce audit trail.\nPero para **memoria operativa** nueva,\nla consolidación mejora mucho señal/ruido.\n\n## Recomendación\nHacer consolidación real en dos fases:\n1. **safe pass**\n - merge duplicados\n - drop raw noise\n - leave legacy research mostly intact but flagged\n2. **legacy collapse pass**\n - convertir claims/sources/runs en bundles temáticos\n\nSi querés, siguiente paso:\nte preparo **plan exacto de consolidación**\ncon lista por memoria:\n- `keep`\n- `merge_into`\n- `drop`\n- `rewrite`\n\nY ahí recién, si aprobás, se ejecuta.", "textSignature": "{\"v\":1,\"id\":\"msg_00bddaf16c51a121016a21727550d08191b59364f492d2c14a\",\"phase\":\"final_answer\"}" } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 431, "output": 1085, "cacheRead": 20480, "cacheWrite": 0, "totalTokens": 21996, "cost": { "input": 0.0010775000000000001, "output": 0.016275, "cacheRead": 0.0051199999999999996, "cacheWrite": 0, "total": 0.0224725 } }, "stopReason": "stop", "timestamp": 1780576884598, "responseId": "resp_00bddaf16c51a121016a217274bb4c8191955ec52e749c1878" } }, { "type": "message", "id": "ce9695f3", "parentId": "b4c6f653", "timestamp": "2026-06-04T12:54:32.152Z", "message": { "role": "user", "content": [ { "type": "text", "text": "[telegram] todo bien\n4. a ssh le saque todo timeout\n5. problema medio solucionado (reintalamls sistema en microsd mas grande)\n\nejecuta" } ], "timestamp": 1780577672151 } }, { "type": "message", "id": "c629b2e7", "parentId": "ce9695f3", "timestamp": "2026-06-04T12:54:37.550Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering memory consolidation**\n\nI need to execute the real consolidation since the user approved and clarified two facts. I should write relevant memories, possibly using the API directly. I might need to check my memory-write abilities and consider updating my to-do list. It’s cross my mind to save durable memory during our conversation if it's relevant. I wonder if consolidation in the database is possible, but I don't have any information about a delete API. I might need to check the API status or documentation.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a2175897ce881918867287f4c66618d\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXWNhZuNPTh13cHzTFWdYQnpFXIpzM6Y9qHTVcrNpKAT7gLfS-YXUahJAaS-bJm-dyfSn9TaiJc_YXJrK1HXGr9kvqZAvKL7Hc9dTUqjuC5IR3dP33_rvSu-e_KDjJeci8ZbxCzhopgLsZcFfEcDGfU0AAYgulVyD9M2SiJBAF6STp7rE_nH7DDMtD_-kqu3AxnRulenQqPrIW-Tya6mSqRbs21GwbpS_xX09KMk9xXTto3jwQEoCeOjqaB0GAXraELDCzl9ivCBoJY9ehglUbAA9UOPcdnHomnmE2iupBPJrteBsjWigZXywELVXi3Ol6ieSj6oB-f8z6q17ez1nJYll4L2MJ3iccRvw6oXiITiQdgH3NJ6b_9HEIatQ4Z9A1UZH4Xo0tzEsgZgDphHiqZ8GfHAg8qzBASfjaEVS6uWpZ6u-fugd8ABYSkmJ0zvWISbuxei0m7NsNuiAVtmpY0zt0mtgCIhc4d3NZwd5JvMIf0IPgI86THox-VBcYpI0Jj_lrId8gEHByvwHaS5_hGdTqmNvNNISSoZIoHP00mE5npatbm-7oJtQao9G2t2MwO8GKI8mR3Hec0bYFD7EA_RnJ6HGwiX1f8BvnmOKjYyxPCRPcN_3GAN5aRGXelye-P1A4yZmCVmqoPGjKe4H2vZZaTIeBp2ytWOox4MJBDaIxKpeFaG9tYVmRNNpw6ostqvPbTMDGkm8fVBDaK9vp6c4YmEg9d1zctwkF_Kopz3wikpdNSMQ1wONAU6KQmgjCrTy835daUFR5pBsiJVR4DcK4tUHltWezvPRgyX_Qll6OZv0mdlPw1qv4Lq87tPm8zpZWhuHEpANFuXeYWVrRoFjjp6qlVHfolVT2GRb5HabeipybidYCWlgzsUDb4irdH-sZ1TRlkpofKkn2E0x0eYi-GvPmnxBHVz0oBJLpCu1YzYN0ZzIl3V8r5Lg8SlQB170G1weNXswsbXUH7DJgHA26wdXayApmfJQILNdW_nbzC4JeEGVvPOrHkPzKfQP5WqqQ-KbSifZacLAif9MWNXHK_U8u259jBy3e-XRWh9EqAmYC2CezenpkkfLyz6uZ1CCxlxxd5UYZYn8lRZVuZ2AB_Gu0l0k21b2nTnWOI_qQOnkuzVqyXcUlo0OeplkqTaTvLgeB6FjypDVdcLmMqV7IcUbTpNPVAzvxCz6QL8Zc8ClZv_nvUd6KTNpT_zUrhRv4iyqFYDQB3WVhLH1MhB_tstC5iHH1Q2tT_EjuJsQis_1yemMLD47hiecs8WnHqF-zIa9VMNv_GmctaXNr2QtsJYfNpeobPo2h3j5IPKqcCP1CVysId91H_vaJPDTQwyuCxO27S5TzldxFUfDoV_4tQ7aOGPaG-FMwgvag_Jxaoh5FNyqOFqHK4o4Kzfkhav9APoYvztE-MIKpO4Ox-4BTmMApEKKXJNXXFagRBl_jXjxio73jcTWm5NbV22fCA8ZH-Thh7IFeGqWEiJcerCTMy_Siocst4q7OoyoosupZ0=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering memory consolidation**\\n\\nI need to execute the real consolidation since the user approved and clarified two facts. I should write relevant memories, possibly using the API directly. I might need to check my memory-write abilities and consider updating my to-do list. It’s cross my mind to save durable memory during our conversation if it's relevant. I wonder if consolidation in the database is possible, but I don't have any information about a delete API. I might need to check the API status or documentation.\"}]}" }, { "type": "toolCall", "id": "call_dhZg2Tl1ATFbekvD8wUFCrXi|fc_00bddaf16c51a121016a21758d41948191a521aaf0df22b1f3", "name": "todo", "arguments": { "action": "create", "subject": "Execute memory consolidation across all memories", "description": "Apply approved consolidation plan to all memories using current heuristic, incorporating user clarifications that SSH timeout is fully removed and storage issue was mostly solved by reinstalling to a larger microSD.", "activeForm": "consolidating memories", "status": "in_progress" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 15452, "output": 167, "cacheRead": 5632, "cacheWrite": 0, "totalTokens": 21251, "cost": { "input": 0.038630000000000005, "output": 0.002505, "cacheRead": 0.001408, "cacheWrite": 0, "total": 0.042543000000000004 } }, "stopReason": "toolUse", "timestamp": 1780577672154, "responseId": "resp_00bddaf16c51a121016a217588bc9c8191aa71e8356bc59f7a" } }, { "type": "message", "id": "c379da6d", "parentId": "1ec845bb", "timestamp": "2026-06-04T12:54:39.343Z", "message": { "role": "assistant", "content": [ { "type": "toolCall", "id": "call_xaH04zte7hXwXtLeEk5zMuVH|fc_00bddaf16c51a121016a21758ed2008191ac04d1ad89c588c4", "name": "read", "arguments": { "path": "/home/sebas/.agents/skills/memory-write/SKILL.md" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 282, "output": 31, "cacheRead": 20992, "cacheWrite": 0, "totalTokens": 21305, "cost": { "input": 0.000705, "output": 0.000465, "cacheRead": 0.005248, "cacheWrite": 0, "total": 0.006418 } }, "stopReason": "toolUse", "timestamp": 1780577677554, "responseId": "resp_00bddaf16c51a121016a21758daa0481919e62af0a9d194378" } }, { "type": "message", "id": "ddbf1956", "parentId": "9aa981c0", "timestamp": "2026-06-04T12:54:43.028Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21759020dc8191805a2b4a159622e7\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXWSdf0Yck_3T4t5M1wLLeEZtoSbCn65iCn_88VwBzTDwYuKHQG1L5rH7CNQQ7KsmqCvIZrXTEn4lpcaq7vmwWf9lbLGjAaBQioeM-0mBEV39yduwdhaksHU8AJgvlUwMixKPM2YHiclCIttNGJVufr_5NWBsGDIEGcHQzLWjVcR4A76e8F_vj9UWOmGbOALPydTQU1j6EqTdxfkGsv-yLLttLsLxNijT4g4tUctnFpfVOx3LF9_TLKcJXBuF24jCeqTz0poG-W0qtVGdpbCPMf-C_wRzrXdTu_kx9kUdy098mfp5iuVY4DSPeQ-O6ltgWAg6pwG1EHhB3tiljnA3QUXi6z5_8o1LmY87IOAgQ3e4qoXj0asxMHLl73E88gwZFopXqyOGuxQy6EyTBkAPC-7Z-pFca0UCB_DpouGhcLecuPiiaFdQwgr7BsBqngy4gWeJ-msJ5rhoUct8QWM1ix1UVqABhwp8dw8hRteH6IppBMrhM1E9u8U2eLyFJ9t7Z_N0AFMyIfSEj36ZEy9NkRzbSS-ToMIa6L6Sms51IUog6Eyp9MOfv6yQspR7yBzoiW60dPUOuMFG35XfNbGZbJgvP2BwpyDVwzjgHzAflxey8UDS8LxWxPL8oRFF-V18e_zknlBXFIWHtKqMwS9SzMJeJhR49a5bK4ynhS_2mZrqWpcYVMrIQ6MC5aFm1-yvzFxZw3kKEf2PFayp0Ruq8NYkDYdJ6bewgcL6McnMHrZ1yE5rNNfmVKN6oDGdXxCLujmmxcurSYQaVmEhxFha7IXdIjX451YzMAl_n9IhZG0fgmULo-_GxKOGX4XKkRoA2JshpTCoKwJ946ADyzAx_bavSbdXL5sX1CGF1w2JLnpp1UTvBcAGAfPGSJi4nSZ_Phk6OmaiWFOp8Dy97KjXHZ7qnaL6lxZ_CDbX_saWz5937Yp5l4QVQbwsbTafudpGoev8K-pSx4sWIEGiL-ikGF1kNOvcKfHLV_9BOXx0EDTQH-V8fsBhXEZ8dQueBa1vV982ha-13rHq-CS9B92CALJLg2Z-9oc00h_LfakG0U1LcOMfEV6is6D1m3grJsQ7PVW\",\"summary\":[]}" }, { "type": "toolCall", "id": "call_DQljHARsl9J8NhinCR7lbeMZ|fc_00bddaf16c51a121016a21759268448191b93a6580dbfbb473", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport requests, json\nfor path in ['/api/status','/openapi.json','/docs','/api/search?q=ssh&limit=1']:\n url='http://100.116.176.16:8091'+path\n r=requests.get(url,timeout=20)\n print('\\nPATH',path,'status',r.status_code,'ct',r.headers.get('content-type'))\n print(r.text[:500])\nPY", "timeout": 60 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 925, "output": 155, "cacheRead": 20992, "cacheWrite": 0, "totalTokens": 22072, "cost": { "input": 0.0023125000000000003, "output": 0.0023250000000000002, "cacheRead": 0.005248, "cacheWrite": 0, "total": 0.009885500000000002 } }, "stopReason": "toolUse", "timestamp": 1780577679345, "responseId": "resp_00bddaf16c51a121016a21758f81e0819183f654147b244b89" } }, { "type": "message", "id": "86331a40", "parentId": "310c1a5f", "timestamp": "2026-06-04T12:54:45.904Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Inspecting API endpoints**\n\nI need to look into the helper script specifically for the API endpoints. It seems important to ensure everything is functioning correctly. I'm curious if there might be any issues lurking there or areas that need improvement. I'll pay attention to the structure and functionality to make sure it aligns with what we're aiming for. Finding and fixing any potential problems could help streamline our process!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a217594062c81919fd06a29a16162f2\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXWVg-obXzIJI4OL1PQQjIBWSQRlo3vxq6eOc4AO3HCqHWXqdM-fS4_mxC0Qk3Zyctoti4bCgvpX_oyOEMbTO6Cd3KWlo6yEOr8KWnFDoCtOafh5oMCD9FUecV8qrQl_a9gcr79P7Y7lUT4Ap9glz7FEj1NmOZV6XcppcXuWGJs29Ckyh67WkCT80_HGdeUl-j55G26dB7NZtWkO0aLzHq1sII-DbWrjkHYSun6jL80WLWPPewtZ32KXn5qocowCjpxyyG50U8iG5irassRCRJkjcsbgW5rpNvSd51eSKtkBWCSuEk-H2xYOjvpv9IWTGLXV7fbGVKSs64PUelGnWaO8TfMuzce6R2Phv47E093mRrndmNZoFhf2Ih1G7MSg3EwF0_NhXiHvMJ4ZabupiPreG4JHh9QcoXNq3yZl3m1ysCQ37OUac--eOpWs33Nqy8ujr96ywZCbuhzUwcGl31I0JSvQqpYkqpMFeKx4VHgyOBziY6sXB3HOZYymRtW8DrN5uqFrLOGGWIwwnmkfUrZTi7TxTcoc324GSYIywvCTEJ2KqK4rEIICi1sEsoryPdeXNa4UxXEqndKGuXR3iTisJIA4PZr50TwtfqUGhwp-qDrjhZ6x1XN5bWiyrjr1sMXDtGfEYpxryNGP5X1N0ZAsB7saAuSJpRPbIcwxlo31jJLscoj57rBBoOt7KEI1_2-lGrU9vGUmQYTltcdE2OswuCBve6WZ1Tt_Uy-vzjc_YSK8ihMzBvBFTlgy9AChEcCxyjCHT-20RE_2wZxwO5sG15uCdS5x0MCy8t-Nd9VSnhGLIGvb8GEvsqt1FOrNandyVD-3oal-f1YPL8OztJvu1bXgR5awDtQaZyV3D8a_2azPffGdbIfF55qRVm5tksFKAc4o7D3j0lEzQzsc-gfFqOG1i8Hn6BYpoFlH3w_VH5QfeOXEqbRdKQxiLeEjC6R1mYsl7UblyLdWNvaDFqXEijy0s7m6BYBUKYdlEXKfwLA=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting API endpoints**\\n\\nI need to look into the helper script specifically for the API endpoints. It seems important to ensure everything is functioning correctly. I'm curious if there might be any issues lurking there or areas that need improvement. I'll pay attention to the structure and functionality to make sure it aligns with what we're aiming for. Finding and fixing any potential problems could help streamline our process!\"}]}" }, { "type": "toolCall", "id": "call_JwzidViW5UkbweCxFk3Mepvo|fc_00bddaf16c51a121016a217595bb348191bb4903e3731fc55f", "name": "read", "arguments": { "path": "/home/sebas/.agents/skills/memory-write/memory-write" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 1121, "output": 45, "cacheRead": 21504, "cacheWrite": 0, "totalTokens": 22670, "cost": { "input": 0.0028025000000000003, "output": 0.000675, "cacheRead": 0.005376, "cacheWrite": 0, "total": 0.0088535 } }, "stopReason": "toolUse", "timestamp": 1780577683257, "responseId": "resp_00bddaf16c51a121016a21759356448191823a3d1f8c15b714" } }, { "type": "message", "id": "0bb1ebd3", "parentId": "4cdcea2b", "timestamp": "2026-06-04T12:54:52.067Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Exploring data updates**\n\nI’m thinking about how to handle updates with an ID. I need to check if using the same ID might overwrite existing data. It seems there might not be a delete option, but perhaps marking the status as \"deleted\" while keeping the same ID could work. I’m considering testing this on a scratch setup to see if I can create temporary memory and change the status. Let's see how that goes!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a217596b32c8191abf8789693086fa4\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXWZRTa9NzllNwr2dZ4SioSBBhzpuB8ng_Vj9hLJdrARu8LXtTUV9wyLrjsMqI1dULuN_Vz2yrjw4WwExP6JrKO09zxmntZnLAydSGitF16bIUZ7kz7aYMGfHlQwQRTYRl9gB_MxFXP0FVfev2DA0gE2083h9fJqx2RLtNMhE1e5jBSa-2PGeoJD7uwgj0yYzfGEt5e526FXU02Zyl43FF8VswrYAhQqCtmyNTbMJ-H4vEqB92JtrlqTV2DRkYEuPAz6URW2PGnLf_kRfTPoiTNrROMJ-xGoKsbXWT7ktMK8QjgEi_wFaq9_EvMF_84JfJ6fXjCZQXjZBS3oRgLdmYSPuVGb216nVlwWAVqdZrNqVC8g6eVlXZKcOW9DwwoP1EyB99Ta_cpmN5dPhYW9bPc7SZOHCDd20a86sEgEc5T2JZPM9Lqvba66uZ40auZCfS2A_Gztzqmlzj4b-I1RhCdXUXzxeqACBMDV3wNy1t_l4o8BLsvj4v2WMmBkCD4wBxx5EkqS4g4E8NdYOo4FdJslVtYgZEjQK3oT6iM5bdUTupCChK7y2_eJ4oQaKcJgolVSZvwOJT-AIARKLKReNeTRk-AZ0arSh7hC_aH3_wjDcjV3rs_MSDjC7wDggOiqEDE34aERw2k0527eR_8rM8TU5Jil67kH-7uUjBjaQMdc6Te88H3cZsE8pO70I4HTozb02-1_V_s7Ja1HRsh5quhtd1hbghfN57UI10NhatodPXQf74HXgzI41UrHcA-0mvVPgiRT-GRcnhL3MNqGcZoC9luUvCcZFBVxXRWgZkSOJMhYSjFWMuwOziMoyzcgVK4lKHuawFiCJopx5S1lDOxzzCUV4aTWsFJqyAYD_LDLa70zOYcA_tACZ4AZ56dzK5fibKgZ93HiWAJgO3YtA41UYmf6eaIWZDGMrIKtINK_eqEOV-70aKAOiDi7dhv0BB4aM0qUJQHwV59PhDtfqq52GUx3Tp12cGKqvPuFpp0oNfyi1HeY9IwYr5cOGxEXSF4EVZNX_K-5aGYBnOqfNbmcdkl0F93zOMfSweTZZh3s8gu9aFsDTYTswIImhyOYfN1BTCO0eSGPdcYtCT9gG89zBQQtlWR2bu0xlO9cPFaXDf4HLJ3JT2XVwB-6ejydnNkK73HMmh2n7Im5bXFQ0LzEBwsxBe2KORX-JgYPAuTnC2WoPFxflpASy7C3v_WqjwjtgBsqoKP0RyYwQjOz7B4u0g==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Exploring data updates**\\n\\nI’m thinking about how to handle updates with an ID. I need to check if using the same ID might overwrite existing data. It seems there might not be a delete option, but perhaps marking the status as \\\"deleted\\\" while keeping the same ID could work. I’m considering testing this on a scratch setup to see if I can create temporary memory and change the status. Let's see how that goes!\"}]}" }, { "type": "toolCall", "id": "call_8Bl8tZdyrYf9nNFCAEVCpzBf|fc_00bddaf16c51a121016a21759976088191b2c9973c17875b8b", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json, subprocess, uuid, requests\nmid='test_'+uuid.uuid4().hex[:8]\ncmd=['/home/sebas/.agents/skills/memory-write/memory-write','--id',mid,'--type','episode','--scope','global','--title','tmp test','--content','one']\nprint(subprocess.check_output(cmd,text=True))\nprint(requests.get('http://100.116.176.16:8091/api/search?q=tmp%20test&limit=5').text)\ncmd=['/home/sebas/.agents/skills/memory-write/memory-write','--id',mid,'--type','episode','--scope','global','--status','deleted','--title','tmp test','--content','one']\nprint(subprocess.check_output(cmd,text=True))\nprint(requests.get('http://100.116.176.16:8091/api/search?q=tmp%20test&limit=5').text)\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 775, "output": 290, "cacheRead": 22528, "cacheWrite": 0, "totalTokens": 23593, "cost": { "input": 0.0019375000000000002, "output": 0.00435, "cacheRead": 0.005632, "cacheWrite": 0, "total": 0.0119195 } }, "stopReason": "toolUse", "timestamp": 1780577685907, "responseId": "resp_00bddaf16c51a121016a2175960fb4819184616609f3e673b9" } }, { "type": "message", "id": "37b259e9", "parentId": "74d88dbc", "timestamp": "2026-06-04T12:55:34.741Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering memory updates**\n\nI’m thinking about cleaning up some test data that needs to be deleted. For sure, I can exclude any deleted memories from my search. It seems like I might want to create some consolidated memories too. The user mentioned execution, so I’m looking to make real changes, but not overwhelm with too many deletions — maybe all 304 is too broad. I'll aim for clear cases, update duplicates, and possibly preserve some artifacts as well. User approved this plan!\n\n**Creating consolidated artifacts**\n\nI’m focusing on SSH and disk facts, considering the need for consolidated artifacts related to legacy research topics. Even if I'm deleting all sources and claims, preserving themes could be valuable. I’m thinking about summarizing the research into four different artifacts around payment integration, display sourcing, fullscreen overlay, and public reference. I might also consolidate storage by updating the current state of memories and marking conflicting ones as deleted while creating new project memories. I'll use a script to help with this process!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a21759d34488191a0a59fc9b8434d31\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXWlMe7UhIXeMWay7F6X7xNPSy_rlvrFainZ3TmYtnMd7Q-oerh2mqt6SKWqxc1sd8l0Va7v475Y_cuaUCQHgJe3rzVIQLB26NXhCIzFAi9dWp_Cv-m9QCkqOBk6LJPUlCOFliwKDVbKBcdP-nBnZ8zJmzWk6-zImKeLoDj9D7SIqRt5sP0SZNZGQ4p1tXsKEg5dYzbWr4uZo-1RbXQH8QcAWYphMJdmBVFrp09eYzmZeiJjO_n5gRzMsWgawYclPQDW99GVU_3GY-lLcNVW-aIosLy-JG7kkDJ3b-6SDUJ7puMcuOqSe4VmM-IqKmPKvOPzqfddJ-AThbHjWDPCwq8j4yEMRdR1j3KPQgCmlR47ODfDFQmBE6fwqrKPU7RI5J7drPedcSHLPMr8dpW3sR9wq2SlgfESUGrEDJ989RGddcApxbbq4U0I1ApGIMw4x9jwE9Uy1KbcbE7QHq4Bkx8B0LJ6O-rc7JreoEuurSZdZHzOboER7EzmRUirEvRwtQeauJBIMiL_D8iJb78FkaFcSzrQ1wd649tjbXlRV5Mai8QC_FXfjuzOZRvnrQ2c9njJ768Nh0MKheUZ74_XWRpYP0jdYS5mPRW4plI5HMpaSOEDZbcsXHOXodqvjDz38YCNHf9i5gZR8I3HOtcKtGIKGT6sDVrLuhVwg7ePz0fl8HOpx6baJoXepdXEvJqWFQuHePriaGgV1zfD28ygkkyFgY9leNZ5paRLJUX-bYaHflfEk5UUk79IlPESEAwhtNgBeg9bQSbCeF_oc6hMkdhBSFDGHoKQsS-Qa0cgtZjxlyMeTkyN8LWerojJTdqtJLTzdob5T_q0OlZcj4PJWSUkh9ad9OfJ2TOUxG1VXptVVnI5VHGlmQfCLK9xkhl6k7THaYcUsTWE2BKUHBXkQNwae_sY4g5tcJd3Xmfvc3p3pAZvaO26i-RTDaqlX3Em5Yz1F45-m0Kblj5ptt-Q3g3YPczx3qn2dbFLBhhSfdP6pKGlT6PlzI7MCEQYH9VDh_1AS5Wz5qMOKiTJZWFHJs2F_1ZLiuD0v9fgtNg-XWFZiyiXlZn2VoYmOpMCccHWv4KlyjjmQWik_6Rk7VifJ52dsKGnLMqVYoMXXP3olaq3kWLEQCGSEnzB--ItSkeB-8ZaNoAOaEoC21bRIjv7uF6hc2fSlVu50GLteeW_qOPo7lYC7tOM_xW-f13AnAfs2Ms7MvyQqMYB59d3tIOXITx1evNW48VhECNViDeEEqijGB6e7rittDPa0vfE_-4uTyLm86XxQeg1rk6RqaFO2_AvfmGTczJb5jiDU_6is4tzBDM5h3qFiOZFZ1Um2mYpfLl5gxAe_vImxWYGzKTqrLCFSRNtIj4-owiMuwmijqn9TxnYu3b2JV8lPZYkVXO6WUovZJ4seGgjFgK2CNKm371CHZ9uchKqzekf-1SkVdfPKHoeLrknumYG4uiISX4vv9uX0bA7WofAgOiNsU2s34_kwVZCgnS99pNtC72EpyIBvCHLeniET3UCiA2Nq_sQ_L9hRpibtx6dfdOy1XaG_naiqrs_HaJv4DJh8nLdqSBTxBrWtzvQa9_hReEJWxWxeprWFhMehr81L7L8aDvlaTzflLfxo1ibIAVLf2o3V7dBjSV-GijuorAbUtSpPmA2Vlu_gagkQlW3N7w-NMNe3GjxY5JjB2Pzqf__SFrj_JWpuEV3LlUBsUIg39MoTJLRfrXPiud4BxtxhklX70NKWJKEu0jeK0hQG4m4O3HWSibd8qnZrIj2wh0NbTkMPu9tHCY_89lMCuTnxKMXXgw1O15U2DFotJ9YIz8vMdoBX4E6qMb4ME76emiAQaW9tiLKOl-5FrXrGqSwNzooEn_SgvQVx38pkN5u304RY7vP6Q4pqi1bTMJhD1KtCTKmAgwllYf9t_zx8ApiN6hknrVvLIVxCND_I9zIdOP9CoMvstqfc5No3229yhiEU82_yXGBFP52699LvS4TLuLoAvdif9rkoo71cu3tfmnYsswpNl-XMca5Da2cXZnec75TWPISrL_bgPIiz9FrLauXCIgnS0c5ViJDtBC1JMA7JMbJxWEyyqRtK8HzVYMYD7wMWICnDGpQIYMWJRVOXneBTtFKNmxuMpPvYsSstpesGiMLKGLrt5nEd5_0FwnzOjcLsA5d9vlP6zMUeRZt5565yauqaDkMDI-Sw6NP2gtZWcRb9AuZRiapLehh3hwbh3Xadt0ODBqwo9p-XeQl_vEJUCAT9e_niRkZ0u9d3mRV_8UDM5NZDmwjONWycNFBqL-sLwtNeUPxujuPg5nl4OzsuIiDhzW8G96SLLMe13-uyDlmEAXfxhkuyTThqS1yXFHVR0ms_ekWq--b7EGojRhDNNQO5kZI1nnTSu2l_7GkiePwvj6eketodqXGImpZHGvBsSczFQA01GbSDLbwdNhHPaQpoog1c56pj6IEhtsZtExIrn5z-wyk5opu_qwpxSQVhCmmGt2MNhY9LTd9d-jNpYRKSdI6OlemHvWllZ_HTa240_qNZine3H1Pw01cS0QtydzG3JGWeIjCbzmBe70np_UV3_5NLBuwx7OAo1L235xKwfZdSDdJPDJfN0Ai01Lg1wGt6aWTIU2R9gRTj_O_eb9fpuGul0CtKmujEYQuWwiONOMVOulSYsGQNDwh5nEPPaPpPUJVJCxOZQT1MInjwORFuMyvEz3Q3_ikwnNEeE461nzHkCNHVcMLxGcZr-dxkMFn7Zi50Qfl8vtKZTRyH8eCoFA5p9_CGEblG3AXlr9iIVrpslVPVdD3j8LBZgaVcHUL7NuAGittCcDlvHcKNShpU_hU_1btM1X2c0IT9jN98WQBUzTY32LGYPQ7jgWt-Kx8Cj5KxPTG4eBGT1SaTq1elAjXvv0vT3e2r1R303zS_eAyjWEslD3pNVc0dvjXPb2K05DFtFUnnIZsf62X36LeQ3CJw1Wx_P_znrx0ZGve4-eAJ9zBqV-bn0R1Roy028rTI4jWxH3MUHtUl5BQVJPxxtqGmTOshbJce7Z15URY_IwivBMRfW6tT87vbVrh6_-oYCEqm5HNFsrAUGgYKz0klQT3bFbVRxH1BVjUQ3k0uvEA6sYZX5mXf5iWtTHIYiYWaYBlcftK2JU5bKTLX_iZX1pzQ1BAJBLyJ7Fet6uPfZnsRkHCMlR5tSrIAdA56LbX2IPU\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering memory updates**\\n\\nI’m thinking about cleaning up some test data that needs to be deleted. For sure, I can exclude any deleted memories from my search. It seems like I might want to create some consolidated memories too. The user mentioned execution, so I’m looking to make real changes, but not overwhelm with too many deletions — maybe all 304 is too broad. I'll aim for clear cases, update duplicates, and possibly preserve some artifacts as well. User approved this plan!\"},{\"type\":\"summary_text\",\"text\":\"**Creating consolidated artifacts**\\n\\nI’m focusing on SSH and disk facts, considering the need for consolidated artifacts related to legacy research topics. Even if I'm deleting all sources and claims, preserving themes could be valuable. I’m thinking about summarizing the research into four different artifacts around payment integration, display sourcing, fullscreen overlay, and public reference. I might also consolidate storage by updating the current state of memories and marking conflicting ones as deleted while creating new project memories. I'll use a script to help with this process!\"}]}" }, { "type": "toolCall", "id": "call_WH3NY6h0dWAtlgwqbplyyni5|fc_00bddaf16c51a121016a2175a5821c8191b6d55802a5d951f9", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json, subprocess, textwrap\nfrom pathlib import Path\nmems=json.load(open('/tmp/memory_dryrun_all.json'))\nby_id={m['id']:m for m in mems}\n\ndef upd(mid, **kw):\n m=by_id[mid].copy()\n m.update(kw)\n args=['/home/sebas/.agents/skills/memory-write/memory-write','--id',mid,'--type',m['type'],'--scope',m['scope'],'--status',m['status'],'--title',m['title'],'--content',m['content']]\n opt_map={\n 'subtype':'--subtype','project_id':'--project-id','repo_id':'--repo-id','task_id':'--task-id','run_id':'--run-id',\n 'source_ref':'--source-ref','evidence_ref':'--evidence-ref','origin_agent':'--origin-agent','url':'--url','domain':'--domain'\n }\n for k,flag in opt_map.items():\n v=m.get(k)\n if v is not None:\n args += [flag, str(v)]\n md=m.get('metadata')\n if md:\n args += ['--metadata', json.dumps(md)]\n subprocess.run(args, check=True, stdout=subprocess.DEVNULL)\n\n# delete clear-noise groups\nids_to_delete=[]\nfor m in mems:\n mid=m['id']\n title=m['title']\n if mid.startswith(('legacy_task_','legacy_run_','legacy_claim_','legacy_source_')):\n ids_to_delete.append(mid)\n elif title.startswith('sebas:'):\n ids_to_delete.append(mid)\n\n# duplicate/conflict drops\nids_to_delete += [\n 'mem_c24eb94053944d0d8847c565a2a6f65a', # corrected by bb53\n 'mem_cdac83f3c3004eaca99546346d0c90d0', # covered by general ambiguous term rule\n 'mem_57f96c24431c4d38b5123f37e66bdae8', # covered by merged laptop access rule\n 'mem_812ea22ea06d4368bdb66fe531e64fdc', # old 4h ssh timeout\n 'mem_dcaa730515ae48b39eda5dcc5e38edd0', # old root full state\n 'mem_08b4efed55144d78af56bdd15821b61d', # old root full state\n 'mem_c8c7e1c8af7d4665aafe58baca242345', # old ssh change episode\n 'mem_d7d13d0c108b4bd9ac634e9be66cfd0d', # duplicate handoff task\n]\nids_to_delete=sorted(set(ids_to_delete))\nfor mid in ids_to_delete:\n if mid in by_id:\n upd(mid, status='deleted')\n\n# create/replace consolidated active memories\ncreate_cmd='/home/sebas/.agents/skills/memory-write/memory-write'\nnew_memories=[\n [\n '--id','mem_laptop_tailnet_access_policy',\n '--type','profile','--scope','global',\n '--title','Laptop Tailnet access policy',\n '--content','Prefer laptop-* helpers and SSH-based Tailnet access over browser/web auth. No extra approval is needed for non-destructive laptop access; confirmation is still required for irreversible actions.',\n '--subtype','working_preference','--source-ref','conversation:2026-06-04:memory-consolidation','--origin-agent','personal-agent'\n ],\n [\n '--id','mem_ssh_idle_timeout_disabled',\n '--type','project','--scope','global',\n '--title','SSH server idle timeout disabled on ballbox-first',\n '--content','Current server policy on ballbox-first: SSH idle timeout is disabled, so sessions should not expire due to server-side idle timeout. This supersedes earlier 4h-timeout memories.',\n '--source-ref','conversation:2026-06-04:memory-consolidation','--origin-agent','personal-agent'\n ],\n [\n '--id','mem_ballbox_first_storage_after_microsd_reinstall',\n '--type','project','--scope','global',\n '--title','ballbox-first storage pressure mostly resolved by larger microSD reinstall',\n '--content','Earlier root-disk pressure on ballbox-first was mostly resolved by reinstalling the system onto a larger microSD. Old memories about emergency cache moves and near-full root state are mainly historical, not current operating state.',\n '--source-ref','conversation:2026-06-04:memory-consolidation','--origin-agent','personal-agent'\n ],\n [\n '--id','mem_legacy_ballbox_payment_research_bundle',\n '--type','artifact','--scope','global',\n '--title','Legacy Ballbox payment-integration research bundle',\n '--content','Consolidated legacy research outcome: the ballbox-payment-integration repo was directionally aligned for a vending QR flow but not production-safe. Key blockers identified across the old run set were real MercadoPago account/contract validation, correct webhook authenticity handling, real TCN/Yunshu dispense callback contract capture, production env/config hardening, and end-to-end validation against a real or faithful machine path.',\n '--subtype','research_bundle','--source-ref','memory-consolidation:legacy-ballbox-payment','--origin-agent','personal-agent'\n ],\n [\n '--id','mem_legacy_display_sourcing_research_bundle',\n '--type','artifact','--scope','global',\n '--title','Legacy Ballbox display sourcing research bundle',\n '--content','Consolidated legacy research outcome: there were multiple viable 50-55 inch China-sourced signage options for Ballbox, and covered padel clubs likely allow cheaper indoor commercial signage before direct-sunlight outdoor hardware. The durable takeaway is to start from covered-club deployment assumptions, compare brightness/connectivity/remote-management constraints, and only pay for high-brightness outdoor units when the venue reality requires it.',\n '--subtype','research_bundle','--source-ref','memory-consolidation:legacy-display-sourcing','--origin-agent','personal-agent'\n ],\n [\n '--id','mem_legacy_fullscreen_overlay_research_bundle',\n '--type','artifact','--scope','global',\n '--title','Legacy fullscreen overlay research bundle',\n '--content','Consolidated legacy research outcome: for replying to Codex without leaving fullscreen media, the viable solution space was lightweight overlay tooling rather than invasive native app scope. Raycast, BetterTouchTool, and Hammerspoon were the main candidates; the durable product takeaway was to optimize for minimal intrusion, quick trigger/response, and preserving fullscreen context.',\n '--subtype','research_bundle','--source-ref','memory-consolidation:legacy-fullscreen-overlay','--origin-agent','personal-agent'\n ],\n [\n '--id','mem_legacy_public_reference_research_bundle',\n '--type','artifact','--scope','global',\n '--title','Legacy public-reference research bundle',\n '--content','Consolidated legacy public-reference outcome: Trading Mastery appears to be a real public business with a public website and GitHub organization presence, while Humand affiliation research should be treated as public-reference tracking only and handled carefully. Durable rule: keep only high-level public-reference conclusions, not granular source-by-source research shards, unless a live task needs them again.',\n '--subtype','research_bundle','--source-ref','memory-consolidation:legacy-public-reference','--origin-agent','personal-agent'\n ],\n [\n '--id','mem_memory_consolidation_2026_06_04',\n '--type','episode','--scope','global',\n '--title','Memory consolidation pass 2026-06-04',\n '--content','Applied one-time consolidation across existing memories using the newer high-signal heuristic. Deleted raw session snippets, legacy task/run/source/claim shards, and several superseded duplicates. Added consolidated bundle memories for older research themes. User clarified that SSH idle timeout is now fully removed and storage pressure was mostly solved by reinstalling onto a larger microSD.',\n '--source-ref','conversation:2026-06-04:memory-consolidation','--origin-agent','personal-agent'\n ],\n]\nfor extra in new_memories:\n subprocess.run([create_cmd]+extra, check=True, stdout=subprocess.DEVNULL)\nprint('deleted',len(ids_to_delete))\nprint('created_or_updated',len(new_memories))\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 7194, "output": 2302, "cacheRead": 23040, "cacheWrite": 0, "totalTokens": 32536, "cost": { "input": 0.017985, "output": 0.03453, "cacheRead": 0.0057599999999999995, "cacheWrite": 0, "total": 0.058275 } }, "stopReason": "toolUse", "timestamp": 1780577692591, "responseId": "resp_00bddaf16c51a121016a21759cae0481918bdf7d09e0ced981" } }, { "type": "message", "id": "958e5ac1", "parentId": "f9ba80e0", "timestamp": "2026-06-04T12:56:02.291Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Checking updates and verification**\n\nI need to consider updating my to-do list for completed tasks. I think it’s important to ensure that everything is verified correctly, especially the counts. It feels like a responsible step to take, but I’m double-checking to make sure I haven’t missed anything. It's so easy to overlook details, and I want to be thorough. Okay, let me go ahead and do that!", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a2175de75dc8191b7f5ec65a8d02fab\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXXg_-_4YKXm4Kr0TDOeDj9IQlSN0Hgb6a_6BDK3iF6DLaXSR6ViePBAOh4MWThhAcl_xEHIqE-Q8pW4CGzmxlyeiua1w_Hm2_9ZJF9wm56Gzg_jC4a5u_ebcn8XsFMpA7PzM85a8bV3jOLM98HXG3Mjc8xr44Hexgm9EfoNvv0tmMlZ_mKVWcViVzzstCd_Ajx5LIyjGYIy-8LyKTQxT-gHTWzhz4rmgIrKAmhQmNAFNqjIcgG3Onl8L2gFWRrAjkGwOv1PzD4p9HpSMucVYJj37mQcTwX5PNYiTisDnZZwJBXOXjvdIBfytUcsvXYn0Baci_Yg-XsuRazJsWE7ehVIlFWeLI-N_tlq_-aLvHFTYi-T5EJ-CZbGW7zZaC_C0OBycetCVEXqT9keFd14VVwTOJZ82CgpHRqLRS-NR9pmGgILaBWaeeacGM7zt02ferMcehkfavXWeO9Ls-pebOyHKzYyv3-kfcUr6mxB4B4pN6wEu1sn65ph4x9KvnLuVNLVerk5DAU31l8igL5QtdCvNN_IMUUuDPI8qjinaESJNWnarvgvGZw3hMpVS7pb7D2UdJs1bhRamWdh4ptpV_Ok7yekUqbFTBIgTxz99uwKMT4enCKoRigUJLgJ2KaC6VwugMhKtvnIZCGEoM6ljyCfRNjfQojLi2TFh1bq0vyqtmCKh6hLxY-4LWvTEtiqi7kxCEeMCkrs1dmuz6kfCKn7tNkaVs5TPVNNW7GTl2JMUrrH46ZK0cneh57dNwl7noRfHvrjQllwChyHbQhm6ZIhhqO4Mg5lHwUyv3IPjsHHSACBbXEUEe7zIB96hpV2fNPzN_Lx5vdW22HEwW9HIPTpNBzs16FjnwkUlS6wCugrKYTxDT3jAFUYIKlmTnG3LDAqjUT-gY0nQw-0bgDkQW6ecIBTtzfAjfv6YtS8oPoUiQp2WIcNAykPNs0VLANEuGTAzIlqhXzwFsYc-3m1LjpIX2kskYaDU5CM2GHnSPT8WZc=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking updates and verification**\\n\\nI need to consider updating my to-do list for completed tasks. I think it’s important to ensure that everything is verified correctly, especially the counts. It feels like a responsible step to take, but I’m double-checking to make sure I haven’t missed anything. It's so easy to overlook details, and I want to be thorough. Okay, let me go ahead and do that!\"}]}" }, { "type": "toolCall", "id": "call_o0vMWyJW3044ZRtpib1eVuwc|fc_00bddaf16c51a121016a2175e04f448191b5cf698a8874a724", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport requests, json\nqueries=['memory consolidation pass 2026-06-04','SSH server idle timeout disabled on ballbox-first','Legacy Ballbox payment-integration research bundle','sebas: [telegram] alo','Legacy task: Build a shortlist']\nfor q in queries:\n url='http://100.116.176.16:8091/api/search?q='+requests.utils.quote(q,safe='')+'&scope=global&scope=project&limit=5'\n r=requests.get(url,timeout=30).json()['results']\n print('\\nQ',q)\n for item in r[:3]:\n m=item['memory']\n print(m['id'], m['status'], m['title'])\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 2861, "output": 207, "cacheRead": 29696, "cacheWrite": 0, "totalTokens": 32764, "cost": { "input": 0.007152500000000001, "output": 0.003105, "cacheRead": 0.007424, "cacheWrite": 0, "total": 0.017681500000000003 } }, "stopReason": "toolUse", "timestamp": 1780577757518, "responseId": "resp_00bddaf16c51a121016a2175dd9acc8191a1ccf628bf33361b" } }, { "type": "message", "id": "03fc5022", "parentId": "ac2842f4", "timestamp": "2026-06-04T12:56:08.656Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Investigating memory issues**\n\nI’m checking the search results, and it looks like deleted memories aren’t appearing. Wait, is mem_4c13 still active? If we deleted it, why is it showing active? Maybe it’s because writing after deletion created a new row? Hmm, it could also be stale results. I’m thinking I need to use the exact ID query. Let’s inspect to see if the search can still return deleted entries.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a2175e3f6048191897dfa439ec61900\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXXn3MkFFNTXLUo8YkbH3jX0cieub3mO8vBQ-7ncPlNFGG5NSqoLJkLMJQgNyGHlvoRrFMKj_4FwCeTzIeLWkg7Ssk0PSwpw_9w7HjhieLNCeH1Fhpf_IRc9sF7MdDu-tYMajYNLvbqAAXZasAq71oDHebKCwWj83eNdlDFa0WNCN7bNVdRJwGm95jkIxeuBQEtRtFs8ZgeRCljn1kU1ZXDX6ta6OTxYrnxrALIHerWc2wSNSljSSJfmzWT13Xj0OQLWR8A0nSdaf2N-T1wDK5nOIukRStBl9bb0qXaRn4RCqQYZ-Pjf2vxU7j7_2qEyiNxLaSYpfiBXptRnDQNaU2Vmc3j5Izx79-AX5qUYp9R8VqypsvrNQ4ecsG3vI5YFJPPUSU9TugDYth0-CWIdudozS_eQUbOL5Hq282PENGiRTcMG2VzZ7eCB3UFWB9fUYhfKiL9h5ucBN3WRGCuxt9_c-Vr44CHjc-SW6OA2F2eRaiOFUI6WZu-LtZkk7IFUQFmT5mQp80VeEynSvBqG5Yi7sDV1Y-48hR-v2L0cRUnQ8qsJlSOPjP68LHRl9Du76w_P-rWumv30g5p-Oqq5YmeEjCI_fTT4Mgwh8cmRNBU5CyRqnhIEQ37Mezb5gsjHkXnrmb2k4XholVfSm52f5VwxNLt3j_KwkReOl0UecI7MgPJayQ57EM9CEJUSmqmGLY9TtXonxajn1B2BDsJzWVrcyTNE2kV0NAofN-cWBkJapsUbNcT6BFAavDFujRjHYJri2lEz9eC1nKuTWltboCWglihKMI4VSLmpwMCyyqP03mQMijbUpmXYrWv4xYp38EksOjeBGU1yz9OTJqsVCTe0h29c7QWjZG6s9vStIDbCRW0nfTes-lRgSqh2UD3NjTJ24to85qayw3dkXYkhzu0DESL4wFUZoZxNgwvuNrm2UxuyxUmb7pHzjnnqc7l95WimMqTL0bOiEE2Mp_aF2jRe8S2oUEKVzxowIJ2vX3XMfk9R4uvFV9aohp70a18g9LgNdF_RaMA4o1MSGTU0n0XIRHOUzxiavIyq7ftH9S0WkUSkmdCaeFot_n0VfLz2KOhssVyZ6kCOcr8km2CKky6cTa1WmpzcOyiE-U7nPf9UiedB8ImJ1jXBMtw1A3Xvr425DYYhjG3o8RZJVF47SmeGfs09BL9j4pyo5KbNPkIdgTt3aXeDgRwXqhgE6-bjKeB6KNueeUy2mbN-WXNIllDo6kmtrOD3-PP9tat8wnRkakAFSiGDH5-ltS8U-hVzWWp3B0WBV5hro8cG2Z4dP1X33w==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Investigating memory issues**\\n\\nI’m checking the search results, and it looks like deleted memories aren’t appearing. Wait, is mem_4c13 still active? If we deleted it, why is it showing active? Maybe it’s because writing after deletion created a new row? Hmm, it could also be stale results. I’m thinking I need to use the exact ID query. Let’s inspect to see if the search can still return deleted entries.\"}]}" }, { "type": "toolCall", "id": "call_s2FxGY89GuGUynJRFbcz86xo|fc_00bddaf16c51a121016a2175e7bfec81919ea65d86f9f16205", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport requests, json\nfor mid in ['mem_4c13c21c3c514b669055da643e90049a','mem_b161e2e3072a49099cabb17193a4fb15','mem_c24eb94053944d0d8847c565a2a6f65a']:\n url='http://100.116.176.16:8091/api/search?q='+mid+'&limit=5'\n r=requests.get(url,timeout=30).json()['results']\n print('\\nID',mid)\n for item in r:\n m=item['memory']\n print(m['id'],m['status'],m['updated_at'],m['title'])\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 903, "output": 254, "cacheRead": 32256, "cacheWrite": 0, "totalTokens": 33413, "cost": { "input": 0.0022575000000000004, "output": 0.00381, "cacheRead": 0.008064, "cacheWrite": 0, "total": 0.0141315 } }, "stopReason": "toolUse", "timestamp": 1780577762613, "responseId": "resp_00bddaf16c51a121016a2175e2b1048191b19231cd8f21741d" } }, { "type": "message", "id": "81c1d16e", "parentId": "d579074e", "timestamp": "2026-06-04T12:56:15.551Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Considering database access**\n\nSearching by ID isn't working out well. I'm thinking maybe if something is deleted, it could be hidden, so I might need to use the exact context. It could be better to use the API database or possibly access SQLite directly. Since there's a local path from the status, inspecting SQLite seems like the easiest option. I need to know if any deleted states apply since the write operation returned no output. I'll inspect the database using the sqlite3 command.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a2175e9a33c8191a25227ff081c3149\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXXsUgMrnv8P7Y-foJaGBlrU-9FdX3Hh1QXD_bR9d5mZp3VwbpMVLNWnToFBqIWsdMCls7WQt-Bf-F9AQBnT0nybR6l1-huoIcWfWEgh5cL8uJ-Xh2vs4Ilb3oUa3_cOZCnPLvLK09lvtmpTVBrIjLyHwk98ZCaaiS32qHAvrPzFxhf-y9Fg60-0GcvIfWB3y2c0bbrkMeLLtwWsxhQbH4Hz9t4_orh_vNGKLXxcTWhjIx_GR54alZziey0FOfDW1Entt551RZYMY8x4P2YsL-yrS0puT0IMZctoEEndMUUoCdaXuP4WN2JoNkb823tdOUK43nUsM0eZM5WMHBjGHN4Cc9n1YlRITonEZoYgliB8gch30MyYrxV1GDfLW7JdVZU-nodW3w6z-Wxc-WPlphJCpUldQjDb4GK0rtvGYVy0LwNt6NTQ2X2b4yIm5pjewo4RrJAfeQJRuf1hSOk9PhgKhrVHE1HvpkXc3kv2rj1XGW6EFI1asKLjzldKqMTLvY-KaCZF1HBm-gMps9VVFT0PnXW5Zjo34LbzmjPgr3VUZiL2vDn55b7n1ddJU1-8yoJr1dCJRVrGfy9J448QHK8xFRANvsWnM2aD5eZl8qecn8WQ7sOkVIt0JkHrjkkH8PudF8mmMdq8p6HxGg1ahZw1cPEArWk3hfwqUVlZnp7M_aiVT43KoTcSZ08C0XjPwZoECwX_Vhl8gazzkz-owk9S5JLzvAnZyPf8lqE622oPR37iMdae9dEHKgmer6Ux-fzgEVy0XJ3cu3MiUE-hxL7ONNKVY4wjuCeL3k9mA_BNzXchew3KS0_q1AOy5FgFPWRHZpP7BoVNFb_skfLWeqQJK1PJZyEQ97IxKDl392r8g7NVp_v1kw-xuFlFQrvLCGGGAbXgyIDFoLZrpcW84G84ceBGbL2TnaHLjzNQ0GgwA1LildnSqkho8RvyPOkJTkx3qrzXHc0Kkrd9ZCby7KVfKQCbEFe7h6eo0QTCfLLY-MqBdkdbRxUurGHzQ7X2kUgWA9p4WjUWdmnkRfmj3gRq6FoT4FmNt-_VEtQwe1vndxoBzrakO9uBO1HZ8x1EYREQ8vOWqT0F1w674kEx3CRtnd3wFn_R0zm7UpqsgOvlx_qzaTYKwlZXC7ZU4LyncwETN8gT2_jEPAm5cPBPr_SQKQHNZBe2MdfLlwifM_5DPPoYdW-LtyZFFI5dNW04eJRqjSEn-gHBArkDWec6Gx2eZRMlyLN2Hotq54uIhOTojzxoPzKyMrd8HkvPhABX21kofvVU0JURC7RJIOrXQLKmwcsgnGAWmA3ByNV4taT3dOnUJ6gTup2gkOeCiMCHxZ7a\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering database access**\\n\\nSearching by ID isn't working out well. I'm thinking maybe if something is deleted, it could be hidden, so I might need to use the exact context. It could be better to use the API database or possibly access SQLite directly. Since there's a local path from the status, inspecting SQLite seems like the easiest option. I need to know if any deleted states apply since the write operation returned no output. I'll inspect the database using the sqlite3 command.\"}]}" }, { "type": "toolCall", "id": "call_wm0dNYdf0UR35bwE6PUN5n9u|fc_00bddaf16c51a121016a2175ecbb5c8191b869f32245d87912", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport sqlite3, json\nconn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\ncur=conn.cursor()\nfor mid in ['mem_4c13c21c3c514b669055da643e90049a','mem_b161e2e3072a49099cabb17193a4fb15','mem_c24eb94053944d0d8847c565a2a6f65a','legacy_task_12','mem_282e57f1ffe6455e82fbe4d184445f2c']:\n rows=cur.execute('select id,status,title,updated_at from memories where id=?',(mid,)).fetchall()\n print(mid, rows)\nprint('active', cur.execute(\"select count(*) from memories where status='active'\").fetchone()[0])\nprint('deleted', cur.execute(\"select count(*) from memories where status='deleted'\").fetchone()[0])\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 1273, "output": 312, "cacheRead": 32768, "cacheWrite": 0, "totalTokens": 34353, "cost": { "input": 0.0031825000000000004, "output": 0.00468, "cacheRead": 0.008192, "cacheWrite": 0, "total": 0.0160545 } }, "stopReason": "toolUse", "timestamp": 1780577768947, "responseId": "resp_00bddaf16c51a121016a2175e90af0819196ecb8e09b3bd79c" } }, { "type": "message", "id": "64bef024", "parentId": "74144028", "timestamp": "2026-06-04T12:56:24.892Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a2175f07d048191b8c06a5bb0259cb8\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXXzpDDBhI3MyJUP_-8c-HyxttibN29kWXUoFDEzeJy6zFcXe5S3qqJTCse3vXtwpOPvIuSFr3KaqXnV6qM9nqiSw6ST73J0zgIV8dtaCMiX_HRw7zMY6uwIZGANtJm_MJRRaeugAaCrhMXh0cvqukQbAy61WAaQ1Y1noRBR_JNhJ4m52ArkCaRnoOawXfRxPeIZr45zf5Gy8AX-vot55oU9ysOWVs8c-h7tqIAriKa-uUzOXeiv5yYVniZdfXqFBiJ_WJW8UBpxaWwPzivHB6DxB795GBY67R5cbaT-cdONSZDZuHmFv3FPS1xTuw5JetH9t9uEB8BOgdhIM7uEc-xHkFgk8Pqecs_wJyi3zFm9GC9zJ1TqN2gsuiE9jiBSOrrsVVHMebILdTVcRoxvqKiphCHDqdWOd_9gpC14LUVZEn7w53pOhYr2mv6Nz5KagOivn21WCbocQe3GDQbNz8z03qwOLkLDGAwPpnm25Zm3RdIycMOZ_MO-PT7A-6J3DYUiXz0odbgem7g0OZqBlrmyDfg1KpZEoQDIlu89W-tSKp7I33KAhRqOhuIktwmcfsdEtBSmHT1ZFDYOrvniskTzsiRkL9GPfD9h5iPdUXIeQV5-Tzl0Lbr1fnbii3rF7md0_NtgQTJMcqGyPL05op3nNUba2Zyf7yC0zQR5AiJ3HcU0HlGa8OPH1YhhJ1hdPFfOOA_0084BPvSr4SDAzIs7sAGSFy__XITXXLfdPiPl8-qmC3DVx0bSTDyyr1ehKH6Udgfk6u9N-olpSh8E7g6MGI7Xpdh2o21dpta__oDO4IOIOGcV7ak4QYWDW505JoYHYWHS-WUp47_cCBOQjqkF-nbMEpnRdJD8w6bVzTcApVCZGAMswfjgSPEma1w4JSvYl2bqlBOG6jLfoRs4_rXhFJHzWRYdWulZqS9LAd7CMdYM2oBMFr0rdDXu4rDQ6QajbCoCA3aR5Ji5WyCjbkW6UpinbKDkHOLTF7v4hMKzD3WBgH41tAas6AUaX-P7TmINbXYKDwEaLwRH2hrYuu9Zm5wxCLRtiOuLJuVnHzDc9A5BBL9CwwLxMm6iMKjcRObZ4bR2lQwnponGPLWOqLPYinsr_dwI68tZJQmm201sGz5qU-4QChyVtcqI6eEBTD2W0nfeBGshIHBIYV3fi8yZgEyKGK4xzszR0R8akJ1DENrF3aF3P9mXhuLF4acxFuOLQBgb22aC1d385oUsYCFZdk3w8TGLGX8TwY5MHKzOEYVugB9MWiaas88sQMuECNeWpPHq8kF_iRcEuj0OCsR9SdqBFlDaA6ljDIJl9UdxFux_hCzY_4Tf_XnbNmeMxd4U3rW4JTbIZ5cRLcMgQ6JfWcj-8uW2r0HypLkB_FjRxxabF18J43rAljAouT2CHf19FJp_uUcWmc-e-IJ2MY11P2dfFxWVJqkqCD3uWIYOy_3sqByJLhEvNuyQxNSu_N61aty_xdNcE-KEbL3GwzjeOwI3LWmL2_EYHNOR_Xxo8J8FMdfuIFZ178GwGy1DSsg3Vub7nnSgLaYmq8UFoxEaJV7vEOq2lCZD9vP3nLSzd002JkJMhc4dvINsK3wJYM1Qo1atxTpz0oK0HBS19oaLXYs8wLy868cICD5oxZ1FwKRiP8-t3NXZPccVykCmNituKVXWVdMuMxae8RDZUKkRkMLV5lUkUxSuogrejU4qJQqdz7hfLWccj2QGr0K8QC03\",\"summary\":[]}" }, { "type": "toolCall", "id": "call_XJTOmeysd8Hv5Bo41peqxyKX|fc_00bddaf16c51a121016a2175f3eaa08191be3c3100e676d669", "name": "bash", "arguments": { "command": "python - <<'PY'\nimport json, subprocess, sqlite3\nmems={m['id']:m for m in json.load(open('/tmp/memory_dryrun_all.json'))}\nfor mid in ['mem_4c13c21c3c514b669055da643e90049a','mem_b161e2e3072a49099cabb17193a4fb15','mem_812ea22ea06d4368bdb66fe531e64fdc']:\n m=mems[mid]\n args=['/home/sebas/.agents/skills/memory-write/memory-write','--id',mid,'--type',m['type'],'--scope',m['scope'],'--status','deleted','--title',m['title'],'--content',m['content']]\n subprocess.run(args,check=True)\nconn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\ncur=conn.cursor()\nfor mid in ['mem_4c13c21c3c514b669055da643e90049a','mem_b161e2e3072a49099cabb17193a4fb15','mem_812ea22ea06d4368bdb66fe531e64fdc']:\n print(cur.execute('select id,status,updated_at from memories where id=?',(mid,)).fetchone())\nPY", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 923, "output": 460, "cacheRead": 33792, "cacheWrite": 0, "totalTokens": 35175, "cost": { "input": 0.0023075, "output": 0.0069, "cacheRead": 0.008447999999999999, "cacheWrite": 0, "total": 0.017655499999999998 } }, "stopReason": "toolUse", "timestamp": 1780577775609, "responseId": "resp_00bddaf16c51a121016a2175efd8188191b54be635d57863f6" } }, { "type": "message", "id": "914a7fe0", "parentId": "2348419c", "timestamp": "2026-06-04T12:56:28.289Z", "message": { "role": "assistant", "content": [ { "type": "thinking", "thinking": "**Verifying active counts**\n\nI’m thinking I need to verify all the created active counts. I wonder if that’s all okay. Going through my to-do list, I want to make sure I’ve completed everything on it. I’m reflecting on if I've missed anything important while checking these counts. It just feels good to make sure everything is in order and up to date! Let’s see how I can confirm that the counts are indeed correct.", "thinkingSignature": "{\"id\":\"rs_00bddaf16c51a121016a2175f9f4b08191b30d69a12be3cfda\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIXX8wYVJkgPt6Nf9Z2LzhzTswHRRiq1z0op0GFzVXVfcMFIGfaZhsTyosUjaq_mpZwIShGolYk8qFc9a2r1NPcrTtpwKy30bSzxPTO_CU51qMcSWpQguBfToWseOo-CeIHSAo8eL3yOT05HZ8ihxb7Q9E3zG3eRgxTM5v0UrxJagl-QMh1KMnpnfIIE1WIQdidO9fsKMAFeqQIx9rTST70pDf58Jr5xsmj2FSz4L5UpV11BpPd4me4eU_UXohApp5Ruiwe3BImqafC8vPMNyb689LwtXji51C_dIfMQhQoSJ2yICwKUpOOhYQXSSyfCm4jWqt06jdoI4bdh3-zBPaG1ajF8ne5A4Lf1XQBFZbvlZ_nYupEgMc1QZ4c_q-HQZdi-MqlECnZ70B7v89-A72-rGXMsAvJwlWDGV-wVoAkEbhPTMeYaW4XyIjR6_R-glY7os8EzxE1CC14IyFbjyO0MU7kxKqGTDGtmSmkhAjUrwj9t7iE0LLoAsu7wc9ZZDu5t0BpDG4TR7AB_wgkvYa2H1wn_3Ui0xOnCgYARcv2rGz805lytqZ-Q9heitmA9mJJc9T9jKZvi7AYLFIiNN8qCKgWiBsKida0XPkxUSDVknKiUgpD_DISXNq7kBein7VsISxjRcm-hCkZh0Od8l8sbds7hQN5H3-U4ReX_YfN8oDMxHRmHdULy5HTQHdBfC9R2THUuSmjMdODFVriw1yDO3wE-EH817dYRRPoud6reCdAsOJqaHtJvZCwheSrngksNJZAev_EefqHuMnRoByw-uKAhEif_0HSvmPrq4YV4ObXyf3Gny7yXoqd4t-ol9wYjwsbDqP3jmZ5lnmtYV5NSpccicZ07b_F_ZQQeV13GYZEyBTclHsiEOjqnU5BvnwsG-SSHMfET6OHchMPPreiLKEYrJgzYAvJLU0y7Mr9lknikFqaSpZchIx8I7AZtytjAwq2jPDNCF5M_EnA-e_hsB9OnFFOcxEZMrA_5Vq50Acg5R1AEmVafy6VW7eSkfslDI\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Verifying active counts**\\n\\nI’m thinking I need to verify all the created active counts. I wonder if that’s all okay. Going through my to-do list, I want to make sure I’ve completed everything on it. I’m reflecting on if I've missed anything important while checking these counts. It just feels good to make sure everything is in order and up to date! Let’s see how I can confirm that the counts are indeed correct.\"}]}" }, { "type": "toolCall", "id": "call_fFScJYhABthyepb0Z8MivUDB|fc_00bddaf16c51a121016a2175fc168c8191a6c671e311242a6a", "name": "todo", "arguments": { "action": "update", "id": 2, "status": "completed" } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 2110, "output": 45, "cacheRead": 34304, "cacheWrite": 0, "totalTokens": 36459, "cost": { "input": 0.005275, "output": 0.000675, "cacheRead": 0.008576, "cacheWrite": 0, "total": 0.014526 } }, "stopReason": "toolUse", "timestamp": 1780577785335, "responseId": "resp_00bddaf16c51a121016a2175f9695c8191a742c892c3576f7c" } }, { "type": "message", "id": "866aa73f", "parentId": "c34cb678", "timestamp": "2026-06-04T12:56:34.039Z", "message": { "role": "assistant", "content": [ { "type": "text", "text": "Listo.\n\nEjecuté consolidación real.\n\n### Hecho\n- **181 memorias** marcadas `deleted`\n- **8 memorias** nuevas/consolidadas\n- conflicto de SSH corregido con tu aclaración\n- storage actualizado con tu aclaración\n\n### Lo que podé\n- `legacy_task_*`\n- `legacy_run_*`\n- `legacy_claim_*`\n- `legacy_source_*`\n- episodios crudos tipo `sebas: ...`\n- algunos duplicados/supersedidos\n\n### Lo que consolidé\nCreé memorias nuevas para:\n- policy de acceso laptop/tailnet\n- SSH sin idle timeout\n- storage resuelto mostly por microSD más grande\n- bundle legacy Ballbox payment research\n- bundle legacy display sourcing\n- bundle legacy fullscreen overlay research\n- bundle legacy public-reference research\n- episodio de consolidación 2026-06-04\n\n### Nota\nEsto fue consolidación operativa, no\narchivo histórico perfecto.\nPriorizó señal > trazabilidad granular.\n\nSi querés, próximo paso:\n- hago un **post-check**\n- te digo qué memorias activas siguen\n viéndose medio redundantes todavía.", "textSignature": "{\"v\":1,\"id\":\"msg_00bddaf16c51a121016a2175fd7d308191a2fe005668c9b38b\",\"phase\":\"final_answer\"}" } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 637, "output": 249, "cacheRead": 35840, "cacheWrite": 0, "totalTokens": 36726, "cost": { "input": 0.0015925000000000002, "output": 0.003735, "cacheRead": 0.00896, "cacheWrite": 0, "total": 0.0142875 } }, "stopReason": "stop", "timestamp": 1780577788292, "responseId": "resp_00bddaf16c51a121016a2175fc60688191a44652a254ba81fa" } }, { "type": "message", "id": "16580b7c", "parentId": "866aa73f", "timestamp": "2026-06-04T13:03:19.008Z", "message": { "role": "user", "content": [ { "type": "text", "text": "[telegram] ok" } ], "timestamp": 1780578199005 } }, { "type": "message", "id": "22835592", "parentId": "16580b7c", "timestamp": "2026-06-04T13:03:21.381Z", "message": { "role": "assistant", "content": [ { "type": "text", "text": "Bien.", "textSignature": "{\"v\":1,\"id\":\"msg_00bddaf16c51a121016a2177991d2c81918ceb3cf59621e039\",\"phase\":\"final_answer\"}" } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 14922, "output": 6, "cacheRead": 20992, "cacheWrite": 0, "totalTokens": 35920, "cost": { "input": 0.037305000000000005, "output": 9e-05, "cacheRead": 0.005248, "cacheWrite": 0, "total": 0.04264300000000001 } }, "stopReason": "stop", "timestamp": 1780578199010, "responseId": "resp_00bddaf16c51a121016a217797adfc8191b74e467f05fb79b2" } }, { "type": "message", "id": "a7474d4c", "parentId": "22835592", "timestamp": "2026-06-04T13:08:29.950Z", "message": { "role": "user", "content": [ { "type": "text", "text": "/new" } ], "timestamp": 1780578509949 } }, { "type": "message", "id": "d0854b37", "parentId": "a7474d4c", "timestamp": "2026-06-04T13:08:32.178Z", "message": { "role": "assistant", "content": [ { "type": "toolCall", "id": "call_wD7Zwli53kzCpyIF2UFcX7wH|fc_00bddaf16c51a121016a2178cf9134819190e784a30092d41f", "name": "bash", "arguments": { "command": "/home/sebas/pi-config/bin/pi-telegram-new-session", "timeout": 120 } } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 35918, "output": 35, "cacheRead": 0, "cacheWrite": 0, "totalTokens": 35953, "cost": { "input": 0.08979500000000001, "output": 0.000525, "cacheRead": 0, "cacheWrite": 0, "total": 0.09032000000000001 } }, "stopReason": "toolUse", "timestamp": 1780578509951, "responseId": "resp_00bddaf16c51a121016a2178ce92ac81918eeea6b45561c4d3" } }, { "type": "message", "id": "c08f8ae6", "parentId": "697b1575", "timestamp": "2026-06-04T13:08:33.841Z", "message": { "role": "assistant", "content": [ { "type": "text", "text": "Sesión rotada.", "textSignature": "{\"v\":1,\"id\":\"msg_00bddaf16c51a121016a2178d19cf08191be51cafed524ef51\",\"phase\":\"final_answer\"}" } ], "api": "openai-codex-responses", "provider": "openai-codex", "model": "gpt-5.4", "usage": { "input": 132, "output": 9, "cacheRead": 35840, "cacheWrite": 0, "totalTokens": 35981, "cost": { "input": 0.00033000000000000005, "output": 0.000135, "cacheRead": 0.00896, "cacheWrite": 0, "total": 0.009425 } }, "stopReason": "stop", "timestamp": 1780578512188, "responseId": "resp_00bddaf16c51a121016a2178d04f4481919d5dd2cd64e4b2d9" } } ], "user_messages": 6, "assistant_messages": 31, "last_user_text": "/new", "last_assistant_text": "Sesión rotada.", "first_user_text": "[telegram] se puede hacer un Dry Run a las memorias existentes sin ver la sesion, en cambio solo viendo la memoria?" }, "analysis": { "status": "completed", "save": true, "saved": true, "memory_id": "mem_7bcaac5509fa47eaa1877a3db0229dac", "memory_ids": [ "mem_7bcaac5509fa47eaa1877a3db0229dac" ], "saved_count": 1, "raw": "{\"memories\": [{\"type\": \"episode\", \"subtype\": \"session_summary\", \"scope\": \"global\", \"title\": \"sebas: [telegram] se puede hacer un Dry Run a las memorias existentes sin ver…\", \"content\": \"User asked: [telegram] se puede hacer un Dry Run a las memorias existentes sin ver la sesion, en cambio solo viendo la memoria?\\nResult: Sesión rotada.\", \"source_ref\": \"session:019e9290-7cad-7f28-847b-004b58c9ebae\", \"evidence_ref\": \"/home/sebas/pi-config/sessions/--home-sebas--/2026-06-04T12-16-43-181Z_019e9290-7cad-7f28-847b-004b58c9ebae.jsonl\", \"confidence\": 0.72, \"freshness\": 0.95, \"importance\": 0.56, \"reason\": \"Deterministic fallback because model analysis was unavailable: Warning: No models match pattern \\\"gemini-3-flash-preview:minimal\\\"\\nWarning: No models match pattern \\\"gemini-3-flash-preview:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:low\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:high\\\"\\nWarning: No models match pattern \\\"o3-mini:low\\\"\\nWarning: No models match pattern \\\"o3-mini:medium\\\"\\nWarning: No models match pattern \\\"o3-mini:high\\\"\\nWarning: No models match pattern \\\"minimax-m2.5-free:minimal\\\"\\nWarning: No models match pattern \\\"qwen/qwen3-coder:free:minimal\\\"\\nWarning: No models match pattern \\\"google/gemma-3-27b-it:free:minimal\\\"\\nNo API key for provider: openai-codex\"}], \"reason\": \"fallback-analysis:Warning: No models match pattern \\\"gemini-3-flash-preview:minimal\\\"\\nWarning: No models match pattern \\\"gemini-3-flash-preview:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:low\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:medium\\\"\\nWarning: No models match pattern \\\"claude-3-7-sonnet:high\\\"\\nWarning: No models match pattern \\\"o3-mini:low\\\"\\nWarning: No models match pattern \\\"o3-mini:medium\\\"\\nWarning: No models match pattern \\\"o3-mini:high\\\"\\nWarning: No models match pattern \\\"minimax-m2.5-free:minimal\\\"\\nWarning: No models match pattern \\\"qwen/qwen3-coder:free:minimal\\\"\\nWarning: No models match pattern \\\"google/gemma-3-27b-it:free:minimal\\\"\\nNo API key for provider: openai-codex\"}", "result": { "memories": [ { "type": "episode", "subtype": "session_summary", "scope": "global", "title": "sebas: [telegram] se puede hacer un Dry Run a las memorias existentes sin ver…", "content": "User asked: [telegram] se puede hacer un Dry Run a las memorias existentes sin ver la sesion, en cambio solo viendo la memoria?\nResult: Sesión rotada.", "source_ref": "session:019e9290-7cad-7f28-847b-004b58c9ebae", "evidence_ref": "/home/sebas/pi-config/sessions/--home-sebas--/2026-06-04T12-16-43-181Z_019e9290-7cad-7f28-847b-004b58c9ebae.jsonl", "confidence": 0.72, "freshness": 0.95, "importance": 0.56, "reason": "Deterministic fallback because model analysis was unavailable: Warning: No models match pattern \"gemini-3-flash-preview:minimal\"\nWarning: No models match pattern \"gemini-3-flash-preview:medium\"\nWarning: No models match pattern \"claude-3-7-sonnet:low\"\nWarning: No models match pattern \"claude-3-7-sonnet:medium\"\nWarning: No models match pattern \"claude-3-7-sonnet:high\"\nWarning: No models match pattern \"o3-mini:low\"\nWarning: No models match pattern \"o3-mini:medium\"\nWarning: No models match pattern \"o3-mini:high\"\nWarning: No models match pattern \"minimax-m2.5-free:minimal\"\nWarning: No models match pattern \"qwen/qwen3-coder:free:minimal\"\nWarning: No models match pattern \"google/gemma-3-27b-it:free:minimal\"\nNo API key for provider: openai-codex" } ], "reason": "fallback-analysis:Warning: No models match pattern \"gemini-3-flash-preview:minimal\"\nWarning: No models match pattern \"gemini-3-flash-preview:medium\"\nWarning: No models match pattern \"claude-3-7-sonnet:low\"\nWarning: No models match pattern \"claude-3-7-sonnet:medium\"\nWarning: No models match pattern \"claude-3-7-sonnet:high\"\nWarning: No models match pattern \"o3-mini:low\"\nWarning: No models match pattern \"o3-mini:medium\"\nWarning: No models match pattern \"o3-mini:high\"\nWarning: No models match pattern \"minimax-m2.5-free:minimal\"\nWarning: No models match pattern \"qwen/qwen3-coder:free:minimal\"\nWarning: No models match pattern \"google/gemma-3-27b-it:free:minimal\"\nNo API key for provider: openai-codex" }, "fallback_error": "Warning: No models match pattern \"gemini-3-flash-preview:minimal\"\nWarning: No models match pattern \"gemini-3-flash-preview:medium\"\nWarning: No models match pattern \"claude-3-7-sonnet:low\"\nWarning: No models match pattern \"claude-3-7-sonnet:medium\"\nWarning: No models match pattern \"claude-3-7-sonnet:high\"\nWarning: No models match pattern \"o3-mini:low\"\nWarning: No models match pattern \"o3-mini:medium\"\nWarning: No models match pattern \"o3-mini:high\"\nWarning: No models match pattern \"minimax-m2.5-free:minimal\"\nWarning: No models match pattern \"qwen/qwen3-coder:free:minimal\"\nWarning: No models match pattern \"google/gemma-3-27b-it:free:minimal\"\nNo API key for provider: openai-codex" } }