{"type":"session","version":3,"id":"019e926c-6feb-7773-8e97-5645482738b0","timestamp":"2026-06-04T11:37:20.619Z","cwd":"/home/sebas"}
{"type":"model_change","id":"a198dcf6","parentId":null,"timestamp":"2026-06-04T11:37:22.867Z","provider":"openai-codex","modelId":"gpt-5.4"}
{"type":"thinking_level_change","id":"6ae42228","parentId":"a198dcf6","timestamp":"2026-06-04T11:37:22.868Z","thinkingLevel":"low"}
{"type":"message","id":"fa6e8b1f","parentId":"6ae42228","timestamp":"2026-06-04T11:37:28.453Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] se estan corriendo memory sweeps de todas las sesiones recientes?"}],"timestamp":1780573048443}}
{"type":"message","id":"f32a713d","parentId":"fa6e8b1f","timestamp":"2026-06-04T11:37:37.903Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting memory sweeps**\n\nI need to check if memory sweeps are running for all recent sessions. Maybe I should look into the process or service related to these sweeps. Understanding the context from memory could help. Searching the agents database might be useful too, but a local setup could provide better insights. My plan is to inspect the running processes related to the memory sweep to get a clearer picture.","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21637a2cec8191af535b70acafe352\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWOBFtDFrG7dWcy6Dq_viwPShNe0b2n9oPt3898HT4ZY099PhbCnbkGymSyw7aqsUT_OI46QIx3T-SQ5GnYrjs2KZBt4dOhwqmYbnSx_bWbXbraOZfm09qOQTefK-efE2d7YxWFqPM_MhTfl8C-uYvlIJr3HDZ_qv04ff-CSbzYBCs-tSJYag4_G7mxp-t5UZ9d8vgHjI_04hgRJ_h6jx6SIo2Ev71wlqcZo0GRamNdmQO7DuqQBGeEhNdZ1gEo1w5ILGwD7D9e4OY6N9IhFN68DvQzCj8mX1CJICLcvSAsBpeVGgTSUFM-ycqHYiAJKkpCu0pS7t5ZvchQhl-FRQ97fD03kndX-kvPLZlgupRD0le5v1HaSe6aIW1cDDbd0C_7b5Ryq9VtulPq9ILuwEtOwxsWDkywgaDd5iBMtrma3P_asbr-QqlxHxiwB7EtcM7NhgOMWtwk8wJFkJwl75wvqC_FkXD6hg5oxfNyWiJAw_sJyscqbmll7DyU6bt-VNxp4f3mls8ifd0fC-VtcpD5uSp8kbX3VpJ8WEXcLNzmwp_youVyvNfxweWyGXr76zvE0fpC6ibzJo6pnuTFs-kay1fyR4k6bN96KW0306gV-N4qjpZJoLujI9uolyoGrtxQ3w9iP7v5r_njPDMHpc63v7N8j2pntqfIGNflIEpIIIsHgr45nub_X-qAf4wZQxA6j3CYgufVW1R4H26Ng97JBGlSQc2-KC-nQsbuyiZfC80iuAOr9ONJ_1o0WIwWfL94XMVQSTytNejpUQtYavCCGveP8FwrONOJlz7E32ZqLrrw4NMFpV0iF7qlchmMlgJc_Af3LSpCCm30LUq7tzAIhZIdtAL_G3FSBtCNeQu5YGd2UO-QddQRS8QeBOX74tyJlscaYZHYCopdyFJrcRuujqPpIROfMHmLrPdmx6cynCrhasXtXYdyzr7lUwKD-EkeR7OUQIe7tln9GEJG31KySUQlp8BmTwcd5EuypKmfGderBtSmQghss2O-FagKsCqAeR0JaIV79Z5mEMADVOuKqkqJI3nbuw8OooIakSm6gmyJ_5HS1n9hxnsrQWUCoRXN12qnn7TG4qxDZjwqYqIYsOk-dzpXxVbcr-QqQmS5Cpm3IUfbUhE5v6Mn-CyxJJju-UjYzHht5p5lkhL6iz5YOnqnOsilfVISKnI8ibvX1okjOe7ZyRGF-hvLu3zOMglHywLxvAyHMBuvdZpe3bt6kNHeDHGzvwZQoYGumVxygs6Kuy3e3Ije6G2Y8x4MocYofnOjvITL8aGM-vHX8zVt31Q==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting memory sweeps**\\n\\nI need to check if memory sweeps are running for all recent sessions. Maybe I should look into the process or service related to these sweeps. Understanding the context from memory could help. Searching the agents database might be useful too, but a local setup could provide better insights. My plan is to inspect the running processes related to the memory sweep to get a clearer picture.\"}]}"},{"type":"toolCall","id":"call_be9WEGihA4KtbTy9B2A6lJf7|fc_08205193eac5c3af016a216381a2208191ab8dc74985178d9d","name":"bash","arguments":{"command":"ps -ef | grep -i 'memory sweep\\|session analysis\\|agents-database\\|ingest' | grep -v grep","timeout":10}},{"type":"toolCall","id":"call_5F1YXR7PYPIR0QnptyaYjapg|fc_08205193eac5c3af016a216381a234819195abe4b09c7cebef","name":"bash","arguments":{"command":"ls -1dt /home/sebas/.pi/sessions/* 2>/dev/null | head -20","timeout":10}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":5428,"output":164,"cacheRead":0,"cacheWrite":0,"totalTokens":5592,"cost":{"input":0.01357,"output":0.00246,"cacheRead":0,"cacheWrite":0,"total":0.01603}},"stopReason":"toolUse","timestamp":1780573048464,"responseId":"resp_08205193eac5c3af016a216378f86081919a35d03df343c294"}}
{"type":"message","id":"2966fea2","parentId":"f32a713d","timestamp":"2026-06-04T11:37:37.935Z","message":{"role":"toolResult","toolCallId":"call_be9WEGihA4KtbTy9B2A6lJf7|fc_08205193eac5c3af016a216381a2208191ab8dc74985178d9d","toolName":"bash","content":[{"type":"text","text":"root        1505    1273  0 May05 ?        00:00:00 sudo -n -u sebas /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data\nsebas       1511    1505  0 May05 ?        00:00:01 /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data\nsebas      48007    1289  0 May05 ?        00:02:25 python3 -m shared_agent_memory.cli --db /home/sebas/agents-database/data/shared-agent-memory.sqlite3 serve-http --host 0.0.0.0 --port 8091\nsebas      48008    1289  0 May05 ?        00:00:09 /home/sebas/agents-database/.venv/bin/python -m shared_agent_memory.cli --db /home/sebas/agents-database/data/shared-agent-memory.sqlite3 maintenance-daemon --interval-seconds 900 --jitter-seconds 60\n"}],"isError":false,"timestamp":1780573057935}}
{"type":"message","id":"dfa40081","parentId":"2966fea2","timestamp":"2026-06-04T11:37:37.935Z","message":{"role":"toolResult","toolCallId":"call_5F1YXR7PYPIR0QnptyaYjapg|fc_08205193eac5c3af016a216381a234819195abe4b09c7cebef","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1780573057935}}
{"type":"message","id":"1b2bbb9d","parentId":"dfa40081","timestamp":"2026-06-04T11:37:41.848Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Searching for sweeps configuration**\n\nI need to determine if sweeps are enabled, and I think the best way to do this is by checking the hooks. I’m planning to search through the configuration files to find the relevant settings. It might take some digging, but I’ll make sure to be thorough to get this right. Let's see what I can find about sweeps and their configurations!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216382c22881919bb86f2344f246ea\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWOFrxmYAu1tFVi0Bf3VgP5yFza4kV2V-qUfn5TcmAyGkEww7X5b30sWCO_Jyu6n0i48wwWzSUQcGts9itBNwyKLPYC-FNcSXJ-wZpoAMzIzCEzeUVX9TdnSfDObUm6EpKgUoeZYlPiAUiK8qSBCNxLla_0cVPadcOTXpocqtAGr22wIvnKcruAbzbxYcGfst5RfscCYR7eQ7wqNZs4sZUhKI_vOzg4yCns3i9bCivVNoSgc_eWmxKLkq1Ul7Xxge8q7OkQ9ylDeMoO3fPYa8UujJ3VNPd3cg_tnFiDvM4uBm83EZ-VH2FG4O1btvM6bMRiaD4Od7QSwWecbExrb7SafRL1DTFziHhS4L_yhiu2pT5TbGJmx3UQauNZ2EFHur1TdzfuizaXWMyk-eB7wmPI_I1a1Yqy506ZtqXD2I_eWGmwZ_HgwmRCLIYmoL2F43Hw2-EcM6k_ZwDrRE1PCyb8tZk3NcfegnlRJnR9a6l5Aj87EAkXyyaCtzoyskxokI5zrV-05TLmwAz4xXHllkaP16NY4K9Ijf_t6y2hsZt0ndK_-l5kLU7H7bTaOQrshZjQYV3LKeJy7pL8iRhJq_hvlPLzsoW_hBTKBrLJDRKtgsgZeiuHC49ZNCze2py7v8vENFBrSl_7YyiLMR5C1bZbXgd60-VO_Y4FKJPGhp7Y-RfRCDNhzvUXbZnlASRUBPhHRfr7NWvFUwPSK50me8rbF1AKzzamvRI5qpepfZGvtSVrfok5GkVs2qziXGwzP-2S5RRzcZwU9CgmzxDr1RXAumWwR2s3tF2V8I8PJo1Akt0heczKz8p5wICEFIj2rgc_SLIT4B3MFyyavJS-fgBOPxBu7YlRm7P7mNaz2-DSdn0v6IHcXXukP968iw7VEOqE2xFcPUs_azLg3rDQvCefoevcysRaZD40jzsJUxT-YikoYSvElJMuhgW7ic63meKCLr60lfxBKy6a-JIXc1E8XRNUmIBBtdO7h3sNV17yeyvAYq9atw3hR5Aqoc9G3QtTd\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Searching for sweeps configuration**\\n\\nI need to determine if sweeps are enabled, and I think the best way to do this is by checking the hooks. I’m planning to search through the configuration files to find the relevant settings. It might take some digging, but I’ll make sure to be thorough to get this right. Let's see what I can find about sweeps and their configurations!\"}]}"},{"type":"toolCall","id":"call_PrGcdQicAI077i2sG9HyR8jm|fc_08205193eac5c3af016a216385c9148191af9bc70f387a9778","name":"bash","arguments":{"command":"grep -Rni \"memory sweep\\|session analysis\\|agents-database\\|POST /api/ingest\\|session end\" /home/sebas/.config /home/sebas/pi-config /home/sebas/.agents 2>/dev/null | head -200","timeout":20}},{"type":"toolCall","id":"call_WlDzcA1lTBdbfFwhqskvb6mm|fc_08205193eac5c3af016a216385c9248191b2202db00498af8b","name":"bash","arguments":{"command":"find /home/sebas -maxdepth 3 \\( -name '*session*' -o -name '*memory*' \\) 2>/dev/null | head -200","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":729,"output":167,"cacheRead":5120,"cacheWrite":0,"totalTokens":6016,"cost":{"input":0.0018225000000000001,"output":0.002505,"cacheRead":0.0012799999999999999,"cacheWrite":0,"total":0.0056075}},"stopReason":"toolUse","timestamp":1780573057936,"responseId":"resp_08205193eac5c3af016a2163823ba48191823cb0232b33eccc"}}
{"type":"message","id":"00aba517","parentId":"1b2bbb9d","timestamp":"2026-06-04T11:37:48.323Z","message":{"role":"toolResult","toolCallId":"call_PrGcdQicAI077i2sG9HyR8jm|fc_08205193eac5c3af016a216385c9148191af9bc70f387a9778","toolName":"bash","content":[{"type":"text","text":"/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:118:{\"type\":\"message\",\"id\":\"55022030\",\"parentId\":\"24a55fd0\",\"timestamp\":\"2026-04-19T22:02:58.641Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_C9KoC5ODzSr8L4MccFNsCkhZ|fc_071e452a5a0ea02e0169e550d7fec48190956ba28739465114\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"{\\n  \\\"projects\\\": {\\n    \\\"copied\\\": 4,\\n    \\\"common_cols\\\": 5\\n  },\\n  \\\"repos\\\": {\\n    \\\"copied\\\": 7,\\n    \\\"common_cols\\\": 6\\n  },\\n  \\\"sources\\\": {\\n    \\\"copied\\\": 0,\\n    \\\"common_cols\\\": 7\\n  },\\n  \\\"ingestion_events\\\": {\\n    \\\"copied\\\": 0,\\n    \\\"common_cols\\\": 11\\n  },\\n  \\\"memories\\\": {\\n    \\\"error\\\": \\\"DatabaseError('database disk image is malformed')\\\"\\n  },\\n  \\\"memory_links\\\": {\\n    \\\"copied\\\": 445,\\n    \\\"common_cols\\\": 6\\n  },\\n  \\\"retrieval_logs\\\": {\\n    \\\"error\\\": \\\"DatabaseError('database disk image is malformed')\\\"\\n  },\\n  \\\"maintenance_jobs\\\": {\\n    \\\"copied\\\": 4,\\n    \\\"common_cols\\\": 14\\n  },\\n  \\\"maintenance_runs\\\": {\\n    \\\"copied\\\": 41,\\n    \\\"common_cols\\\": 8\\n  },\\n  \\\"memory_conflicts\\\": {\\n    \\\"copied\\\": 0,\\n    \\\"common_cols\\\": 6\\n  },\\n  \\\"profiles\\\": {\\n    \\\"copied\\\": 0,\\n    \\\"common_cols\\\": 5\\n  },\\n  \\\"tasks\\\": {\\n    \\\"copied\\\": 194,\\n    \\\"common_cols\\\": 19\\n  },\\n  \\\"task_runs\\\": {\\n    \\\"copied\\\": 63,\\n    \\\"common_cols\\\": 12\\n  },\\n  \\\"artifacts\\\": {\\n    \\\"copied\\\": 163,\\n    \\\"common_cols\\\": 11\\n  },\\n  \\\"fts\\\": \\\"rebuilt\\\",\\n  \\\"quick_check\\\": [\\n    [\\n      \\\"ok\\\"\\n    ]\\n  ],\\n  \\\"count_memories\\\": 288,\\n  \\\"count_tasks\\\": 194,\\n  \\\"count_artifacts\\\": 163,\\n  \\\"count_projects\\\": 4,\\n  \\\"count_repos\\\": 7,\\n  \\\"count_retrieval_logs\\\": 6848,\\n  \\\"count_task_runs\\\": 63,\\n  \\\"count_maintenance_jobs\\\": 4,\\n  \\\"count_maintenance_runs\\\": 41\\n}\\n-rw-r--r-- 1 sebas sebas 102M Apr 19 19:02 /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3\\n\"}],\"isError\":false,\"timestamp\":1776636178641}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:119:{\"type\":\"message\",\"id\":\"ba794162\",\"parentId\":\"55022030\",\"timestamp\":\"2026-04-19T22:03:07.154Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_LjHjJLglZe2edpbbetueCNQ0|fc_071e452a5a0ea02e0169e551161d688190b1905dbc09c46210\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3\\nfor p in [\\n '/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3',\\n '/mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3',\\n]:\\n    con=sqlite3.connect(p)\\n    cur=con.cursor()\\n    print('DB', p)\\n    for q in [\\n        'SELECT COUNT(*) FROM memories',\\n        'SELECT COUNT(*) FROM retrieval_logs',\\n        'SELECT COUNT(*) FROM memory_links',\\n        'SELECT COUNT(*) FROM tasks',\\n        'SELECT COUNT(*) FROM artifacts',\\n    ]:\\n        try:\\n            print(q, cur.execute(q).fetchone()[0])\\n        except Exception as e:\\n            print(q, 'ERR', repr(e))\\n    try:\\n        print('sample memories ids', cur.execute('SELECT id, title FROM memories LIMIT 5').fetchall())\\n    except Exception as e:\\n        print('sample memories ERR', repr(e))\\n    print()\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":692,\"output\":267,\"cacheRead\":34048,\"cacheWrite\":0,\"totalTokens\":35007,\"cost\":{\"input\":0.0017300000000000002,\"output\":0.004005,\"cacheRead\":0.008512,\"cacheWrite\":0,\"total\":0.014247000000000001}},\"stopReason\":\"toolUse\",\"timestamp\":1776636178644,\"responseId\":\"resp_071e452a5a0ea02e0169e551134c008190b69ada5e4206106e\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:120:{\"type\":\"message\",\"id\":\"e9c30105\",\"parentId\":\"ba794162\",\"timestamp\":\"2026-04-19T22:03:07.207Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_LjHjJLglZe2edpbbetueCNQ0|fc_071e452a5a0ea02e0169e551161d688190b1905dbc09c46210\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"DB /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\nSELECT COUNT(*) FROM memories 320\\nSELECT COUNT(*) FROM retrieval_logs 6901\\nSELECT COUNT(*) FROM memory_links 445\\nSELECT COUNT(*) FROM tasks 194\\nSELECT COUNT(*) FROM artifacts 163\\nsample memories ids [('legacy_run_9fdccb34-cf1e-47bd-b133-a224f363a6d4', 'Research run: Investigate personal assistant patterns'), ('legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1', 'Research run: Verify capture-url flow'), ('legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb', 'Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow'), ('legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7', 'Research run: Source 50+ inch China displays for Ballbox advertising in padel venues'), ('legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0', 'Research run: Test intake flow')]\\n\\nDB /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3\\nSELECT COUNT(*) FROM memories 288\\nSELECT COUNT(*) FROM retrieval_logs 6848\\nSELECT COUNT(*) FROM memory_links 445\\nSELECT COUNT(*) FROM tasks 194\\nSELECT COUNT(*) FROM artifacts 163\\nsample memories ids [('legacy_run_9fdccb34-cf1e-47bd-b133-a224f363a6d4', 'Research run: Investigate personal assistant patterns'), ('legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1', 'Research run: Verify capture-url flow'), ('legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb', 'Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow'), ('legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7', 'Research run: Source 50+ inch China displays for Ballbox advertising in padel venues'), ('legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0', 'Research run: Test intake flow')]\\n\\n\"}],\"isError\":false,\"timestamp\":1776636187207}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:121:{\"type\":\"message\",\"id\":\"81cfa66e\",\"parentId\":\"e9c30105\",\"timestamp\":\"2026-04-19T22:03:13.390Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_o9Ym3DQMttfl2ZSWaFDO8pdU|fc_071e452a5a0ea02e0169e5511ce6208190a835991207d5f592\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3, json\\nsrc='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\nout='/mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3'\\n\\ns=sqlite3.connect(src)\\nd=sqlite3.connect(out)\\nsc=s.cursor(); dc=d.cursor()\\n\\nsrc_ids=[]\\ntry:\\n    for row in sc.execute('SELECT id FROM memories'):\\n        src_ids.append(row[0])\\nexcept Exception as e:\\n    print('src_iter_err', repr(e))\\n\\ndst_ids={row[0] for row in dc.execute('SELECT id FROM memories')}\\nmissing=[x for x in src_ids if x not in dst_ids]\\nprint('src_seen', len(src_ids))\\nprint('dst_ids', len(dst_ids))\\nprint('missing', len(missing))\\nprint(json.dumps(missing[:50], indent=2))\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":742,\"output\":242,\"cacheRead\":34816,\"cacheWrite\":0,\"totalTokens\":35800,\"cost\":{\"input\":0.001855,\"output\":0.00363,\"cacheRead\":0.008704,\"cacheWrite\":0,\"total\":0.014189}},\"stopReason\":\"toolUse\",\"timestamp\":1776636187209,\"responseId\":\"resp_071e452a5a0ea02e0169e5511c1f3881908aa288077c0d6c44\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:123:{\"type\":\"message\",\"id\":\"27abb143\",\"parentId\":\"a35b6063\",\"timestamp\":\"2026-04-19T22:03:19.976Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_jXrvytr2Ylm2GoGZHrYzJeCm|fc_071e452a5a0ea02e0169e5512306748190b5706aab0666d340\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3\\nsrc='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\ncon=sqlite3.connect(src)\\ncur=con.cursor()\\nids=[\\n'9be235c5-e438-4f89-a35e-9c9582574016',\\n'mem_10b251a5cd6844faaeec93ff7019c086',\\n'mem_1349693993ae4981b89dd9dab290d66d',\\n'mem_139063138eea4721bdac8d830fc0f49b',\\n'mem_30a32043ffd8430fa1b3d82e409d7b53',\\n]\\nfor mid in ids:\\n    try:\\n        row=cur.execute('SELECT id,title,type,subtype,run_id,task_id FROM memories WHERE id=?',(mid,)).fetchone()\\n        print(mid, row)\\n    except Exception as e:\\n        print(mid, 'ERR', repr(e))\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":628,\"output\":264,\"cacheRead\":35712,\"cacheWrite\":0,\"totalTokens\":36604,\"cost\":{\"input\":0.0015700000000000002,\"output\":0.00396,\"cacheRead\":0.008928,\"cacheWrite\":0,\"total\":0.014458}},\"stopReason\":\"toolUse\",\"timestamp\":1776636193450,\"responseId\":\"resp_071e452a5a0ea02e0169e55121f1d481908fc634d1088ac984\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:125:{\"type\":\"message\",\"id\":\"49a8e791\",\"parentId\":\"67a875ec\",\"timestamp\":\"2026-04-19T22:03:29.651Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_B4261hos9XGzvPEy6DS0VxPD|fc_071e452a5a0ea02e0169e5512963d88190af324c78af63c4fe\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\ncp -a /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3 /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\nsha256sum /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3 /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3\\nsystemctl --user restart agents-database-http.service\\nsleep 2\\ncurl -sS -m 10 -D - -o /tmp/adbstatus3.out http://127.0.0.1:8091/api/status\\nprintf '\\\\n--- body ---\\\\n'\\ncat /tmp/adbstatus3.out\\nprintf '\\\\n--- write test ---\\\\n'\\npython3 - <<'PY'\\nimport json, urllib.request\\npayload={\\n  'type':'episode','scope':'global','title':'repair test memory','content':'post-rebuild write test','source_ref':'conversation:2026-04-19','origin_agent':'personal-agent'\\n}\\nreq=urllib.request.Request('http://127.0.0.1:8091/api/ingest', data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'}, method='POST')\\nwith urllib.request.urlopen(req, timeout=20) as r:\\n    print(r.status)\\n    print(r.read().decode())\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":345,\"output\":321,\"cacheRead\":36480,\"cacheWrite\":0,\"totalTokens\":37146,\"cost\":{\"input\":0.0008625000000000001,\"output\":0.004815,\"cacheRead\":0.00912,\"cacheWrite\":0,\"total\":0.0147975}},\"stopReason\":\"toolUse\",\"timestamp\":1776636200031,\"responseId\":\"resp_071e452a5a0ea02e0169e551286190819089d205830c69cefa\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:126:{\"type\":\"message\",\"id\":\"62196669\",\"parentId\":\"49a8e791\",\"timestamp\":\"2026-04-19T22:03:47.437Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_B4261hos9XGzvPEy6DS0VxPD|fc_071e452a5a0ea02e0169e5512963d88190af324c78af63c4fe\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"d9fbc9cddbdfe0cbdef6fd881c3c3d39404cbc858a04137542b3c4020183d47b  /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\nd9fbc9cddbdfe0cbdef6fd881c3c3d39404cbc858a04137542b3c4020183d47b  /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3\\nHTTP/1.0 200 OK\\r\\nServer: BaseHTTP/0.6 Python/3.13.5\\r\\nDate: Sun, 19 Apr 2026 22:03:47 GMT\\r\\nContent-Type: application/json; charset=utf-8\\r\\nContent-Length: 261\\r\\nAccess-Control-Allow-Origin: *\\r\\nAccess-Control-Allow-Headers: Content-Type\\r\\nAccess-Control-Allow-Methods: GET,POST,PATCH,OPTIONS\\r\\n\\r\\n\\n--- body ---\\n{\\\"ok\\\": true, \\\"db_path\\\": \\\"/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\\", \\\"dashboard\\\": {\\\"memories\\\": 288, \\\"tasks\\\": 194, \\\"artifacts\\\": 163, \\\"projects\\\": 4, \\\"repos\\\": 7}, \\\"audit\\\": {\\\"schema_version\\\": \\\"shared-agent-memory-v2\\\", \\\"memories\\\": 288, \\\"tasks\\\": 194}}\\n--- write test ---\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 6, in <module>\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 189, in urlopen\\n    return opener.open(url, data, timeout)\\n           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 489, in open\\n    response = self._open(req, data)\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 506, in _open\\n    result = self._call_chain(self.handle_open, protocol, protocol +\\n                              '_open', req)\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 466, in _call_chain\\n    result = func(*args)\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 1348, in http_open\\n    return self.do_open(http.client.HTTPConnection, req)\\n           ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 1323, in do_open\\n    r = h.getresponse()\\n  File \\\"/usr/lib/python3.13/http/client.py\\\", line 1430, in getresponse\\n    response.begin()\\n    ~~~~~~~~~~~~~~^^\\n  File \\\"/usr/lib/python3.13/http/client.py\\\", line 331, in begin\\n    version, status, reason = self._read_status()\\n                              ~~~~~~~~~~~~~~~~~^^\\n  File \\\"/usr/lib/python3.13/http/client.py\\\", line 300, in _read_status\\n    raise RemoteDisconnected(\\\"Remote end closed connection without\\\"\\n                             \\\" response\\\")\\nhttp.client.RemoteDisconnected: Remote end closed connection without response\\n\\n\\nCommand exited with code 1\"}],\"details\":{},\"isError\":true,\"timestamp\":1776636227436}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:127:{\"type\":\"message\",\"id\":\"fd24ce17\",\"parentId\":\"62196669\",\"timestamp\":\"2026-04-19T22:04:04.908Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_fZ6yY6znCPk9GBlFtr5GUVb7|fc_071e452a5a0ea02e0169e551547274819099b4b07a06b4c69d\",\"name\":\"bash\",\"arguments\":{\"command\":\"journalctl --user -u agents-database-http.service -n 60 --no-pager | tail -n 40\",\"timeout\":120}},{\"type\":\"toolCall\",\"id\":\"call_ZVFdrgIgxBNBIL9dEScK4jmg|fc_071e452a5a0ea02e0169e5515472948190ac199473798ae803\",\"name\":\"bash\",\"arguments\":{\"command\":\"python3 - <<'PY'\\nimport sqlite3\\np='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\ncon=sqlite3.connect(p)\\ncur=con.cursor()\\nfor q in [\\n 'PRAGMA quick_check;',\\n 'SELECT COUNT(*) FROM memories',\\n 'SELECT COUNT(*) FROM memories_fts',\\n 'SELECT COUNT(*) FROM sqlite_master WHERE name = \\\"memories_fts\\\"',\\n]:\\n    try:\\n        print('---', q)\\n        print(cur.execute(q).fetchall()[:5])\\n    except Exception as e:\\n        print('ERR', q, repr(e))\\nPY\",\"timeout\":120}},{\"type\":\"toolCall\",\"id\":\"call_KHKMyCT7Em8TWV8fFDlNcWo4|fc_071e452a5a0ea02e0169e5515472a48190873ce9e06e64e468\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/service.py\",\"offset\":60,\"limit\":220}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":824,\"output\":258,\"cacheRead\":36992,\"cacheWrite\":0,\"totalTokens\":38074,\"cost\":{\"input\":0.00206,\"output\":0.00387,\"cacheRead\":0.009248,\"cacheWrite\":0,\"total\":0.015178}},\"stopReason\":\"toolUse\",\"timestamp\":1776636227438,\"responseId\":\"resp_071e452a5a0ea02e0169e5514642f48190b77576bd22f36a9e\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:131:{\"type\":\"message\",\"id\":\"31d3b7de\",\"parentId\":\"e0cdbde0\",\"timestamp\":\"2026-04-19T22:04:08.884Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_EzGruatKiJxKRLUVaO0ISz5a|fc_071e452a5a0ea02e0169e5515651848190bc46f2eb94acb920\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/service.py\",\"offset\":1460,\"limit\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":2430,\"output\":39,\"cacheRead\":37888,\"cacheWrite\":0,\"totalTokens\":40357,\"cost\":{\"input\":0.0060750000000000005,\"output\":0.000585,\"cacheRead\":0.009472,\"cacheWrite\":0,\"total\":0.016132}},\"stopReason\":\"toolUse\",\"timestamp\":1776636245056,\"responseId\":\"resp_071e452a5a0ea02e0169e5515565808190800d39dfb9a68e51\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:133:{\"type\":\"message\",\"id\":\"62f12576\",\"parentId\":\"660104e5\",\"timestamp\":\"2026-04-19T22:04:13.997Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_Z6UREPAfKIxPRNuUuBwvu351|fc_071e452a5a0ea02e0169e5515a11208190ab3e24541340dc9d\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3, json, traceback\\nfrom pathlib import Path\\nimport sys\\nsys.path.insert(0, '/mnt/rpi/agents-database/src')\\nfrom shared_agent_memory.service import MemoryService\\n\\np='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\ntry:\\n    svc=MemoryService(p)\\n    print('service_init_ok')\\n    rec=svc.create_memory({\\n      'type':'episode','scope':'global','title':'repair test memory 2','content':'post-rebuild direct service write test','source_ref':'conversation:2026-04-19','origin_agent':'personal-agent'\\n    })\\n    print(json.dumps(rec, indent=2))\\nexcept Exception as e:\\n    print('ERR', repr(e))\\n    traceback.print_exc()\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":1387,\"output\":212,\"cacheRead\":40192,\"cacheWrite\":0,\"totalTokens\":41791,\"cost\":{\"input\":0.0034675,\"output\":0.00318,\"cacheRead\":0.010048,\"cacheWrite\":0,\"total\":0.016695500000000002}},\"stopReason\":\"toolUse\",\"timestamp\":1776636248907,\"responseId\":\"resp_071e452a5a0ea02e0169e551592f308190863e9b40a912d98c\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:135:{\"type\":\"message\",\"id\":\"b583a3a1\",\"parentId\":\"462df25a\",\"timestamp\":\"2026-04-19T22:04:22.336Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_wC9PqdsD4tS0saEmdtHR4Yu3|fc_071e452a5a0ea02e0169e55164b3b08190a251d1f23154b983\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py\",\"offset\":160,\"limit\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":506,\"output\":39,\"cacheRead\":41600,\"cacheWrite\":0,\"totalTokens\":42145,\"cost\":{\"input\":0.001265,\"output\":0.000585,\"cacheRead\":0.0104,\"cacheWrite\":0,\"total\":0.01225}},\"stopReason\":\"toolUse\",\"timestamp\":1776636255042,\"responseId\":\"resp_071e452a5a0ea02e0169e5515ff7708190ae13a324740a2320\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:137:{\"type\":\"message\",\"id\":\"a05bc419\",\"parentId\":\"369c7887\",\"timestamp\":\"2026-04-19T22:04:24.709Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_al3dMjxANfFx8AmYm7F8H09A|fc_071e452a5a0ea02e0169e55167dd108190bab71c966acc9da8\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py\",\"offset\":280,\"limit\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":1131,\"output\":39,\"cacheRead\":41984,\"cacheWrite\":0,\"totalTokens\":43154,\"cost\":{\"input\":0.0028275,\"output\":0.000585,\"cacheRead\":0.010496,\"cacheWrite\":0,\"total\":0.0139085}},\"stopReason\":\"toolUse\",\"timestamp\":1776636262361,\"responseId\":\"resp_071e452a5a0ea02e0169e55166c04c8190a7585d031f1d8b08\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:139:{\"type\":\"message\",\"id\":\"1456369e\",\"parentId\":\"b51bfbd4\",\"timestamp\":\"2026-04-19T22:04:32.337Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_2TY02jFDtzuYwpPyDik8CCWG|fc_071e452a5a0ea02e0169e55169efa08190ba098da50066a942\",\"name\":\"edit\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py\",\"edits\":[{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n                return\\n            except ValueError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n                return\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n                return\\n            except ValueError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n                return\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n                return\\n\"},{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n                return\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n                return\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n                return\\n\"},{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n\"}]}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":806,\"output\":349,\"cacheRead\":43008,\"cacheWrite\":0,\"totalTokens\":44163,\"cost\":{\"input\":0.0020150000000000003,\"output\":0.005235,\"cacheRead\":0.010752,\"cacheWrite\":0,\"total\":0.018002}},\"stopReason\":\"toolUse\",\"timestamp\":1776636264723,\"responseId\":\"resp_071e452a5a0ea02e0169e5516914b0819096ade06ccdff6de9\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:140:{\"type\":\"message\",\"id\":\"a583af89\",\"parentId\":\"1456369e\",\"timestamp\":\"2026-04-19T22:04:32.364Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_2TY02jFDtzuYwpPyDik8CCWG|fc_071e452a5a0ea02e0169e55169efa08190ba098da50066a942\",\"toolName\":\"edit\",\"content\":[{\"type\":\"text\",\"text\":\"Found 2 occurrences of edits[2] in /mnt/rpi/agents-database/src/shared_agent_memory/http_api.py. Each oldText must be unique. Please provide more context to make it unique.\"}],\"details\":{},\"isError\":true,\"timestamp\":1776636272364}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:141:{\"type\":\"message\",\"id\":\"fca57b28\",\"parentId\":\"a583af89\",\"timestamp\":\"2026-04-19T22:04:47.252Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_ExB5sJPrcCT5nS8W1jPK7dOI|fc_071e452a5a0ea02e0169e55172482c81908cb38592b44bad1d\",\"name\":\"edit\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py\",\"edits\":[{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n                return\\n            except ValueError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n                return\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n                return\\n            except ValueError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n                return\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n                return\\n\"},{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n                return\\n\\n            self.send_error(HTTPStatus.NOT_FOUND)\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n                return\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n                return\\n\\n            self.send_error(HTTPStatus.NOT_FOUND)\\n\"},{\"oldText\":\"            try:\\n                self._send_json(\\n                    service.update_task(\\n                        parsed.path.split(\\\"/\\\")[3],\\n                        status=payload.get(\\\"status\\\"),\\n                        owner_agent=payload.get(\\\"owner_agent\\\"),\\n                        blocked_reason=payload.get(\\\"blocked_reason\\\"),\\n                        requires_human_input=payload.get(\\\"requires_human_input\\\"),\\n                        metadata=payload.get(\\\"metadata\\\"),\\n                        run_id=payload.get(\\\"run_id\\\"),\\n                    )\\n                )\\n            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n\",\"newText\":\"            try:\\n                self._send_json(\\n                    service.update_task(\\n                        parsed.path.split(\\\"/\\\")[3],\\n                        status=payload.get(\\\"status\\\"),\\n                        owner_agent=payload.get(\\\"owner_agent\\\"),\\n                        blocked_reason=payload.get(\\\"blocked_reason\\\"),\\n                        requires_human_input=payload.get(\\\"requires_human_input\\\"),\\n                        metadata=payload.get(\\\"metadata\\\"),\\n                        run_id=payload.get(\\\"run_id\\\"),\\n                    )\\n                )\\n            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n\"}]}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":183,\"output\":601,\"cacheRead\":44032,\"cacheWrite\":0,\"totalTokens\":44816,\"cost\":{\"input\":0.00045750000000000006,\"output\":0.009015,\"cacheRead\":0.011007999999999999,\"cacheWrite\":0,\"total\":0.0204805}},\"stopReason\":\"toolUse\",\"timestamp\":1776636272366,\"responseId\":\"resp_071e452a5a0ea02e0169e55170adc08190b779d9650c4fb47a\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:142:{\"type\":\"message\",\"id\":\"543b2034\",\"parentId\":\"fca57b28\",\"timestamp\":\"2026-04-19T22:04:47.270Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_ExB5sJPrcCT5nS8W1jPK7dOI|fc_071e452a5a0ea02e0169e55172482c81908cb38592b44bad1d\",\"toolName\":\"edit\",\"content\":[{\"type\":\"text\",\"text\":\"Successfully replaced 3 block(s) in /mnt/rpi/agents-database/src/shared_agent_memory/http_api.py.\"}],\"details\":{\"diff\":\"     ...\\n 191                 return\\n 192             except ValueError as exc:\\n 193                 self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n 194                 return\\n+195             except Exception as exc:\\n+196                 self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n+197                 return\\n 195 \\n 196             self.send_error(HTTPStatus.NOT_FOUND)\\n 197 \\n 198         def do_POST(self) -> None:  # noqa: N802\\n     ...\\n 289                     return\\n 290             except KeyError as exc:\\n 291                 self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n 292                 return\\n+296             except Exception as exc:\\n+297                 self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n+298                 return\\n 293 \\n 294             self.send_error(HTTPStatus.NOT_FOUND)\\n 295 \\n 296         def do_PATCH(self) -> None:  # noqa: N802\\n     ...\\n 316                     )\\n 317                 )\\n 318             except KeyError as exc:\\n 319                 self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n+326             except Exception as exc:\\n+327                 self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n 320 \\n 321         def log_message(self, fmt: str, *args: Any) -> None:\\n 322             return\\n 323 \\n     ...\",\"firstChangedLine\":195},\"isError\":false,\"timestamp\":1776636287270}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:143:{\"type\":\"message\",\"id\":\"59b19039\",\"parentId\":\"543b2034\",\"timestamp\":\"2026-04-19T22:04:53.167Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_wUDe2Y50EeMG0BmP1F7WgEEh|fc_071e452a5a0ea02e0169e55181460c81909662a07d72eebd8a\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\nsystemctl --user restart agents-database-http.service\\nsleep 2\\npython3 - <<'PY'\\nimport json, urllib.request, urllib.error\\npayload={\\n  'type':'episode','scope':'global','title':'repair test memory 3','content':'post-rebuild http write test','source_ref':'conversation:2026-04-19','origin_agent':'personal-agent'\\n}\\nreq=urllib.request.Request('http://127.0.0.1:8091/api/ingest', data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'}, method='POST')\\ntry:\\n    with urllib.request.urlopen(req, timeout=20) as r:\\n        print('status', r.status)\\n        print(r.read().decode())\\nexcept urllib.error.HTTPError as e:\\n    print('http_error', e.code)\\n    print(e.read().decode())\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":178,\"output\":221,\"cacheRead\":44672,\"cacheWrite\":0,\"totalTokens\":45071,\"cost\":{\"input\":0.00044500000000000003,\"output\":0.0033150000000000002,\"cacheRead\":0.011167999999999999,\"cacheWrite\":0,\"total\":0.014928}},\"stopReason\":\"toolUse\",\"timestamp\":1776636287272,\"responseId\":\"resp_071e452a5a0ea02e0169e5517fc18481909a7051312556392f\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:145:{\"type\":\"message\",\"id\":\"ce245cf0\",\"parentId\":\"ab030fb1\",\"timestamp\":\"2026-04-19T22:04:58.026Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_sCmMrxmJWtlr5hFmVXIYueLJ|fc_071e452a5a0ea02e0169e551891b148190874a581b13729c43\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/cli.py\",\"offset\":130,\"limit\":80}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":265,\"output\":39,\"cacheRead\":44928,\"cacheWrite\":0,\"totalTokens\":45232,\"cost\":{\"input\":0.0006625,\"output\":0.000585,\"cacheRead\":0.011231999999999999,\"cacheWrite\":0,\"total\":0.0124795}},\"stopReason\":\"toolUse\",\"timestamp\":1776636295281,\"responseId\":\"resp_071e452a5a0ea02e0169e55187e6448190aa4e592ddb2ce715\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:147:{\"type\":\"message\",\"id\":\"4dcdc694\",\"parentId\":\"7e7ce863\",\"timestamp\":\"2026-04-19T22:05:07.423Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_l4ECxAxuvMAGE6vhgjruzdvj|fc_071e452a5a0ea02e0169e5518b6a9c81908d4b6e672f309cb0\",\"name\":\"edit\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/cli.py\",\"edits\":[{\"oldText\":\"    if args.command == \\\"serve-http\\\":\\n        from .store import SQLiteStore\\n        from .service import SCHEMA_VERSION\\n\\n        class StatusOnlyService:\\n            def __init__(self, db_path: str) -> None:\\n                self.store = SQLiteStore(db_path)\\n                self.store.initialize()\\n\\n            def dashboard_snapshot(self):\\n                with self.store.connection() as conn:\\n                    return {\\n                        \\\"memories\\\": conn.execute(\\\"SELECT COUNT(*) FROM memories\\\").fetchone()[0],\\n                        \\\"tasks\\\": conn.execute(\\\"SELECT COUNT(*) FROM tasks\\\").fetchone()[0],\\n                        \\\"artifacts\\\": conn.execute(\\\"SELECT COUNT(*) FROM artifacts\\\").fetchone()[0],\\n                        \\\"projects\\\": conn.execute(\\\"SELECT COUNT(*) FROM projects\\\").fetchone()[0],\\n                        \\\"repos\\\": conn.execute(\\\"SELECT COUNT(*) FROM repos\\\").fetchone()[0],\\n                    }\\n\\n            def audit_v2(self):\\n                with self.store.connection() as conn:\\n                    return {\\n                        \\\"schema_version\\\": SCHEMA_VERSION,\\n                        \\\"memories\\\": conn.execute(\\\"SELECT COUNT(*) FROM memories\\\").fetchone()[0],\\n                        \\\"tasks\\\": conn.execute(\\\"SELECT COUNT(*) FROM tasks\\\").fetchone()[0],\\n                    }\\n\\n        service = StatusOnlyService(args.db)\\n        from .http_api import build_handler\\n        from http.server import ThreadingHTTPServer\\n\\n        server = ThreadingHTTPServer((args.host, args.port), build_handler(service))\\n        try:\\n            server.serve_forever()\\n        finally:\\n            server.server_close()\\n        return\\n\\n    service = MemoryService(args.db)\\n    engine = MemoryEngine(service)\\n\",\"newText\":\"    service = MemoryService(args.db)\\n    engine = MemoryEngine(service)\\n\"}]}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":880,\"output\":436,\"cacheRead\":45056,\"cacheWrite\":0,\"totalTokens\":46372,\"cost\":{\"input\":0.0022,\"output\":0.00654,\"cacheRead\":0.011264,\"cacheWrite\":0,\"total\":0.020004}},\"stopReason\":\"toolUse\",\"timestamp\":1776636298041,\"responseId\":\"resp_071e452a5a0ea02e0169e5518a7860819082ff96dea055bda0\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:148:{\"type\":\"message\",\"id\":\"0ebbde07\",\"parentId\":\"4dcdc694\",\"timestamp\":\"2026-04-19T22:05:07.430Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_l4ECxAxuvMAGE6vhgjruzdvj|fc_071e452a5a0ea02e0169e5518b6a9c81908d4b6e672f309cb0\",\"toolName\":\"edit\",\"content\":[{\"type\":\"text\",\"text\":\"Successfully replaced 1 block(s) in /mnt/rpi/agents-database/src/shared_agent_memory/cli.py.\"}],\"details\":{\"diff\":\"     ...\\n 134 def main() -> None:\\n 135     parser = build_parser()\\n 136     args = parser.parse_args()\\n 137 \\n-138     if args.command == \\\"serve-http\\\":\\n-139         from .store import SQLiteStore\\n-140         from .service import SCHEMA_VERSION\\n-141 \\n-142         class StatusOnlyService:\\n-143             def __init__(self, db_path: str) -> None:\\n-144                 self.store = SQLiteStore(db_path)\\n-145                 self.store.initialize()\\n-146 \\n-147             def dashboard_snapshot(self):\\n-148                 with self.store.connection() as conn:\\n-149                     return {\\n-150                         \\\"memories\\\": conn.execute(\\\"SELECT COUNT(*) FROM memories\\\").fetchone()[0],\\n-151                         \\\"tasks\\\": conn.execute(\\\"SELECT COUNT(*) FROM tasks\\\").fetchone()[0],\\n-152                         \\\"artifacts\\\": conn.execute(\\\"SELECT COUNT(*) FROM artifacts\\\").fetchone()[0],\\n-153                         \\\"projects\\\": conn.execute(\\\"SELECT COUNT(*) FROM projects\\\").fetchone()[0],\\n-154                         \\\"repos\\\": conn.execute(\\\"SELECT COUNT(*) FROM repos\\\").fetchone()[0],\\n-155                     }\\n-156 \\n-157             def audit_v2(self):\\n-158                 with self.store.connection() as conn:\\n-159                     return {\\n-160                         \\\"schema_version\\\": SCHEMA_VERSION,\\n-161                         \\\"memories\\\": conn.execute(\\\"SELECT COUNT(*) FROM memories\\\").fetchone()[0],\\n-162                         \\\"tasks\\\": conn.execute(\\\"SELECT COUNT(*) FROM tasks\\\").fetchone()[0],\\n-163                     }\\n-164 \\n-165         service = StatusOnlyService(args.db)\\n-166         from .http_api import build_handler\\n-167         from http.server import ThreadingHTTPServer\\n-168 \\n-169         server = ThreadingHTTPServer((args.host, args.port), build_handler(service))\\n-170         try:\\n-171             server.serve_forever()\\n-172         finally:\\n-173             server.server_close()\\n-174         return\\n-175 \\n 176     service = MemoryService(args.db)\\n 177     engine = MemoryEngine(service)\\n 178 \\n 179     if args.command == \\\"init-db\\\":\\n     ...\",\"firstChangedLine\":138},\"isError\":false,\"timestamp\":1776636307429}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:149:{\"type\":\"message\",\"id\":\"7658a5c1\",\"parentId\":\"0ebbde07\",\"timestamp\":\"2026-04-19T22:05:15.847Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_os4zNaUTaG4Hxpk2FbRfV5LJ|fc_071e452a5a0ea02e0169e55196f5f08190a87d37c1d58107ac\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\ncd /mnt/rpi/agents-database\\nPYTHONPATH=src python3 -m py_compile src/shared_agent_memory/cli.py src/shared_agent_memory/http_api.py src/shared_agent_memory/service.py\\nsystemctl --user restart agents-database-http.service\\nsleep 2\\ncurl -sS -m 10 http://127.0.0.1:8091/api/status\\nprintf '\\\\n--- ingest ---\\\\n'\\npython3 - <<'PY'\\nimport json, urllib.request, urllib.error\\npayload={\\n  'type':'episode','scope':'global','title':'repair test memory 4','content':'post-rebuild http write test ok','source_ref':'conversation:2026-04-19','origin_agent':'personal-agent'\\n}\\nreq=urllib.request.Request('http://127.0.0.1:8091/api/ingest', data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'}, method='POST')\\nwith urllib.request.urlopen(req, timeout=20) as r:\\n    print('status', r.status)\\n    print(r.read().decode())\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":198,\"output\":265,\"cacheRead\":46208,\"cacheWrite\":0,\"totalTokens\":46671,\"cost\":{\"input\":0.000495,\"output\":0.003975,\"cacheRead\":0.011552,\"cacheWrite\":0,\"total\":0.016022}},\"stopReason\":\"toolUse\",\"timestamp\":1776636307431,\"responseId\":\"resp_071e452a5a0ea02e0169e5519418e88190a7e2d526413ddfcd\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:153:{\"type\":\"message\",\"id\":\"7ae83668\",\"parentId\":\"15ee688d\",\"timestamp\":\"2026-04-19T22:05:35.669Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"Done, mostly.\\n\\nWhat I completed\\n\\n1. Backed up SQLite first\\n- Created:\\n  - `/mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3`\\n\\n2. Rebuilt the malformed DB into a clean file\\n- New rebuilt DB:\\n  - `/mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3`\\n- Replaced live DB with rebuilt copy:\\n  - `/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3`\\n\\n3. Restored agents-database to normal HTTP behavior\\n- Removed the degraded status-only workaround\\n- Added safer HTTP error handling so bad endpoints return `500` instead of empty reply\\n- Restarted service\\n- Verified write path works again:\\n  - `POST /api/ingest` returned `201`\\n\\nCurrent `agents-database` state\\n- service up on `:8091`\\n- `/api/status` works\\n- `/api/ingest` works\\n- DB `quick_check` on rebuilt DB: `ok`\\n\\nImportant caveat\\n- Rebuild recovered most, not all, rows.\\n- Before:\\n  - memories: `320`\\n  - retrieval_logs: `6901`\\n- After rebuild:\\n  - memories: `288` + new test writes\\n  - retrieval_logs: `6848`\\n- So some damaged rows were lost during rebuild. This is the tradeoff that got the service healthy again.\\n\\nRoot `personal-agent.service`\\n- I confirmed the broken root unit is still the stale one in:\\n  - `/etc/systemd/system/personal-agent.service`\\n- I tried to remove/disable it properly, but file operations on `/etc/systemd/system` need elevated rights.\\n- The healthy user unit is still serving `:8082`.\\n\\nBlocked item\\n- I cannot finish cleanup of the root unit without privilege.\\n\\nYou should run:\\n```bash\\nsudo mkdir -p /mnt/rpi/service-backups/systemd-disabled\\nsudo mv /etc/systemd/system/personal-agent.service /mnt/rpi/service-backups/systemd-disabled/personal-agent.service.disabled-$(date +%Y%m%d-%H%M%S)\\nsudo systemctl daemon-reload\\nsudo systemctl reset-failed personal-agent.service || true\\nsystemctl status personal-agent.service --no-pager -n 20 || true\\n```\\n\\nBusiness outcome\\n- Shared memory service is working again for normal API use.\\n- Personal agent remains available.\\n- Only remaining cleanup is the stale root systemd unit.\\n\\nIf you want, next I can:\\n- diff old vs rebuilt DB counts in more detail\\n- save a repair summary into agents-database\\n- inspect whether any key memory types were among the lost rows\",\"textSignature\":\"{\\\"v\\\":1,\\\"id\\\":\\\"msg_071e452a5a0ea02e0169e551a3999c8190abae122cd32fd8ed\\\",\\\"phase\\\":\\\"final_answer\\\"}\"}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":173,\"output\":566,\"cacheRead\":46976,\"cacheWrite\":0,\"totalTokens\":47715,\"cost\":{\"input\":0.00043250000000000005,\"output\":0.008490000000000001,\"cacheRead\":0.011744,\"cacheWrite\":0,\"total\":0.0206665}},\"stopReason\":\"stop\",\"timestamp\":1776636322453,\"responseId\":\"resp_071e452a5a0ea02e0169e551a2dd68819085a062b9659bb870\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:160:{\"type\":\"message\",\"id\":\"a330d67d\",\"parentId\":\"370dd3ad\",\"timestamp\":\"2026-04-19T22:21:17.098Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"Only minor cleanup.\\n\\nDone:\\n- SSH tmux picker with “new session”\\n- tmux scrolling enabled\\n- root stale `personal-agent.service` removed\\n- `personal-agent` healthy on user service\\n- `agents-database` repaired enough to serve and write again\\n- SQLite backed up before repair\\n\\nLeft:\\n1. optional: save a repair summary into agents-database\\n2. optional: inspect what data was lost in DB rebuild\\n   - rebuilt DB recovered most rows, but not all\\n3. optional: clean extra DB artifacts if you want less clutter\\n   - backups/recovered copies in `/mnt/rpi/agents-database/data/`\\n\\nWorth noting:\\n- `agents-database` is healthy now, but the rebuild was a recovery, not a perfect repair. If some old memories matter, we should audit missing rows.\\n\\nIf you want the system “fully closed out,” I’d do:\\n- save memory of the tmux preference + repair summary\\n- produce a short lost-row report\\n- stop there\\n\\nIf not, you’re basically done.\",\"textSignature\":\"{\\\"v\\\":1,\\\"id\\\":\\\"msg_071e452a5a0ea02e0169e55558cabc81909f84d0eb814fcfce\\\",\\\"phase\\\":\\\"final_answer\\\"}\"}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":152,\"output\":222,\"cacheRead\":62976,\"cacheWrite\":0,\"totalTokens\":63350,\"cost\":{\"input\":0.00038,\"output\":0.00333,\"cacheRead\":0.015743999999999998,\"cacheWrite\":0,\"total\":0.019454}},\"stopReason\":\"stop\",\"timestamp\":1776637271150,\"responseId\":\"resp_071e452a5a0ea02e0169e555578e3481908277117feff18574\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:162:{\"type\":\"message\",\"id\":\"6eebe0a9\",\"parentId\":\"dda84e8f\",\"timestamp\":\"2026-04-19T22:22:12.015Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"Already rebuilt.\\n\\nCurrent live DB is the rebuilt one:\\n- `/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3`\\n\\nBackups kept:\\n- original pre-repair backup:\\n  - `/mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3`\\n- earlier copies:\\n  - `.bak-20260419-184938`\\n  - `.recovered-20260419-184949.sqlite3`\\n  - `.rebuild-20260419.sqlite3`\\n\\nResult of rebuild:\\n- service health restored\\n- writes restored\\n- some rows lost during salvage\\n\\nSo if by “try rebuild” you mean “attempt a better second-pass recovery from the original corrupted backup,” yes, that’s still possible.\\n\\nBest next attempt:\\n- keep live DB as-is\\n- use the original corrupted backup\\n- do a more targeted salvage of missing rows, especially `memories` and `retrieval_logs`\\n- merge any recovered rows into the healthy live DB\\n\\nThat is safe if done against copies only.\\n\\nIf you want, I can do that now:\\n1. diff live rebuilt DB vs original corrupted backup\\n2. try to recover missing rows from the original backup\\n3. merge recovered rows into live DB\\n4. verify API again\",\"textSignature\":\"{\\\"v\\\":1,\\\"id\\\":\\\"msg_071e452a5a0ea02e0169e5558ef05081908bfac82c8521afe7\\\",\\\"phase\\\":\\\"final_answer\\\"}\"}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":126,\"output\":267,\"cacheRead\":63232,\"cacheWrite\":0,\"totalTokens\":63625,\"cost\":{\"input\":0.000315,\"output\":0.004005,\"cacheRead\":0.015808,\"cacheWrite\":0,\"total\":0.020128}},\"stopReason\":\"stop\",\"timestamp\":1776637325459,\"responseId\":\"resp_071e452a5a0ea02e0169e5558e0a048190ae9b7785a6f38936\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:164:{\"type\":\"message\",\"id\":\"d251b709\",\"parentId\":\"66a0eb8c\",\"timestamp\":\"2026-04-19T22:22:37.747Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_LinsHcyHu9iH02c0MTiwtZws|fc_071e452a5a0ea02e0169e555ad6e4c8190b29ad0967e9a9415\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3, json\\nfor p in [\\n '/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3',\\n '/mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3',\\n]:\\n    con=sqlite3.connect(p)\\n    cur=con.cursor()\\n    print('DB', p)\\n    for q in [\\n      'SELECT COUNT(*) FROM memories',\\n      'SELECT COUNT(*) FROM retrieval_logs',\\n      'SELECT COUNT(*) FROM tasks',\\n      'SELECT COUNT(*) FROM artifacts',\\n      'SELECT COUNT(*) FROM memory_links',\\n    ]:\\n      try:\\n        print(q, cur.execute(q).fetchone()[0])\\n      except Exception as e:\\n        print(q, 'ERR', repr(e))\\n    print()\\nPY\",\"timeout\":120}},{\"type\":\"toolCall\",\"id\":\"call_Rw3vjGxuZZsWsDpifSDNMrXN|fc_071e452a5a0ea02e0169e555ad6e6081908393dd01742ec996\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\ncp -a /mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3 /mnt/rpi/agents-database/data/shared-agent-memory.recovery-work.sqlite3\\nls -lh /mnt/rpi/agents-database/data/shared-agent-memory.recovery-work.sqlite3\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":149,\"output\":330,\"cacheRead\":63488,\"cacheWrite\":0,\"totalTokens\":63967,\"cost\":{\"input\":0.00037250000000000006,\"output\":0.00495,\"cacheRead\":0.015872,\"cacheWrite\":0,\"total\":0.0211945}},\"stopReason\":\"toolUse\",\"timestamp\":1776637348607,\"responseId\":\"resp_071e452a5a0ea02e0169e555a503448190843d93bbabf86b1e\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:165:{\"type\":\"message\",\"id\":\"1081500b\",\"parentId\":\"d251b709\",\"timestamp\":\"2026-04-19T22:22:39.513Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_LinsHcyHu9iH02c0MTiwtZws|fc_071e452a5a0ea02e0169e555ad6e4c8190b29ad0967e9a9415\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"DB /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\nSELECT COUNT(*) FROM memories 290\\nSELECT COUNT(*) FROM retrieval_logs 6848\\nSELECT COUNT(*) FROM tasks 194\\nSELECT COUNT(*) FROM artifacts 163\\nSELECT COUNT(*) FROM memory_links 465\\n\\nDB /mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3\\nSELECT COUNT(*) FROM memories 320\\nSELECT COUNT(*) FROM retrieval_logs 6901\\nSELECT COUNT(*) FROM tasks 194\\nSELECT COUNT(*) FROM artifacts 163\\nSELECT COUNT(*) FROM memory_links 445\\n\\n\"}],\"isError\":false,\"timestamp\":1776637359513}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:166:{\"type\":\"message\",\"id\":\"8387cb19\",\"parentId\":\"1081500b\",\"timestamp\":\"2026-04-19T22:22:43.873Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_Rw3vjGxuZZsWsDpifSDNMrXN|fc_071e452a5a0ea02e0169e555ad6e6081908393dd01742ec996\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"-rw-r--r-- 1 sebas sebas 104M Apr 18 10:35 /mnt/rpi/agents-database/data/shared-agent-memory.recovery-work.sqlite3\\n\"}],\"isError\":false,\"timestamp\":1776637363873}}\n\n[Showing lines 174-200 of 200 (50.0KB limit). Full output: /tmp/pi-bash-0db4f6e09bf8e7fa.log]"}],"details":{"truncation":{"content":"/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:118:{\"type\":\"message\",\"id\":\"55022030\",\"parentId\":\"24a55fd0\",\"timestamp\":\"2026-04-19T22:02:58.641Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_C9KoC5ODzSr8L4MccFNsCkhZ|fc_071e452a5a0ea02e0169e550d7fec48190956ba28739465114\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"{\\n  \\\"projects\\\": {\\n    \\\"copied\\\": 4,\\n    \\\"common_cols\\\": 5\\n  },\\n  \\\"repos\\\": {\\n    \\\"copied\\\": 7,\\n    \\\"common_cols\\\": 6\\n  },\\n  \\\"sources\\\": {\\n    \\\"copied\\\": 0,\\n    \\\"common_cols\\\": 7\\n  },\\n  \\\"ingestion_events\\\": {\\n    \\\"copied\\\": 0,\\n    \\\"common_cols\\\": 11\\n  },\\n  \\\"memories\\\": {\\n    \\\"error\\\": \\\"DatabaseError('database disk image is malformed')\\\"\\n  },\\n  \\\"memory_links\\\": {\\n    \\\"copied\\\": 445,\\n    \\\"common_cols\\\": 6\\n  },\\n  \\\"retrieval_logs\\\": {\\n    \\\"error\\\": \\\"DatabaseError('database disk image is malformed')\\\"\\n  },\\n  \\\"maintenance_jobs\\\": {\\n    \\\"copied\\\": 4,\\n    \\\"common_cols\\\": 14\\n  },\\n  \\\"maintenance_runs\\\": {\\n    \\\"copied\\\": 41,\\n    \\\"common_cols\\\": 8\\n  },\\n  \\\"memory_conflicts\\\": {\\n    \\\"copied\\\": 0,\\n    \\\"common_cols\\\": 6\\n  },\\n  \\\"profiles\\\": {\\n    \\\"copied\\\": 0,\\n    \\\"common_cols\\\": 5\\n  },\\n  \\\"tasks\\\": {\\n    \\\"copied\\\": 194,\\n    \\\"common_cols\\\": 19\\n  },\\n  \\\"task_runs\\\": {\\n    \\\"copied\\\": 63,\\n    \\\"common_cols\\\": 12\\n  },\\n  \\\"artifacts\\\": {\\n    \\\"copied\\\": 163,\\n    \\\"common_cols\\\": 11\\n  },\\n  \\\"fts\\\": \\\"rebuilt\\\",\\n  \\\"quick_check\\\": [\\n    [\\n      \\\"ok\\\"\\n    ]\\n  ],\\n  \\\"count_memories\\\": 288,\\n  \\\"count_tasks\\\": 194,\\n  \\\"count_artifacts\\\": 163,\\n  \\\"count_projects\\\": 4,\\n  \\\"count_repos\\\": 7,\\n  \\\"count_retrieval_logs\\\": 6848,\\n  \\\"count_task_runs\\\": 63,\\n  \\\"count_maintenance_jobs\\\": 4,\\n  \\\"count_maintenance_runs\\\": 41\\n}\\n-rw-r--r-- 1 sebas sebas 102M Apr 19 19:02 /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3\\n\"}],\"isError\":false,\"timestamp\":1776636178641}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:119:{\"type\":\"message\",\"id\":\"ba794162\",\"parentId\":\"55022030\",\"timestamp\":\"2026-04-19T22:03:07.154Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_LjHjJLglZe2edpbbetueCNQ0|fc_071e452a5a0ea02e0169e551161d688190b1905dbc09c46210\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3\\nfor p in [\\n '/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3',\\n '/mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3',\\n]:\\n    con=sqlite3.connect(p)\\n    cur=con.cursor()\\n    print('DB', p)\\n    for q in [\\n        'SELECT COUNT(*) FROM memories',\\n        'SELECT COUNT(*) FROM retrieval_logs',\\n        'SELECT COUNT(*) FROM memory_links',\\n        'SELECT COUNT(*) FROM tasks',\\n        'SELECT COUNT(*) FROM artifacts',\\n    ]:\\n        try:\\n            print(q, cur.execute(q).fetchone()[0])\\n        except Exception as e:\\n            print(q, 'ERR', repr(e))\\n    try:\\n        print('sample memories ids', cur.execute('SELECT id, title FROM memories LIMIT 5').fetchall())\\n    except Exception as e:\\n        print('sample memories ERR', repr(e))\\n    print()\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":692,\"output\":267,\"cacheRead\":34048,\"cacheWrite\":0,\"totalTokens\":35007,\"cost\":{\"input\":0.0017300000000000002,\"output\":0.004005,\"cacheRead\":0.008512,\"cacheWrite\":0,\"total\":0.014247000000000001}},\"stopReason\":\"toolUse\",\"timestamp\":1776636178644,\"responseId\":\"resp_071e452a5a0ea02e0169e551134c008190b69ada5e4206106e\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:120:{\"type\":\"message\",\"id\":\"e9c30105\",\"parentId\":\"ba794162\",\"timestamp\":\"2026-04-19T22:03:07.207Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_LjHjJLglZe2edpbbetueCNQ0|fc_071e452a5a0ea02e0169e551161d688190b1905dbc09c46210\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"DB /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\nSELECT COUNT(*) FROM memories 320\\nSELECT COUNT(*) FROM retrieval_logs 6901\\nSELECT COUNT(*) FROM memory_links 445\\nSELECT COUNT(*) FROM tasks 194\\nSELECT COUNT(*) FROM artifacts 163\\nsample memories ids [('legacy_run_9fdccb34-cf1e-47bd-b133-a224f363a6d4', 'Research run: Investigate personal assistant patterns'), ('legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1', 'Research run: Verify capture-url flow'), ('legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb', 'Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow'), ('legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7', 'Research run: Source 50+ inch China displays for Ballbox advertising in padel venues'), ('legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0', 'Research run: Test intake flow')]\\n\\nDB /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3\\nSELECT COUNT(*) FROM memories 288\\nSELECT COUNT(*) FROM retrieval_logs 6848\\nSELECT COUNT(*) FROM memory_links 445\\nSELECT COUNT(*) FROM tasks 194\\nSELECT COUNT(*) FROM artifacts 163\\nsample memories ids [('legacy_run_9fdccb34-cf1e-47bd-b133-a224f363a6d4', 'Research run: Investigate personal assistant patterns'), ('legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1', 'Research run: Verify capture-url flow'), ('legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb', 'Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow'), ('legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7', 'Research run: Source 50+ inch China displays for Ballbox advertising in padel venues'), ('legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0', 'Research run: Test intake flow')]\\n\\n\"}],\"isError\":false,\"timestamp\":1776636187207}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:121:{\"type\":\"message\",\"id\":\"81cfa66e\",\"parentId\":\"e9c30105\",\"timestamp\":\"2026-04-19T22:03:13.390Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_o9Ym3DQMttfl2ZSWaFDO8pdU|fc_071e452a5a0ea02e0169e5511ce6208190a835991207d5f592\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3, json\\nsrc='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\nout='/mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3'\\n\\ns=sqlite3.connect(src)\\nd=sqlite3.connect(out)\\nsc=s.cursor(); dc=d.cursor()\\n\\nsrc_ids=[]\\ntry:\\n    for row in sc.execute('SELECT id FROM memories'):\\n        src_ids.append(row[0])\\nexcept Exception as e:\\n    print('src_iter_err', repr(e))\\n\\ndst_ids={row[0] for row in dc.execute('SELECT id FROM memories')}\\nmissing=[x for x in src_ids if x not in dst_ids]\\nprint('src_seen', len(src_ids))\\nprint('dst_ids', len(dst_ids))\\nprint('missing', len(missing))\\nprint(json.dumps(missing[:50], indent=2))\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":742,\"output\":242,\"cacheRead\":34816,\"cacheWrite\":0,\"totalTokens\":35800,\"cost\":{\"input\":0.001855,\"output\":0.00363,\"cacheRead\":0.008704,\"cacheWrite\":0,\"total\":0.014189}},\"stopReason\":\"toolUse\",\"timestamp\":1776636187209,\"responseId\":\"resp_071e452a5a0ea02e0169e5511c1f3881908aa288077c0d6c44\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:123:{\"type\":\"message\",\"id\":\"27abb143\",\"parentId\":\"a35b6063\",\"timestamp\":\"2026-04-19T22:03:19.976Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_jXrvytr2Ylm2GoGZHrYzJeCm|fc_071e452a5a0ea02e0169e5512306748190b5706aab0666d340\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3\\nsrc='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\ncon=sqlite3.connect(src)\\ncur=con.cursor()\\nids=[\\n'9be235c5-e438-4f89-a35e-9c9582574016',\\n'mem_10b251a5cd6844faaeec93ff7019c086',\\n'mem_1349693993ae4981b89dd9dab290d66d',\\n'mem_139063138eea4721bdac8d830fc0f49b',\\n'mem_30a32043ffd8430fa1b3d82e409d7b53',\\n]\\nfor mid in ids:\\n    try:\\n        row=cur.execute('SELECT id,title,type,subtype,run_id,task_id FROM memories WHERE id=?',(mid,)).fetchone()\\n        print(mid, row)\\n    except Exception as e:\\n        print(mid, 'ERR', repr(e))\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":628,\"output\":264,\"cacheRead\":35712,\"cacheWrite\":0,\"totalTokens\":36604,\"cost\":{\"input\":0.0015700000000000002,\"output\":0.00396,\"cacheRead\":0.008928,\"cacheWrite\":0,\"total\":0.014458}},\"stopReason\":\"toolUse\",\"timestamp\":1776636193450,\"responseId\":\"resp_071e452a5a0ea02e0169e55121f1d481908fc634d1088ac984\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:125:{\"type\":\"message\",\"id\":\"49a8e791\",\"parentId\":\"67a875ec\",\"timestamp\":\"2026-04-19T22:03:29.651Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_B4261hos9XGzvPEy6DS0VxPD|fc_071e452a5a0ea02e0169e5512963d88190af324c78af63c4fe\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\ncp -a /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3 /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\nsha256sum /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3 /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3\\nsystemctl --user restart agents-database-http.service\\nsleep 2\\ncurl -sS -m 10 -D - -o /tmp/adbstatus3.out http://127.0.0.1:8091/api/status\\nprintf '\\\\n--- body ---\\\\n'\\ncat /tmp/adbstatus3.out\\nprintf '\\\\n--- write test ---\\\\n'\\npython3 - <<'PY'\\nimport json, urllib.request\\npayload={\\n  'type':'episode','scope':'global','title':'repair test memory','content':'post-rebuild write test','source_ref':'conversation:2026-04-19','origin_agent':'personal-agent'\\n}\\nreq=urllib.request.Request('http://127.0.0.1:8091/api/ingest', data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'}, method='POST')\\nwith urllib.request.urlopen(req, timeout=20) as r:\\n    print(r.status)\\n    print(r.read().decode())\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":345,\"output\":321,\"cacheRead\":36480,\"cacheWrite\":0,\"totalTokens\":37146,\"cost\":{\"input\":0.0008625000000000001,\"output\":0.004815,\"cacheRead\":0.00912,\"cacheWrite\":0,\"total\":0.0147975}},\"stopReason\":\"toolUse\",\"timestamp\":1776636200031,\"responseId\":\"resp_071e452a5a0ea02e0169e551286190819089d205830c69cefa\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:126:{\"type\":\"message\",\"id\":\"62196669\",\"parentId\":\"49a8e791\",\"timestamp\":\"2026-04-19T22:03:47.437Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_B4261hos9XGzvPEy6DS0VxPD|fc_071e452a5a0ea02e0169e5512963d88190af324c78af63c4fe\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"d9fbc9cddbdfe0cbdef6fd881c3c3d39404cbc858a04137542b3c4020183d47b  /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\nd9fbc9cddbdfe0cbdef6fd881c3c3d39404cbc858a04137542b3c4020183d47b  /mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3\\nHTTP/1.0 200 OK\\r\\nServer: BaseHTTP/0.6 Python/3.13.5\\r\\nDate: Sun, 19 Apr 2026 22:03:47 GMT\\r\\nContent-Type: application/json; charset=utf-8\\r\\nContent-Length: 261\\r\\nAccess-Control-Allow-Origin: *\\r\\nAccess-Control-Allow-Headers: Content-Type\\r\\nAccess-Control-Allow-Methods: GET,POST,PATCH,OPTIONS\\r\\n\\r\\n\\n--- body ---\\n{\\\"ok\\\": true, \\\"db_path\\\": \\\"/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\\", \\\"dashboard\\\": {\\\"memories\\\": 288, \\\"tasks\\\": 194, \\\"artifacts\\\": 163, \\\"projects\\\": 4, \\\"repos\\\": 7}, \\\"audit\\\": {\\\"schema_version\\\": \\\"shared-agent-memory-v2\\\", \\\"memories\\\": 288, \\\"tasks\\\": 194}}\\n--- write test ---\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 6, in <module>\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 189, in urlopen\\n    return opener.open(url, data, timeout)\\n           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 489, in open\\n    response = self._open(req, data)\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 506, in _open\\n    result = self._call_chain(self.handle_open, protocol, protocol +\\n                              '_open', req)\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 466, in _call_chain\\n    result = func(*args)\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 1348, in http_open\\n    return self.do_open(http.client.HTTPConnection, req)\\n           ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/usr/lib/python3.13/urllib/request.py\\\", line 1323, in do_open\\n    r = h.getresponse()\\n  File \\\"/usr/lib/python3.13/http/client.py\\\", line 1430, in getresponse\\n    response.begin()\\n    ~~~~~~~~~~~~~~^^\\n  File \\\"/usr/lib/python3.13/http/client.py\\\", line 331, in begin\\n    version, status, reason = self._read_status()\\n                              ~~~~~~~~~~~~~~~~~^^\\n  File \\\"/usr/lib/python3.13/http/client.py\\\", line 300, in _read_status\\n    raise RemoteDisconnected(\\\"Remote end closed connection without\\\"\\n                             \\\" response\\\")\\nhttp.client.RemoteDisconnected: Remote end closed connection without response\\n\\n\\nCommand exited with code 1\"}],\"details\":{},\"isError\":true,\"timestamp\":1776636227436}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:127:{\"type\":\"message\",\"id\":\"fd24ce17\",\"parentId\":\"62196669\",\"timestamp\":\"2026-04-19T22:04:04.908Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_fZ6yY6znCPk9GBlFtr5GUVb7|fc_071e452a5a0ea02e0169e551547274819099b4b07a06b4c69d\",\"name\":\"bash\",\"arguments\":{\"command\":\"journalctl --user -u agents-database-http.service -n 60 --no-pager | tail -n 40\",\"timeout\":120}},{\"type\":\"toolCall\",\"id\":\"call_ZVFdrgIgxBNBIL9dEScK4jmg|fc_071e452a5a0ea02e0169e5515472948190ac199473798ae803\",\"name\":\"bash\",\"arguments\":{\"command\":\"python3 - <<'PY'\\nimport sqlite3\\np='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\ncon=sqlite3.connect(p)\\ncur=con.cursor()\\nfor q in [\\n 'PRAGMA quick_check;',\\n 'SELECT COUNT(*) FROM memories',\\n 'SELECT COUNT(*) FROM memories_fts',\\n 'SELECT COUNT(*) FROM sqlite_master WHERE name = \\\"memories_fts\\\"',\\n]:\\n    try:\\n        print('---', q)\\n        print(cur.execute(q).fetchall()[:5])\\n    except Exception as e:\\n        print('ERR', q, repr(e))\\nPY\",\"timeout\":120}},{\"type\":\"toolCall\",\"id\":\"call_KHKMyCT7Em8TWV8fFDlNcWo4|fc_071e452a5a0ea02e0169e5515472a48190873ce9e06e64e468\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/service.py\",\"offset\":60,\"limit\":220}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":824,\"output\":258,\"cacheRead\":36992,\"cacheWrite\":0,\"totalTokens\":38074,\"cost\":{\"input\":0.00206,\"output\":0.00387,\"cacheRead\":0.009248,\"cacheWrite\":0,\"total\":0.015178}},\"stopReason\":\"toolUse\",\"timestamp\":1776636227438,\"responseId\":\"resp_071e452a5a0ea02e0169e5514642f48190b77576bd22f36a9e\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:131:{\"type\":\"message\",\"id\":\"31d3b7de\",\"parentId\":\"e0cdbde0\",\"timestamp\":\"2026-04-19T22:04:08.884Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_EzGruatKiJxKRLUVaO0ISz5a|fc_071e452a5a0ea02e0169e5515651848190bc46f2eb94acb920\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/service.py\",\"offset\":1460,\"limit\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":2430,\"output\":39,\"cacheRead\":37888,\"cacheWrite\":0,\"totalTokens\":40357,\"cost\":{\"input\":0.0060750000000000005,\"output\":0.000585,\"cacheRead\":0.009472,\"cacheWrite\":0,\"total\":0.016132}},\"stopReason\":\"toolUse\",\"timestamp\":1776636245056,\"responseId\":\"resp_071e452a5a0ea02e0169e5515565808190800d39dfb9a68e51\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:133:{\"type\":\"message\",\"id\":\"62f12576\",\"parentId\":\"660104e5\",\"timestamp\":\"2026-04-19T22:04:13.997Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_Z6UREPAfKIxPRNuUuBwvu351|fc_071e452a5a0ea02e0169e5515a11208190ab3e24541340dc9d\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3, json, traceback\\nfrom pathlib import Path\\nimport sys\\nsys.path.insert(0, '/mnt/rpi/agents-database/src')\\nfrom shared_agent_memory.service import MemoryService\\n\\np='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\\ntry:\\n    svc=MemoryService(p)\\n    print('service_init_ok')\\n    rec=svc.create_memory({\\n      'type':'episode','scope':'global','title':'repair test memory 2','content':'post-rebuild direct service write test','source_ref':'conversation:2026-04-19','origin_agent':'personal-agent'\\n    })\\n    print(json.dumps(rec, indent=2))\\nexcept Exception as e:\\n    print('ERR', repr(e))\\n    traceback.print_exc()\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":1387,\"output\":212,\"cacheRead\":40192,\"cacheWrite\":0,\"totalTokens\":41791,\"cost\":{\"input\":0.0034675,\"output\":0.00318,\"cacheRead\":0.010048,\"cacheWrite\":0,\"total\":0.016695500000000002}},\"stopReason\":\"toolUse\",\"timestamp\":1776636248907,\"responseId\":\"resp_071e452a5a0ea02e0169e551592f308190863e9b40a912d98c\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:135:{\"type\":\"message\",\"id\":\"b583a3a1\",\"parentId\":\"462df25a\",\"timestamp\":\"2026-04-19T22:04:22.336Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_wC9PqdsD4tS0saEmdtHR4Yu3|fc_071e452a5a0ea02e0169e55164b3b08190a251d1f23154b983\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py\",\"offset\":160,\"limit\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":506,\"output\":39,\"cacheRead\":41600,\"cacheWrite\":0,\"totalTokens\":42145,\"cost\":{\"input\":0.001265,\"output\":0.000585,\"cacheRead\":0.0104,\"cacheWrite\":0,\"total\":0.01225}},\"stopReason\":\"toolUse\",\"timestamp\":1776636255042,\"responseId\":\"resp_071e452a5a0ea02e0169e5515ff7708190ae13a324740a2320\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:137:{\"type\":\"message\",\"id\":\"a05bc419\",\"parentId\":\"369c7887\",\"timestamp\":\"2026-04-19T22:04:24.709Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_al3dMjxANfFx8AmYm7F8H09A|fc_071e452a5a0ea02e0169e55167dd108190bab71c966acc9da8\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py\",\"offset\":280,\"limit\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":1131,\"output\":39,\"cacheRead\":41984,\"cacheWrite\":0,\"totalTokens\":43154,\"cost\":{\"input\":0.0028275,\"output\":0.000585,\"cacheRead\":0.010496,\"cacheWrite\":0,\"total\":0.0139085}},\"stopReason\":\"toolUse\",\"timestamp\":1776636262361,\"responseId\":\"resp_071e452a5a0ea02e0169e55166c04c8190a7585d031f1d8b08\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:139:{\"type\":\"message\",\"id\":\"1456369e\",\"parentId\":\"b51bfbd4\",\"timestamp\":\"2026-04-19T22:04:32.337Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_2TY02jFDtzuYwpPyDik8CCWG|fc_071e452a5a0ea02e0169e55169efa08190ba098da50066a942\",\"name\":\"edit\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py\",\"edits\":[{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n                return\\n            except ValueError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n                return\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n                return\\n            except ValueError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n                return\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n                return\\n\"},{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n                return\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n                return\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n                return\\n\"},{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n\"}]}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":806,\"output\":349,\"cacheRead\":43008,\"cacheWrite\":0,\"totalTokens\":44163,\"cost\":{\"input\":0.0020150000000000003,\"output\":0.005235,\"cacheRead\":0.010752,\"cacheWrite\":0,\"total\":0.018002}},\"stopReason\":\"toolUse\",\"timestamp\":1776636264723,\"responseId\":\"resp_071e452a5a0ea02e0169e5516914b0819096ade06ccdff6de9\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:140:{\"type\":\"message\",\"id\":\"a583af89\",\"parentId\":\"1456369e\",\"timestamp\":\"2026-04-19T22:04:32.364Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_2TY02jFDtzuYwpPyDik8CCWG|fc_071e452a5a0ea02e0169e55169efa08190ba098da50066a942\",\"toolName\":\"edit\",\"content\":[{\"type\":\"text\",\"text\":\"Found 2 occurrences of edits[2] in /mnt/rpi/agents-database/src/shared_agent_memory/http_api.py. Each oldText must be unique. Please provide more context to make it unique.\"}],\"details\":{},\"isError\":true,\"timestamp\":1776636272364}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:141:{\"type\":\"message\",\"id\":\"fca57b28\",\"parentId\":\"a583af89\",\"timestamp\":\"2026-04-19T22:04:47.252Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_ExB5sJPrcCT5nS8W1jPK7dOI|fc_071e452a5a0ea02e0169e55172482c81908cb38592b44bad1d\",\"name\":\"edit\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/http_api.py\",\"edits\":[{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n                return\\n            except ValueError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n                return\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n                return\\n            except ValueError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n                return\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n                return\\n\"},{\"oldText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n                return\\n\\n            self.send_error(HTTPStatus.NOT_FOUND)\\n\",\"newText\":\"            except KeyError as exc:\\n                self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n                return\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n                return\\n\\n            self.send_error(HTTPStatus.NOT_FOUND)\\n\"},{\"oldText\":\"            try:\\n                self._send_json(\\n                    service.update_task(\\n                        parsed.path.split(\\\"/\\\")[3],\\n                        status=payload.get(\\\"status\\\"),\\n                        owner_agent=payload.get(\\\"owner_agent\\\"),\\n                        blocked_reason=payload.get(\\\"blocked_reason\\\"),\\n                        requires_human_input=payload.get(\\\"requires_human_input\\\"),\\n                        metadata=payload.get(\\\"metadata\\\"),\\n                        run_id=payload.get(\\\"run_id\\\"),\\n                    )\\n                )\\n            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n\",\"newText\":\"            try:\\n                self._send_json(\\n                    service.update_task(\\n                        parsed.path.split(\\\"/\\\")[3],\\n                        status=payload.get(\\\"status\\\"),\\n                        owner_agent=payload.get(\\\"owner_agent\\\"),\\n                        blocked_reason=payload.get(\\\"blocked_reason\\\"),\\n                        requires_human_input=payload.get(\\\"requires_human_input\\\"),\\n                        metadata=payload.get(\\\"metadata\\\"),\\n                        run_id=payload.get(\\\"run_id\\\"),\\n                    )\\n                )\\n            except KeyError as exc:\\n                self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n            except Exception as exc:\\n                self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n\"}]}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":183,\"output\":601,\"cacheRead\":44032,\"cacheWrite\":0,\"totalTokens\":44816,\"cost\":{\"input\":0.00045750000000000006,\"output\":0.009015,\"cacheRead\":0.011007999999999999,\"cacheWrite\":0,\"total\":0.0204805}},\"stopReason\":\"toolUse\",\"timestamp\":1776636272366,\"responseId\":\"resp_071e452a5a0ea02e0169e55170adc08190b779d9650c4fb47a\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:142:{\"type\":\"message\",\"id\":\"543b2034\",\"parentId\":\"fca57b28\",\"timestamp\":\"2026-04-19T22:04:47.270Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_ExB5sJPrcCT5nS8W1jPK7dOI|fc_071e452a5a0ea02e0169e55172482c81908cb38592b44bad1d\",\"toolName\":\"edit\",\"content\":[{\"type\":\"text\",\"text\":\"Successfully replaced 3 block(s) in /mnt/rpi/agents-database/src/shared_agent_memory/http_api.py.\"}],\"details\":{\"diff\":\"     ...\\n 191                 return\\n 192             except ValueError as exc:\\n 193                 self.send_error(HTTPStatus.BAD_REQUEST, str(exc))\\n 194                 return\\n+195             except Exception as exc:\\n+196                 self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n+197                 return\\n 195 \\n 196             self.send_error(HTTPStatus.NOT_FOUND)\\n 197 \\n 198         def do_POST(self) -> None:  # noqa: N802\\n     ...\\n 289                     return\\n 290             except KeyError as exc:\\n 291                 self.send_error(HTTPStatus.BAD_REQUEST, f\\\"Missing field: {exc}\\\")\\n 292                 return\\n+296             except Exception as exc:\\n+297                 self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n+298                 return\\n 293 \\n 294             self.send_error(HTTPStatus.NOT_FOUND)\\n 295 \\n 296         def do_PATCH(self) -> None:  # noqa: N802\\n     ...\\n 316                     )\\n 317                 )\\n 318             except KeyError as exc:\\n 319                 self.send_error(HTTPStatus.NOT_FOUND, str(exc))\\n+326             except Exception as exc:\\n+327                 self.send_error(HTTPStatus.INTERNAL_SERVER_ERROR, str(exc))\\n 320 \\n 321         def log_message(self, fmt: str, *args: Any) -> None:\\n 322             return\\n 323 \\n     ...\",\"firstChangedLine\":195},\"isError\":false,\"timestamp\":1776636287270}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:143:{\"type\":\"message\",\"id\":\"59b19039\",\"parentId\":\"543b2034\",\"timestamp\":\"2026-04-19T22:04:53.167Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_wUDe2Y50EeMG0BmP1F7WgEEh|fc_071e452a5a0ea02e0169e55181460c81909662a07d72eebd8a\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\nsystemctl --user restart agents-database-http.service\\nsleep 2\\npython3 - <<'PY'\\nimport json, urllib.request, urllib.error\\npayload={\\n  'type':'episode','scope':'global','title':'repair test memory 3','content':'post-rebuild http write test','source_ref':'conversation:2026-04-19','origin_agent':'personal-agent'\\n}\\nreq=urllib.request.Request('http://127.0.0.1:8091/api/ingest', data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'}, method='POST')\\ntry:\\n    with urllib.request.urlopen(req, timeout=20) as r:\\n        print('status', r.status)\\n        print(r.read().decode())\\nexcept urllib.error.HTTPError as e:\\n    print('http_error', e.code)\\n    print(e.read().decode())\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":178,\"output\":221,\"cacheRead\":44672,\"cacheWrite\":0,\"totalTokens\":45071,\"cost\":{\"input\":0.00044500000000000003,\"output\":0.0033150000000000002,\"cacheRead\":0.011167999999999999,\"cacheWrite\":0,\"total\":0.014928}},\"stopReason\":\"toolUse\",\"timestamp\":1776636287272,\"responseId\":\"resp_071e452a5a0ea02e0169e5517fc18481909a7051312556392f\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:145:{\"type\":\"message\",\"id\":\"ce245cf0\",\"parentId\":\"ab030fb1\",\"timestamp\":\"2026-04-19T22:04:58.026Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_sCmMrxmJWtlr5hFmVXIYueLJ|fc_071e452a5a0ea02e0169e551891b148190874a581b13729c43\",\"name\":\"read\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/cli.py\",\"offset\":130,\"limit\":80}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":265,\"output\":39,\"cacheRead\":44928,\"cacheWrite\":0,\"totalTokens\":45232,\"cost\":{\"input\":0.0006625,\"output\":0.000585,\"cacheRead\":0.011231999999999999,\"cacheWrite\":0,\"total\":0.0124795}},\"stopReason\":\"toolUse\",\"timestamp\":1776636295281,\"responseId\":\"resp_071e452a5a0ea02e0169e55187e6448190aa4e592ddb2ce715\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:147:{\"type\":\"message\",\"id\":\"4dcdc694\",\"parentId\":\"7e7ce863\",\"timestamp\":\"2026-04-19T22:05:07.423Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_l4ECxAxuvMAGE6vhgjruzdvj|fc_071e452a5a0ea02e0169e5518b6a9c81908d4b6e672f309cb0\",\"name\":\"edit\",\"arguments\":{\"path\":\"/mnt/rpi/agents-database/src/shared_agent_memory/cli.py\",\"edits\":[{\"oldText\":\"    if args.command == \\\"serve-http\\\":\\n        from .store import SQLiteStore\\n        from .service import SCHEMA_VERSION\\n\\n        class StatusOnlyService:\\n            def __init__(self, db_path: str) -> None:\\n                self.store = SQLiteStore(db_path)\\n                self.store.initialize()\\n\\n            def dashboard_snapshot(self):\\n                with self.store.connection() as conn:\\n                    return {\\n                        \\\"memories\\\": conn.execute(\\\"SELECT COUNT(*) FROM memories\\\").fetchone()[0],\\n                        \\\"tasks\\\": conn.execute(\\\"SELECT COUNT(*) FROM tasks\\\").fetchone()[0],\\n                        \\\"artifacts\\\": conn.execute(\\\"SELECT COUNT(*) FROM artifacts\\\").fetchone()[0],\\n                        \\\"projects\\\": conn.execute(\\\"SELECT COUNT(*) FROM projects\\\").fetchone()[0],\\n                        \\\"repos\\\": conn.execute(\\\"SELECT COUNT(*) FROM repos\\\").fetchone()[0],\\n                    }\\n\\n            def audit_v2(self):\\n                with self.store.connection() as conn:\\n                    return {\\n                        \\\"schema_version\\\": SCHEMA_VERSION,\\n                        \\\"memories\\\": conn.execute(\\\"SELECT COUNT(*) FROM memories\\\").fetchone()[0],\\n                        \\\"tasks\\\": conn.execute(\\\"SELECT COUNT(*) FROM tasks\\\").fetchone()[0],\\n                    }\\n\\n        service = StatusOnlyService(args.db)\\n        from .http_api import build_handler\\n        from http.server import ThreadingHTTPServer\\n\\n        server = ThreadingHTTPServer((args.host, args.port), build_handler(service))\\n        try:\\n            server.serve_forever()\\n        finally:\\n            server.server_close()\\n        return\\n\\n    service = MemoryService(args.db)\\n    engine = MemoryEngine(service)\\n\",\"newText\":\"    service = MemoryService(args.db)\\n    engine = MemoryEngine(service)\\n\"}]}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":880,\"output\":436,\"cacheRead\":45056,\"cacheWrite\":0,\"totalTokens\":46372,\"cost\":{\"input\":0.0022,\"output\":0.00654,\"cacheRead\":0.011264,\"cacheWrite\":0,\"total\":0.020004}},\"stopReason\":\"toolUse\",\"timestamp\":1776636298041,\"responseId\":\"resp_071e452a5a0ea02e0169e5518a7860819082ff96dea055bda0\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:148:{\"type\":\"message\",\"id\":\"0ebbde07\",\"parentId\":\"4dcdc694\",\"timestamp\":\"2026-04-19T22:05:07.430Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_l4ECxAxuvMAGE6vhgjruzdvj|fc_071e452a5a0ea02e0169e5518b6a9c81908d4b6e672f309cb0\",\"toolName\":\"edit\",\"content\":[{\"type\":\"text\",\"text\":\"Successfully replaced 1 block(s) in /mnt/rpi/agents-database/src/shared_agent_memory/cli.py.\"}],\"details\":{\"diff\":\"     ...\\n 134 def main() -> None:\\n 135     parser = build_parser()\\n 136     args = parser.parse_args()\\n 137 \\n-138     if args.command == \\\"serve-http\\\":\\n-139         from .store import SQLiteStore\\n-140         from .service import SCHEMA_VERSION\\n-141 \\n-142         class StatusOnlyService:\\n-143             def __init__(self, db_path: str) -> None:\\n-144                 self.store = SQLiteStore(db_path)\\n-145                 self.store.initialize()\\n-146 \\n-147             def dashboard_snapshot(self):\\n-148                 with self.store.connection() as conn:\\n-149                     return {\\n-150                         \\\"memories\\\": conn.execute(\\\"SELECT COUNT(*) FROM memories\\\").fetchone()[0],\\n-151                         \\\"tasks\\\": conn.execute(\\\"SELECT COUNT(*) FROM tasks\\\").fetchone()[0],\\n-152                         \\\"artifacts\\\": conn.execute(\\\"SELECT COUNT(*) FROM artifacts\\\").fetchone()[0],\\n-153                         \\\"projects\\\": conn.execute(\\\"SELECT COUNT(*) FROM projects\\\").fetchone()[0],\\n-154                         \\\"repos\\\": conn.execute(\\\"SELECT COUNT(*) FROM repos\\\").fetchone()[0],\\n-155                     }\\n-156 \\n-157             def audit_v2(self):\\n-158                 with self.store.connection() as conn:\\n-159                     return {\\n-160                         \\\"schema_version\\\": SCHEMA_VERSION,\\n-161                         \\\"memories\\\": conn.execute(\\\"SELECT COUNT(*) FROM memories\\\").fetchone()[0],\\n-162                         \\\"tasks\\\": conn.execute(\\\"SELECT COUNT(*) FROM tasks\\\").fetchone()[0],\\n-163                     }\\n-164 \\n-165         service = StatusOnlyService(args.db)\\n-166         from .http_api import build_handler\\n-167         from http.server import ThreadingHTTPServer\\n-168 \\n-169         server = ThreadingHTTPServer((args.host, args.port), build_handler(service))\\n-170         try:\\n-171             server.serve_forever()\\n-172         finally:\\n-173             server.server_close()\\n-174         return\\n-175 \\n 176     service = MemoryService(args.db)\\n 177     engine = MemoryEngine(service)\\n 178 \\n 179     if args.command == \\\"init-db\\\":\\n     ...\",\"firstChangedLine\":138},\"isError\":false,\"timestamp\":1776636307429}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:149:{\"type\":\"message\",\"id\":\"7658a5c1\",\"parentId\":\"0ebbde07\",\"timestamp\":\"2026-04-19T22:05:15.847Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_os4zNaUTaG4Hxpk2FbRfV5LJ|fc_071e452a5a0ea02e0169e55196f5f08190a87d37c1d58107ac\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\ncd /mnt/rpi/agents-database\\nPYTHONPATH=src python3 -m py_compile src/shared_agent_memory/cli.py src/shared_agent_memory/http_api.py src/shared_agent_memory/service.py\\nsystemctl --user restart agents-database-http.service\\nsleep 2\\ncurl -sS -m 10 http://127.0.0.1:8091/api/status\\nprintf '\\\\n--- ingest ---\\\\n'\\npython3 - <<'PY'\\nimport json, urllib.request, urllib.error\\npayload={\\n  'type':'episode','scope':'global','title':'repair test memory 4','content':'post-rebuild http write test ok','source_ref':'conversation:2026-04-19','origin_agent':'personal-agent'\\n}\\nreq=urllib.request.Request('http://127.0.0.1:8091/api/ingest', data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'}, method='POST')\\nwith urllib.request.urlopen(req, timeout=20) as r:\\n    print('status', r.status)\\n    print(r.read().decode())\\nPY\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":198,\"output\":265,\"cacheRead\":46208,\"cacheWrite\":0,\"totalTokens\":46671,\"cost\":{\"input\":0.000495,\"output\":0.003975,\"cacheRead\":0.011552,\"cacheWrite\":0,\"total\":0.016022}},\"stopReason\":\"toolUse\",\"timestamp\":1776636307431,\"responseId\":\"resp_071e452a5a0ea02e0169e5519418e88190a7e2d526413ddfcd\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:153:{\"type\":\"message\",\"id\":\"7ae83668\",\"parentId\":\"15ee688d\",\"timestamp\":\"2026-04-19T22:05:35.669Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"Done, mostly.\\n\\nWhat I completed\\n\\n1. Backed up SQLite first\\n- Created:\\n  - `/mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3`\\n\\n2. Rebuilt the malformed DB into a clean file\\n- New rebuilt DB:\\n  - `/mnt/rpi/agents-database/data/shared-agent-memory.rebuild-20260419.sqlite3`\\n- Replaced live DB with rebuilt copy:\\n  - `/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3`\\n\\n3. Restored agents-database to normal HTTP behavior\\n- Removed the degraded status-only workaround\\n- Added safer HTTP error handling so bad endpoints return `500` instead of empty reply\\n- Restarted service\\n- Verified write path works again:\\n  - `POST /api/ingest` returned `201`\\n\\nCurrent `agents-database` state\\n- service up on `:8091`\\n- `/api/status` works\\n- `/api/ingest` works\\n- DB `quick_check` on rebuilt DB: `ok`\\n\\nImportant caveat\\n- Rebuild recovered most, not all, rows.\\n- Before:\\n  - memories: `320`\\n  - retrieval_logs: `6901`\\n- After rebuild:\\n  - memories: `288` + new test writes\\n  - retrieval_logs: `6848`\\n- So some damaged rows were lost during rebuild. This is the tradeoff that got the service healthy again.\\n\\nRoot `personal-agent.service`\\n- I confirmed the broken root unit is still the stale one in:\\n  - `/etc/systemd/system/personal-agent.service`\\n- I tried to remove/disable it properly, but file operations on `/etc/systemd/system` need elevated rights.\\n- The healthy user unit is still serving `:8082`.\\n\\nBlocked item\\n- I cannot finish cleanup of the root unit without privilege.\\n\\nYou should run:\\n```bash\\nsudo mkdir -p /mnt/rpi/service-backups/systemd-disabled\\nsudo mv /etc/systemd/system/personal-agent.service /mnt/rpi/service-backups/systemd-disabled/personal-agent.service.disabled-$(date +%Y%m%d-%H%M%S)\\nsudo systemctl daemon-reload\\nsudo systemctl reset-failed personal-agent.service || true\\nsystemctl status personal-agent.service --no-pager -n 20 || true\\n```\\n\\nBusiness outcome\\n- Shared memory service is working again for normal API use.\\n- Personal agent remains available.\\n- Only remaining cleanup is the stale root systemd unit.\\n\\nIf you want, next I can:\\n- diff old vs rebuilt DB counts in more detail\\n- save a repair summary into agents-database\\n- inspect whether any key memory types were among the lost rows\",\"textSignature\":\"{\\\"v\\\":1,\\\"id\\\":\\\"msg_071e452a5a0ea02e0169e551a3999c8190abae122cd32fd8ed\\\",\\\"phase\\\":\\\"final_answer\\\"}\"}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":173,\"output\":566,\"cacheRead\":46976,\"cacheWrite\":0,\"totalTokens\":47715,\"cost\":{\"input\":0.00043250000000000005,\"output\":0.008490000000000001,\"cacheRead\":0.011744,\"cacheWrite\":0,\"total\":0.0206665}},\"stopReason\":\"stop\",\"timestamp\":1776636322453,\"responseId\":\"resp_071e452a5a0ea02e0169e551a2dd68819085a062b9659bb870\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:160:{\"type\":\"message\",\"id\":\"a330d67d\",\"parentId\":\"370dd3ad\",\"timestamp\":\"2026-04-19T22:21:17.098Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"Only minor cleanup.\\n\\nDone:\\n- SSH tmux picker with “new session”\\n- tmux scrolling enabled\\n- root stale `personal-agent.service` removed\\n- `personal-agent` healthy on user service\\n- `agents-database` repaired enough to serve and write again\\n- SQLite backed up before repair\\n\\nLeft:\\n1. optional: save a repair summary into agents-database\\n2. optional: inspect what data was lost in DB rebuild\\n   - rebuilt DB recovered most rows, but not all\\n3. optional: clean extra DB artifacts if you want less clutter\\n   - backups/recovered copies in `/mnt/rpi/agents-database/data/`\\n\\nWorth noting:\\n- `agents-database` is healthy now, but the rebuild was a recovery, not a perfect repair. If some old memories matter, we should audit missing rows.\\n\\nIf you want the system “fully closed out,” I’d do:\\n- save memory of the tmux preference + repair summary\\n- produce a short lost-row report\\n- stop there\\n\\nIf not, you’re basically done.\",\"textSignature\":\"{\\\"v\\\":1,\\\"id\\\":\\\"msg_071e452a5a0ea02e0169e55558cabc81909f84d0eb814fcfce\\\",\\\"phase\\\":\\\"final_answer\\\"}\"}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":152,\"output\":222,\"cacheRead\":62976,\"cacheWrite\":0,\"totalTokens\":63350,\"cost\":{\"input\":0.00038,\"output\":0.00333,\"cacheRead\":0.015743999999999998,\"cacheWrite\":0,\"total\":0.019454}},\"stopReason\":\"stop\",\"timestamp\":1776637271150,\"responseId\":\"resp_071e452a5a0ea02e0169e555578e3481908277117feff18574\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:162:{\"type\":\"message\",\"id\":\"6eebe0a9\",\"parentId\":\"dda84e8f\",\"timestamp\":\"2026-04-19T22:22:12.015Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"Already rebuilt.\\n\\nCurrent live DB is the rebuilt one:\\n- `/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3`\\n\\nBackups kept:\\n- original pre-repair backup:\\n  - `/mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3`\\n- earlier copies:\\n  - `.bak-20260419-184938`\\n  - `.recovered-20260419-184949.sqlite3`\\n  - `.rebuild-20260419.sqlite3`\\n\\nResult of rebuild:\\n- service health restored\\n- writes restored\\n- some rows lost during salvage\\n\\nSo if by “try rebuild” you mean “attempt a better second-pass recovery from the original corrupted backup,” yes, that’s still possible.\\n\\nBest next attempt:\\n- keep live DB as-is\\n- use the original corrupted backup\\n- do a more targeted salvage of missing rows, especially `memories` and `retrieval_logs`\\n- merge any recovered rows into the healthy live DB\\n\\nThat is safe if done against copies only.\\n\\nIf you want, I can do that now:\\n1. diff live rebuilt DB vs original corrupted backup\\n2. try to recover missing rows from the original backup\\n3. merge recovered rows into live DB\\n4. verify API again\",\"textSignature\":\"{\\\"v\\\":1,\\\"id\\\":\\\"msg_071e452a5a0ea02e0169e5558ef05081908bfac82c8521afe7\\\",\\\"phase\\\":\\\"final_answer\\\"}\"}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":126,\"output\":267,\"cacheRead\":63232,\"cacheWrite\":0,\"totalTokens\":63625,\"cost\":{\"input\":0.000315,\"output\":0.004005,\"cacheRead\":0.015808,\"cacheWrite\":0,\"total\":0.020128}},\"stopReason\":\"stop\",\"timestamp\":1776637325459,\"responseId\":\"resp_071e452a5a0ea02e0169e5558e0a048190ae9b7785a6f38936\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:164:{\"type\":\"message\",\"id\":\"d251b709\",\"parentId\":\"66a0eb8c\",\"timestamp\":\"2026-04-19T22:22:37.747Z\",\"message\":{\"role\":\"assistant\",\"content\":[{\"type\":\"toolCall\",\"id\":\"call_LinsHcyHu9iH02c0MTiwtZws|fc_071e452a5a0ea02e0169e555ad6e4c8190b29ad0967e9a9415\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\npython3 - <<'PY'\\nimport sqlite3, json\\nfor p in [\\n '/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3',\\n '/mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3',\\n]:\\n    con=sqlite3.connect(p)\\n    cur=con.cursor()\\n    print('DB', p)\\n    for q in [\\n      'SELECT COUNT(*) FROM memories',\\n      'SELECT COUNT(*) FROM retrieval_logs',\\n      'SELECT COUNT(*) FROM tasks',\\n      'SELECT COUNT(*) FROM artifacts',\\n      'SELECT COUNT(*) FROM memory_links',\\n    ]:\\n      try:\\n        print(q, cur.execute(q).fetchone()[0])\\n      except Exception as e:\\n        print(q, 'ERR', repr(e))\\n    print()\\nPY\",\"timeout\":120}},{\"type\":\"toolCall\",\"id\":\"call_Rw3vjGxuZZsWsDpifSDNMrXN|fc_071e452a5a0ea02e0169e555ad6e6081908393dd01742ec996\",\"name\":\"bash\",\"arguments\":{\"command\":\"set -euo pipefail\\ncp -a /mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3 /mnt/rpi/agents-database/data/shared-agent-memory.recovery-work.sqlite3\\nls -lh /mnt/rpi/agents-database/data/shared-agent-memory.recovery-work.sqlite3\",\"timeout\":120}}],\"api\":\"openai-codex-responses\",\"provider\":\"openai-codex\",\"model\":\"gpt-5.4\",\"usage\":{\"input\":149,\"output\":330,\"cacheRead\":63488,\"cacheWrite\":0,\"totalTokens\":63967,\"cost\":{\"input\":0.00037250000000000006,\"output\":0.00495,\"cacheRead\":0.015872,\"cacheWrite\":0,\"total\":0.0211945}},\"stopReason\":\"toolUse\",\"timestamp\":1776637348607,\"responseId\":\"resp_071e452a5a0ea02e0169e555a503448190843d93bbabf86b1e\"}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:165:{\"type\":\"message\",\"id\":\"1081500b\",\"parentId\":\"d251b709\",\"timestamp\":\"2026-04-19T22:22:39.513Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_LinsHcyHu9iH02c0MTiwtZws|fc_071e452a5a0ea02e0169e555ad6e4c8190b29ad0967e9a9415\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"DB /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3\\nSELECT COUNT(*) FROM memories 290\\nSELECT COUNT(*) FROM retrieval_logs 6848\\nSELECT COUNT(*) FROM tasks 194\\nSELECT COUNT(*) FROM artifacts 163\\nSELECT COUNT(*) FROM memory_links 465\\n\\nDB /mnt/rpi/agents-database/data/shared-agent-memory.pre-repair-20260419-185338.sqlite3\\nSELECT COUNT(*) FROM memories 320\\nSELECT COUNT(*) FROM retrieval_logs 6901\\nSELECT COUNT(*) FROM tasks 194\\nSELECT COUNT(*) FROM artifacts 163\\nSELECT COUNT(*) FROM memory_links 445\\n\\n\"}],\"isError\":false,\"timestamp\":1776637359513}}\n/home/sebas/pi-config/sessions/--home-sebas--/2026-04-19T21-08-22-321Z_019da792-b2b1-701a-b70b-d0ab6d2ed023.jsonl:166:{\"type\":\"message\",\"id\":\"8387cb19\",\"parentId\":\"1081500b\",\"timestamp\":\"2026-04-19T22:22:43.873Z\",\"message\":{\"role\":\"toolResult\",\"toolCallId\":\"call_Rw3vjGxuZZsWsDpifSDNMrXN|fc_071e452a5a0ea02e0169e555ad6e6081908393dd01742ec996\",\"toolName\":\"bash\",\"content\":[{\"type\":\"text\",\"text\":\"-rw-r--r-- 1 sebas sebas 104M Apr 18 10:35 /mnt/rpi/agents-database/data/shared-agent-memory.recovery-work.sqlite3\\n\"}],\"isError\":false,\"timestamp\":1776637363873}}","truncated":true,"truncatedBy":"bytes","totalLines":200,"totalBytes":1012905,"outputLines":27,"outputBytes":47746,"lastLinePartial":false,"firstLineExceedsLimit":false,"maxLines":2000,"maxBytes":51200},"fullOutputPath":"/tmp/pi-bash-0db4f6e09bf8e7fa.log"},"isError":false,"timestamp":1780573068323}}
{"type":"message","id":"fad75846","parentId":"00aba517","timestamp":"2026-06-04T11:37:48.325Z","message":{"role":"toolResult","toolCallId":"call_WlDzcA1lTBdbfFwhqskvb6mm|fc_08205193eac5c3af016a216385c9248191b2202db00498af8b","toolName":"bash","content":[{"type":"text","text":"/home/sebas/pi-config/bin/pi-telegram-new-session\n/home/sebas/pi-config/sessions\n/home/sebas/pi-config/sessions/spec-memory-layer-agents.md\n/home/sebas/pi-config/sessions/RFC-memory-layer-agents.md\n/home/sebas/pi-config/sessions/session-memory-jobs\n/home/sebas/pi-config/sessions/RFC-memory-layer-agents.html\n/home/sebas/pi-config/sessions/memory-techniques-review.html\n/home/sebas/pi-config/sessions/memory-techniques-review.md\n/home/sebas/pi-config/scripts/session-memory-sweeper.service\n/home/sebas/pi-config/scripts/session-memory-sweeper.timer\n/home/sebas/pi-config/scripts/install-session-memory-dispatcher.service\n/home/sebas/pi-config/scripts/session-memory-sweeper.py\n/home/sebas/pi-config/scripts/session-memory-analyzer.py\n/home/sebas/pi-config/scripts/session-memory-dispatcher.py\n/home/sebas/pi-config/docs/agents-database-memory-contract.md\n/home/sebas/pi-config/runtime/session-memory-sweeper-state.json\n/home/sebas/pi-config/extensions/session-switch\n/home/sebas/pi-config/extensions/session-memory-dispatcher.ts\n/home/sebas/.xsession-errors.old\n/home/sebas/.claude/session-env\n/home/sebas/.claude/statsig/statsig.session_id.2656274335\n/home/sebas/.pi/agent/sessions\n/home/sebas/.pi/pi-acp/session-map.json\n/home/sebas/.codex/sessions\n/home/sebas/.config/copyparty/sessions.db.lock\n/home/sebas/.config/copyparty/sessions.db\n/home/sebas/.agents/skills/memory-read\n/home/sebas/.agents/skills/memory-write\n/home/sebas/.agents/skills.bck/personal-memory-search\n/home/sebas/.xsession-errors\n/home/sebas/outputs/reports/memory-audit-report.html\n/home/sebas/agents-database/docs/shared-agent-memory-rfc.md\n/home/sebas/agents-database/docs/memory-consolidation-rfc.md\n/home/sebas/agents-database/tests/test_memory_service.py\n/home/sebas/agents-database/src/shared_agent_memory\n/home/sebas/agents-database/data/shared-agent-memory.sqlite3\n"}],"isError":false,"timestamp":1780573068325}}
{"type":"message","id":"d9ccc356","parentId":"fad75846","timestamp":"2026-06-04T11:37:53.112Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting service states**\n\nI need to check the timer and service state, along with the sweeper state. It seems like there are multiple components to look into, so I'll take a moment to organize what I need to do. I want to make sure everything is functioning properly, as that’s important for smooth operations. Let’s explore the specifics of each state to ensure there are no issues. I’ll start analyzing them now!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21638e47948191b9712feab8b4860c\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWORHLBZe5eggXptV8QqNOlULuLL6fzO8QNyv20qGR7gkAEWcvjR9l1li1JFS9I2Fqr_acvtSGN9kmfBKEjYiE78fo23_Rk_VDday5hR256TxA5RT--P9i6mCHW5yj2YSnOARrFEHDFY4-bW0kJ7iqiFi1fP0N19Fi9RrBpaoMuU_jecsvKhb-opliX0PLswua4-PkWQaAaXN68OzGCrKx_mXJApd_qwUKtXPgIHA18AxWGMM1sqZJaqF0eC5gaaxN86FYz4DgE3kXlQUsx2KrxJGLDnnqTWEAIxwFidT7O5Kx3fOPpvtWNpa_HezftrzfCEYk6_5pbVMWBZpRUpuGSKI9cZW_evx-TkdCHqSmfN4q7NgxR7Au4ygp6AG4MT8B31G1qfe3fwoPoss7RspX6e3TuW26oEknXK3F1KqhnsiwHL_00G2Eb5QokrXe-S9VMaAHOjS3ddMUzqv6ryHfq6DQ5IPzVcF30fRJprt99LS5rtiiiKObcy9ZccVa04_2jxHQbeHmt_s1yxj1sYAnJjtxNprGkGSsoIrrcopkmIoXeup4y3oWtWVIfZ_cMWUr32bT44Kw14VtOkLEqDWn-ji2jvPjPbpm1LL-LduzZjrewWN_pBDXeL_nKYh0VYKCgKdbBbBylqAYUHySt_hwq4c2ldMfhODXgW-GnmyV2KjlAnviMuRgkUV3UM4XRbTm_XzwjEF_KXubWRIE2AOoiaG2qZPAMaJMp9SwaQzAMvkFI3EAd3UQqcPJKCZtVzqgHtHK4x_sRFUsvgp_Y1zrfw9RvehFXIjnY-NTy15rJeYT-hDxyF_6wo7Q-SsuPiblFRnIDYjMCcdjLXEmpQYb82p0_mzeUqI1lR3_yvZ9iLC9r9vbNcCq9kVNia-5Z69yk802AHCLin8uPDYQm8YSZFbsmtdNodvu3niLTRq4iRksWvRPxkvHz6dRMNVKL2uSyK77iiYmTHbcEYhEKgz1CjAYKmmDfM9zkqSZC8WRFD8tM=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting service states**\\n\\nI need to check the timer and service state, along with the sweeper state. It seems like there are multiple components to look into, so I'll take a moment to organize what I need to do. I want to make sure everything is functioning properly, as that’s important for smooth operations. Let’s explore the specifics of each state to ensure there are no issues. I’ll start analyzing them now!\"}]}"},{"type":"toolCall","id":"call_H1WoD88Q5sicDp2952kcOOti|fc_08205193eac5c3af016a21639106e08191bbf47702a6e1ce21","name":"bash","arguments":{"command":"systemctl --user status session-memory-sweeper.timer --no-pager -n 20","timeout":20}},{"type":"toolCall","id":"call_tF88SOgpwteOSqQNzv7PzJkQ|fc_08205193eac5c3af016a21639107008191966b19ee2f390a0f","name":"bash","arguments":{"command":"systemctl --user status session-memory-sweeper.service --no-pager -n 40","timeout":20}},{"type":"toolCall","id":"call_RgZkTprS3REtUpph5epMc0wx|fc_08205193eac5c3af016a21639107088191883b2ce26574e2ac","name":"read","arguments":{"path":"/home/sebas/pi-config/runtime/session-memory-sweeper-state.json"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":16691,"output":134,"cacheRead":5120,"cacheWrite":0,"totalTokens":21945,"cost":{"input":0.0417275,"output":0.00201,"cacheRead":0.0012799999999999999,"cacheWrite":0,"total":0.0450175}},"stopReason":"toolUse","timestamp":1780573068326,"responseId":"resp_08205193eac5c3af016a21638d5a2081919ded19efe854ad33"}}
{"type":"message","id":"df321d9c","parentId":"d9ccc356","timestamp":"2026-06-04T11:37:53.162Z","message":{"role":"toolResult","toolCallId":"call_H1WoD88Q5sicDp2952kcOOti|fc_08205193eac5c3af016a21639106e08191bbf47702a6e1ce21","toolName":"bash","content":[{"type":"text","text":"● session-memory-sweeper.timer - Run Pi session memory sweeper periodically\n     Loaded: loaded (/home/sebas/.config/systemd/user/session-memory-sweeper.timer; enabled; preset: enabled)\n     Active: active (waiting) since Sun 2026-05-24 06:14:10 -03; 1 week 4 days ago\n Invocation: 6c24261dbf5f45c6a1b23dc5a0193bd3\n    Trigger: Thu 2026-06-04 12:28:57 -03; 3h 51min left\n   Triggers: ● session-memory-sweeper.service\n\nNotice: journal has been rotated since unit was started, output may be incomplete.\n"}],"isError":false,"timestamp":1780573073161}}
{"type":"message","id":"ddc8e9c3","parentId":"df321d9c","timestamp":"2026-06-04T11:37:53.162Z","message":{"role":"toolResult","toolCallId":"call_tF88SOgpwteOSqQNzv7PzJkQ|fc_08205193eac5c3af016a21639107008191966b19ee2f390a0f","toolName":"bash","content":[{"type":"text","text":"○ session-memory-sweeper.service - Pi session memory sweeper\n     Loaded: loaded (/home/sebas/.config/systemd/user/session-memory-sweeper.service; static)\n     Active: inactive (dead) since Thu 2026-06-04 06:28:57 -03; 2h 8min ago\n Invocation: dccf76edf26e4b0c997b6e764247a8bf\nTriggeredBy: ● session-memory-sweeper.timer\n    Process: 510149 ExecStart=/usr/bin/env python3 /home/sebas/pi-config/scripts/session-memory-sweeper.py --once (code=exited, status=0/SUCCESS)\n   Main PID: 510149 (code=exited, status=0/SUCCESS)\n        CPU: 226ms\n\nJun 04 06:28:57 ballbox-first systemd[1289]: Starting session-memory-sweeper.service - Pi session memory sweeper...\nJun 04 06:28:57 ballbox-first env[510149]: {\"processed_now\": 0, \"results\": [], \"maintenance\": null, \"notifications\": []}\nJun 04 06:28:57 ballbox-first systemd[1289]: Finished session-memory-sweeper.service - Pi session memory sweeper.\n\n\nCommand exited with code 3"}],"details":{},"isError":true,"timestamp":1780573073162}}
{"type":"message","id":"30f5cb86","parentId":"ddc8e9c3","timestamp":"2026-06-04T11:37:53.162Z","message":{"role":"toolResult","toolCallId":"call_RgZkTprS3REtUpph5epMc0wx|fc_08205193eac5c3af016a21639107088191883b2ce26574e2ac","toolName":"read","content":[{"type":"text","text":"{\n  \"processed\": {\n    \"/home/sebas/pi-config/sessions/--home-sebas--/2026-05-04T21-17-14-403Z_019df4da-3522-776c-986b-78e057101a55.jsonl\": \"1777929749621373908:111064\",\n    \"/home/sebas/pi-config/sessions/--tmp-pi-tau-mux-test-project-a--/2026-04-29T16-49-04-845Z_019dda24-e74d-72aa-8f07-5b90064db8f0.jsonl\": \"1777949543345967536:6964\",\n    \"/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T02-53-43-386Z_019df60e-4459-772c-a00f-05bb1f067639.jsonl\": \"1777949835211273563:136731\",\n    \"/home/sebas/pi-config/sessions/--home-sebas--/2026-05-04T20-31-12-064Z_019df4b0-0ebf-777a-8efb-4a9a002cf97c.jsonl\": \"1777982856008548859:152220\",\n    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\"/home/sebas/pi-config/sessions/--home-sebas--/2026-06-02T19-08-10-186Z_019e89bc-764a-7995-bbce-27de9b19c956.jsonl\": \"1780526568955933290:1393116\"\n  },\n  \"last_run_at\": \"2026-06-04T09:28:57Z\"\n}\n"}],"isError":false,"timestamp":1780573073162}}
{"type":"message","id":"7367f980","parentId":"30f5cb86","timestamp":"2026-06-04T11:37:57.680Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Checking recent sessions**\n\nI need to compare all recent sessions, particularly looking beyond June 2, since those haven't been processed yet. It might be helpful to list the latest files after that date to ensure I'm capturing everything necessary. This way, I can get a clearer picture of what's been handled and what still requires attention. Let’s figure out what those files are and make sure nothing is overlooked!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216391b9d081919127150d50117416\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWOVlKVnvSdyVfIxuF95uVVIIzACPc6R9qxIjaJfCyF3icnK4gDpCJ5DwbawbbrTSrw89gdlVbbJQmDPtw6CGIvHCeT-xrVLjTj-3KtLaQUYdGyZ2ao6LKZzeywzCZ9rKfz-5dAgpw1cuq_-npVeCq8O5bzMf2oer3J3Kw0uU7C9inb-buNbHP516bRS0nMBZ8SG15l5q--i5tC3gKqAJxRTrJn9xLgwKz7szq8xtoGpoIin_oHiQEPG3V7Q0ghUdq6Ze5hIKvuhmnjg1wp4yPjrbPWcaS4K6ujUmMaTqS-tuwW40pHaawLkaZ3lCXHXGIXwI4qNh7H-h9l5UA_zyjgsxfuqJIPPLedt9YCbD-WICTN51jAZqT0eGxvgEvbMrsnC5XJT5X0QmAocMfCgYGNDJuXsT6dHVCW-9zwyRmZ1HPJpcMr789hUycVcDDjZSEr_mo68JvTvByuDcqWIxQ3Tec7xlzwPCpwF5h3YFJCHoogx4eN5PtLxsWvAq6gbhxAsve79ZmhuWA4a-llPwHla6_9WsoHCIGK9okcdqsftnYOfun8MCpKgLxAlSfSpI3z09EK-9dIWKiL1YPzzNq2U2ZadCLJ3XLKQda66gxByDezSCBQD-UEErDYuSM6Y2uxjG8VDttmsvxHnNVus4uxxxMb5slWrd14DJWS9FZ1iFg82nbFP_R960_gjKHYagHnkVQZtlqHhRNxliOW265ZBl-z_spcEJmNJcbSSikubJEXNnasBz3vek5pj-Lk8lLkMjL1DKSwl1U5E0bUAuQVuk2l-6n9wisbCqi7ue-kPOYDuFnppLLTihvIOPZbiu2ZLubswIs3-2VyvHHd5rsW9mkzRjyizqn4T6QU6zYrWYELjGU9Q5C2a966tPBTC-6nU2rTZfxwZrhc_Ub6Jxx8H-6FGR_JmaaG9af5Su5fBEkpyuVpRG6GBZ7NQ-Ftlei3hsWRf2Gktl5Hmb0IoTFGTAZCYqsXANEtYdcL5fwQyJkftN5qeGSxQynnkxdz1qAh_dvLWtIpTP5CUbPLf8UJ1RiHqmcVRHWg95CPz9AafbgITz60coFXnZ-pcTzt2PLqpqqULKjQqqTBd9BQr1JLXMQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking recent sessions**\\n\\nI need to compare all recent sessions, particularly looking beyond June 2, since those haven't been processed yet. It might be helpful to list the latest files after that date to ensure I'm capturing everything necessary. This way, I can get a clearer picture of what's been handled and what still requires attention. Let’s figure out what those files are and make sure nothing is overlooked!\"}]}"},{"type":"toolCall","id":"call_B72MZtciUAus34IRpLbYnX1p|fc_08205193eac5c3af016a2163959c988191a99740dab4f3bdf6","name":"bash","arguments":{"command":"find /home/sebas/pi-config/sessions -type f -name '*.jsonl' | sort | tail -20","timeout":20}},{"type":"toolCall","id":"call_5UHgNPHWrq1iTiuLSDtPZz3k|fc_08205193eac5c3af016a2163959cb88191b6909f162ba8a2b7","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json,glob,os\nstate=json.load(open('/home/sebas/pi-config/runtime/session-memory-sweeper-state.json'))\nprocessed=set(state['processed'])\nfiles=sorted(glob.glob('/home/sebas/pi-config/sessions/**/*.jsonl', recursive=True))\nrecent=files[-20:]\nfor f in recent:\n    print(('OK ' if f in processed else 'MISS'), f)\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":7795,"output":209,"cacheRead":21504,"cacheWrite":0,"totalTokens":29508,"cost":{"input":0.0194875,"output":0.003135,"cacheRead":0.005376,"cacheWrite":0,"total":0.0279985}},"stopReason":"toolUse","timestamp":1780573073163,"responseId":"resp_08205193eac5c3af016a2163913de48191923f938d64ef051c"}}
{"type":"message","id":"9fad3438","parentId":"7367f980","timestamp":"2026-06-04T11:37:57.744Z","message":{"role":"toolResult","toolCallId":"call_B72MZtciUAus34IRpLbYnX1p|fc_08205193eac5c3af016a2163959c988191a99740dab4f3bdf6","toolName":"bash","content":[{"type":"text","text":"/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T00-40-43-848Z_019da855-1e48-73f6-9fb3-3e7af0384b1a.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T20-38-09-531Z_019dac9d-657a-7261-bcfe-1e8aba68425f.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-32-15-820Z_019db52d-43cb-7714-b600-a8e0da2d5b72.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-35-06-723Z_019db52f-df63-716e-b5fc-10f13fd9de1b.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T13-09-51-204Z_019db54f-ade4-70ca-bccf-03ddd7fc60d9.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-31-46-594Z_019db59a-aea2-71dd-aca1-e14a4a32409b.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-38-27-079Z_019db5a0-cb07-701e-b971-284bf7a60070.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T18-10-19-700Z_019db662-c5b3-70c9-a569-597a7f9d15cf.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-23T13-38-40-507Z_019dba90-6cfb-70df-8ba6-034822682371.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-23T19-52-56-556Z_019dbbe7-13ec-73a6-9faf-d597676a0900.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-28T21-03-55-211Z_019dd5e7-db4a-76e2-8ccd-e4e657863880.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-29T20-32-52-925Z_019ddaf1-ccbc-77cd-af19-d9a9c2666da1.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-30T19-17-03-147Z_019ddfd2-bc2a-76e9-a0f2-9cdd36ca3335.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-34-306Z_019dfbb6-7e21-74de-a57e-8c42b778991b.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-41-291Z_019dfbb6-996a-744c-a2ab-a1e71dc26506.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-48-674Z_019dfbb6-b641-700d-b562-ac63d9b7d0cb.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-25-240Z_019dfbb8-2f77-7021-8f8b-950f41ddcae8.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-31-864Z_019dfbb8-4957-77ab-8b81-ac7e4734bbe1.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-38-831Z_019dfbb8-648e-7115-8f04-27ca57c09fc6.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-tau-mux-test-project-a--/2026-04-29T16-49-04-845Z_019dda24-e74d-72aa-8f07-5b90064db8f0.jsonl\n"}],"isError":false,"timestamp":1780573077744}}
{"type":"message","id":"624c14d7","parentId":"9fad3438","timestamp":"2026-06-04T11:37:57.744Z","message":{"role":"toolResult","toolCallId":"call_5UHgNPHWrq1iTiuLSDtPZz3k|fc_08205193eac5c3af016a2163959cb88191b6909f162ba8a2b7","toolName":"bash","content":[{"type":"text","text":"MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T00-40-43-848Z_019da855-1e48-73f6-9fb3-3e7af0384b1a.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T20-38-09-531Z_019dac9d-657a-7261-bcfe-1e8aba68425f.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-32-15-820Z_019db52d-43cb-7714-b600-a8e0da2d5b72.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-35-06-723Z_019db52f-df63-716e-b5fc-10f13fd9de1b.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T13-09-51-204Z_019db54f-ade4-70ca-bccf-03ddd7fc60d9.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-31-46-594Z_019db59a-aea2-71dd-aca1-e14a4a32409b.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-38-27-079Z_019db5a0-cb07-701e-b971-284bf7a60070.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T18-10-19-700Z_019db662-c5b3-70c9-a569-597a7f9d15cf.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-23T13-38-40-507Z_019dba90-6cfb-70df-8ba6-034822682371.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-23T19-52-56-556Z_019dbbe7-13ec-73a6-9faf-d597676a0900.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-28T21-03-55-211Z_019dd5e7-db4a-76e2-8ccd-e4e657863880.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-29T20-32-52-925Z_019ddaf1-ccbc-77cd-af19-d9a9c2666da1.jsonl\nMISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-30T19-17-03-147Z_019ddfd2-bc2a-76e9-a0f2-9cdd36ca3335.jsonl\nOK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-34-306Z_019dfbb6-7e21-74de-a57e-8c42b778991b.jsonl\nOK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-41-291Z_019dfbb6-996a-744c-a2ab-a1e71dc26506.jsonl\nOK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-48-674Z_019dfbb6-b641-700d-b562-ac63d9b7d0cb.jsonl\nOK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-25-240Z_019dfbb8-2f77-7021-8f8b-950f41ddcae8.jsonl\nOK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-31-864Z_019dfbb8-4957-77ab-8b81-ac7e4734bbe1.jsonl\nOK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-38-831Z_019dfbb8-648e-7115-8f04-27ca57c09fc6.jsonl\nOK  /home/sebas/pi-config/sessions/--tmp-pi-tau-mux-test-project-a--/2026-04-29T16-49-04-845Z_019dda24-e74d-72aa-8f07-5b90064db8f0.jsonl\n"}],"isError":false,"timestamp":1780573077744}}
{"type":"message","id":"f9951808","parentId":"624c14d7","timestamp":"2026-06-04T11:38:03.433Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Looking into file sorting**\n\nI think I need to improve the sorting by modification time, especially to identify files that are newer than what I last processed back in June 2. I’ll count the files that are missing and use Python to compare all the files against the latest dates. It seems like a logical way to ensure everything's organized correctly and nothing gets overlooked. Let's see how that works out!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216396e4948191ab932a5e91772c75\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWOarkk8wEOD4B86wMIGdP35esSa42B2LURxLEzVmdGKHkLGMeKsAi2ze4ba0SXVIIlX2JDMKntkqI90LQHHjk073AgYKWTkJp8-71Lue7J13aLxmmRvByeDAhSGRyqh6JJYbYvxHEBXLCHDgPO75uXSeJ4AGVUqEjzuxor0qgarp7vPvMFFELTV6_k-cIKoqlOT3b7-mSQ6sN4s9Rh90FNMeYc3Xi0MmotdGFrNTPXRvqulHR4XqV8FkHHfhL0Mvi6XgBGIsDinor2dPHuorh85rp9OeJLxQUK0CjP_tB4dMdUbsmCtfyFrknQ9Zt7DY21Cb0nUUONGiO4_sI7-l94Ch0A05U-TjmKwGmtRsOmgFwkVn06RVTshCds5lGyuMCgXfiq3eGntVDlYHlO9kAP0DR0YJfdjID6x_7ggXfOjSy7HHOG5oKYaU-No1td9WcmCmRTleK_4OucACbB0UFwo5Zrn0ijrteRx2CXOB81r1xO_nIqzpu3ewURL2Lu51IJUmrgsioQjknzUcgY9fYxDayijqR7Cfm5VI20GdwPnZCDWxPGDx1CNgV4rrrfpJm3mW3jitaeXKLZEBAVkyCsvblkPve-ND5tzrxaK9wlt0icqyprWxUTrOgFH2PcAqbSrjG8cySYBO3MUVIVe3qS-Hf9BTlFOX1YM6-IB0wuN-jRPOZzJeZzneoky4XvK4E4RXSKeUqGuME6amsyIwNtoeJMvGXc1n7tMIV5FplEQIm0JJ-oc1vpGt37PsXI0JHngbL9wZDTfHbQOCdqa2ENM-FhNiSYXAi3mw6hSRBcWcJ-oyfmisz2gD6GovNhHBdcEjYWd6Vht7C54FmVcBXzd0vVfEo85OvQdTuzYG2Tfbg3SjlF3eFS0MS5mKDlRZVM8QxOJcuOP-CWzzc_tXeyKNFoLAPoV9IJ__CBVJgfmgzdSkv27CtxPPBaG81_nRCK_8IhYsWwXvytg-fGA-fnRe0ChLQEvm_AwJ4vN6UlJifCLLs9nZjnE6epoeyAMOjIAaDvGe2yuVFh9NX4p_X4-tfvfNx6dokv50EbGvGiLFky_A_vWoZlMm3H6jHfrP8odZ11uKnlsBbZzBEEJrfpJOBfs-u-BFw2JZVnu0QcitxXngTl5D4MWkmYoRAp6Yqgg\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Looking into file sorting**\\n\\nI think I need to improve the sorting by modification time, especially to identify files that are newer than what I last processed back in June 2. I’ll count the files that are missing and use Python to compare all the files against the latest dates. It seems like a logical way to ensure everything's organized correctly and nothing gets overlooked. Let's see how that works out!\"}]}"},{"type":"toolCall","id":"call_ZoXJnlNlgCVJTQ4ZmHt6NkKw|fc_08205193eac5c3af016a21639aece08191b8f3ec09cf001183","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json,glob,os\nfrom pathlib import Path\nstate=json.load(open('/home/sebas/pi-config/runtime/session-memory-sweeper-state.json'))\nprocessed=set(state['processed'])\nfiles=sorted(glob.glob('/home/sebas/pi-config/sessions/**/*.jsonl', recursive=True))\nmissing=[f for f in files if f not in processed]\nprint('total_files', len(files))\nprint('processed', len(processed))\nprint('missing', len(missing))\nprint('latest_missing:')\nfor f in missing[-15:]:\n    print(f)\nprint('latest_processed:')\nfor f in sorted(processed)[-10:]:\n    print(f)\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":26279,"output":226,"cacheRead":5632,"cacheWrite":0,"totalTokens":32137,"cost":{"input":0.0656975,"output":0.0033900000000000002,"cacheRead":0.001408,"cacheWrite":0,"total":0.07049550000000002}},"stopReason":"toolUse","timestamp":1780573077745,"responseId":"resp_08205193eac5c3af016a216395d5248191985c74af888dde51"}}
{"type":"message","id":"83b815ca","parentId":"f9951808","timestamp":"2026-06-04T11:38:03.492Z","message":{"role":"toolResult","toolCallId":"call_ZoXJnlNlgCVJTQ4ZmHt6NkKw|fc_08205193eac5c3af016a21639aece08191b8f3ec09cf001183","toolName":"bash","content":[{"type":"text","text":"total_files 201\nprocessed 97\nmissing 104\nlatest_missing:\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-19T18-04-00-040Z_019da6e9-e6a7-739b-a2e1-0b237f665f7e.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-19T20-59-28-042Z_019da78a-8ba9-736d-93ab-3796d2a1b909.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T00-40-43-848Z_019da855-1e48-73f6-9fb3-3e7af0384b1a.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T20-38-09-531Z_019dac9d-657a-7261-bcfe-1e8aba68425f.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-32-15-820Z_019db52d-43cb-7714-b600-a8e0da2d5b72.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-35-06-723Z_019db52f-df63-716e-b5fc-10f13fd9de1b.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T13-09-51-204Z_019db54f-ade4-70ca-bccf-03ddd7fc60d9.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-31-46-594Z_019db59a-aea2-71dd-aca1-e14a4a32409b.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-38-27-079Z_019db5a0-cb07-701e-b971-284bf7a60070.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T18-10-19-700Z_019db662-c5b3-70c9-a569-597a7f9d15cf.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-23T13-38-40-507Z_019dba90-6cfb-70df-8ba6-034822682371.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-23T19-52-56-556Z_019dbbe7-13ec-73a6-9faf-d597676a0900.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-28T21-03-55-211Z_019dd5e7-db4a-76e2-8ccd-e4e657863880.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-29T20-32-52-925Z_019ddaf1-ccbc-77cd-af19-d9a9c2666da1.jsonl\n/home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-30T19-17-03-147Z_019ddfd2-bc2a-76e9-a0f2-9cdd36ca3335.jsonl\nlatest_processed:\n/home/sebas/pi-config/sessions/--home-sebas-work-projects-vending-status-pages--/2026-05-22T15-04-40-182Z_019e5037-93f5-722e-a4eb-1b4f1b298466.jsonl\n/home/sebas/pi-config/sessions/--home-sebas-work-projects-vending-status-pages--/2026-05-22T15-05-06-897Z_019e5037-fc50-734f-8fed-18aa25043384.jsonl\n/home/sebas/pi-config/sessions/--home-sebas-work-projects-vending-status-pages--/2026-05-22T15-06-13-702Z_019e5039-0145-746b-a4be-37c5c81d82aa.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-34-306Z_019dfbb6-7e21-74de-a57e-8c42b778991b.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-41-291Z_019dfbb6-996a-744c-a2ab-a1e71dc26506.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-48-674Z_019dfbb6-b641-700d-b562-ac63d9b7d0cb.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-25-240Z_019dfbb8-2f77-7021-8f8b-950f41ddcae8.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-31-864Z_019dfbb8-4957-77ab-8b81-ac7e4734bbe1.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-38-831Z_019dfbb8-648e-7115-8f04-27ca57c09fc6.jsonl\n/home/sebas/pi-config/sessions/--tmp-pi-tau-mux-test-project-a--/2026-04-29T16-49-04-845Z_019dda24-e74d-72aa-8f07-5b90064db8f0.jsonl\n"}],"isError":false,"timestamp":1780573083492}}
{"type":"message","id":"8c2aa07e","parentId":"83b815ca","timestamp":"2026-06-04T11:38:09.598Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Analyzing recent sessions**\n\nI need a clear answer about the timer and session status. It seems the last run was successful, but I've only processed 97 out of 201 sessions, leaving 104 missing, mostly older files under mnt-rpi-repos-ballbox. Nothing newer than June 2 shows in the processed list, but wait—June 2 is noted. The missing files are older, which is strange since the processed set isn't monotonic. I want to check for any very recent sessions after June 2. Let’s count files newer than that.","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21639c39ec81918870af6c603bdca0\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWOh6CfseJC8tSfTlR1ZtCxpsc0BdMp2qd2jMnn4ItpeJMYGTxyPtSs2vAVaTV-0GppRh2gSPiCHmkYjUYHpiwfOedDS7kgs8RnEpI3qU6w0fewL-2Zr7BrBuoBsrJv7J4QsYZ9_O_bd97jpE80alh3zSAuV9lHon0kaRZQlwaf6RtT3MsXX6TDcvYvMAjm1tLdY4DGMBNWCZ-7pDFsVU_EfaZxj_6JPHOTcd0eRUxYIbKnaMHqHHJJT-iSA6LGhQG_ccZ3tqwh-RmlEXKLQCOZvN6eYEpSEYQuELCXowsYanQshn2oHaHWBSanBytcFePI2RkuM2QZMgMO_OvF4dtIKKDrSHdxLs9HJDH5NqA3nmXWIKNfyBudfXH5ahGXEK7zF7oj7LnL8IMjXzmzC4QB47J6R7hHQp2HSCjtllGWbgvI6gfHy6othYR348_oYibbnJ27qTdgnIO4CDrwLpeKwLfZKAA09T415wrxnuOOYqt_BrYMdPkRrB7kfMmfU4OhMn8XnCpyhvjQ3ZkydYVhj4ti-BtmD2rUuRNt732fMF36rCLy77YG2V4aYS1-Nqz6-f_HO_L_OfyAmh1ttBw-U2nK1JjNbwcnSXWgMYVD_Vcs_WzPkCobVqyWjqe7OoSBd3sKSHL8TPgfOwmnP-RZS7vDQU5w00YpjJO-W5XU8AC2PgjYL5fKYrKiypgRGaFXYZ1Q9vyDxNX2N2dUzhGwl4yh6KRd9IwWNtAQx38gD97126AqsNzg8lNEHX_K4z9-fHaN7xqcpiXZBwgJ7N8sqQbdVSwCyQaqyVkuoZpKvHPTzItk054_urDtMMTpjlECVG0GqsNw9DEmWAEtAX948VvJDonh58EFJ5PAhWSx9JoeBKieXaxNA9mn_FwTwa3lEpWPiAqwqy_Bqd8b-kW8lz8jlbilMy9RWao-FYuDHxhoR85wUsiKJnW4eKqCpA051pv3wCTBvd93WQ9miISWLBMAwNKAFuoHd9cLI0TXvuO5qXFkt2T_Y_sp6D2GlcTACaXMsiWhMP6hsYMHC1kWsx4zHyYQCKPtSvdFJQSWwFsTVW7KUOy2V9pF4-eeaMXT74rY_jHNc59Tl8iPpNFWLfijuRBg-FIDgHMnv-uSk0tw_jCjPmIvL9EKXIwROV6xk-jxkSym-SisFsxiwNld_O6_mU50tcnKmrdkfZivnD5HCQZiL4hx256A1kw7G4m4BQukCGlPVsAbd9LbOejqUwQooOTXGG6J2ihsCju65Uj31Tbf7ziNQNlJHLWHhOwzjxVk1-VT9RAwO8kK3d4SSmoZNzJRJuo0p_HvScJsxcSbiw9UL63MaBm7DIZf17fznyTajrFHMYB4Fug59imXqCOxmNO38DrqI5dlCfcIyA92db_cZLqtSkMuo5jwTYBU708qCP_6mpAa7YZ7tEh5qX80itvOqZWyhOt3EFsaAhCucEjdRPGB7xxfZmbVwL9A55tK8zq2zSyLk6k1_WofRyNkTKhkOw2zA5B-2BZQa51egrBgi8Ssw0O4KGBuNz_SKndol3DC76up1GEzUPPJOzwql8wPUnAO0MFHWM-pBx0SiW8f0J0-K5PSPz4ziMRaEMSrjCrtcfTWf__tNbMTQF6WmmOHRfYbQAc7YgZv-Iwg=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Analyzing recent sessions**\\n\\nI need a clear answer about the timer and session status. It seems the last run was successful, but I've only processed 97 out of 201 sessions, leaving 104 missing, mostly older files under mnt-rpi-repos-ballbox. Nothing newer than June 2 shows in the processed list, but wait—June 2 is noted. The missing files are older, which is strange since the processed set isn't monotonic. I want to check for any very recent sessions after June 2. Let’s count files newer than that.\"}]}"},{"type":"toolCall","id":"call_EiNQ0lECHHiMLxrqP9hvdYLG|fc_08205193eac5c3af016a2163a1124481919a0dbaf17109aaca","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json,glob,os,re\nstate=json.load(open('/home/sebas/pi-config/runtime/session-memory-sweeper-state.json'))\nprocessed=set(state['processed'])\nfiles=sorted(glob.glob('/home/sebas/pi-config/sessions/**/*.jsonl', recursive=True))\nrecent=[f for f in files if '/2026-06-' in f]\nprint('june files', len(recent))\nfor f in recent:\n    print(('OK ' if f in processed else 'MISS'), f)\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1897,"output":261,"cacheRead":31744,"cacheWrite":0,"totalTokens":33902,"cost":{"input":0.0047425,"output":0.003915,"cacheRead":0.007936,"cacheWrite":0,"total":0.0165935}},"stopReason":"toolUse","timestamp":1780573083493,"responseId":"resp_08205193eac5c3af016a21639b9860819194a8ab750a413ffd"}}
{"type":"message","id":"9560f32b","parentId":"8c2aa07e","timestamp":"2026-06-04T11:38:09.650Z","message":{"role":"toolResult","toolCallId":"call_EiNQ0lECHHiMLxrqP9hvdYLG|fc_08205193eac5c3af016a2163a1124481919a0dbaf17109aaca","toolName":"bash","content":[{"type":"text","text":"june files 10\nOK  /home/sebas/pi-config/sessions/--home-sebas--/2026-06-01T12-33-34-483Z_019e832c-d712-76b7-9953-0074f47c6d77.jsonl\nOK  /home/sebas/pi-config/sessions/--home-sebas--/2026-06-01T20-22-11-303Z_019e84d9-de67-704a-81ce-f506986a41f0.jsonl\nOK  /home/sebas/pi-config/sessions/--home-sebas--/2026-06-02T14-45-26-796Z_019e88cb-ee8c-70d7-990a-2cd779f85ec1.jsonl\nOK  /home/sebas/pi-config/sessions/--home-sebas--/2026-06-02T19-08-10-186Z_019e89bc-764a-7995-bbce-27de9b19c956.jsonl\nMISS /home/sebas/pi-config/sessions/--home-sebas--/2026-06-04T11-36-30-837Z_019e926b-ad75-7aee-8e2a-e659e86435b9.jsonl\nMISS /home/sebas/pi-config/sessions/--home-sebas--/2026-06-04T11-37-20-619Z_019e926c-6feb-7773-8e97-5645482738b0.jsonl\nOK  /home/sebas/pi-config/sessions/--home-sebas-pi-config--/2026-06-01T07-30-39-919Z_019e8217-84ac-71b8-91fc-1fa48ecce09a.jsonl\nOK  /home/sebas/pi-config/sessions/--home-sebas-pi-config--/2026-06-01T07-31-31-861Z_019e8218-4f95-71e8-9dc6-33b2c06dd0aa.jsonl\nOK  /home/sebas/pi-config/sessions/--home-sebas-pi-config--/2026-06-01T07-38-42-520Z_019e821e-e1d8-7429-a663-9fef969f67e8.jsonl\nOK  /home/sebas/pi-config/sessions/--home-sebas-pi-config--/2026-06-01T07-40-11-504Z_019e8220-3d70-7779-8f73-11bb956240c7.jsonl\n"}],"isError":false,"timestamp":1780573089650}}
{"type":"message","id":"e3393463","parentId":"9560f32b","timestamp":"2026-06-04T11:38:13.349Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a2163a249e48191bfc7062c6cf40ac0\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWOiOCKimox9aoji156KAj1JGh6S-bgRoxLHVCFjOmHVOcDlQ9ScY6K1hP1EZxu6vpL2YptJosWllUdFxVtIvbnIa7ZwDxnh3USLvkvUfkzxB2WVoJkvpIz5XbXwxJ8-c_ANOl99WJR1BueiptPakUvkw-v2ltaxkbYsvpaGwEWuA1EjSLd6eOTLH7ELcmFU4DuACv2Lsynp0Myf6hwhffvLGl1MXviNgPSa5coc7YfWzxt0sRJ4e6kKyAeL5BIQ8unhm5mk_DikwTXYKx8ONaMwaOdv6a2vpC0RjTFGwlwwWbiDM7rHfLQkcaNU3SJGnM7ZhzLOelFqpeSusIsLvmAc_7kRsF44TxVEaAdL2UeZ1IRXzbc_xHUxPYQK9ggak0b47d4eDhuV1sI2cqPGixwfJGErNZoW531XXvWC8rcjTs4-C9aqAwfpTD1CJZlH2fbGF9JcLX_HngNDtBIGuGEr-rlIkh6k-KF2sSr1aadY5MANP_uI3I6ShhDMH-PBHb7fnGozPtnUwzn2yryoylifWgijHaVLv24GIG3OYV5VNwd-_LKedjq-r6_SSOna0WwfMeewBmycyKjEXb-etL0Rec5qvrX_nmcCOkFYj13RvPoyK7mPGGVtxUr0R2WL3w6hAfRF55eMmk3rz3-2uUzzG7JuuQUdOeMycldHWNja8mMkvd1S4Wsi-wKL3dnipBqHa84F4h0NvgwSem5JrgfrihG47N84mkeHw1NiU1cqWna-_-8nL2_Vd9UJfBID8aAIdr4IKvL8n1U6QHU9Tw2dvgtKVvhIl1JZmIof1AdLU4gfR6MHQVnsCrI8qZdwNtJ2oDIrvW3DBBbYtDeXHffMBDXB0AyQqUUmH4fSsJxA5i32VtQXcZxrTe0kJWfmNwvuwoAdkK5RQ3NAn-jqU7hGfQUt7iXTzFvBGdmFnOcCd-0=\",\"summary\":[]}"},{"type":"text","text":"No.\n\nState now:\n- sweeper timer: running\n- last run: `2026-06-04 06:28 -03`\n- last run result: success\n- processed this run: `0`\n\nCoverage:\n- processed sessions: `97`\n- total sessions: `201`\n- missing: `104`\n\nFor truly recent ones:\n- June 1–2 sessions: swept\n- two new June 4 sessions: not swept yet\n- next scheduled run: ~`12:28 -03`\n\nAlso:\n- many older `--mnt-rpi-repos-ballbox--` sessions were never swept.\n\nIf you want, I can inspect why backlog coverage is partial and fix it.","textSignature":"{\"v\":1,\"id\":\"msg_08205193eac5c3af016a2163a2d6f08191bf5c375b09d4fa37\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1228,"output":157,"cacheRead":33280,"cacheWrite":0,"totalTokens":34665,"cost":{"input":0.0030700000000000002,"output":0.002355,"cacheRead":0.00832,"cacheWrite":0,"total":0.013745}},"stopReason":"stop","timestamp":1780573089651,"responseId":"resp_08205193eac5c3af016a2163a1bbdc8191807cb41d99cbf0fc"}}
{"type":"message","id":"e05dfc6b","parentId":"e3393463","timestamp":"2026-06-04T11:40:18.451Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] viejas y muy recientes tiene sentido que no se hayan corrido. solo quiero saber si ahora se van a correr eventualmente para todas"}],"timestamp":1780573218450}}
{"type":"message","id":"e72e387a","parentId":"e05dfc6b","timestamp":"2026-06-04T11:40:30.138Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216424b0348191bf103ae9aed80027\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWQoAj5DDDltWoa1D_UCETXVXUf663tdlHLuOPkGdHeUfStoZrdvjY3Yl31Pmo3-1MaEHsUy8mfSrX9uaWDE4alVRvMNjgirhktQ7Am24emRv9QUgqhsep8elm_4aPfC9ktj9BTghUJ6cP6MzZ2ZB-qe0g_4O4wTlHnt2zVx2Ng6MCGELaripR9IeWSSOG909D5YiFupkvLXrIz4SHCqqNDPmc3jxEUSOfWTQCD7UPYfe47B6GU_nU9kJp-BNQEnLWgh5OYdm_90VtuSmkz086cPuDz-gR9ouF44qJW-Tnf7ITB4hux4tgJej-6_RGuZ1LZxDDp1kaxzSwNAZEvAlPFyt4wluCtEawfi4javdlymxxX4o9ZksfTiE6wCphbhIOSGivJq3UG_-Orst5jMfFeGNSNlwM-DCrmNHCNuCln5RXCV9LR_9AkgyhVlTyAAIHLzdrZ662E8FUdR1NPTBqCviDih2zPDvwFaeQMwqrLWdIS3DXIqJGQzonBOcr8it3DgXemMvivag8barQoViL9-eRmXW8lOXyIc8pvnp51DfkgbVWQ0rAdb7EkBUShgaaFbxJTu_5X4WM-ERdVPOsJCGSgOR_LaqjcR9IDbkRPJRjboMIk3WexmRUTq5kuf26l4XpGff_Ag_9drQy6KtxQ-9PdBkaW9kTSTC2nM7RTtsA58YlLvyT-Xmih53YzYW4vTuZri1N3veKRI-HI-Xo3ZF7bR1mxxI5OUSni6huX1UxpA3cvsNmOzrq1docKtjMDWUasY5v3CcyZVBkhSKId_Zugkqu4Ab00BdP46oZD5yD1H3iExfEUZQpDEyoDKjxd6uP14lZQk5XXdXSMqYwP3fkEzRuid9NcFy9kARaLhOBN2SPreI4rQbOPHPoYkTASJ8ueg2AStNvhwQPT0GgPFmKH6tLImwdN9OC8MbG8katJ1oMbt_wL_Mk6NcNeWGj26\",\"summary\":[]}"},{"type":"text","text":"Sí, en principio sí.\n\nAhora:\n- el timer está activo\n- el sweeper corre periódico\n- las nuevas sesiones deberían entrar solas en próximas corridas\n\nPero no puedo decir “todas” con certeza total porque:\n- hoy hay backlog viejo sin procesar\n- la última corrida hizo `0`, o sea no agarró nada nuevo todavía\n\nConclusión:\n- las recientes: sí, deberían correrse eventualmente\n- “todas todas”, incluyendo backlog viejo: no garantizado con lo visto\n\nSi querés, reviso la regla exacta de selección y te digo si el diseño excluye viejas a propósito o si hay bug.","textSignature":"{\"v\":1,\"id\":\"msg_08205193eac5c3af016a216427fce48191b926bc48b686d979\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":29285,"output":147,"cacheRead":5120,"cacheWrite":0,"totalTokens":34552,"cost":{"input":0.0732125,"output":0.002205,"cacheRead":0.0012799999999999999,"cacheWrite":0,"total":0.0766975}},"stopReason":"stop","timestamp":1780573218452,"responseId":"resp_08205193eac5c3af016a2164228c288191a68ca8340631c36d"}}
{"type":"message","id":"6abde102","parentId":"e72e387a","timestamp":"2026-06-04T11:41:00.653Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] esta bien asi. promotions/demotions/conflicts hubieron las ultimas dos semanas?"}],"timestamp":1780573260652}}
{"type":"message","id":"632326b2","parentId":"6abde102","timestamp":"2026-06-04T11:41:08.149Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating memory maintenance tasks**\n\nI need to look into memory maintenance jobs regarding promotions, demotions, and conflicts from the last two weeks. It looks like I should inspect the agents database and check the maintenance logs. I’m thinking about using a memory-read skill, but that might not be necessary. Instead, I could use bash or curl to query the sqlite database. I'll keep my approach concise as I gather the necessary information.","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21644e0d64819189309d1451431f88\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWRUaZcpfQj3zW04lRzhc7bYhg90A03hWbl1GDj9Bixpqg8fyia3yq_jzybqt4RsUXogG8iCJjD_KbFWox9GM2ZKYqGmQgZTnmpQpcPQf1lAxNKuIGjf3rkaIU5cJM31BTr3XFxx-NK_KRsbyPRyyyZqzLRQ6uRLVBxBXmBeE66KPBdUstUo2b-Z4RiGVrZpbZaPXq1AkhJqao7-O6wPbd__v3FA6nei8hwUXBPmy6VfpAuL8G0OjuPkRqkPO8sHmVm6WOCZFY1mspiVIxa6IbdRaSMOanF2SjdYGSS8ySlR-fwW3HUOMRE3r_mVqfVySqNYPezJcAapIVMydAiT2tPLlbz5j_MhkZGHo42OEdHbowFAMAB-GONWTu7fgp8f-hC_fa-52mUMnt_6gn7nyGLm1iwslBjABq3rGAQunnDFy9nIppqYhfrZkpFMI4Bq7XSEHBoujt9dd34uJtEPJyNIWUaKD0kqJC0uhcW-dUDOfKutCvMhlGokIynXyPCNFgoXNsH1_8fipQQWDxrZE9rVZZ86CZYtSAbPRsmMuv5lOb4rQn1X8QqfDfcrnyYiXerDqkKOJhOFYdP44JolfGo8LQPyZiCpTXLELSUB5D3ieEUIZbrBSUBglPwK4AChRl6Z3NeHQe9WBQuE0eMUkQk0LYKpRfk4Z37h_NF19ttJxKftlXGjpaVqB6ENz-zAlz0UuElrksTzYzXLGLv10DcUBSv56Fi7F6_T6G_CoBOjtzd4lTv7iwolbXhISm6zpMwi9JXX7kz3F8UBvJcnrC9Wwfqmsy4-1siXG8w6V4PRjpm0_7_X3HfI7sP_Nj6GhQfUwMM9-5SrGTc0QTV4f8M0LmxLyYab4ldIyKO3jV1Febqq01ba7Bp27FL_QmxK18502PblhjgGMD9InNKC8IgjzsPOprPr_idxtg9_p_yWIW9TtTQlYmMGSV-ST4hVBMpGkgpCKvpaB17NPQP1kFJYMXdS7-CtMghnBHypBmstkKibGqr0l_Rgr8Vpg_WqKqYXGt_BHdshqb7u99_hos-64-AmjIxTpVbQfPXUXMwl3FIPUtBpIPLPtE5J48B3_E9vTOuwqRAPpSHQoIp-sDgZ_HJWIQN4Y-TOpaLYK5BHxmxtFQ5jro2VRBylHn42PNfgG2uetfIHyvoYXQ2ODiaAnS8uT_beLSkqIEtM4i0cPNzgHWARz34UWLOHYZ5PkagawAQlUnhRev3MYIsNnnvh3gsA7M74_hnNSDSQFU5wdS8=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Evaluating memory maintenance tasks**\\n\\nI need to look into memory maintenance jobs regarding promotions, demotions, and conflicts from the last two weeks. It looks like I should inspect the agents database and check the maintenance logs. I’m thinking about using a memory-read skill, but that might not be necessary. Instead, I could use bash or curl to query the sqlite database. I'll keep my approach concise as I gather the necessary information.\"}]}"},{"type":"toolCall","id":"call_lUT9dnJrVmXXADpRzSM1vSDo|fc_08205193eac5c3af016a21645415a08191ae48e9feee9529ea","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, datetime\np='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'\ncon=sqlite3.connect(p)\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\nfor table in ['maintenance_runs','memory_conflicts']:\n    print('\\nTABLE', table)\n    cols=cur.execute(f'PRAGMA table_info({table})').fetchall()\n    print([c['name'] for c in cols])\n    rows=cur.execute(f'SELECT * FROM {table} ORDER BY rowid DESC LIMIT 5').fetchall()\n    for r in rows:\n        print(dict(r))\nPY","timeout":20}},{"type":"toolCall","id":"call_5l6TvC7No42bXZJX0L5w0moQ|fc_08205193eac5c3af016a21645415b88191ac919eed9a6446e0","name":"bash","arguments":{"command":"date -u +%Y-%m-%dT%H:%M:%SZ","timeout":5}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":265,"output":276,"cacheRead":34304,"cacheWrite":0,"totalTokens":34845,"cost":{"input":0.0006625,"output":0.0041400000000000005,"cacheRead":0.008576,"cacheWrite":0,"total":0.013378500000000002}},"stopReason":"toolUse","timestamp":1780573260655,"responseId":"resp_08205193eac5c3af016a21644cc518819192b6baaa3cc82f29"}}
{"type":"message","id":"8ff0f669","parentId":"632326b2","timestamp":"2026-06-04T11:41:08.201Z","message":{"role":"toolResult","toolCallId":"call_lUT9dnJrVmXXADpRzSM1vSDo|fc_08205193eac5c3af016a21645415a08191ae48e9feee9529ea","toolName":"bash","content":[{"type":"text","text":"\nTABLE maintenance_runs\n['id', 'job_id', 'status', 'started_at', 'completed_at', 'summary', 'stats_json', 'error_message']\n{'id': 'mrun_7d98a0dc24cb4e3ebcb70bb9aef9568f', 'job_id': 'job_consolidation_daily_10', 'status': 'completed', 'started_at': '2026-06-04T10:11:45.195571+00:00', 'completed_at': '2026-06-04T10:11:45.618558+00:00', 'summary': 'consolidation completed', 'stats_json': '{\"archived\": 0, \"candidates\": 67, \"conflicts\": 0, \"linked\": 6, \"llm_summaries\": 0, \"promoted\": 67}', 'error_message': None}\n{'id': 'mrun_c29ed1ba48554b3a9aabce81aaea9fa2', 'job_id': 'job_consolidation_daily_15', 'status': 'completed', 'started_at': '2026-06-03T15:06:06.733033+00:00', 'completed_at': '2026-06-03T15:06:07.087991+00:00', 'summary': 'consolidation completed', 'stats_json': '{\"archived\": 0, \"candidates\": 67, \"conflicts\": 0, \"linked\": 6, \"llm_summaries\": 0, \"promoted\": 67}', 'error_message': None}\n{'id': 'mrun_042a89d336d449e4968d601a35ff03c8', 'job_id': 'job_consolidation_daily_10', 'status': 'completed', 'started_at': '2026-06-03T10:11:50.353042+00:00', 'completed_at': '2026-06-03T10:11:50.696561+00:00', 'summary': 'consolidation completed', 'stats_json': '{\"archived\": 0, \"candidates\": 67, \"conflicts\": 0, \"linked\": 7, \"llm_summaries\": 0, \"promoted\": 67}', 'error_message': None}\n{'id': 'mrun_87d62b70f8444ebbb25dee421600a4ae', 'job_id': 'job_consolidation_daily_15', 'status': 'completed', 'started_at': '2026-06-02T15:00:21.204263+00:00', 'completed_at': '2026-06-02T15:00:21.501701+00:00', 'summary': 'consolidation completed', 'stats_json': '{\"archived\": 0, \"candidates\": 65, \"conflicts\": 0, \"linked\": 7, \"llm_summaries\": 0, \"promoted\": 65}', 'error_message': None}\n{'id': 'mrun_e27716cb7ad74533a6c40023d5460534', 'job_id': 'job_consolidation_daily_10', 'status': 'completed', 'started_at': '2026-06-02T10:11:57.861053+00:00', 'completed_at': '2026-06-02T10:11:58.236029+00:00', 'summary': 'consolidation completed', 'stats_json': '{\"archived\": 0, \"candidates\": 65, \"conflicts\": 0, \"linked\": 7, \"llm_summaries\": 0, \"promoted\": 65}', 'error_message': None}\n\nTABLE memory_conflicts\n['id', 'memory_id', 'conflicting_memory_id', 'reason', 'created_at', 'metadata_json']\n"}],"isError":false,"timestamp":1780573268201}}
{"type":"message","id":"f5294090","parentId":"8ff0f669","timestamp":"2026-06-04T11:41:08.202Z","message":{"role":"toolResult","toolCallId":"call_5l6TvC7No42bXZJX0L5w0moQ|fc_08205193eac5c3af016a21645415b88191ac919eed9a6446e0","toolName":"bash","content":[{"type":"text","text":"2026-06-04T11:41:08Z\n"}],"isError":false,"timestamp":1780573268202}}
{"type":"message","id":"57462426","parentId":"f5294090","timestamp":"2026-06-04T11:41:18.959Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21645500648191b8134af1f271ab28\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWRWeZSFg93nob8MK7myGIR0PFz2pA6hdN8ia4eMlMAskULncvCZP0vl5qIciYoSiX_gRNpPNh_HdlZBw5KxDBQV-KgcPTouaLM75dsIctb6IUb5TmtC0DicGdsVnE6_LNVzLUIXpS6mjlHcEPaIS8KcPuUZWfmcO9tE-GQjy-VxW_T3eTFYlh4tn-976rOBfWouP92EM1RjzECGys0GsADqD2gtigTiaywwfT8V31ip2tWVX090K2xBqVn2m8KsshGusVSCsEmQJJOkFYX6UmqwKgVicHfG172qeojFySvKpWMhjYXX4-wQHZ2hN8bqvfsOEm4HHCQPGt4X9XM1I_FgLwMKK4RXo-PWBGWp3bbuKXSdCDWq4sZom_LXEkExr1WSSSiGLvj7edU6z0wNKxaNopcLiakHtTZqCNauB5WLquyG4buzjW2ycUQYLTDgsO4sK8hiIDXe47uudBfswP2Flc64CnCakk-SKMnELJWrXLuUlpAByqceXRhmNXQ70a-Va6uE5dcMIWx_QL8uv1yLWnWLg5nitutXG24L0QyzrvQLCeuaMah1aTOX1Ig7NLhiU1h2mfnyBvLmtO0DBDNjsq3hWEQxoOb3GRy0XkMrT_wzYSsQIDQDfWOa0yKuUwJgLwd6bABxyJsFZLjAZqhjZSVtgS5AQBUtNefBm9G_aiynzFwp-Qapv8CltwGtROdQMoci93vEmYOsEa6eyRhH8VDX-KX8-JdjrlkFtpfRgep0RaJxi5cmORWNdmvKv-PoVsDxmLBQ4_w2V9XZjpMt3tPs6WLcOokOTGK-AwhEMPly5ukN17R2AeXTuy7rWd0wfKMY65AZ9fweR9GrEnqYBvAubT1QqEsBwqD2mnX1N8SGgJdrO4KTnkYUHo18X8D2A7nV4Pjw3Iq2ClX-Piu700XgAsldwheYanZLeICnuNnoZF68sZr3dSCNX5WiW9LN\",\"summary\":[]}"},{"type":"toolCall","id":"call_JHEHCpyLSh1OYzuSuG5IgZS1|fc_08205193eac5c3af016a216456bca4819190f1f4fceabc9fec","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json, datetime\np='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'\ncon=sqlite3.connect(p)\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\nnow=datetime.datetime.fromisoformat('2026-06-04T11:41:08+00:00')\ncutoff=now-datetime.timedelta(days=14)\nrows=cur.execute(\"SELECT * FROM maintenance_runs WHERE started_at >= ? ORDER BY started_at\", (cutoff.isoformat(),)).fetchall()\nprom=dem=conf=arch=0\nruns=0\nbyjob={}\nfor r in rows:\n    runs+=1\n    stats=json.loads(r['stats_json'] or '{}')\n    p_=int(stats.get('promoted',0) or 0)\n    a_=int(stats.get('archived',0) or 0)\n    c_=int(stats.get('conflicts',0) or 0)\n    prom+=p_; arch+=a_; conf+=c_\n    byjob.setdefault(r['job_id'], {'runs':0,'promoted':0,'archived':0,'conflicts':0})\n    byjob[r['job_id']]['runs']+=1\n    byjob[r['job_id']]['promoted']+=p_\n    byjob[r['job_id']]['archived']+=a_\n    byjob[r['job_id']]['conflicts']+=c_\nprint('cutoff', cutoff.isoformat())\nprint('runs', runs)\nprint('promoted', prom)\nprint('archived', arch)\nprint('conflicts_stats', conf)\nprint('conflict_rows', cur.execute('SELECT COUNT(*) FROM memory_conflicts WHERE created_at >= ?', (cutoff.isoformat(),)).fetchone()[0])\nprint('jobs', json.dumps(byjob, indent=2, sort_keys=True))\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1429,"output":454,"cacheRead":34304,"cacheWrite":0,"totalTokens":36187,"cost":{"input":0.0035725,"output":0.00681,"cacheRead":0.008576,"cacheWrite":0,"total":0.0189585}},"stopReason":"toolUse","timestamp":1780573268204,"responseId":"resp_08205193eac5c3af016a2164544840819187b2f2a61ad966a2"}}
{"type":"message","id":"072e2f59","parentId":"57462426","timestamp":"2026-06-04T11:41:19.005Z","message":{"role":"toolResult","toolCallId":"call_JHEHCpyLSh1OYzuSuG5IgZS1|fc_08205193eac5c3af016a216456bca4819190f1f4fceabc9fec","toolName":"bash","content":[{"type":"text","text":"cutoff 2026-05-21T11:41:08+00:00\nruns 30\npromoted 1434\narchived 0\nconflicts_stats 0\nconflict_rows 0\njobs {\n  \"job_consolidation_daily_10\": {\n    \"archived\": 0,\n    \"conflicts\": 0,\n    \"promoted\": 714,\n    \"runs\": 14\n  },\n  \"job_consolidation_daily_15\": {\n    \"archived\": 0,\n    \"conflicts\": 0,\n    \"promoted\": 647,\n    \"runs\": 14\n  },\n  \"job_consolidation_weekly\": {\n    \"archived\": 0,\n    \"conflicts\": 0,\n    \"promoted\": 73,\n    \"runs\": 2\n  }\n}\n"}],"isError":false,"timestamp":1780573279005}}
{"type":"message","id":"94ec51df","parentId":"072e2f59","timestamp":"2026-06-04T11:41:24.813Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering promotions and demotions**\n\nI should mention demotions and potentially classify them as archived. The user asked about promotions, demotions, and conflicts, so I want to address those properly. It seems sensible to refer to archived status when discussing demotions. I’ll keep my response concise to ensure clarity without overwhelming details. It’s important to capture the user’s inquiry accurately while being straightforward about the terminology used for demotions.","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21646180148191a6094709d9eefa88\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWRkwMd8oKMCb9MwZj_-MWb94wimBQP_1rsV3MZGH9hWJwVbLZhvP-PNF2PrS964zvSbQQYU4OM6NaumjZdfpArond06JA_97zMLkoF-DgTuRXVZOYFgcM1VzmEE4LKnQyNWoVge14icqjXUrb5U4Ijb8wTFXgo5fqjcZUft8Z9BFklvF2jvM1s4RY1KlzJ_nVF4f94bLISbcTZoGGLY-uQ7Df3S2Vfii1USP0xwD_9s0SPMDxMUSPARMAN9Ecft6DXyE4Cq2P1ex36RdqULuSvdSlIzJVCWgZWzHia34YF52qLrOd3VlmpVUSeqJ1hYrrSHcm64v1PdorPqkB8iisZBvphrkooFaxLNyGwlV6l2jI6-SCe6_wndo8jRnRW-PpCgFZqLtniuvuYnkMAw70TMXV_5eTnMM6-Gk3Dpf1lIXq8OmDyGqWodA-VIJaAAY4umwn4xRIaL3YeuX7msZKdUSxZEK-vugtIMSdK2mqfeZl5nnJrQeJ0gB_W9aI8yU8N6zGYf1ooSNUvpeGhsNUHkCx2OdbWvnwrCjFSP-bZfr9GSIRPN94_ikPZu6E4RN8kzPDu5yHvSzSCw1kR1S6EbhCv1Z-GAEbu9Xko8g2Fx6uwzbU0xFlcEHjPI0Wx75h-JQVI-omoX1lv8jpKzswRBJ81lEaBRWud4er_kZSouqqHjkqBVbbuFAAuIWHvKhe7Qgq9TAB50lptDGPAsRh4hcu81AUJX3sVTAXubWY9hhh_qK4OZt9mFmi8SvvboMQe-4JKjcQuW3y4-iHPw2SfGxUqi2p8EtdJlLUdRxBIc7lBpXVrHGLYRmJtzJxfknfFGr4K8qRgWbLgv4bzYhtBrkMzf1IMwOoHgAKe-uNhLyQxOqknr47j24m92FHIOIrqHk6jWzu4EShmUCHX800TRnwR4cYdpyV_a2PENIXL5UXdnKT5fdeHoSpCapWAT1LQawOKqx-ADHGsXyYrnEdtZ2NUJ8M22Pv-o4PPgDtnYktFL7N5OWr0XMspZxL2SCARZi6y7GfO_oOqo-Ohpr8KW4m_mcjxM0wv37acCTyDOGIm_yM8L49JsLofIIshhsRjLKTDmZANc4Uk4SPQrC0gpSQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering promotions and demotions**\\n\\nI should mention demotions and potentially classify them as archived. The user asked about promotions, demotions, and conflicts, so I want to address those properly. It seems sensible to refer to archived status when discussing demotions. I’ll keep my response concise to ensure clarity without overwhelming details. It’s important to capture the user’s inquiry accurately while being straightforward about the terminology used for demotions.\"}]}"},{"type":"text","text":"Sí.\n\nÚltimas 2 semanas:\n- promotions: `1434`\n- demotions/archives: `0`\n- conflicts: `0`\n\nDetalle:\n- corridas de consolidación: `30`\n- daily_10: `714` promotions\n- daily_15: `647` promotions\n- weekly: `73` promotions\n\nO sea: hubo muchas promotions, ninguna demotion, ningún conflict.","textSignature":"{\"v\":1,\"id\":\"msg_08205193eac5c3af016a21646487e08191a156d553802af685\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1051,"output":123,"cacheRead":35328,"cacheWrite":0,"totalTokens":36502,"cost":{"input":0.0026275,"output":0.001845,"cacheRead":0.008832,"cacheWrite":0,"total":0.0133045}},"stopReason":"stop","timestamp":1780573279006,"responseId":"resp_08205193eac5c3af016a21645f42c88191a82c92c9fe11452f"}}
{"type":"message","id":"0ba98309","parentId":"94ec51df","timestamp":"2026-06-04T11:42:53.303Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] okk. como ves las memorias en su totalidad moviendose?"}],"timestamp":1780573373302}}
{"type":"message","id":"8f52865b","parentId":"0ba98309","timestamp":"2026-06-04T11:43:00.675Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering promotional strategies**\n\nI’m thinking about giving an opinion based on data, focusing on how promotions might only mean accumulating rather than pruning. It’s interesting to consider how this impacts decisions. I don’t think I’ll need any tools for this unless absolutely necessary, but I want to ensure I’m clear on what I’m expressing. So, I’ll lay out insights based on the data I have and see how they relate to promotions.","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a2164c07dc08191b128a249bb627812\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWTDd9g0PPKLRMeacuf8qSg5lS3BmlB73nwD1fMhF3YGaaOjvHs0aItN9BQX4ngahC3Av-UDzJXTOYnVFKfVZJ1mvIOAVDQ64SUpPs5YlN31jWMTX8e64ijUsEERcI_BN6yMt5BkuFsVjxL7vU2FFWxIHD4pKegi-dwMkq79ihTtM21QQtqxdMNurpb0HyW16una_9jNzzFOjBr0lmJ7UMLGlSoUQ_Y7taAMjFAwrbvYJnS1qvar87E941len6uzpGF4su4gDh-RoTpQ6XrK-OhZ3PMHFaIQDw6SEEHIzgjgtD_7gnPaDNNUpqV_wPWm3n2JEUG7yi43KkjliAssBEehZutC_py8b4wA3bTNdttJ0cxRstg9Xt_07OihgyNrtB5_LXwAfGewYZdL3lVBl6vQKsoJBUDPNp-LosaV71pNUJMaudSZ7yjp7YdgXmWVOEBtN4PcxqmJ0YlUjnYWtR11qCaBIhHmiJtYnsc5qKdnf-BHj81kAtN_N4HSrmbmTzCZB_vYr600hBuZ_T1sDuJeB3tId7O-K0YmR18SDaHd7-TE_X4h7w1EalM77NbtRXcTpBKn-wysZp5XGuAy2biSOjFwtGFPh7zfUQo74JSOfxTX8DsJOZ4qyWF5ml_46cHgss4w0Zj-EYY-hKMHyFj34Or42rGU-VId3FYDyONRosWyRy3YRd7xg2aHy0GnKj0zOl7CIgKHFFRpLLB3F45oTxd9MvWg4d_KwEHcD2eSLTgHlUdKpIqpcjxQnoKp4-OCf173HHdSi1PVgyTUIIenEZUYTqV9Hjp4LLuF_8OBT3z0DSLMlSx3gyEMhePDdP0S5S07WL-P9LQV0FTXVa244C_OQf3aLJ-VVzju2ZeGH-yfiWHSoCCofg0Br7vY2eitmu-T8eDusVkeTX3rNhA2cXjzB7C9T6DN7eaB5bcbyO8ocEiRRyqijY1VChx5zYAcx2EhEjl3rPYUWiTnN2HQok9fiO5kQfHDd02wbmEkR-n5jB6BGHeqKXTeshddkWPrpCS38qiQvcrgewVQcVvMQw44beCVG8ok6s6Q14vOYHhJ9w9ZFeCIto5W6gBQWRTfd6WokKLSje06RvaweX8Jtg==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering promotional strategies**\\n\\nI’m thinking about giving an opinion based on data, focusing on how promotions might only mean accumulating rather than pruning. It’s interesting to consider how this impacts decisions. I don’t think I’ll need any tools for this unless absolutely necessary, but I want to ensure I’m clear on what I’m expressing. So, I’ll lay out insights based on the data I have and see how they relate to promotions.\"}]}"},{"type":"text","text":"Se ven vivas, pero desbalanceadas.\n\nPatrón:\n- entra material nuevo\n- consolidación promueve bastante\n- casi no poda\n- no aparecen conflictos\n\nLectura:\n- el sistema está moviendo memorias hacia arriba\n- pero no está haciendo casi trabajo de depurar,\n  competir, fusionar, o bajar cosas\n\nEso puede significar dos cosas:\n1. todavía está en fase de acumulación\n2. los criterios de archive/conflict están flojos\n   y casi todo pasa como “promote”\n\nMi lectura corta:\n- movimiento sí\n- metabolismo incompleto\n\nMás simple:\n- está creciendo\n- no está adelgazando\n\nSi querés, después te hago un juicio más duro:\n- sano\n- inflado\n- estancado\n- o mintiendo métricas","textSignature":"{\"v\":1,\"id\":\"msg_08205193eac5c3af016a2164c3997081918d9c511d0de851a3\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":2119,"output":198,"cacheRead":34304,"cacheWrite":0,"totalTokens":36621,"cost":{"input":0.0052975,"output":0.00297,"cacheRead":0.008576,"cacheWrite":0,"total":0.0168435}},"stopReason":"stop","timestamp":1780573373304,"responseId":"resp_08205193eac5c3af016a2164bda3548191babfa2982f338134"}}
{"type":"message","id":"195e9e49","parentId":"8f52865b","timestamp":"2026-06-04T11:43:55.033Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] hace un dry run con un criterio mas estricto de conflicto/consolidacion a ver que salta"}],"timestamp":1780573435032}}
{"type":"message","id":"9f70016d","parentId":"195e9e49","timestamp":"2026-06-04T11:44:02.373Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting scripts**\n\nI think we need to inspect the scripts thoroughly. Maybe I could use a to-do list to organize the steps, since it's a small multi-step process. I’ll note down what needs to be done, which helps me stay on track and ensures I don't miss anything important. It's definitely important to approach this systematically, so I'll get started on that and make sure everything is covered!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a2164fbc40881919a3fc161ebd9a5f0\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWT_KuDj5wi_M04EQfU4nXm_9Du3_GoYsH8HTCEfkmrd_1-OdAL1jyp07ewa_IeEp_MT0xvptp0wXAjCe26xm6OG2d8d31Afw5SKm0S1qa-pllhHwbwBgQz2ueLP412p5gxFaV9gfwuiemM83WrpSUm0bWXJiLWy8BP-iGiLFhn7qhAJbtP4yJuJCxD6PVbH1LYqa9a6qCZq3SDqvFy88Ama1HdieKpl6M3vnoUN-80JmvXzvhs_pUmMESoirtaoinD3ioxQ8VJMum86y17MwAUXaOKdFZM-r1tSo8ZuUU7RH1Jb3wrvia6BrILAHgPHDjYMOAQlG_sIvbuLFLK9mBo5L7LOMEIC4NFrJqpfDCOmExpOJLEKoBe7jjkEHmzrW7MbkjY1NUEpe_HFplUlQku7t5PRtS2NErhvITC0LuAwDSKfDYuWuTbMsDRpN__GLo4IZv71O8b64SC8z62LkhJ9dhUPn6eRzoo28BkanDuDk4plLPHIqMJYxj7gJIWIQVjXiblbloRZZG7v7LLDVhJ0oFs3WE7e_MpghL5ElYeFFFrK85pfEzNklglEyIsvQQTXuPA4i_bgFzuLuCJTQXDPYDoIpg_ERBHTu1MLxBwpF0VGZ3pl9s1OZtQ4kYZj1ubU5G1CW4KgDnMiHrGIkraAO7S03KOfMPoxeoB-T2fPiVVfrkAGrDZxQDnQWEqqTiMU2i9mEcpm1w351D8TodyLlVZzij4jMAFJxI2tn4txjegRzmIfiOgudsMDvUir7cPd_xw1KYnl8uRBJIj71jBLyu_NqkGzMeBEWrI2yisJxxNVyOJl8Fcbh01J5Z3cTgid8Fj-HnwytKy4GPw6RiDciX5RCwWwQhXJQI2vZD7ft2RlpWN6ynGpcdsBqXrmkKXD8RmZjW3gOQRO7sZAJjKiDgctJRfmPTB9npiRQw8dUIwZuqwCD6WpejXKB4zqQEVZlX_EDQS7_DfCP0-D38b6fcvRwqzZFVGZ321wWttpKDY=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting scripts**\\n\\nI think we need to inspect the scripts thoroughly. Maybe I could use a to-do list to organize the steps, since it's a small multi-step process. I’ll note down what needs to be done, which helps me stay on track and ensures I don't miss anything important. It's definitely important to approach this systematically, so I'll get started on that and make sure everything is covered!\"}]}"},{"type":"toolCall","id":"call_5ShPbRLVbhSSAQjFidEIh6Ik|fc_08205193eac5c3af016a2164ffa5548191a6d24d865eaddb87","name":"todo","arguments":{"action":"create","subject":"Dry run stricter memory consolidation/conflict detection","description":"Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.","activeForm":"running stricter memory dry run","status":"in_progress"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":271,"output":96,"cacheRead":36352,"cacheWrite":0,"totalTokens":36719,"cost":{"input":0.0006775,"output":0.00144,"cacheRead":0.009087999999999999,"cacheWrite":0,"total":0.011205499999999998}},"stopReason":"toolUse","timestamp":1780573435033,"responseId":"resp_08205193eac5c3af016a2164fb22088191a4d99395eecae5c2"}}
{"type":"message","id":"b45a6dce","parentId":"9f70016d","timestamp":"2026-06-04T11:44:02.379Z","message":{"role":"toolResult","toolCallId":"call_5ShPbRLVbhSSAQjFidEIh6Ik|fc_08205193eac5c3af016a2164ffa5548191a6d24d865eaddb87","toolName":"todo","content":[{"type":"text","text":"Created #1: Dry run stricter memory consolidation/conflict detection (pending)"}],"details":{"action":"create","params":{"action":"create","subject":"Dry run stricter memory consolidation/conflict detection","description":"Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.","activeForm":"running stricter memory dry run","status":"in_progress"},"tasks":[{"id":1,"subject":"Dry run stricter memory consolidation/conflict detection","status":"pending","description":"Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.","activeForm":"running stricter memory dry run"}],"nextId":2},"isError":false,"timestamp":1780573442379}}
{"type":"message","id":"30c3faaa","parentId":"b45a6dce","timestamp":"2026-06-04T11:44:04.162Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_DG10iyJbriVbM8xZeOpGlGF1|fc_08205193eac5c3af016a216503bb0481918cc748fe9c623337","name":"todo","arguments":{"action":"update","id":1,"status":"in_progress"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":393,"output":26,"cacheRead":36352,"cacheWrite":0,"totalTokens":36771,"cost":{"input":0.0009825,"output":0.00039,"cacheRead":0.009087999999999999,"cacheWrite":0,"total":0.0104605}},"stopReason":"toolUse","timestamp":1780573442380,"responseId":"resp_08205193eac5c3af016a21650281d481918d6fd04860570f13"}}
{"type":"message","id":"26165fa9","parentId":"30c3faaa","timestamp":"2026-06-04T11:44:04.163Z","message":{"role":"toolResult","toolCallId":"call_DG10iyJbriVbM8xZeOpGlGF1|fc_08205193eac5c3af016a216503bb0481918cc748fe9c623337","toolName":"todo","content":[{"type":"text","text":"Updated #1 (pending → in_progress)"}],"details":{"action":"update","params":{"action":"update","id":1,"status":"in_progress"},"tasks":[{"id":1,"subject":"Dry run stricter memory consolidation/conflict detection","status":"in_progress","description":"Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.","activeForm":"running stricter memory dry run"}],"nextId":2},"isError":false,"timestamp":1780573444163}}
{"type":"message","id":"7290c8f2","parentId":"26165fa9","timestamp":"2026-06-04T11:44:07.694Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_8FTx50kt4zOkKIPZU3lwr3j8|fc_08205193eac5c3af016a216507a1808191816d1fe3299408c6","name":"read","arguments":{"path":"/home/sebas/pi-config/scripts/session-memory-sweeper.py","offset":1,"limit":260}},{"type":"toolCall","id":"call_jamxrp8VaItneGN857A75so7|fc_08205193eac5c3af016a216507a1c881919d34135833ad93df","name":"read","arguments":{"path":"/home/sebas/agents-database/src/shared_agent_memory/service.py","offset":1,"limit":340}},{"type":"toolCall","id":"call_U5WKqyS8EkYynL8E2NXC1ueA|fc_08205193eac5c3af016a216507a1d081919c8b31a2f6a82734","name":"bash","arguments":{"command":"grep -Rni \"promoted\\|archived\\|conflict\\|consolid\" /home/sebas/agents-database/src/shared_agent_memory /home/sebas/pi-config/scripts 2>/dev/null | head -200","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":438,"output":152,"cacheRead":36352,"cacheWrite":0,"totalTokens":36942,"cost":{"input":0.001095,"output":0.00228,"cacheRead":0.009087999999999999,"cacheWrite":0,"total":0.012462999999999998}},"stopReason":"toolUse","timestamp":1780573444164,"responseId":"resp_08205193eac5c3af016a21650441d08191914ec3dee2f05010"}}
{"type":"message","id":"14b5300f","parentId":"7290c8f2","timestamp":"2026-06-04T11:44:07.716Z","message":{"role":"toolResult","toolCallId":"call_8FTx50kt4zOkKIPZU3lwr3j8|fc_08205193eac5c3af016a216507a1808191816d1fe3299408c6","toolName":"read","content":[{"type":"text","text":"#!/usr/bin/env python3\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport shlex\nimport subprocess\nimport sys\nimport time\nfrom pathlib import Path\nfrom typing import Any\n\nBASE_DIR = Path(__file__).resolve().parent.parent\nSESSIONS_DIR = Path(os.environ.get(\"PI_SESSION_DIR\", BASE_DIR / \"sessions\"))\nJOB_DIR = Path(os.environ.get(\"PI_SESSION_MEMORY_JOB_DIR\", SESSIONS_DIR / \"session-memory-jobs\"))\nSTATE_FILE = Path(os.environ.get(\"PI_SESSION_MEMORY_SWEEP_STATE\", BASE_DIR / \"runtime\" / \"session-memory-sweeper-state.json\"))\nANALYZER = Path(os.environ.get(\"PI_SESSION_MEMORY_ANALYZER\", BASE_DIR / \"scripts\" / \"session-memory-analyzer.py\"))\nLOOKBACK_DAYS = int(os.environ.get(\"PI_SESSION_MEMORY_LOOKBACK_DAYS\", \"21\"))\nMAX_SESSIONS_PER_RUN = int(os.environ.get(\"PI_SESSION_MEMORY_MAX_SESSIONS_PER_RUN\", \"12\"))\nMIN_TOTAL_MESSAGES = int(os.environ.get(\"PI_SESSION_MEMORY_MIN_TOTAL_MESSAGES\", \"6\"))\nMIN_ASSISTANT_TEXT = int(os.environ.get(\"PI_SESSION_MEMORY_MIN_ASSISTANT_TEXT\", \"120\"))\nTELEGRAM_NOTIFY_BIN = Path(os.environ.get(\"TELEGRAM_NOTIFY_BIN\", str(Path.home() / \".agents\" / \"skills\" / \"telegram-notify\" / \"telegram-notify\")))\nMAINTENANCE_SCRIPT = Path(os.environ.get(\"AGENTS_DB_MAINTENANCE_SCRIPT\", \"/home/sebas/agents-database/scripts/run-maintenance-daemon.sh\"))\nDB_PATH = os.environ.get(\"DB_PATH\", \"/home/sebas/agents-database/data/shared-agent-memory.sqlite3\")\n\nSKIP_PREFIXES = (\n    \"you are iteration \",\n    \"continue the bounded ralph-loop\",\n)\n\n\ndef load_state() -> dict[str, Any]:\n    if not STATE_FILE.exists():\n        return {\"processed\": {}, \"last_run_at\": None}\n    try:\n        return json.loads(STATE_FILE.read_text(encoding=\"utf-8\"))\n    except Exception:\n        return {\"processed\": {}, \"last_run_at\": None}\n\n\ndef save_state(state: dict[str, Any]) -> None:\n    STATE_FILE.parent.mkdir(parents=True, exist_ok=True)\n    STATE_FILE.write_text(json.dumps(state, indent=2, ensure_ascii=False) + \"\\n\", encoding=\"utf-8\")\n\n\ndef iter_session_files() -> list[Path]:\n    cutoff = time.time() - LOOKBACK_DAYS * 86400\n    paths = [p for p in SESSIONS_DIR.rglob(\"*.jsonl\") if p.is_file() and p.stat().st_mtime >= cutoff]\n    return sorted(paths, key=lambda p: p.stat().st_mtime)\n\n\ndef parse_session(path: Path) -> dict[str, Any]:\n    session_id = None\n    cwd = None\n    branch: list[dict[str, Any]] = []\n    user_messages = 0\n    assistant_messages = 0\n    last_user_text = \"\"\n    last_assistant_text = \"\"\n    first_user_text = \"\"\n\n    with path.open(\"r\", encoding=\"utf-8\", errors=\"ignore\") as handle:\n        for line in handle:\n            try:\n                entry = json.loads(line)\n            except Exception:\n                continue\n            if entry.get(\"type\") == \"session\":\n                session_id = entry.get(\"id\") or session_id\n                cwd = entry.get(\"cwd\") or cwd\n                continue\n            if entry.get(\"type\") != \"message\":\n                continue\n            message = entry.get(\"message\") or {}\n            role = message.get(\"role\")\n            if role not in {\"user\", \"assistant\"}:\n                continue\n            text_parts = []\n            for part in message.get(\"content\") or []:\n                if part.get(\"type\") == \"text\" and isinstance(part.get(\"text\"), str):\n                    text_parts.append(part[\"text\"])\n            text = \"\\n\".join(text_parts).strip()\n            branch.append(entry)\n            if role == \"user\":\n                user_messages += 1\n                if text and not first_user_text:\n                    first_user_text = text\n                if text:\n                    last_user_text = text\n            elif role == \"assistant\":\n                assistant_messages += 1\n                if text:\n                    last_assistant_text = text\n\n    return {\n        \"session_id\": session_id,\n        \"session_file\": str(path),\n        \"cwd\": cwd or str(Path.home()),\n        \"scope\": infer_scope(cwd or str(Path.home())),\n        \"reason\": \"periodic_sweep\",\n        \"trigger\": \"periodic_sweeper\",\n        \"branch\": branch,\n        \"user_messages\": user_messages,\n        \"assistant_messages\": assistant_messages,\n        \"last_user_text\": truncate(last_user_text, 2000),\n        \"last_assistant_text\": truncate(last_assistant_text, 2000),\n        \"first_user_text\": truncate(first_user_text, 400),\n    }\n\n\ndef infer_scope(cwd: str) -> str:\n    home = str(Path.home())\n    try:\n        resolved = str(Path(cwd).resolve())\n    except Exception:\n        resolved = cwd\n    if resolved == home or resolved.startswith(str(Path(home) / \".pi\")):\n        return \"global\"\n    return \"project\"\n\n\ndef truncate(text: str, max_len: int) -> str:\n    text = \" \".join(text.split())\n    if len(text) <= max_len:\n        return text\n    return text[: max_len - 1] + \"…\"\n\n\ndef should_skip(meta: dict[str, Any]) -> tuple[bool, str | None]:\n    if meta[\"user_messages\"] <= 0 or meta[\"assistant_messages\"] <= 0:\n        return True, \"no_dialogue\"\n    first = (meta.get(\"first_user_text\") or \"\").strip().lower()\n    if first.startswith(SKIP_PREFIXES):\n        return True, \"loop_session\"\n    total_messages = meta[\"user_messages\"] + meta[\"assistant_messages\"]\n    if total_messages < MIN_TOTAL_MESSAGES and len(meta.get(\"last_assistant_text\") or \"\") < MIN_ASSISTANT_TEXT:\n        return True, \"too_small\"\n    return False, None\n\n\ndef existing_job_for_session(session_file: str) -> bool:\n    for path in JOB_DIR.glob(\"*.json\"):\n        try:\n            payload = json.loads(path.read_text(encoding=\"utf-8\"))\n        except Exception:\n            continue\n        if payload.get(\"payload\", {}).get(\"session_file\") == session_file:\n            return True\n    return False\n\n\ndef write_job(payload: dict[str, Any]) -> Path:\n    JOB_DIR.mkdir(parents=True, exist_ok=True)\n    job_id = time.strftime(\"%Y%m%dT%H%M%S\", time.gmtime()) + f\"{int(time.time_ns()%1_000_000):06d}Z\"\n    job_file = JOB_DIR / f\"{job_id}.json\"\n    job = {\n        \"job_id\": job_id,\n        \"queued_at\": time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.gmtime()),\n        \"status\": \"queued\",\n        \"job_type\": \"session_memory_analysis\",\n        \"payload\": payload,\n        \"analysis\": {\n            \"status\": \"queued\",\n            \"save\": None,\n            \"memory_id\": None,\n        },\n    }\n    job_file.write_text(json.dumps(job, indent=2, ensure_ascii=False) + \"\\n\", encoding=\"utf-8\")\n    return job_file\n\n\ndef run_analyzer(job_file: Path) -> int:\n    proc = subprocess.run([sys.executable, str(ANALYZER), \"--job-file\", str(job_file)], capture_output=True, text=True)\n    log_file = job_file.with_suffix(\".log\")\n    log_file.write_text((proc.stdout or \"\") + (proc.stderr or \"\"), encoding=\"utf-8\")\n    return proc.returncode\n\n\ndef classify_agents_candidates(analysis: dict[str, Any]) -> list[str]:\n    items = []\n    for memory in (analysis.get(\"result\") or {}).get(\"memories\") or []:\n        if not isinstance(memory, dict):\n            continue\n        if memory.get(\"scope\") != \"global\":\n            continue\n        if memory.get(\"type\") not in {\"profile\", \"decision\", \"procedural\"}:\n            continue\n        if float(memory.get(\"confidence\") or 0) < 0.85 or float(memory.get(\"importance\") or 0) < 0.8:\n            continue\n        text = f\"{memory.get('title','')} {memory.get('content','')}\".lower()\n        if any(token in text for token in [\"always\", \"default\", \"prefer\", \"should\", \"avoid\", \"never\", \"every conversation\", \"working rule\"]):\n            items.append(memory.get(\"title\") or \"untitled\")\n    return items\n\n\ndef maybe_send_telegram(message: str) -> None:\n    if not message.strip() or not TELEGRAM_NOTIFY_BIN.exists():\n        return\n    subprocess.run([str(TELEGRAM_NOTIFY_BIN), message], check=False, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n\n\ndef run_maintenance_once() -> dict[str, Any] | None:\n    if not MAINTENANCE_SCRIPT.exists():\n        return None\n    env = dict(os.environ)\n    env[\"DB_PATH\"] = DB_PATH\n    proc = subprocess.run([str(MAINTENANCE_SCRIPT), \"--once\"], capture_output=True, text=True, env=env)\n    raw = (proc.stdout or \"\").strip().splitlines()\n    if proc.returncode != 0 or not raw:\n        return None\n    try:\n        payload = json.loads(raw[-1])\n    except Exception:\n        return None\n    promoted = 0\n    archived = 0\n    linked = 0\n    conflicts = 0\n    for item in payload:\n        stats = item.get(\"stats\") or {}\n        promoted += int(stats.get(\"promoted\") or 0)\n        archived += int(stats.get(\"archived\") or 0)\n        linked += int(stats.get(\"linked\") or 0)\n        conflicts += int(stats.get(\"conflicts\") or 0)\n    return {\"promoted\": promoted, \"archived\": archived, \"linked\": linked, \"conflicts\": conflicts}\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--once\", action=\"store_true\")\n    args = parser.parse_args()\n    state = load_state()\n    processed = state.setdefault(\"processed\", {})\n    results = []\n    notifications: list[str] = []\n\n    for session_file in iter_session_files():\n        if len(results) >= MAX_SESSIONS_PER_RUN:\n            break\n        stat = session_file.stat()\n        key = str(session_file)\n        marker = f\"{stat.st_mtime_ns}:{stat.st_size}\"\n        if processed.get(key) == marker:\n            continue\n        meta = parse_session(session_file)\n        skip, reason = should_skip(meta)\n        if existing_job_for_session(key):\n            processed[key] = marker\n            results.append({\"session_file\": key, \"status\": \"already_queued\"})\n            continue\n        if skip:\n            processed[key] = marker\n            results.append({\"session_file\": key, \"status\": \"skipped\", \"reason\": reason})\n            continue\n        job_file = write_job(meta)\n        exit_code = run_analyzer(job_file)\n        processed[key] = marker\n        job = json.loads(job_file.read_text(encoding=\"utf-8\"))\n        analysis = job.get(\"analysis\") or {}\n\n[33 more lines in file. Use offset=261 to continue.]"}],"isError":false,"timestamp":1780573447716}}
{"type":"message","id":"c0c83928","parentId":"14b5300f","timestamp":"2026-06-04T11:44:07.716Z","message":{"role":"toolResult","toolCallId":"call_jamxrp8VaItneGN857A75so7|fc_08205193eac5c3af016a216507a1c881919d34135833ad93df","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nimport json\nimport math\nimport subprocess\nimport uuid\nfrom dataclasses import dataclass\nfrom datetime import UTC, datetime, timedelta\nfrom typing import Any\nfrom urllib.parse import urlparse\n\nfrom .schema import SCHEMA_VERSION\nfrom .store import SQLiteStore\n\n\ndef utcnow() -> str:\n    return datetime.now(UTC).isoformat()\n\n\ndef make_id(prefix: str) -> str:\n    return f\"{prefix}_{uuid.uuid4().hex}\"\n\n\ndef normalize_text(value: str) -> str:\n    return \" \".join(value.lower().split())\n\n\ndef _line_value(content: str, label: str) -> str:\n    prefix = f\"{label}:\"\n    for line in content.splitlines():\n        if line.startswith(prefix):\n            return line[len(prefix) :].strip()\n    return \"\"\n\n\n@dataclass(slots=True)\nclass SearchResult:\n    memory: dict[str, Any]\n    score: float\n    explanation: str\n    facet_score: float\n    lexical_score: float\n    semantic_score: float\n\n    def as_dict(self) -> dict[str, Any]:\n        return {\n            \"memory\": self.memory,\n            \"score\": self.score,\n            \"score_breakdown\": {\n                \"facet\": self.facet_score,\n                \"lexical\": self.lexical_score,\n                \"semantic\": self.semantic_score,\n                \"confidence\": self.memory[\"confidence\"],\n                \"freshness\": self.memory[\"freshness\"],\n            },\n            \"matched_scope\": self.memory[\"scope\"],\n            \"evidence_ref\": self.memory[\"evidence_ref\"],\n            \"explanation\": self.explanation,\n        }\n\n\nclass MemoryService:\n    def __init__(self, db_path: str) -> None:\n        self.store = SQLiteStore(db_path)\n        self._maintenance_check_running = False\n        self.store.initialize()\n        self.migrate_v2()\n        self._ensure_default_jobs()\n        self._run_opportunistic_maintenance(limit=10)\n\n    def create_memory(self, memory_input: dict[str, Any]) -> dict[str, Any]:\n        now = utcnow()\n        metadata = dict(memory_input.get(\"metadata\") or {})\n        subtype = memory_input.get(\"subtype\")\n        run_id = memory_input.get(\"run_id\")\n        task_id = memory_input.get(\"task_id\")\n        url = memory_input.get(\"url\")\n        domain = memory_input.get(\"domain\") or (urlparse(url).netloc if url else None)\n        origin_agent = memory_input.get(\"origin_agent\") or metadata.get(\"origin_agent\") or memory_input.get(\"agent_id\")\n        record = {\n            \"id\": memory_input.get(\"id\", make_id(\"mem\")),\n            \"schema_version\": memory_input.get(\"schema_version\", SCHEMA_VERSION),\n            \"type\": memory_input[\"type\"],\n            \"subtype\": subtype,\n            \"scope\": memory_input[\"scope\"],\n            \"status\": memory_input.get(\"status\", \"active\"),\n            \"project_id\": memory_input.get(\"project_id\"),\n            \"repo_id\": memory_input.get(\"repo_id\"),\n            \"agent_id\": memory_input.get(\"agent_id\"),\n            \"origin_agent\": origin_agent,\n            \"run_id\": run_id,\n            \"task_id\": task_id,\n            \"url\": url,\n            \"domain\": domain,\n            \"source_kind\": memory_input.get(\"source_kind\", \"manual\"),\n            \"title\": memory_input[\"title\"],\n            \"content\": memory_input[\"content\"],\n            \"summary\": memory_input.get(\"summary\") or self._make_summary(memory_input[\"content\"]),\n            \"confidence\": float(memory_input.get(\"confidence\", 1.0)),\n            \"freshness\": float(memory_input.get(\"freshness\", 1.0)),\n            \"created_at\": memory_input.get(\"created_at\", now),\n            \"updated_at\": now,\n            \"observed_at\": memory_input.get(\"observed_at\", now),\n            \"source_ref\": memory_input.get(\"source_ref\"),\n            \"evidence_ref\": memory_input.get(\"evidence_ref\"),\n            \"embedding_json\": self._encode_embedding(memory_input.get(\"embedding\")),\n            \"metadata_json\": self.store.dumps(metadata),\n        }\n        with self.store.connection() as conn:\n            conn.execute(\n                \"\"\"\n                INSERT OR REPLACE INTO memories (\n                    id, schema_version, type, subtype, scope, status, project_id, repo_id, agent_id, origin_agent,\n                    run_id, task_id, url, domain, source_kind, title, content, summary, confidence, freshness,\n                    created_at, updated_at, observed_at, source_ref, evidence_ref, embedding_json, metadata_json\n                ) VALUES (\n                    :id, :schema_version, :type, :subtype, :scope, :status, :project_id, :repo_id, :agent_id, :origin_agent,\n                    :run_id, :task_id, :url, :domain, :source_kind, :title, :content, :summary, :confidence, :freshness,\n                    :created_at, :updated_at, :observed_at, :source_ref, :evidence_ref, :embedding_json, :metadata_json\n                )\n                \"\"\",\n                record,\n            )\n            self._upsert_fts(conn, record)\n        self._run_opportunistic_maintenance(limit=1)\n        return self.get_memory(record[\"id\"])\n\n    def ingest(self, memory_input: dict[str, Any]) -> dict[str, Any]:\n        return self.create_memory(memory_input)\n\n    def capture_session(self, source_ref: str, scope: str, content: str, **metadata: Any) -> dict[str, Any]:\n        return self._capture_source(\n            trigger_type=\"session_close\",\n            source_kind=\"run\",\n            source_ref=source_ref,\n            scope=scope,\n            content=content,\n            metadata=metadata,\n        )\n\n    def capture_conversation(\n        self, source_ref: str, scope: str, content: str, capture_mode: str = \"manual_import\", **metadata: Any\n    ) -> dict[str, Any]:\n        metadata = {\"capture_mode\": capture_mode, **metadata}\n        return self._capture_source(\n            trigger_type=\"conversation_capture\",\n            source_kind=\"conversation\",\n            source_ref=source_ref,\n            scope=scope,\n            content=content,\n            metadata=metadata,\n        )\n\n    def search(\n        self,\n        query: str,\n        scopes: list[str] | None = None,\n        filters: dict[str, Any] | None = None,\n        limit: int = 10,\n        include_inbox: bool = False,\n    ) -> dict[str, Any]:\n        filters = filters or {}\n        query_terms = [term for term in normalize_text(query).split() if term]\n        sql = [\"SELECT * FROM memories WHERE 1=1\"]\n        params: list[Any] = []\n\n        if scopes:\n            sql.append(f\"AND scope IN ({','.join('?' for _ in scopes)})\")\n            params.extend(scopes)\n        if not include_inbox:\n            sql.append(\"AND status = ?\")\n            params.append(\"active\")\n        params.extend(self._memory_filter_sql(sql, filters))\n\n        with self.store.connection() as conn:\n            rows = [self._row_to_memory(row) for row in conn.execute(\" \".join(sql), params)]\n\n        results: list[SearchResult] = []\n        query_embedding = self._text_embedding(query) if query_terms else []\n        for row in rows:\n            haystack = normalize_text(\" \".join([row[\"title\"], row[\"summary\"], row[\"content\"]]))\n            lexical = self._lexical_score(query_terms, haystack)\n            semantic = self._semantic_score(query_embedding, row.get(\"embedding\")) if query_terms else 0.0\n            facet = self._facet_score(row, filters)\n            if query_terms and lexical == 0 and semantic == 0 and facet == 0:\n                continue\n            score = facet * 0.45 + lexical * 0.3 + semantic * 0.15 + row[\"confidence\"] * 0.06 + row[\"freshness\"] * 0.04\n            explanation = (\n                f\"facets={facet:.2f} lexical={lexical:.2f} semantic={semantic:.2f} \"\n                f\"confidence={row['confidence']:.2f} freshness={row['freshness']:.2f}\"\n            )\n            results.append(SearchResult(row, score, explanation, facet, lexical, semantic))\n\n        ordered = sorted(\n            results,\n            key=lambda item: (\n                item.facet_score,\n                item.lexical_score,\n                item.semantic_score,\n                item.memory[\"confidence\"],\n                item.memory[\"freshness\"],\n                item.memory[\"updated_at\"],\n            ),\n            reverse=True,\n        )[:limit]\n        payload = [item.as_dict() for item in ordered]\n\n        retrieval_id = make_id(\"ret\")\n        with self.store.connection() as conn:\n            conn.execute(\n                \"INSERT INTO retrieval_logs (id, query_text, filters_json, results_json, created_at) VALUES (?, ?, ?, ?, ?)\",\n                (\n                    retrieval_id,\n                    query,\n                    self.store.dumps({\"scopes\": scopes, \"filters\": filters, \"include_inbox\": include_inbox}),\n                    self.store.dumps(payload),\n                    utcnow(),\n                ),\n            )\n        return {\"retrieval_id\": retrieval_id, \"results\": payload}\n\n    def context_for(\n        self, project: str | None = None, repo: str | None = None, agent: str | None = None, task: str | None = None\n    ) -> dict[str, Any]:\n        profile_facts = self.list_memories(status=\"active\", scope=\"global\", memory_type=\"profile\", limit=8)\n        project_filters = {\"project_id\": project, \"repo_id\": repo}\n        active_decisions = self.list_memories(\n            status=\"active\",\n            scope=\"project\" if project else None,\n            memory_type=\"decision\",\n            project_id=project,\n            repo_id=repo,\n            limit=8,\n        )\n        project_memories = self.list_memories(\n            status=\"active\",\n            project_id=project,\n            repo_id=repo,\n            limit=12,\n        )\n        project_memories = [item for item in project_memories if item[\"type\"] in {\"project\", \"decision\", \"artifact\", \"episode\"}][:12]\n        recent_episodes = self.list_memories(\n            status=\"active\",\n            memory_type=\"episode\",\n            project_id=project,\n            repo_id=repo,\n            limit=10,\n        )\n        task_relevant_artifacts = self.list_memories(\n            status=\"active\",\n            memory_type=\"artifact\",\n            project_id=project,\n            repo_id=repo,\n            task_id=task,\n            limit=10,\n        )\n        if task and not task_relevant_artifacts:\n            task_relevant_artifacts = self.search(task, filters={**project_filters, \"type\": \"artifact\"}, limit=5)[\"results\"]\n        citations = [\n            item[\"id\"]\n            for item in [*profile_facts, *project_memories, *active_decisions, *recent_episodes]\n            if isinstance(item, dict) and \"id\" in item\n        ]\n        return {\n            \"profile_facts\": profile_facts,\n            \"project_memories\": project_memories,\n            \"active_decisions\": active_decisions,\n            \"recent_episodes\": recent_episodes,\n            \"task_relevant_artifacts\": task_relevant_artifacts,\n            \"citations\": citations,\n            \"agent_id\": agent,\n        }\n\n    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:\n        promoted: list[str] = []\n        linked: list[dict[str, str]] = []\n        with self.store.connection() as conn:\n            for memory_id in inbox_items:\n                row = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (memory_id,)).fetchone()\n                if row is None:\n                    continue\n                memory = self._row_to_memory(row)\n                duplicate = conn.execute(\n                    \"\"\"\n                    SELECT id FROM memories\n                    WHERE id != ?\n                      AND status = 'active'\n                      AND type = ?\n                      AND scope = ?\n                      AND content = ?\n                    LIMIT 1\n                    \"\"\",\n                    (memory_id, memory[\"type\"], memory[\"scope\"], memory[\"content\"]),\n                ).fetchone()\n                if duplicate:\n                    link_id = make_id(\"lnk\")\n                    conn.execute(\n                        \"\"\"\n                        INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)\n                        VALUES (?, ?, ?, ?, ?, ?)\n                        \"\"\",\n                        (\n                            link_id,\n                            memory_id,\n                            duplicate[\"id\"],\n                            \"related_to\",\n                            utcnow(),\n                            self.store.dumps({\"reason\": \"exact_duplicate\"}),\n                        ),\n                    )\n                    conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"archived\", utcnow(), memory_id))\n                    linked.append({\"from\": memory_id, \"to\": duplicate[\"id\"], \"relation\": \"related_to\"})\n                    continue\n                conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"active\", utcnow(), memory_id))\n                promoted.append(memory_id)\n        return {\"promoted\": promoted, \"linked\": linked}\n\n    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:\n        stats = {\n            \"candidates\": 0,\n            \"promoted\": 0,\n            \"archived\": 0,\n            \"linked\": 0,\n            \"conflicts\": 0,\n            \"llm_summaries\": 0,\n        }\n        age_days = int(config.get(\"candidate_age_days\", 7))\n        max_candidates = int(config.get(\"max_candidates\", 200))\n        high_threshold = float(config.get(\"dedupe_high_threshold\", 0.9))\n        mid_threshold = float(config.get(\"dedupe_mid_threshold\", 0.75))\n        promote_threshold = float(config.get(\"promote_confidence\", 0.7))\n        conflict_threshold = float(config.get(\"conflict_threshold\", 0.55))\n        llm_enabled = bool(config.get(\"llm_enabled\", False))\n        llm_provider = config.get(\"llm_provider\") or \"opencode\"\n        llm_command = config.get(\"llm_command\") or [\"opencode\", \"-m\", \"MiniMax M2.5 Free\"]\n        cutoff = (datetime.now(UTC) - timedelta(days=age_days)).isoformat()\n\n        with self.store.connection() as conn:\n            rows = conn.execute(\n                \"\"\"\n\n[1369 more lines in file. Use offset=341 to continue.]"}],"isError":false,"timestamp":1780573447716}}
{"type":"message","id":"ddb6ba94","parentId":"c0c83928","timestamp":"2026-06-04T11:44:07.716Z","message":{"role":"toolResult","toolCallId":"call_U5WKqyS8EkYynL8E2NXC1ueA|fc_08205193eac5c3af016a216507a1d081919c8b31a2f6a82734","toolName":"bash","content":[{"type":"text","text":"/home/sebas/agents-database/src/shared_agent_memory/engine.py:76:        if job_type == \"consolidation\":\n/home/sebas/agents-database/src/shared_agent_memory/engine.py:77:            return self.service.consolidate_candidates(config=job.get(\"metadata\") or {})\n/home/sebas/agents-database/src/shared_agent_memory/service.py:274:    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:\n/home/sebas/agents-database/src/shared_agent_memory/service.py:275:        promoted: list[str] = []\n/home/sebas/agents-database/src/shared_agent_memory/service.py:311:                    conn.execute(\"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\", (\"archived\", utcnow(), memory_id))\n/home/sebas/agents-database/src/shared_agent_memory/service.py:315:                promoted.append(memory_id)\n/home/sebas/agents-database/src/shared_agent_memory/service.py:316:        return {\"promoted\": promoted, \"linked\": linked}\n/home/sebas/agents-database/src/shared_agent_memory/service.py:318:    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:\n/home/sebas/agents-database/src/shared_agent_memory/service.py:321:            \"promoted\": 0,\n/home/sebas/agents-database/src/shared_agent_memory/service.py:322:            \"archived\": 0,\n/home/sebas/agents-database/src/shared_agent_memory/service.py:324:            \"conflicts\": 0,\n/home/sebas/agents-database/src/shared_agent_memory/service.py:332:        conflict_threshold = float(config.get(\"conflict_threshold\", 0.55))\n/home/sebas/agents-database/src/shared_agent_memory/service.py:388:                        (\"archived\", utcnow(), candidate[\"id\"]),\n/home/sebas/agents-database/src/shared_agent_memory/service.py:390:                    stats[\"archived\"] += 1\n/home/sebas/agents-database/src/shared_agent_memory/service.py:398:                        (\"archived\", utcnow(), candidate[\"id\"]),\n/home/sebas/agents-database/src/shared_agent_memory/service.py:400:                    stats[\"archived\"] += 1\n/home/sebas/agents-database/src/shared_agent_memory/service.py:433:                if best_match and best_score >= conflict_threshold and self._conflict_signal(candidate, best_match):\n/home/sebas/agents-database/src/shared_agent_memory/service.py:434:                    self._record_conflict(conn, candidate[\"id\"], best_match[\"id\"], \"potential_contradiction\")\n/home/sebas/agents-database/src/shared_agent_memory/service.py:436:                    stats[\"conflicts\"] += 1\n/home/sebas/agents-database/src/shared_agent_memory/service.py:443:                    stats[\"promoted\"] += 1\n/home/sebas/agents-database/src/shared_agent_memory/service.py:445:                    # Normalize qualifying active records as already-promoted durable memories.\n/home/sebas/agents-database/src/shared_agent_memory/service.py:446:                    stats[\"promoted\"] += 1\n/home/sebas/agents-database/src/shared_agent_memory/service.py:492:            \"memory_conflicts\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:516:            \"memory_conflicts\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1059:                            \"archived\" if approval[\"status\"] != \"pending\" else \"active\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1246:                \"id\": \"job_consolidation_daily_10\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1247:                \"job_type\": \"consolidation\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1259:                    \"conflict_threshold\": 0.55,\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1266:                \"id\": \"job_consolidation_daily_15\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1267:                \"job_type\": \"consolidation\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1279:                    \"conflict_threshold\": 0.55,\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1286:                \"id\": \"job_consolidation_weekly\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1287:                \"job_type\": \"consolidation\",\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1299:                    \"conflict_threshold\": 0.55,\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1307:            legacy = conn.execute(\"SELECT id FROM maintenance_jobs WHERE id = ?\", (\"job_consolidation_daily\",)).fetchone()\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1315:                    (\"deprecated: split into 10:00 and 15:00 jobs\", utcnow(), \"job_consolidation_daily\"),\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1659:    def _record_conflict(self, conn: Any, memory_id: str, conflicting_id: str, reason: str) -> None:\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1662:            INSERT INTO memory_conflicts (id, memory_id, conflicting_memory_id, reason, created_at, metadata_json)\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1665:            (make_id(\"mcf\"), memory_id, conflicting_id, reason, utcnow(), self.store.dumps({})),\n/home/sebas/agents-database/src/shared_agent_memory/service.py:1668:    def _conflict_signal(self, left: dict[str, Any], right: dict[str, Any]) -> bool:\n/home/sebas/agents-database/src/shared_agent_memory/cli.py:99:    consolidate = subparsers.add_parser(\"consolidate\")\n/home/sebas/agents-database/src/shared_agent_memory/cli.py:100:    consolidate.add_argument(\"memory_ids\", nargs=\"+\")\n/home/sebas/agents-database/src/shared_agent_memory/cli.py:238:    elif args.command == \"consolidate\":\n/home/sebas/agents-database/src/shared_agent_memory/cli.py:239:        print(json.dumps(service.consolidate(args.memory_ids), indent=2))\n/home/sebas/agents-database/src/shared_agent_memory/schema.py:129:CREATE TABLE IF NOT EXISTS memory_conflicts (\n/home/sebas/agents-database/src/shared_agent_memory/schema.py:132:    conflicting_memory_id TEXT NOT NULL,\n/home/sebas/agents-database/src/shared_agent_memory/schema.py:137:    FOREIGN KEY(conflicting_memory_id) REFERENCES memories(id)\n/home/sebas/agents-database/src/shared_agent_memory/schema.py:224:CREATE INDEX IF NOT EXISTS idx_memory_conflicts_mem ON memory_conflicts(memory_id);\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:216:    promoted = 0\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:217:    archived = 0\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:219:    conflicts = 0\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:222:        promoted += int(stats.get(\"promoted\") or 0)\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:223:        archived += int(stats.get(\"archived\") or 0)\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:225:        conflicts += int(stats.get(\"conflicts\") or 0)\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:226:    return {\"promoted\": promoted, \"archived\": archived, \"linked\": linked, \"conflicts\": conflicts}\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:276:            f\"promoted {maintenance['promoted']}, \"\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:277:            f\"archived {maintenance['archived']}, \"\n/home/sebas/pi-config/scripts/session-memory-sweeper.py:279:            f\"conflicts {maintenance['conflicts']}\"\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:139:- Include strong durable memories and weaker candidate memories that may be worth later consolidation.\n/home/sebas/pi-config/scripts/session-memory-analyzer.py:145:- It is OK to include lower-confidence candidates if they could become useful after consolidation.\n"}],"isError":false,"timestamp":1780573447716}}
{"type":"message","id":"a931ac4f","parentId":"ddb6ba94","timestamp":"2026-06-04T11:44:13.557Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_RJSEOwbP84pcukkd7R1IrBMw|fc_08205193eac5c3af016a21650d7d7881918fd5a9691a12a54d","name":"read","arguments":{"path":"/home/sebas/agents-database/src/shared_agent_memory/service.py","offset":318,"limit":220}},{"type":"toolCall","id":"call_rRW5xrJCMkJGkIWWIrfdbUh3|fc_08205193eac5c3af016a21650d7d9081918ff51a43cf0bab65","name":"read","arguments":{"path":"/home/sebas/agents-database/src/shared_agent_memory/service.py","offset":1650,"limit":80}},{"type":"toolCall","id":"call_LK1GMtv9AIhRW2im4afWmgcm|fc_08205193eac5c3af016a21650d7d988191a0f3571df2e3eb07","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json\np='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'\ncon=sqlite3.connect(p)\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\nprint('status counts')\nfor r in cur.execute(\"SELECT status, COUNT(*) c FROM memories GROUP BY status ORDER BY c DESC\"):\n    print(dict(r))\nprint('\\nrecent active sample')\nfor r in cur.execute(\"SELECT id,type,scope,status,title,updated_at,confidence,freshness FROM memories WHERE status='active' ORDER BY updated_at DESC LIMIT 20\"):\n    print(dict(r))\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":7910,"output":253,"cacheRead":36352,"cacheWrite":0,"totalTokens":44515,"cost":{"input":0.019775,"output":0.003795,"cacheRead":0.009087999999999999,"cacheWrite":0,"total":0.032658}},"stopReason":"toolUse","timestamp":1780573447718,"responseId":"resp_08205193eac5c3af016a216507d3548191b1023a9c2eb0a93a"}}
{"type":"message","id":"c7ec5b20","parentId":"a931ac4f","timestamp":"2026-06-04T11:44:13.622Z","message":{"role":"toolResult","toolCallId":"call_RJSEOwbP84pcukkd7R1IrBMw|fc_08205193eac5c3af016a21650d7d7881918fd5a9691a12a54d","toolName":"read","content":[{"type":"text","text":"    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:\n        stats = {\n            \"candidates\": 0,\n            \"promoted\": 0,\n            \"archived\": 0,\n            \"linked\": 0,\n            \"conflicts\": 0,\n            \"llm_summaries\": 0,\n        }\n        age_days = int(config.get(\"candidate_age_days\", 7))\n        max_candidates = int(config.get(\"max_candidates\", 200))\n        high_threshold = float(config.get(\"dedupe_high_threshold\", 0.9))\n        mid_threshold = float(config.get(\"dedupe_mid_threshold\", 0.75))\n        promote_threshold = float(config.get(\"promote_confidence\", 0.7))\n        conflict_threshold = float(config.get(\"conflict_threshold\", 0.55))\n        llm_enabled = bool(config.get(\"llm_enabled\", False))\n        llm_provider = config.get(\"llm_provider\") or \"opencode\"\n        llm_command = config.get(\"llm_command\") or [\"opencode\", \"-m\", \"MiniMax M2.5 Free\"]\n        cutoff = (datetime.now(UTC) - timedelta(days=age_days)).isoformat()\n\n        with self.store.connection() as conn:\n            rows = conn.execute(\n                \"\"\"\n                SELECT * FROM memories\n                WHERE (\n                    status = 'inbox'\n                    OR (type = 'episode' AND status = 'active')\n                )\n                  AND created_at <= ?\n                ORDER BY created_at ASC\n                LIMIT ?\n                \"\"\",\n                (cutoff, max_candidates),\n            ).fetchall()\n\n            candidates = [self._row_to_memory(row) for row in rows]\n            stats[\"candidates\"] = len(candidates)\n\n            for candidate in candidates:\n                related = conn.execute(\n                    \"\"\"\n                    SELECT * FROM memories\n                    WHERE status = 'active'\n                      AND id != ?\n                      AND type = ?\n                      AND scope = ?\n                    ORDER BY updated_at DESC\n                    LIMIT 50\n                    \"\"\",\n                    (candidate[\"id\"], candidate[\"type\"], candidate[\"scope\"]),\n                ).fetchall()\n                related_memories = [self._row_to_memory(row) for row in related]\n\n                best_match = None\n                best_score = 0.0\n                for other in related_memories:\n                    lexical = self._lexical_score(\n                        normalize_text(candidate[\"content\"]).split(),\n                        normalize_text(other[\"content\"]),\n                    )\n                    semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n                    if score > best_score:\n                        best_score = score\n                        best_match = other\n\n                if best_match and candidate[\"content\"] == best_match[\"content\"]:\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"exact_duplicate\")\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"archived\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"archived\"] += 1\n                    stats[\"linked\"] += 1\n                    continue\n\n                if best_match and best_score >= high_threshold:\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"high_similarity\")\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"archived\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"archived\"] += 1\n                    stats[\"linked\"] += 1\n                    continue\n\n                if best_match and best_score >= mid_threshold:\n                    if llm_enabled:\n                        synthesized = self._llm_summarize_pair(\n                            candidate,\n                            best_match,\n                            provider=llm_provider,\n                            command=llm_command,\n                        )\n                        if synthesized:\n                            conn.execute(\n                                \"UPDATE memories SET summary = ?, updated_at = ?, metadata_json = ? WHERE id = ?\",\n                                (\n                                    synthesized,\n                                    utcnow(),\n                                    self.store.dumps(\n                                        {\n                                            **candidate.get(\"metadata\", {}),\n                                            \"synthesis_sources\": [candidate[\"id\"], best_match[\"id\"]],\n                                            \"synthesis_provider\": llm_provider,\n                                            \"synthesis_command\": llm_command,\n                                        }\n                                    ),\n                                    candidate[\"id\"],\n                                ),\n                            )\n                            stats[\"llm_summaries\"] += 1\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"mid_similarity\")\n                    stats[\"linked\"] += 1\n\n                if best_match and best_score >= conflict_threshold and self._conflict_signal(candidate, best_match):\n                    self._record_conflict(conn, candidate[\"id\"], best_match[\"id\"], \"potential_contradiction\")\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"contradicts\", \"heuristic\")\n                    stats[\"conflicts\"] += 1\n\n                if candidate[\"status\"] == \"inbox\" and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"active\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"promoted\"] += 1\n                elif candidate[\"status\"] == \"active\" and candidate[\"type\"] in {\"episode\", \"artifact\", \"decision\", \"project\", \"procedural\", \"semantic\"} and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    # Normalize qualifying active records as already-promoted durable memories.\n                    stats[\"promoted\"] += 1\n\n        return stats\n\n    def upsert_profile_fact(\n        self, title: str, content: str, evidence_ref: str | None = None, source_ref: str | None = None\n    ) -> dict[str, Any]:\n        return self.create_memory(\n            {\n                \"type\": \"profile\",\n                \"scope\": \"global\",\n                \"title\": title,\n                \"content\": content,\n                \"summary\": self._make_summary(content),\n                \"source_ref\": source_ref,\n                \"evidence_ref\": evidence_ref,\n                \"metadata\": {\"kind\": \"profile_fact\"},\n            }\n        )\n\n    def explain(self, identifier: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            retrieval = conn.execute(\"SELECT * FROM retrieval_logs WHERE id = ?\", (identifier,)).fetchone()\n            if retrieval:\n                return {\n                    \"kind\": \"retrieval\",\n                    \"id\": retrieval[\"id\"],\n                    \"query_text\": retrieval[\"query_text\"],\n                    \"filters\": self.store.loads(retrieval[\"filters_json\"]),\n                    \"results\": self.store.loads(retrieval[\"results_json\"]),\n                }\n            memory = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (identifier,)).fetchone()\n            if memory:\n                mem = self._row_to_memory(memory)\n                sources = conn.execute(\"SELECT * FROM sources WHERE source_ref = ? OR id = ?\", (mem[\"source_ref\"], mem[\"source_ref\"])).fetchall()\n                return {\"kind\": \"memory\", \"memory\": mem, \"sources\": [dict(source) for source in sources]}\n        raise KeyError(f\"Unknown identifier: {identifier}\")\n\n    def export(self) -> dict[str, Any]:\n        tables = [\n            \"projects\",\n            \"repos\",\n            \"sources\",\n            \"ingestion_events\",\n            \"memories\",\n            \"memory_links\",\n            \"memory_conflicts\",\n            \"retrieval_logs\",\n            \"maintenance_jobs\",\n            \"maintenance_runs\",\n            \"profiles\",\n            \"tasks\",\n            \"task_runs\",\n            \"artifacts\",\n        ]\n        exported: dict[str, list[dict[str, Any]]] = {}\n        with self.store.connection() as conn:\n            for table in tables:\n                rows = conn.execute(f\"SELECT * FROM {table}\").fetchall()\n                exported[table] = [dict(row) for row in rows]\n        return exported\n\n    def import_data(self, payload: dict[str, Any]) -> None:\n        tables = [\n            \"projects\",\n            \"repos\",\n            \"sources\",\n            \"ingestion_events\",\n            \"memories\",\n            \"memory_links\",\n            \"memory_conflicts\",\n            \"retrieval_logs\",\n            \"maintenance_jobs\",\n            \"maintenance_runs\",\n            \"profiles\",\n            \"tasks\",\n            \"task_runs\",\n            \"artifacts\",\n        ]\n        with self.store.connection() as conn:\n            for table in tables:\n                rows = payload.get(table, [])\n                if not rows:\n                    continue\n                columns = list(rows[0].keys())\n                placeholder = \",\".join(\"?\" for _ in columns)\n                sql = f\"INSERT OR REPLACE INTO {table} ({','.join(columns)}) VALUES ({placeholder})\"\n                conn.executemany(sql, [tuple(row.get(col) for col in columns) for row in rows])\n                if table == \"memories\":\n                    for row in rows:\n                        self._upsert_fts(conn, row)\n        self.migrate_v2()\n\n[1172 more lines in file. Use offset=538 to continue.]"}],"isError":false,"timestamp":1780573453622}}
{"type":"message","id":"75ef0796","parentId":"c7ec5b20","timestamp":"2026-06-04T11:44:13.622Z","message":{"role":"toolResult","toolCallId":"call_rRW5xrJCMkJGkIWWIrfdbUh3|fc_08205193eac5c3af016a21650d7d9081918ff51a43cf0bab65","toolName":"read","content":[{"type":"text","text":"    def _link_memories(self, conn: Any, from_id: str, to_id: str, relation: str, reason: str) -> None:\n        conn.execute(\n            \"\"\"\n            INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)\n            VALUES (?, ?, ?, ?, ?, ?)\n            \"\"\",\n            (make_id(\"lnk\"), from_id, to_id, relation, utcnow(), self.store.dumps({\"reason\": reason})),\n        )\n\n    def _record_conflict(self, conn: Any, memory_id: str, conflicting_id: str, reason: str) -> None:\n        conn.execute(\n            \"\"\"\n            INSERT INTO memory_conflicts (id, memory_id, conflicting_memory_id, reason, created_at, metadata_json)\n            VALUES (?, ?, ?, ?, ?, ?)\n            \"\"\",\n            (make_id(\"mcf\"), memory_id, conflicting_id, reason, utcnow(), self.store.dumps({})),\n        )\n\n    def _conflict_signal(self, left: dict[str, Any], right: dict[str, Any]) -> bool:\n        allowed_types = {\"profile\", \"decision\", \"project\"}\n        if left.get(\"type\") not in allowed_types or right.get(\"type\") not in allowed_types:\n            return False\n        negations = {\"not\", \"no\", \"never\", \"nunca\", \"sin\"}\n        left_tokens = set(normalize_text(left[\"content\"]).split())\n        right_tokens = set(normalize_text(right[\"content\"]).split())\n        left_neg = bool(negations & left_tokens)\n        right_neg = bool(negations & right_tokens)\n        overlap = len(left_tokens & right_tokens)\n        return overlap >= 3 and left_neg != right_neg\n\n    def _llm_summarize_pair(\n        self,\n        left: dict[str, Any],\n        right: dict[str, Any],\n        *,\n        provider: str,\n        command: list[str],\n    ) -> str | None:\n        if provider != \"opencode\":\n            return None\n        prompt = (\n            \"Summarize and merge the two memory notes into one concise statement. Preserve key facts and avoid speculation.\\n\\n\"\n            f\"Memory A:\\n{left['content']}\\n\\nMemory B:\\n{right['content']}\\n\"\n        )\n        try:\n            result = subprocess.run(\n                command,\n                input=prompt,\n                text=True,\n                capture_output=True,\n                timeout=60,\n                check=False,\n            )\n            if result.returncode != 0:\n                return None\n            output = result.stdout.strip()\n            return output or None\n        except Exception:\n            return None\n"}],"isError":false,"timestamp":1780573453622}}
{"type":"message","id":"dc3b3502","parentId":"75ef0796","timestamp":"2026-06-04T11:44:13.622Z","message":{"role":"toolResult","toolCallId":"call_LK1GMtv9AIhRW2im4afWmgcm|fc_08205193eac5c3af016a21650d7d988191a0f3571df2e3eb07","toolName":"bash","content":[{"type":"text","text":"status counts\n{'status': 'active', 'c': 317}\n\nrecent active sample\n{'id': 'mem_1bd596d2f0854d058b37e0a75d74587c', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] hace poco hicimos en un repo modificacion de zips para hacer…', 'updated_at': '2026-06-03T10:11:50.691236+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_c8667f8f64f747c7a7cc1407da9ff8bb', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] en el pdf diario de rss podriamos agregar el titulo del feed…', 'updated_at': '2026-06-03T10:11:50.684291+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_4870951158a94c72ac1dd324c720731e', 'type': 'decision', 'scope': 'project', 'status': 'active', 'title': 'rss-offline PDF uses reader-style print layout', 'updated_at': '2026-06-02T15:00:21.183318+00:00', 'confidence': 1.0, 'freshness': 1.0}\n{'id': 'mem_31a3021ad8f349e19bc839199ae6697d', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] te animas a reproducir este logo a la perfeccion pero vector…', 'updated_at': '2026-06-02T10:11:58.227925+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_d356ae644c4f4f90982d6024a49085c0', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] limpia la carparta de dropzone que te voy a pasar nuevos', 'updated_at': '2026-06-02T10:11:58.222933+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_e80d2be0aecc4eed84d5aa179f83bfdd', 'type': 'episode', 'scope': 'project', 'status': 'active', 'title': 'pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…', 'updated_at': '2026-06-01T10:07:54.414849+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_89d7f9db9cb04d0da7539c33e3ccc024', 'type': 'episode', 'scope': 'project', 'status': 'active', 'title': 'pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…', 'updated_at': '2026-06-01T10:07:54.414093+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_a986376af1554bec8e3909c656585bb6', 'type': 'episode', 'scope': 'project', 'status': 'active', 'title': 'pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…', 'updated_at': '2026-06-01T10:07:54.413440+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_15c297bc9c334fed8e31075b26130378', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] loved this repo’s idea https://github.com/workos/case and it…', 'updated_at': '2026-06-01T10:07:54.412728+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_4814b92ec95a4376a9927668421d8503', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] te fijas que los links a pdfs en https://ballbox-first.emper…', 'updated_at': '2026-05-31T10:05:58.694147+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_f69a512f8b034dba821f31efc6b046db', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] busca una conversación de una sesión de pi de hoy, en donde…', 'updated_at': '2026-05-31T10:05:58.683372+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_0ffba9b736bf4c0aae1d3696fb36c65e', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] hay una sesion de tmux abierta?', 'updated_at': '2026-05-31T10:05:58.670977+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_a238b1334bbf4bba900d3d91b83f5174', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: hay una sesion de tmux abierta?', 'updated_at': '2026-05-31T10:05:58.664635+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_d45915de1d704362a10b78a4beb0a0b9', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] dale hacelo', 'updated_at': '2026-05-31T10:05:58.661030+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_1e0572197f9048ba9f99f84fbd0a0592', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] te llegaron?', 'updated_at': '2026-05-31T10:05:58.657005+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_165ae24535224d79b9e0370d64c1e7b4', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] el zip que paso son los archivos en una máquina expendedora.…', 'updated_at': '2026-05-31T10:05:58.649423+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_e9134aea6bfb4091b7051a97af2ae9e1', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'Ballbox machine rebrand releases published in dropbox repo', 'updated_at': '2026-05-31T08:07:39.615946+00:00', 'confidence': 1.0, 'freshness': 1.0}\n{'id': 'mem_edf34d5e3b9a4dbe9276d288255bade7', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] resume ftp investigation for ballbox. Since we need a passwo…', 'updated_at': '2026-05-28T10:15:31.691079+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_3bca1201a684427e87bca9e7cc806afc', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] how do we set up a deepseek api key to pi?', 'updated_at': '2026-05-28T10:15:31.681910+00:00', 'confidence': 0.72, 'freshness': 0.95}\n{'id': 'mem_8517007aa63c464c9b84431f6562769a', 'type': 'artifact', 'scope': 'project', 'status': 'active', 'title': 'OurVend FTP host is cloud infra, not machine access', 'updated_at': '2026-05-27T21:59:48.292980+00:00', 'confidence': 1.0, 'freshness': 1.0}\n"}],"isError":false,"timestamp":1780573453622}}
{"type":"message","id":"d9016f12","parentId":"dc3b3502","timestamp":"2026-06-04T11:44:41.588Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Running conflict checks**\n\nI need to perform a dry run, possibly with a copy of the existing script. I'll inspect the default job configurations to adjust the thresholds. Using a Python script, I plan to simulate stricter thresholds and enhance conflict detection. The user has asked for a stricter conflict criterion. I’ll define a lower conflict threshold and look for near-duplicates. To run this, I’ll check memory schema fields and recent episodes for results without modifying the database.","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216511d0048191ac4acc1bb5f8a254\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWUWn2H-K04oTzTMe-OK41V_ndYdHc5xCjEWpkxAcFEaJXdygOlN7D51GFTodI3VCOuod-ycwmbfz5mkVFkXuYF8uL39XvGlU0m2t5xwnMTg6m_MnIU09jSWUlCNgnftw93wHWz65Qaj_M1eUTjGINl3qHBsxz6z43LzaQdh09E7RX7PjTzPh06UAzae0wlUUsnHuWyxkJ0Jf9kryw2VzNLQC3wkzFB5dFjAstHRh7JpBoRq4q9TdLUcY8K5UdVZEHaE-ChVTksXw0Kp7hd-lbrgklAmFoB40HxXPZRNuiTA0SPBT0J5mhohG9quT82yiGbMu87e6ZKCImneUbk0QQ2itWpBKv3gBLK-D1-M2JrwbaO_CrDvQ8z3YwsT9UyNV7xmBB5CZijSQpe53OdUVplb4KUcEZHkRaXvXwj9QSv89PCFzetthISjUfSqrNXB73x0HuLUT1FpPzV3RD3jDFgIUmPr_g4cNPmAqj1eTFeIdBF81mR_5M0u2D9RDzQL6HqXpiXRrjfq-t1sIW91_mHQElSQTmQEv9X_UPPVxw6cCBkL96IAZg6l2p_qri3uxRJXjr3hd4M1WT2qvUPwJ22qZKLBsEuZBb55dd6Y6QC8Ag0bF2PJpmPYDgnuwFzjMkDt02hNW699cI6qS6N6e69CuFvO3VChUTMThvMa_6u3__7Cz7-3lpkDiR8exd_7JE1PxzVu7NtNbL4NtFWLcOxn30Mx5FLrPqt8qTo75Hlhtsp_G60fshUvIV4X7pkp7QXvJ1Z-WVZsmHqsBhF5-JaasU_f3qXLVbAeEx8AMlCEtkgVuE4Gbx-cjpUs3u1pURLQlFbuVzAiinaN5CAboJLrS3BuL74rsjMHawd0gQi5lbhQeCZrCM8X-c3AeOiwx_WAx_tuie-OhUCwAk2he7lJbn1j-gdfxqmE7-NSPa-RTFfvKqBC9QulCoy-2ycDOWc1ZHGqbKB-oOsS3FKAw9fWZEKZqnRFuDbgFe7GaHJ80FzVpZpCzYKLs_ZBNaqXXAWpmug5JkrMUCYqDeufoTK9dCFJR_dY9o9ySUczwvAMa95FzN0g0GoojrRrsujgh9O0NjleaDZGjg8OCPlHFDx1QPAbRds1O_EW2RHCavkUbQCDYqob-A-ld_Ft15BN6PbjqYMz4j__AmN6cfsokysgnxl4WcWaIJ_5fYXtDtEQhLJ7SaofR-0IDrnm-M7hQyzFY6qhylmpaJCAJ8yTiuiiO7o7_Z_ob4HlXWCVvOv2JB8n55FgfZqPz0snmo56cIf_AjUWd2kzDOqLr_bksG2v4SZDZehIjfxc4QN8rwuJCfRUx-iQpDYWXaIH6tp3XMv9voDpVACOLCPJbmvZcTRcYrq-tj7dZcuNUGLlBOIkv0pXOLNdg49Z49vgPFkn0_lMSfaPNC1TipInw3Iunawpf-zeoXmX-YeeCsWuO7g_zRSeEkqhy357prqPsgiPNSyJ3gEVx-CmHnwURsbg4PtSzdnNVyZQXgLEQRXLyCuhMBs73JI3rZ63LnIw8qZntkcmyZ_meF-sAw9lGog7GuogjSEgGvzcDP5vycGEyl_hqEWbq7iWBg_y0ujIXQ2nvP-k2yMmyGkusGCcQ0GePo3YiL8zjHP9c9hAdeYeZRggV0a1lZVOcaYjZEtiWYz1eDHSlxIIjNReG2CbYBrUN8ZdhP5U-Cv3JJnH6VRYSY5rEb2r4XbwIqURHtLDKZqwrP1tBckQ-bvqrS3zQt-AU0aEQsK1d_d8CB3Yvc9MWNBbhHJSpx3DZjAMK504myf6TJfz6Tb72-XwWF1aPyA56wpOW4ng8xSJD1bCuznybdMKt71PwD4Yx28bF4uzwbObBsnjczKMCfxW92REtKaiME16iEgY8xa4tyUdGerazd07Jkx8B6HhYXErhOuVGGtlbllMEyZyjO3eQmFSrNTUY6t-C6TLescX_pm1ISSuDhUEwrBcNCbyZW1wD1R5QFxZp3zInybAr146aL8XGBAjRbK4b3fswpvL_vRzxeUGny569W72lavh_LClaf0ZV_qxtBQ2BTN-VfiHKROdlOig2ozE8YL7MTu3m_6JKV634gtry1YAVEDsEgj4ivfzyorDX1Ig_theGjd5cGIpeb2K0e3ruuoLzDk53EpyeFxHo9jC7YbkjF4RIw6tmAvMosJpD-A7SdPEO8cZt97Ae0RfNR5KIdAt1YlFYpSdgHopfzzs7HxKHGjwo7MAtEinRWtDC1t4Wot_SxycBehHmqA6pcDv5cKD51XfdP5wLO2a3XRY4bJcufuVeAbdMaKn-9PYGkWC0i-_L7li56CHcZXeH74wOQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Running conflict checks**\\n\\nI need to perform a dry run, possibly with a copy of the existing script. I'll inspect the default job configurations to adjust the thresholds. Using a Python script, I plan to simulate stricter thresholds and enhance conflict detection. The user has asked for a stricter conflict criterion. I’ll define a lower conflict threshold and look for near-duplicates. To run this, I’ll check memory schema fields and recent episodes for results without modifying the database.\"}]}"},{"type":"toolCall","id":"call_NWih1mF47eSXsHsBpWxkHiUc|fc_08205193eac5c3af016a2165167a388191a15b42c69e1c3d36","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json, re\nfrom datetime import datetime, timezone, timedelta\nfrom pathlib import Path\n\nDB='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'\ncon=sqlite3.connect(DB)\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\n\ncutoff=(datetime.now(timezone.utc)-timedelta(days=7)).isoformat()\nrows=cur.execute('''\nSELECT * FROM memories\nWHERE ((status='inbox') OR (type='episode' AND status='active'))\n  AND created_at <= ?\nORDER BY created_at ASC\nLIMIT 200\n''',(cutoff,)).fetchall()\nmems=[dict(r) for r in rows]\nprint('candidates', len(mems))\n\ndef norm(s):\n    s=s.lower()\n    s=re.sub(r'\\[telegram\\]', ' ', s)\n    s=re.sub(r'https?://\\S+', ' URL ', s)\n    s=re.sub(r'[^\\w\\s]', ' ', s)\n    s=' '.join(s.split())\n    return s\n\ndef toks(s):\n    return set(norm(s).split())\n\ndef jacc(a,b):\n    if not a or not b: return 0.0\n    return len(a&b)/len(a|b)\n\ndef overlap_ratio(a,b):\n    if not a or not b: return 0.0\n    return len(a&b)/min(len(a),len(b))\nneg={'not','no','never','nunca','sin'}\nconflict_pairs=[]\ndupe_pairs=[]\nlink_pairs=[]\nfor i,m in enumerate(mems):\n    mt=toks(m['content'])\n    for j,n in enumerate(mems[:i]):\n        if m['type']!=n['type'] or m['scope']!=n['scope']:\n            continue\n        nt=toks(n['content'])\n        jac=jacc(mt,nt)\n        ov=overlap_ratio(mt,nt)\n        # stricter consolidation: normalize channel noise and catch heavy overlap\n        if jac>=0.62 or ov>=0.78:\n            dupe_pairs.append((jac,ov,m,n))\n        elif jac>=0.42 or ov>=0.62:\n            link_pairs.append((jac,ov,m,n))\n        # stricter conflict: same type/scope, overlap, opposite negation, or explicit antonym-ish cues\n        if m['type'] in {'profile','decision','project','artifact','episode'}:\n            mn=bool(neg & mt); nn=bool(neg & nt)\n            anti=False\n            terms=[('always','never'),('with','without'),('enable','disable'),('enabled','disabled'),('public','private'),('local','remote')]\n            ms=' '+norm(m['content'])+' '\n            ns=' '+norm(n['content'])+' '\n            for a,b in terms:\n                if ((f' {a} ' in ms and f' {b} ' in ns) or (f' {b} ' in ms and f' {a} ' in ns)):\n                    anti=True; break\n            if (jac>=0.28 or ov>=0.5) and ((mn!=nn) or anti):\n                conflict_pairs.append((jac,ov,m,n,'negation' if mn!=nn else 'antonym'))\n\ndupe_pairs=sorted(dupe_pairs,key=lambda x:(x[0],x[1]), reverse=True)\nlink_pairs=sorted(link_pairs,key=lambda x:(x[0],x[1]), reverse=True)\nconflict_pairs=sorted(conflict_pairs,key=lambda x:(x[0],x[1]), reverse=True)\nprint('dryrun_dupe_archive', len(dupe_pairs))\nprint('dryrun_related_link', len(link_pairs))\nprint('dryrun_conflicts', len(conflict_pairs))\nprint('\\nTOP DUPES')\nfor jac,ov,m,n in dupe_pairs[:12]:\n    print(json.dumps({\n        'score': round(jac,3), 'overlap': round(ov,3),\n        'a': m['id'], 'a_title': m['title'][:90],\n        'b': n['id'], 'b_title': n['title'][:90]\n    }, ensure_ascii=False))\nprint('\\nTOP LINKS')\nfor jac,ov,m,n in link_pairs[:12]:\n    print(json.dumps({\n        'score': round(jac,3), 'overlap': round(ov,3),\n        'a': m['id'], 'a_title': m['title'][:90],\n        'b': n['id'], 'b_title': n['title'][:90]\n    }, ensure_ascii=False))\nprint('\\nTOP CONFLICTS')\nfor jac,ov,m,n,reason in conflict_pairs[:12]:\n    print(json.dumps({\n        'reason': reason, 'score': round(jac,3), 'overlap': round(ov,3),\n        'a': m['id'], 'a_title': m['title'][:90],\n        'b': n['id'], 'b_title': n['title'][:90]\n    }, ensure_ascii=False))\nPY","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":5140,"output":1419,"cacheRead":44032,"cacheWrite":0,"totalTokens":50591,"cost":{"input":0.01285,"output":0.021285000000000002,"cacheRead":0.011007999999999999,"cacheWrite":0,"total":0.045142999999999996}},"stopReason":"toolUse","timestamp":1780573453623,"responseId":"resp_08205193eac5c3af016a21650db86c8191b0b124074da13013"}}
{"type":"message","id":"13136191","parentId":"d9016f12","timestamp":"2026-06-04T11:44:41.736Z","message":{"role":"toolResult","toolCallId":"call_NWih1mF47eSXsHsBpWxkHiUc|fc_08205193eac5c3af016a2165167a388191a15b42c69e1c3d36","toolName":"bash","content":[{"type":"text","text":"candidates 51\ndryrun_dupe_archive 2\ndryrun_related_link 4\ndryrun_conflicts 3\n\nTOP DUPES\n{\"score\": 0.623, \"overlap\": 0.786, \"a\": \"mem_17eea5c0658f4cd3beece90a93d35e36\", \"a_title\": \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\", \"b\": \"mem_04dc02a90cad40cc826ed1e8f93aff36\", \"b_title\": \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\"}\n{\"score\": 0.52, \"overlap\": 0.788, \"a\": \"legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff\", \"a_title\": \"Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered pade\", \"b\": \"legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\", \"b_title\": \"Research run: Source 50+ inch China displays for Ballbox advertising in padel venues\"}\n\nTOP LINKS\n{\"score\": 0.489, \"overlap\": 0.697, \"a\": \"legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7\", \"a_title\": \"Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment f\", \"b\": \"legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb\", \"b_title\": \"Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machin\"}\n{\"score\": 0.167, \"overlap\": 0.667, \"a\": \"legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0\", \"a_title\": \"Research run: Test intake flow\", \"b\": \"legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb\", \"b_title\": \"Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machin\"}\n{\"score\": 0.15, \"overlap\": 0.667, \"a\": \"legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7\", \"a_title\": \"Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment f\", \"b\": \"legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0\", \"b_title\": \"Research run: Test intake flow\"}\n{\"score\": 0.15, \"overlap\": 0.667, \"a\": \"legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7\", \"a_title\": \"Research run: Analyze the best next steps to make ballbox-payment-integration production-r\", \"b\": \"legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0\", \"b_title\": \"Research run: Test intake flow\"}\n\nTOP CONFLICTS\n{\"reason\": \"negation\", \"score\": 0.623, \"overlap\": 0.786, \"a\": \"mem_17eea5c0658f4cd3beece90a93d35e36\", \"a_title\": \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\", \"b\": \"mem_04dc02a90cad40cc826ed1e8f93aff36\", \"b_title\": \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\"}\n{\"reason\": \"negation\", \"score\": 0.154, \"overlap\": 0.5, \"a\": \"legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54\", \"a_title\": \"Research run: Production-readiness plan for ballbox-payment-integration\", \"b\": \"legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1\", \"b_title\": \"Research run: Verify capture-url flow\"}\n{\"reason\": \"negation\", \"score\": 0.087, \"overlap\": 0.606, \"a\": \"mem_c1cd76a28d434774ad10b1507577bab3\", \"a_title\": \"sebas: [telegram] necesito para ballbox un entregable en pdf profesional para…\", \"b\": \"mem_a1f632320762487c8864dd45f2cbf394\", \"b_title\": \"sebas: [telegram] hazme en inglés, un texto explicando con flechas, el flujo m…\"}\n"}],"isError":false,"timestamp":1780573481736}}
{"type":"message","id":"c5328386","parentId":"13136191","timestamp":"2026-06-04T11:44:50.786Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Investigating suspicious items**\n\nI think I need to closely inspect the most suspicious items because some could be false positives. I want to ensure that I'm reading the contents carefully to verify their legitimacy. It's always tricky to identify what's really off versus what's just standard. I'll take my time to analyze everything thoroughly to avoid any mistakes. Let’s make sure I'm getting it right this time!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21652c638881919556dec990be9737\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWUvXGDZdTXNGtasjmillojJlmaQ9Aa3L6Ik7MyGW1N5ctG2UC0RLfYGMQydGZj1tOwMUCZA4RX4zrxu3okqPYRpDEzqTUzYHVc7P5AjMQ7n1PZJ1FXLTbL-1iUVnHhnULS3gFGd_jLl4WzzZaJVZaJXF3-m4vlEzj4cD4FZTAQWw-gEdT1pZQtOq0U098hFJBDQ0KFazUpMjKi1YioRaZeqUw26ZKyURkbjRNeyQWqyPkvmEYvz_HwviYLrHJqzbOxAYoHB7ap_GexNVrZpMzW1nh7ACcliZSrCLSei1T-WXnSSq43d8_k1oBnQ9ZpOOoMQIiiPbDatDMQaR8rbdMMDDF4_211hvEWTfRDCKA9zjFo2uUUW4PidD-fQN4YQQeSdHQ76v38iVwBJp5EUEXXT2UsQvpWn4RuEeNt4TdENXR9qKAx48M4EtNJ6I9j_jmXrhDViy8ybHAxFHKjcnrEFmyfi8pDWcZY_QzXy3RvmSzFnyhvKhhPTqxlIfHgM-Kesj776E9MxvTWb8seHmjgzpi7SewlzsU35zrysS7jHcs6Xg4foBO_Lupv1ZYRmynaWKi99DYfdzbeUeQiiFHCqPKMlZAWhTG3MX4UfAOP5q9nGfRi5pCR6dag7qtAjeD-B6qF4UQ5aorNc0qxlQn6LhuwdEJRQ47COmA4bm3yGpo1AkOXjM75hmorZg0RxmkroaNSyl3hUa9jqf2q6yoeGjUFyhiQ95eIhAbbeNC6VH42CdS1XAM7n7PfzMJ5aTpiC640Dl7G88jwSRqvZp37hkI7k1F73-hcjneCNkP4nMBSM8HT8lLE7WJPcpJsYWC60dXAVaT9m8KwVjX_d0ulyYS8kkGmDQgrF4w_wZqETJ8MhEROnvGAsHbuEbQijPwlbd2hQrFP4Fd7lruMwy-rnLNLWYtP_L1al74oBrrhgUqQAlT1JFUuaIsziJse8q4EdLYLQP7cPJCSlzsF0QW_PyfdJk76QwwPvl3Oohzw_R6OBN8TgR3UbhTRnEPOfLy8QoWzMGd63ryjMWR4QyOyX1g==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Investigating suspicious items**\\n\\nI think I need to closely inspect the most suspicious items because some could be false positives. I want to ensure that I'm reading the contents carefully to verify their legitimacy. It's always tricky to identify what's really off versus what's just standard. I'll take my time to analyze everything thoroughly to avoid any mistakes. Let’s make sure I'm getting it right this time!\"}]}"},{"type":"toolCall","id":"call_pefcQg3Ezk0xapcTwZWwXYjv|fc_08205193eac5c3af016a21652fdbb48191bd2247104a26760b","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json\nids=[\n'mem_17eea5c0658f4cd3beece90a93d35e36','mem_04dc02a90cad40cc826ed1e8f93aff36',\n'legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff','legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7',\n'legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54','legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1',\n'mem_c1cd76a28d434774ad10b1507577bab3','mem_a1f632320762487c8864dd45f2cbf394'\n]\ncon=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\nfor mid in ids:\n r=cur.execute('SELECT id,type,scope,title,content,created_at FROM memories WHERE id=?',(mid,)).fetchone()\n print('\\nID',mid)\n print(json.dumps(dict(r), ensure_ascii=False, indent=2))\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":3097,"output":361,"cacheRead":48640,"cacheWrite":0,"totalTokens":52098,"cost":{"input":0.0077425,"output":0.0054150000000000005,"cacheRead":0.012159999999999999,"cacheWrite":0,"total":0.0253175}},"stopReason":"toolUse","timestamp":1780573481738,"responseId":"resp_08205193eac5c3af016a21652a493881918751b98fa23087a0"}}
{"type":"message","id":"36b3df9e","parentId":"c5328386","timestamp":"2026-06-04T11:44:50.863Z","message":{"role":"toolResult","toolCallId":"call_pefcQg3Ezk0xapcTwZWwXYjv|fc_08205193eac5c3af016a21652fdbb48191bd2247104a26760b","toolName":"bash","content":[{"type":"text","text":"\nID mem_17eea5c0658f4cd3beece90a93d35e36\n{\n  \"id\": \"mem_17eea5c0658f4cd3beece90a93d35e36\",\n  \"type\": \"episode\",\n  \"scope\": \"agent\",\n  \"title\": \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\",\n  \"content\": \"Task ID: task_2e1c07a93b9d40598bb4bfa504a5d726\\nOrigin: personal-agent\\nReason: code work (repo)\\nPayload: {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\", \\\"intent\\\": \\\"Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (repo)\\\", \\\"secondary_agent\\\": null}, \\\"task_id\\\": \\\"task_2e1c07a93b9d40598bb4bfa504a5d726\\\"}\",\n  \"created_at\": \"2026-03-15T14:18:48.581429+00:00\"\n}\n\nID mem_04dc02a90cad40cc826ed1e8f93aff36\n{\n  \"id\": \"mem_04dc02a90cad40cc826ed1e8f93aff36\",\n  \"type\": \"episode\",\n  \"scope\": \"agent\",\n  \"title\": \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\",\n  \"content\": \"Task ID: task_ed6a38c1b49f4e829e067d49d206148b\\nOrigin: personal-agent\\nReason: code work (fix)\\nPayload: {\\\"expected_artifacts\\\": [\\\"report\\\"], \\\"idempotency_key\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\", \\\"intent\\\": \\\"Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\\\", \\\"route\\\": {\\\"codex_instruction\\\": \\\"\\\", \\\"delegation_target\\\": \\\"ai-dev-workflow\\\", \\\"planning_source\\\": \\\"fallback\\\", \\\"primary_agent\\\": \\\"code\\\", \\\"reason\\\": \\\"code work (fix)\\\", \\\"secondary_agent\\\": null}, \\\"task_id\\\": \\\"task_ed6a38c1b49f4e829e067d49d206148b\\\"}\",\n  \"created_at\": \"2026-03-15T14:18:33.490715+00:00\"\n}\n\nID legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff\n{\n  \"id\": \"legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff\",\n  \"type\": \"episode\",\n  \"scope\": \"global\",\n  \"title\": \"Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs\",\n  \"content\": \"Goal: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs\\nScope: Prioritize covered clubs first; shortlist suppliers/screens from Alibaba, Made-in-China, or similar\\nAssumptions: Fullscreen browser preferred; exact hardware/software stack can be validated later; no final purchase decision yet\\nSummary: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs\",\n  \"created_at\": \"2026-03-13T14:01:39.638922+00:00\"\n}\n\nID legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\n{\n  \"id\": \"legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\",\n  \"type\": \"episode\",\n  \"scope\": \"global\",\n  \"title\": \"Research run: Source 50+ inch China displays for Ballbox advertising in padel venues\",\n  \"content\": \"Goal: Source 50+ inch China displays for Ballbox advertising in padel venues\\nScope: Made-in-China, Alibaba, similar suppliers; indoor or outdoor; browser/fullscreen capable preferred\\nAssumptions: Need shortlist first, not final purchase decision\\nSummary: Source 50+ inch China displays for Ballbox advertising in padel venues\",\n  \"created_at\": \"2026-03-13T14:01:39.634873+00:00\"\n}\n\nID legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54\n{\n  \"id\": \"legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54\",\n  \"type\": \"episode\",\n  \"scope\": \"global\",\n  \"title\": \"Research run: Production-readiness plan for ballbox-payment-integration\",\n  \"content\": \"Goal: Production-readiness plan for ballbox-payment-integration\\nScope: Capture repository-based evidence, claims, and follow-up tasks for first production deployment readiness.\\nAssumptions: Local repo assessment only; external provider contracts and production infra not yet validated.\\nSummary: Production-readiness plan for ballbox-payment-integration\",\n  \"created_at\": \"2026-03-13T14:01:39.642436+00:00\"\n}\n\nID legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1\n{\n  \"id\": \"legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1\",\n  \"type\": \"episode\",\n  \"scope\": \"global\",\n  \"title\": \"Research run: Verify capture-url flow\",\n  \"content\": \"Goal: Verify capture-url flow\\nScope: local file capture\\nAssumptions: e2e verification\\nSummary: Verify capture-url flow\",\n  \"created_at\": \"2026-03-13T14:01:39.632489+00:00\"\n}\n\nID mem_c1cd76a28d434774ad10b1507577bab3\n{\n  \"id\": \"mem_c1cd76a28d434774ad10b1507577bab3\",\n  \"type\": \"episode\",\n  \"scope\": \"global\",\n  \"title\": \"sebas: [telegram] necesito para ballbox un entregable en pdf profesional para…\",\n  \"content\": \"User asked: [telegram] necesito para ballbox un entregable en pdf profesional para mandar a una imprenta a que hagan un ploteo de las maquinas. tiene que ser el logo de ballbox con un qr a su derecha que lleve a https://ballbox.app el logo sacalo de la webapp clonada local y corre todo esto en un ralph loop con 5 iteraciones para estar bien seguros de que el trabajo este hecho. mandame el link cuando termine…\\nResult: Usá este: **Prompt** Diseñá un logo nuevo para una startup llamada **Ballbox**. Contexto de marca: - Ballbox combina **máquinas físicas inteligentes** con **software** - vende **tubos de pelotas** y también puede mostrar **publicidad en pantallas integradas** - foco inicial: **padel clubs** - la marca tiene que sentirse: - **tecnológica** - **moderna** - **simple** - **premium** - **fácil de reconocer a distancia** - **aplicable tanto en máquina física como en app/web** Objetivo del logo: - que funcione muy bien en: - frente de una **máquina expendedora** - **pantalla grande** - **app móvil** - **favicon** - **vinilo / ploteo** - **redes** - debe verse fuerte tanto en tamaños grandes como chicos - debe ser **minimalista**, no recargado - debe transmitir una mezcla de: - **deporte** - **automatización** - **tecnología** - **movimiento** - **confianza** Dirección visual: - evitar look infantil - evitar estilo genérico de startup SaaS - evitar parecer marca de energía, cripto o gaming - tomar inspiración sutil de: - **pelota / tubo de pelotas** - **padel / racket sports** - **módulo / caja / dispenser** - **pantalla / frame / interfaz** - **flujo físico + digital** - no usar demasiados detalles finos - formas limpias, geométricas, memorables - idealmente que tenga: - **isotipo** - **wordmark** - y versión combinada Color: - explorar una paleta principal con: - **verde lima / verde tenis sofisticado** - **negro / carbón** - **blanco** - opcional: variantes monocromo - el verde debe sentirse más **premium-tech** que deportivo barato Entrega esperada: - 3 a 5 conceptos claramente distintos - para cada concepto: - breve racional - qué simboliza - por qué sirve para Ballbox - mostrar variantes: - fondo blanco - fondo negro - solo isotipo - horizontal - vertical - priorizar logos que se puedan producir fácil en: - impresión - corte de vinilo - señalética - pantalla Estilo de salida: - alta calidad de branding - dirección creativa profesional - aspecto de marca real lista pa…\",\n  \"created_at\": \"2026-05-25T03:14:20.619368+00:00\"\n}\n\nID mem_a1f632320762487c8864dd45f2cbf394\n{\n  \"id\": \"mem_a1f632320762487c8864dd45f2cbf394\",\n  \"type\": \"episode\",\n  \"scope\": \"global\",\n  \"title\": \"sebas: [telegram] hazme en inglés, un texto explicando con flechas, el flujo m…\",\n  \"content\": \"User asked: [telegram] hazme en inglés, un texto explicando con flechas, el flujo muy simple de cómo se paga en máquina expendedora. Muy grandes rasgos para explicar el rol del código QR\\nResult: Hecho. Roté la sesión.\",\n  \"created_at\": \"2026-05-24T15:14:28.619357+00:00\"\n}\n"}],"isError":false,"timestamp":1780573490863}}
{"type":"message","id":"330e88c8","parentId":"36b3df9e","timestamp":"2026-06-04T11:44:54.420Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering database checks**\n\nI’m thinking it might be important to run a better dry run against the entire database or perhaps focus on the most recent two weeks. The user is interested in seeing what results come up, particularly concerning any false positives or key issues. I could also take a closer look at the default configurations to ensure everything is in order. There’s definitely a lot to consider here!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a2165338234819184da4dfdf3623a75\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWU1M8X_Ycm62vW5wIq9hySIGmAiDJvb7TktqlNCSYxfn71W0UAnSvZhxwJy4Ds6IJzJsAi0UBKgH7E4hRfmqLKmL3blYHx66xn3np5W9XNC8Q5h_0LWFsDlsmATwmitgHBA4_skRo8o2Hsj--zjGkLksnRLTYTfrwUMcEFGv6d2wSXpZ775dWsQgoGqySdfZdr8V6QErjENUuAWj4iDriDQZsDlQNatQPaZrUm7cS-BK9YnhQrRQCidtUP7O4pvWCCdVvPkF9syjqYDMHXjtcqZH9uqgSp4UmBore8dWYppVymV03Ryr_srVBsvL4ZYCnRTEk66l-q-G5tZMO7yfBGIxY9o3x9ToZPaE802c47ioxkCyid0aEWYX4N1aNXSAH0ptP8SPq8xatdp4p5GF4clXkarkCG15VxyuuHuc-HlkPoD5qWA_uLtQ6Y_LY4q1PHtbLW6GuR1EciwhTjHppuG0VCsEEJQFBqdlgjBXdCGQBVJt1njKmhm2Uk4dwYblHRB6aKN0OgGqpNKQ6xCZfd3XXxOs-dYeCHNqTKUOqYKzLzGyHdZSL9L0af13EdlPYxFNmX6LMeZsn8ROA5qSYx0Jms42fqZhoKngjZMuvTrIFSNFLbpo-wmJw0QtttdtxxM01aSOl6UiHnsIo1TyyJFGWL0DcCdvu2GQVWAC3fRDbZMJBXYIXOt6bYFIL91Ui7RLgOJvkbSgA3rhhLbJU9VlrEmAC30jLtS0_US37eo5hmiIGKorvlXtT6wl5EPdsHs9Kb_5SIq98UB0oEk4xFxKbhIvbZSllwxZll8UaAkTAN9iGoXoBz4DR4r7b13nmj-q-GLEzO8C0Vg6AuVgq_GuLYFylZb3XKjWiEJHBkjT6qsJIXQ4ZLHqK0sWn-GJ56yeqde8en2pVy33G4v2uvqgnfn7TGZwMpVhugD0F0EGaNif-FdYlxemIdHNI7yTf7PCCpEuse8P-EIqVCWGOc0t_0T73yTqtLQddE845CqqY2U2XJp-1cJ-UDAUbLNgmlEkOijeKD0jd_aflK2ckuJOEpYX1XbqCil89n5qZMEx2RPJIr1m8Hbzfhkppk6gxtIhaNfxnZFUTnMjDMEDVu6bxAzp7OgrjCsg0ci-HA4wiU7GcZxnZsIed8LaiKdUhwv\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering database checks**\\n\\nI’m thinking it might be important to run a better dry run against the entire database or perhaps focus on the most recent two weeks. The user is interested in seeing what results come up, particularly concerning any false positives or key issues. I could also take a closer look at the default configurations to ensure everything is in order. There’s definitely a lot to consider here!\"}]}"},{"type":"toolCall","id":"call_neQwimKUU82h2Lgb8g02zuwt|fc_08205193eac5c3af016a216535a0888191b26cd3f4c6ad1ba9","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json\ncon=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\nfor job in cur.execute(\"SELECT id, metadata_json FROM maintenance_jobs WHERE job_type='consolidation' ORDER BY id\"):\n    print(job['id'], job['metadata_json'])\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":3204,"output":152,"cacheRead":51200,"cacheWrite":0,"totalTokens":54556,"cost":{"input":0.008010000000000001,"output":0.00228,"cacheRead":0.012799999999999999,"cacheWrite":0,"total":0.02309}},"stopReason":"toolUse","timestamp":1780573490865,"responseId":"resp_08205193eac5c3af016a216532f39c81919f5226c2a5811212"}}
{"type":"message","id":"d2b70ec8","parentId":"330e88c8","timestamp":"2026-06-04T11:44:54.457Z","message":{"role":"toolResult","toolCallId":"call_neQwimKUU82h2Lgb8g02zuwt|fc_08205193eac5c3af016a216535a0888191b26cd3f4c6ad1ba9","toolName":"bash","content":[{"type":"text","text":"job_consolidation_daily_10 {\"candidate_age_days\": 0, \"conflict_threshold\": 0.55, \"dedupe_high_threshold\": 0.9, \"dedupe_mid_threshold\": 0.75, \"llm_command\": [\"opencode\", \"-m\", \"MiniMax M2.5 Free\"], \"llm_enabled\": false, \"llm_provider\": \"opencode\", \"max_candidates\": 200, \"mode\": \"daily_10\", \"promote_confidence\": 0.7}\njob_consolidation_daily_15 {\"candidate_age_days\": 0, \"conflict_threshold\": 0.55, \"dedupe_high_threshold\": 0.9, \"dedupe_mid_threshold\": 0.75, \"llm_command\": [\"opencode\", \"-m\", \"MiniMax M2.5 Free\"], \"llm_enabled\": false, \"llm_provider\": \"opencode\", \"max_candidates\": 200, \"mode\": \"daily_15\", \"promote_confidence\": 0.7}\njob_consolidation_weekly {\"candidate_age_days\": 0, \"conflict_threshold\": 0.55, \"dedupe_high_threshold\": 0.9, \"dedupe_mid_threshold\": 0.75, \"llm_command\": [\"opencode\", \"-m\", \"MiniMax M2.5 Free\"], \"llm_enabled\": true, \"llm_provider\": \"opencode\", \"max_candidates\": 800, \"mode\": \"weekly\", \"promote_confidence\": 0.7}\n"}],"isError":false,"timestamp":1780573494457}}
{"type":"message","id":"859f2359","parentId":"d2b70ec8","timestamp":"2026-06-04T11:45:25.223Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Explaining candidate age logic**\n\nI need to clarify that a candidate age of 0 means it includes all candidates older than the current date. Next, I'll consider running a more faithful dry run using the actual algorithm but with stricter thresholds and no writing involved. It's essential to count how everything archives, links, promotes, or conflicts among all 200 candidates. I think I can implement this based on the actual logic, only changing the thresholds and normalization. That might actually be sufficient.","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21653908bc81919fe1320539f33e6b\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWU85zHiS8W2FAWH0li7n27Wa9E_bjvBA4A_AMgHpo4yHHqx_k5ZbQsSbUL0c-s_dD3YF5-y-5RNpkI7Yp01_c4jpkQVR-Xx8t4LejqakzSS7XLL5BeNHpzLBJcTbzm3pQynC2citqGsIemdAXn5VUCDlZE5avACZaNLTtfEaQ2iea_y3ToxAKxVlAILkkKdhYxvFgKQLFIjiHnnty2Wynv1jmyU3S47xWG952tGgUsqLonKlSy95pmsHsyO9nKUrPbWbDW9Wm2HVzCI-6Ohfwagk0kf8WD1XBMXfd1rLUrd1f2kSIDYcTwtGvdW5AnsgnLYGrr2YVxlcTgUFPI1HDn2GDb9YRVHiFT7m2vEPMZs7Hi6eVJ6wLrHkHcsoQmI_RKo_ZMx6wYX0JzouSpXaL37epVpyGhki0J87TQgqeH_llO9oRcNLM42pbnIiX5n6ib6A8qNBI2N2jIj4luoEnFqDzC4DszxCM99UOetd2ZYVXTDvgabCmM7JST6N7LmJ3jp8kh3UY5m-I70wYA0TaM69T8AbzmNYMu4rgdx3wky4QDNQcjiIS6MMKnqrwbBe4or8k2tZ5mO8Kc4lKiNw8tI6uRmjOCtn9VOQhpCzJu7FR4XX7lW4VspSohRuZL_bMODxfyEjJb8V68ww43cz7V6GLFrC-ZfVKN-j47IzXVe3d0ZnphrQJsnloMW9zMZ1Cc40dK59bRsiOqiz4rwgyqNCoJbxQ-ngEOctyKCqtKk_n28DSuG_N2WQYufm7yOKSwvQIvkbXLm2CYnyJXh7GEmXbj8TI2_YVdDRyeS0hRnxIVuIVsA3n4HvtnD-dA3tACbLpvD2xuOj_aG2tqaITRTE751nRdp-L5KUDvFwfL7qsestrMmZN8maLq6H-nFT1jS0hfYEZO3P6iKemUB0OkCnVg_ONfYg-dqIWJP2ACQBxGE6dHqg3C8tUNJSZOpv88Nm1KWMAlYCJC00Jgf9p0-Ka37dtAKSMZyyTK5tgMgS2RM-s8p-Y5TGoIewTGNPpfNhIEdFmxiP2pmykVluK7I71ipMyNXnH2FzG8CHijT5sVG8fOh-af7WJZp9PHXzNTr1-2yEUsdtwJxq6_m05QsDNx_jznzrrEhuwQKcT9FEK_mqiv9LxCm7NnEEAhxnCQmT6M7d12rmgBk7hDNFgxFTQ0QNVInfhHWEptRLaKPFQrnuF6wphT77x2rZIYhPOym2iXeJHmlnMNG5nmQTm1VXDuMN2DJkp-TizFDPpS-WAZDJk0JEK0uGnvEwPE16wjdQJqIHE6M2NF8kEFMOskSbjRq-y0wW0f5LxEqoNPUp6inZg73sjc3duZDrW-da14W2Cjaa9GV80CKZOIGZGgDoQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Explaining candidate age logic**\\n\\nI need to clarify that a candidate age of 0 means it includes all candidates older than the current date. Next, I'll consider running a more faithful dry run using the actual algorithm but with stricter thresholds and no writing involved. It's essential to count how everything archives, links, promotes, or conflicts among all 200 candidates. I think I can implement this based on the actual logic, only changing the thresholds and normalization. That might actually be sufficient.\"}]}"},{"type":"toolCall","id":"call_u0mK1ZNk2DrdkdgexpGqthgd|fc_08205193eac5c3af016a21653c542881919424c21d7d9c51f0","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3, json, math, re\nfrom datetime import datetime, timezone, timedelta\n\nDB='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'\ncon=sqlite3.connect(DB)\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\ncutoff=datetime.now(timezone.utc).isoformat()  # mirrors candidate_age_days=0\nrows=cur.execute('''\nSELECT * FROM memories\nWHERE ((status='inbox') OR (type='episode' AND status='active'))\n  AND created_at <= ?\nORDER BY created_at ASC\nLIMIT 200\n''',(cutoff,)).fetchall()\nmems=[dict(r) for r in rows]\nactive=[dict(r) for r in cur.execute(\"SELECT * FROM memories WHERE status='active'\")]\nby_id={m['id']:m for m in active}\n\n# stricter-than-live dry run\nHIGH=0.80\nMID=0.55\nCONFLICT=0.35\nPROMOTE=0.75\n\ndef normalize_text(v:str)->str:\n    return ' '.join(v.lower().split())\n\ndef lexical_score(query_terms, haystack:str)->float:\n    if not query_terms:\n        return 0.0\n    hay_terms=set(normalize_text(haystack).split())\n    if not hay_terms:\n        return 0.0\n    q_terms=set(query_terms)\n    inter=len(q_terms & hay_terms)\n    union=len(q_terms | hay_terms)\n    return inter/union if union else 0.0\n\ndef semantic_score(left,right):\n    return 0.0\n\ndef clean(v:str)->str:\n    v=v.lower()\n    v=re.sub(r'\\[telegram\\]',' ',v)\n    v=re.sub(r'https?://\\S+',' URL ',v)\n    v=re.sub(r'[^\\w\\s]',' ',v)\n    return ' '.join(v.split())\n\ndef strict_conflict(left,right):\n    if left.get('type') not in {'profile','decision','project','episode'} or right.get('type') not in {'profile','decision','project','episode'}:\n        return False,None\n    lt=set(clean(left['content']).split())\n    rt=set(clean(right['content']).split())\n    overlap=len(lt & rt)\n    if overlap < 4:\n        return False,None\n    neg={'not','no','never','nunca','sin'}\n    ln=bool(neg & lt); rn=bool(neg & rt)\n    if ln != rn:\n        return True,'negation'\n    antonyms=[('always','never'),('with','without'),('enabled','disabled'),('public','private'),('local','remote'),('first','later')]\n    ls=' '+clean(left['content'])+' '; rs=' '+clean(right['content'])+' '\n    for a,b in antonyms:\n        if ((f' {a} ' in ls and f' {b} ' in rs) or (f' {b} ' in ls and f' {a} ' in rs)):\n            return True,f'antonym:{a}/{b}'\n    return False,None\n\nstats={'candidates':len(mems),'promoted':0,'archived':0,'linked':0,'conflicts':0}\ndupes=[]; links=[]; conflicts=[]\nfor candidate in mems:\n    related=[m for m in active if m['id']!=candidate['id'] and m['type']==candidate['type'] and m['scope']==candidate['scope']][:50]\n    best_match=None; best_score=0.0\n    cand_terms=clean(candidate['content']).split()\n    for other in related:\n        lexical=lexical_score(cand_terms, clean(other['content']))\n        semantic=semantic_score([],[])\n        score=lexical if semantic==0.0 else lexical*0.6+semantic*0.4\n        if score>best_score:\n            best_score=score; best_match=other\n    if best_match and clean(candidate['content'])==clean(best_match['content']):\n        stats['archived']+=1; stats['linked']+=1\n        dupes.append((best_score,candidate,best_match,'exact_normalized'))\n        continue\n    if best_match and best_score>=HIGH:\n        stats['archived']+=1; stats['linked']+=1\n        dupes.append((best_score,candidate,best_match,'high_similarity'))\n        continue\n    if best_match and best_score>=MID:\n        stats['linked']+=1\n        links.append((best_score,candidate,best_match,'mid_similarity'))\n    if best_match and best_score>=CONFLICT:\n        ok,reason=strict_conflict(candidate,best_match)\n        if ok:\n            stats['conflicts']+=1\n            conflicts.append((best_score,candidate,best_match,reason))\n    if candidate['status']=='inbox' and float(candidate.get('confidence') or 0)>=PROMOTE:\n        stats['promoted']+=1\n    elif candidate['status']=='active' and candidate['type'] in {'episode','artifact','decision','project','procedural','semantic'} and float(candidate.get('confidence') or 0)>=PROMOTE:\n        stats['promoted']+=1\n\nprint(json.dumps(stats, indent=2))\nprint('\\nTOP_ARCHIVE')\nfor score,a,b,reason in sorted(dupes,key=lambda x:x[0], reverse=True)[:10]:\n    print(json.dumps({'score':round(score,3),'reason':reason,'a':a['id'],'a_title':a['title'][:85],'b':b['id'],'b_title':b['title'][:85]}, ensure_ascii=False))\nprint('\\nTOP_LINK')\nfor score,a,b,reason in sorted(links,key=lambda x:x[0], reverse=True)[:10]:\n    print(json.dumps({'score':round(score,3),'reason':reason,'a':a['id'],'a_title':a['title'][:85],'b':b['id'],'b_title':b['title'][:85]}, ensure_ascii=False))\nprint('\\nTOP_CONFLICT')\nfor score,a,b,reason in sorted(conflicts,key=lambda x:x[0], reverse=True)[:10]:\n    print(json.dumps({'score':round(score,3),'reason':reason,'a':a['id'],'a_title':a['title'][:85],'b':b['id'],'b_title':b['title'][:85]}, ensure_ascii=False))\nPY","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":49774,"output":1557,"cacheRead":5120,"cacheWrite":0,"totalTokens":56451,"cost":{"input":0.124435,"output":0.023355,"cacheRead":0.0012799999999999999,"cacheWrite":0,"total":0.14907}},"stopReason":"toolUse","timestamp":1780573494459,"responseId":"resp_08205193eac5c3af016a2165368988819184c359822a8a1467"}}
{"type":"message","id":"f8046955","parentId":"859f2359","timestamp":"2026-06-04T11:45:25.485Z","message":{"role":"toolResult","toolCallId":"call_u0mK1ZNk2DrdkdgexpGqthgd|fc_08205193eac5c3af016a21653c542881919424c21d7d9c51f0","toolName":"bash","content":[{"type":"text","text":"{\n  \"candidates\": 67,\n  \"promoted\": 31,\n  \"archived\": 0,\n  \"linked\": 5,\n  \"conflicts\": 7\n}\n\nTOP_ARCHIVE\n\nTOP_LINK\n{\"score\": 0.722, \"reason\": \"mid_similarity\", \"a\": \"mem_a986376af1554bec8e3909c656585bb6\", \"a_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\", \"b\": \"mem_89d7f9db9cb04d0da7539c33e3ccc024\", \"b_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\"}\n{\"score\": 0.722, \"reason\": \"mid_similarity\", \"a\": \"mem_89d7f9db9cb04d0da7539c33e3ccc024\", \"a_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\", \"b\": \"mem_a986376af1554bec8e3909c656585bb6\", \"b_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\"}\n{\"score\": 0.712, \"reason\": \"mid_similarity\", \"a\": \"mem_e80d2be0aecc4eed84d5aa179f83bfdd\", \"a_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\", \"b\": \"mem_89d7f9db9cb04d0da7539c33e3ccc024\", \"b_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\"}\n{\"score\": 0.623, \"reason\": \"mid_similarity\", \"a\": \"mem_04dc02a90cad40cc826ed1e8f93aff36\", \"a_title\": \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\", \"b\": \"mem_17eea5c0658f4cd3beece90a93d35e36\", \"b_title\": \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\"}\n{\"score\": 0.623, \"reason\": \"mid_similarity\", \"a\": \"mem_17eea5c0658f4cd3beece90a93d35e36\", \"a_title\": \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\", \"b\": \"mem_04dc02a90cad40cc826ed1e8f93aff36\", \"b_title\": \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\"}\n\nTOP_CONFLICT\n{\"score\": 0.722, \"reason\": \"negation\", \"a\": \"mem_a986376af1554bec8e3909c656585bb6\", \"a_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\", \"b\": \"mem_89d7f9db9cb04d0da7539c33e3ccc024\", \"b_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\"}\n{\"score\": 0.722, \"reason\": \"negation\", \"a\": \"mem_89d7f9db9cb04d0da7539c33e3ccc024\", \"a_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\", \"b\": \"mem_a986376af1554bec8e3909c656585bb6\", \"b_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\"}\n{\"score\": 0.712, \"reason\": \"negation\", \"a\": \"mem_e80d2be0aecc4eed84d5aa179f83bfdd\", \"a_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\", \"b\": \"mem_89d7f9db9cb04d0da7539c33e3ccc024\", \"b_title\": \"pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…\"}\n{\"score\": 0.623, \"reason\": \"negation\", \"a\": \"mem_04dc02a90cad40cc826ed1e8f93aff36\", \"a_title\": \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\", \"b\": \"mem_17eea5c0658f4cd3beece90a93d35e36\", \"b_title\": \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\"}\n{\"score\": 0.623, \"reason\": \"negation\", \"a\": \"mem_17eea5c0658f4cd3beece90a93d35e36\", \"a_title\": \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\", \"b\": \"mem_04dc02a90cad40cc826ed1e8f93aff36\", \"b_title\": \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\"}\n{\"score\": 0.52, \"reason\": \"antonym:first/later\", \"a\": \"legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\", \"a_title\": \"Research run: Source 50+ inch China displays for Ballbox advertising in padel venues\", \"b\": \"legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff\", \"b_title\": \"Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered\"}\n{\"score\": 0.52, \"reason\": \"antonym:first/later\", \"a\": \"legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff\", \"a_title\": \"Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered\", \"b\": \"legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\", \"b_title\": \"Research run: Source 50+ inch China displays for Ballbox advertising in padel venues\"}\n"}],"isError":false,"timestamp":1780573525485}}
{"type":"message","id":"1ac0c206","parentId":"f8046955","timestamp":"2026-06-04T11:45:32.536Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting conflicts and results**\n\nI'm noticing that most of these conflicts are actually false positives due to the negation catching phrases like \"no repo work.\" I need to look into why that's happening. However, it's helpful that the stricter heuristic is revealing junk duplicates. I should explain this further, perhaps by inspecting the types of candidates and their confidence levels. That might give me more clarity on these issues!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a2165566734819184b9c72e7987e738\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWVYd0pegdFrs0kwV6do1c-I3eoUNN2wPUle7kd82HB_Nz4HUHMKnp3GuHlqv8RVOwW2clZ6FW3doIURwEcOphepaY-A7DoIOnUbH58kA5ezEu-mDXnAQprG-oiXneBB0dgKW5qK0twfGLxlM8dx5ruK_zR42PBUp15JZWxweYaYvXYuAXazB9-PYY88v5SUC1gW85JPXiTWSc9EzdQ2a5IBYeNeXdmleeis5PFMUargQqw54RTQ5UXtYxeL5IcNnAeYHwvzNvevcrGi4SQ3_6gJIC3ipiobgXBgsRPdzCHMoGRUhLXK7pWE1Mb-i9IohdgHzzSvBZLTTYWCG_F28Rejxu5thOSZOWq-ZuhDIyzYSU2SHdec3maWyWWkj6VQx262ZbVBrNzkmeoxPPmuWmMq2S_5dWTDpihnP2d1JhXP325qer2tC2Ek6QaBPkzCMBtn4CV1n8tGAnyxBzEnV84PMI67MsIm56T11jbS-rI7g0uKEcA26RZhKDuQSEXW1E7CEHfIboRL2O7W00a4kYDavuq1WaVy-uPcMuHxN8bPYWBFveRA70NS4kff48jEDOBZlPTsLgrntfq1D0zlciKPGq0pprkmzhergxZMaff8k2LwjtQN_SjRkFNRwkQXk9JN4AHSaDCdmKLxGBMEXlu8Q9_xoy5jcJqqM8tMWKxpLpcIQTk1MkyP9oGa-arixmXqH-XiQPRVtuqWR78HMn_xLRu3bFznYvx5n8UczON2mnT4BeV2vQgefXxZHpdQVef-_2SCNtvJ7QvQva6Qs_zmDbvrcr3N_75td8PFoVDKJmPQ5tVho5TnMH6K7YArZ1XOJokeq4Q5R9Uh-Pz3iTAOHZkvd8VBkIBj4UvsA_qbS_OY1tMe3pPaCFGla1xB50qILnfermoI3hIKcSUHnss-BVMT0TshAMq2N1UnJ36oO0PKG0D299AIK097uR5YobZKdQJOQ97Rq4uDdti9yJLsp73mDFe-yBLZMkaxwjXSKdGk8WhIhmCgtP4MxoNTP5oBPIfIH3dyD72OqAW6x_-btP9bf9luMafR2TRhiS9gAWVLWDyf3-OsgBoK0ZPbFIoBID_W0jBBO9Z-8R5cnhpBHUT2V8lnz9yWq0LKcFdkaZF-CQ_CJ8E2cVP2JKBhSvb_kGOXgU7GPGkXWov58QJxihPNXwN8rbEYz9HDZUEi2Y9iwj0gmlhCKks6msNMk6D0RdfjRCmVnzCRX0fVqFYaCw==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting conflicts and results**\\n\\nI'm noticing that most of these conflicts are actually false positives due to the negation catching phrases like \\\"no repo work.\\\" I need to look into why that's happening. However, it's helpful that the stricter heuristic is revealing junk duplicates. I should explain this further, perhaps by inspecting the types of candidates and their confidence levels. That might give me more clarity on these issues!\"}]}"},{"type":"toolCall","id":"call_iyztt2LoS9XT6PQ5uvjherzt|fc_08205193eac5c3af016a216558e3e081918e47bd2a603b4f00","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport sqlite3\ncon=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\nfor r in cur.execute(\"SELECT type, scope, ROUND(confidence,2) conf, COUNT(*) c FROM memories WHERE ((status='inbox') OR (type='episode' AND status='active')) GROUP BY type,scope,conf ORDER BY c DESC LIMIT 20\"):\n    print(dict(r))\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":3184,"output":183,"cacheRead":54784,"cacheWrite":0,"totalTokens":58151,"cost":{"input":0.00796,"output":0.002745,"cacheRead":0.013696,"cacheWrite":0,"total":0.024401}},"stopReason":"toolUse","timestamp":1780573525486,"responseId":"resp_08205193eac5c3af016a216555920c8191b8dbc4dd32e67fbb"}}
{"type":"message","id":"35226b73","parentId":"1ac0c206","timestamp":"2026-06-04T11:45:32.590Z","message":{"role":"toolResult","toolCallId":"call_iyztt2LoS9XT6PQ5uvjherzt|fc_08205193eac5c3af016a216558e3e081918e47bd2a603b4f00","toolName":"bash","content":[{"type":"text","text":"{'type': 'episode', 'scope': 'global', 'conf': 0.72, 'c': 32}\n{'type': 'episode', 'scope': 'global', 'conf': 0.85, 'c': 14}\n{'type': 'episode', 'scope': 'project', 'conf': 0.72, 'c': 4}\n{'type': 'episode', 'scope': 'global', 'conf': 1.0, 'c': 3}\n{'type': 'episode', 'scope': 'agent', 'conf': 0.91, 'c': 2}\n{'type': 'episode', 'scope': 'global', 'conf': 0.95, 'c': 2}\n{'type': 'episode', 'scope': 'project', 'conf': 0.99, 'c': 2}\n{'type': 'episode', 'scope': 'agent', 'conf': 1.0, 'c': 1}\n{'type': 'episode', 'scope': 'global', 'conf': 0.78, 'c': 1}\n{'type': 'episode', 'scope': 'global', 'conf': 0.81, 'c': 1}\n{'type': 'episode', 'scope': 'global', 'conf': 0.94, 'c': 1}\n{'type': 'episode', 'scope': 'global', 'conf': 0.96, 'c': 1}\n{'type': 'episode', 'scope': 'project', 'conf': 0.9, 'c': 1}\n{'type': 'episode', 'scope': 'project', 'conf': 0.94, 'c': 1}\n{'type': 'episode', 'scope': 'project', 'conf': 1.0, 'c': 1}\n"}],"isError":false,"timestamp":1780573532590}}
{"type":"message","id":"c854739f","parentId":"35226b73","timestamp":"2026-06-04T11:45:34.937Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a21655df67881918ffbd6b4f62107e7\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWVe1F-chL_PQtayGsNByV0iSTBx70J_D9SnqVZL3EHRO_yAjpPzczSwOicbkql14D9WfIVS2C3XVAV-SN5xGSkOwKt6zzqkWd6qlrU-AOKU7US-of1C9filkkz3pKyjELjDuQ59r4_c0k5EgyhZWoTdeYRahN6141WEbSZkExSDL57dpIONqp0RGexgOWVqvLuk2fF_JZI4-8UEXi3L4LSvFJA5CRTXLmhFGFKUCDjsdDyQgi3VDsWvLa3waxcY-MXLec05zhlnKLTe4aGQ94GXszOyW3TFTQjpYScJxh-HI4q_qFqOI5bDJQnOtGszny6FkH5MPvvZywkNQU2jB-FZNUuCNIZngNYpMmms9mnhZ8_h_UKsXl0mol3Cf8mxIu5iF04wYmigpsZnAV1w_rwp5286nPbXyN503T4EUkJgHD1fPrJREA_XWtjaABUWsd8TNE2mtSju8iVHDpZ1ky0NvfROidTautm4AGy5MynZxT8I72PJ4wrCBIq2D3Y9YKNWxvl50byr9j-VwaZXW5Q7bZx4JN4jAsRZ7m7x1txIJnUMoQXBmVWD1q4Cju1JelbLgmpEMCdZN0R-c5Fha4M5dRon7OcF__eISk4fx7vE_ydVTAvTXBvk2gWf6q_KnXz7VXuuP_AzXtXuqAQLygXev0O51hBWlKjkR8mMyp_vCywTfmorgu9PnMpa-FDzSG3ZOLJ3uLDbAj2oV3rdYtMAC0UhdcH1JrJRVuDsnNTbuWl1fTjeO_U41av6hiH8U0kkbIZcPR2z0hdBr8kCTsNm4-A-nnQXceDamcRQdtz0wq0QJn3enxP72kNEF3wKABXwj7AFfWZT6q1x2pume8Cz6qu_eBjkR0N6hKVQ_Uef4QBziEJvrW0_rWie6VjFHAB4hTLuYG_E0T1QR_oGxHid5_Wqg3uoajC5x54xtn30f_k=\",\"summary\":[]}"},{"type":"toolCall","id":"call_g1SZQ4fcUwM1prDWaBZvMrpS|fc_08205193eac5c3af016a21655eb83c8191963e6e45bb6d506c","name":"todo","arguments":{"action":"update","id":1,"status":"completed"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":696,"output":35,"cacheRead":57856,"cacheWrite":0,"totalTokens":58587,"cost":{"input":0.0017400000000000002,"output":0.000525,"cacheRead":0.014464,"cacheWrite":0,"total":0.016729}},"stopReason":"toolUse","timestamp":1780573532591,"responseId":"resp_08205193eac5c3af016a21655d51248191ac0e3a6c64d2aa57"}}
{"type":"message","id":"22328f70","parentId":"c854739f","timestamp":"2026-06-04T11:45:34.939Z","message":{"role":"toolResult","toolCallId":"call_g1SZQ4fcUwM1prDWaBZvMrpS|fc_08205193eac5c3af016a21655eb83c8191963e6e45bb6d506c","toolName":"todo","content":[{"type":"text","text":"Updated #1 (in_progress → completed)"}],"details":{"action":"update","params":{"action":"update","id":1,"status":"completed"},"tasks":[{"id":1,"subject":"Dry run stricter memory consolidation/conflict detection","status":"completed","description":"Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.","activeForm":"running stricter memory dry run"}],"nextId":2},"isError":false,"timestamp":1780573534939}}
{"type":"message","id":"016ba4f8","parentId":"22328f70","timestamp":"2026-06-04T11:45:43.717Z","message":{"role":"assistant","content":[{"type":"text","text":"Sí. Hice dry run, sin tocar nada.\n\nBase real hoy:\n- job live:\n  - `dedupe_high`: `0.90`\n  - `dedupe_mid`: `0.75`\n  - `conflict`: `0.55`\n  - `promote_conf`: `0.70`\n\nDry run más estricto/agresivo:\n- `dedupe_high`: `0.80`\n- `dedupe_mid`: `0.55`\n- `conflict`: `0.35`\n- `promote_conf`: `0.75`\n- además normalicé ruido tipo `[telegram]`, urls, puntuación\n\nQué salió sobre `67` candidatos:\n- promotions: `31`\n- archive por dedupe fuerte: `0`\n- links por similitud: `5`\n- conflicts: `7`\n\nPero lo importante:\n- casi todo lo que “saltó” fue basura estructural\n- no aparecieron contradicciones de negocio fuertes\n- sí aparecieron memorias repetitivas de sistema\n\nLo que saltó de verdad:\n1. episodios repetidos de `pi-config`\n   - “You are running one deterministic phase-machine pass…”\n2. episodios repetidos de `Subagent intake task_...`\n3. un par de `legacy_run` muy parecidos\n   - displays China / Ballbox\n   - investigación parecida\n\nLectura:\n- el problema no es que haya conflictos semánticos ocultos grandes\n- el problema es que se están guardando muchos episodios operativos parecidos\n- el motor actual promueve mucho y limpia poco\n- si lo hacés más agresivo, lo primero que emerge es ruido de sistema, no verdad contradictoria\n\nConclusión corta:\n- sí, con criterio más duro aparecen cosas\n- pero mayormente son duplicados/near-duplicates operativos\n- no vi una mina de conflictos útiles esperando salir\n\nSi querés, siguiente paso útil:\n1. excluir memorias operativas de sistema\n2. consolidar episodios repetidos por plantilla\n3. recién después endurecer conflicto semántico\n\nEso sí movería la aguja.","textSignature":"{\"v\":1,\"id\":\"msg_08205193eac5c3af016a21655f96c08191ac833821da58c322\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":238,"output":446,"cacheRead":58368,"cacheWrite":0,"totalTokens":59052,"cost":{"input":0.000595,"output":0.00669,"cacheRead":0.014591999999999999,"cacheWrite":0,"total":0.021877}},"stopReason":"stop","timestamp":1780573534940,"responseId":"resp_08205193eac5c3af016a21655f0b248191aaf11d99dae8fa5f"}}
{"type":"message","id":"db95395f","parentId":"016ba4f8","timestamp":"2026-06-04T11:49:54.372Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] si coincido entonces no enfoquemos en mejorar conflict detection sino en sacar ruido del sistema, consolidar memorias similares"}],"timestamp":1780573794371}}
{"type":"message","id":"228d666f","parentId":"db95395f","timestamp":"2026-06-04T11:49:59.272Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering memory creation**\n\nI think I need to respond concisely and consider creating a memory for the user’s decision, which seems durable. It might be helpful to save it for later, possibly even mid-conversation. I could acknowledge their statement and outline the next steps, but since they didn’t explicitly ask me to execute anything, I should frame my response appropriately. Let's ensure I’m clear without assuming too much!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216663996881918ea3ce73b2f88c85\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWZmZJvkNcnJutteptcm4Jw2U89ZLO3NMReqoKxy8QMwBilIyqcRQNT7XOClZLMvb5RL3R0XHpQnac0yABXP4fV7lDj6czqgOkKZWcax9WG3pc87ACtdfmOsTOjo8ofIVLjkA5Nk0F4FYTeg8ITMmdHsOgVbgz5qhbUg83LAe1EUr85K6cwZcOPnkk23lq-ROyK_G3Bb9I_HJn4lrA-Pvq7U3Obw4SOWQHp39is_Yav9PwV8CHzm7PdPLcW73Hvbon7ZdOYWOX2tsa1eN5UUMuzMyK1AjpKkD05CqtnpruM06kPo0KJdOPWW26Pv8AqBXZjq4Quz3GdJTXwynYyvDLyPMT4MlzI4Mv8GOBJ_DmrwfBAYqhB5gttp33Io7MxPkNWXfhoTw1DbNmY2i1TN0hGLSobg3F7A7SGAVuSNGCLe3_LTSCSL2hEEYTAsOEJ51YKLLQPc1OPTm7M-HY6SeXLjoQedh72O5cQ1EjkkTaHEcdozd3h_Bey5FLMxF28kXcYx1ZB6qGr3tmb9U9t-gtTr-aMKzuvad90n-tEy7uol0dMe6wRdoLcSiKhnFMbiYWhe7d4qLx0Md6GQqw4irAN9YpPPM86bVr2iGwoXyOxxBYLrlQw86Ji9tXjaYxLajiXKJG2jA-V_BwIVUZoXfXcRlmPSq1WlpbGnf7m2e-Qgl9rPcdYcICqgKn2eepCxRz4mtRxcOZxa2GyKGDm8uxRRxi9W5YFhrMV4hLvp48KVhEw0LJ9vrQ2dW7mYNPj6I6yEC7kynFOeFq-l6fN_IthgZkoxyWWxI9hWMHc3J1XoEd5Ep1WW-IETosfAxsSe8HBfmGy1aFTZeFAXetTlX5F4JMAmHAK4VTDpepP30Z0wTOocClUjwNhJCnVhI20ma2ibAdgNRPN73btZDE6rMbBsGQVeX3WYYg5RuoU7DZ8LOfisW4g4V3ZsU78uQXYYS40iJ7Fxclzk_xfP0ZaIGAlKhhLcPwjkqYcgJPjSo3cHgVJR5YSMNPZtSLKzV-8Av1wI3AgFIeQKu9kmPUj7UNKE1DcqXmkMd8TRxUSYZnMQ-Mt_9yYIGSgciE-RvRkdzmq9xK7L7-hjf1yIAh214ZeIqjTawitVR-BPDWoIyYdR7nwgSlpvund_lIPv8htswsahIRWJucpnktz2axtjcMZJY3tCrGWYznRIxAZ1cUjkSU8vIa50BKSAFRK7W30YuMhpZrZuL-TJEzv-R-Ki4Ru94y3MLJF5gTNITkdlkD-o3KA=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering memory creation**\\n\\nI think I need to respond concisely and consider creating a memory for the user’s decision, which seems durable. It might be helpful to save it for later, possibly even mid-conversation. I could acknowledge their statement and outline the next steps, but since they didn’t explicitly ask me to execute anything, I should frame my response appropriately. Let's ensure I’m clear without assuming too much!\"}]}"},{"type":"text","text":"Sí. Mejor foco.\n\nPrioridad buena:\n1. filtrar ruido de sistema\n2. consolidar memorias repetidas\n3. dejar conflict detection para después\n\nPorque hoy:\n- el falso volumen viene más por duplicación\n  que por contradicción real\n- mejorar conflicts ahora te da poco valor\n\nCambio de criterio recomendado:\n- no guardar episodios de prompts internos\n- no guardar intake/subagent boilerplate\n- colapsar runs casi iguales\n- endurecer dedupe de episodios operativos\n\nResultado esperado:\n- menos inflación\n- métricas más honestas\n- memoria más útil para retrieval\n\nSi querés, próximo paso hago plan concreto de limpieza y reglas.","textSignature":"{\"v\":1,\"id\":\"msg_08205193eac5c3af016a216666a1188191a093d46ae39d4f87\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":22323,"output":197,"cacheRead":36352,"cacheWrite":0,"totalTokens":58872,"cost":{"input":0.0558075,"output":0.002955,"cacheRead":0.009087999999999999,"cacheWrite":0,"total":0.06785050000000001}},"stopReason":"stop","timestamp":1780573794374,"responseId":"resp_08205193eac5c3af016a216662786481918b2724f92f966443"}}
{"type":"message","id":"acf5fe1c","parentId":"228d666f","timestamp":"2026-06-04T11:51:13.527Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] dale"}],"timestamp":1780573873526}}
{"type":"message","id":"7910d9d3","parentId":"acf5fe1c","timestamp":"2026-06-04T11:51:22.660Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Creating a concise plan**\n\nI want to focus on providing a clear and concise answer with a concrete plan. It seems like there's no need for tools in this case, so I'll keep it straightforward. My aim is to present the information directly and in an easily digestible format. This way, the user can understand what they need without any unnecessary complexity. Let's get started on that concise plan!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a2166b2cdd081918cfe8ebe4a08feac\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWa1OM0OxHSd9DvJLspLq_HKaSXMR6doYkvj4cXi85OMtwQ8tXE2iqhYqW_3L3gFYcQ1EfGTLlm23fPQFI-dp_uTvvup9bEaEbMJ-T0-sGmU_XVC3ecjpxJko9TpdL_lksRxp8I6lczfE3-u7WoSrH3gNrCi2cZ0olD0S8oHugh9UgAZXS5FAkGTUjnNKokGB4lM6LFq1DKY4CC_-NnwKswrql98OIuuWUKCMbjnwigBwpY4qMl9TXP7HFK3U6y-dH5vbFfiS7awxkqiE42Bft8SgjLF8DQ4-fmGTcX6ZAJ_yGsGbVr-wlqxJrIGZEr96GxtECcuUDdXmG0d8mIDD_Izpdz8bsoOJ9zGlw_00d-V_SdgXAhyXr-Jv2IvonFFLHikjh0B0F7xk8HGRpdrEokmEpG-Ljfpx18u6KVOG4ysbwvUOZrc9JV9BMGGi803EOP0eCuaJat2gJSly6vVo4aOcKWOCH1Wsx4Xs40cZJvjm8t31kktdp-eyvAZ59tjp2zev3D6jXeu4V6gJEwSbhCHQHSeaaGYgj-Je6zzauFjzk9lSZxvbo74pOPNAurwlQJPLJMfT65uuOz1i0YTzUuiZBnsvi1fMXH68PWZ8pmuhbGhXq4oVedBdhizxOPNCulx6ErURCNE7IpehrxfOlafxPoTnSCKSvXCG_AieRMZzq8CXO0FC5oa8ELtn2xGgKQotzjk4VTUa9NnmrIBAFt7XsnmamBek_GR231tnH2qYOLhBDh-ZCpR6XGW7YAMARXMYomaFTOX3F2EzVSzgABQ_3LsXCRxh04T7KvXwcv9gsSsqIMhnvQLN5sP7a4MBqJq1asg556hNXfkKrqJJQZi7J1t8mSYuHdluETWkkflcJ45YILqP3WykCXJXkbxzP4bYY65AwHj1t4sOoHuw4xG3AxPgx5Sx2LHnJTskwuuaaLXvUtyejWJ7Upl8kwyV0SgjEpAzUVM5HlTUlICD3u0lxJvq2nZIfue_f5mkYL-e28=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Creating a concise plan**\\n\\nI want to focus on providing a clear and concise answer with a concrete plan. It seems like there's no need for tools in this case, so I'll keep it straightforward. My aim is to present the information directly and in an easily digestible format. This way, the user can understand what they need without any unnecessary complexity. Let's get started on that concise plan!\"}]}"},{"type":"text","text":"Plan concreto:\n\n1. cortar ingreso de ruido\n- excluir episodios que sean:\n  - prompts de sistema\n  - phase-machine / loop boilerplate\n  - subagent intake boilerplate\n  - job envelopes / routing payloads\n- regla: si el valor durable es casi cero,\n  no entra a memoria\n\n2. bajar promoción automática\n- no tratar `episode active` como\n  “ya promovido” por default\n- exigir más señal para episodios\n  operativos antes de contarlos\n\n3. dedupe por normalización\n- comparar contenido normalizado:\n  - sin `[telegram]`\n  - sin urls\n  - sin ids/task ids\n  - sin timestamps\n  - sin payload json crudo\n- eso va a unir muchas memorias\n  que hoy parecen distintas\n\n4. consolidación por plantilla\n- detectar familias repetidas:\n  - `Subagent intake task_*`\n  - `You are running one deterministic...`\n  - runs/research con mismo goal\n- convertir varias en una sola memoria\n  resumida o archivar duplicadas\n\n5. separar “operativo” de “durable”\n- episodios operativos:\n  - default inbox o archivables\n- memorias durables:\n  - decisiones\n  - preferencias\n  - facts de proyecto\n  - artifacts útiles\n- así retrieval deja de mezclar ruido\n  con cosas importantes\n\n6. métricas más honestas\n- reportar separado:\n  - promoted durable\n  - linked duplicates\n  - archived noise\n  - skipped system noise\n- no inflar “promoted” con episodios\n  activos casi automáticos\n\nOrden:\n- primero filtro de ingreso\n- después dedupe/consolidación\n- después métricas\n\nImpacto esperado:\n- menos volumen basura\n- mejor search/context\n- promotions más creíbles\n\nSi querés, lo próximo hago ya:\n- reglas exactas\n- archivos a tocar\n- cambio mínimo seguro primero","textSignature":"{\"v\":1,\"id\":\"msg_08205193eac5c3af016a2166b586908191bb25c9bd2c1fd361\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":461,"output":429,"cacheRead":58368,"cacheWrite":0,"totalTokens":59258,"cost":{"input":0.0011525,"output":0.006435,"cacheRead":0.014591999999999999,"cacheWrite":0,"total":0.022179499999999998}},"stopReason":"stop","timestamp":1780573873529,"responseId":"resp_08205193eac5c3af016a2166b1d1c48191ac8c7895d5ad4266"}}
{"type":"message","id":"5811a0a6","parentId":"7910d9d3","timestamp":"2026-06-04T12:10:48.753Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] si"}],"timestamp":1780575048752}}
{"type":"message","id":"27107b1d","parentId":"5811a0a6","timestamp":"2026-06-04T12:10:58.455Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216b4a73ac8191918c2531b4a00d56\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWtLhSpumSuddyjqlLdo5YT4mdnNoM03sEF_W2_SqZTsE3UuGnzeMDZ5lfLM-Nknxe_jeHFuF4dHRIYE2zRDo9MF7kHa4cYMJkLoWQhqFhHevaCoyKh6xrPd6J8IGgGVswktWxgWVCkpjEm5vnnq66zMpSn1Y1zHtk6ELo22I0GVzEqU8xC2SRRK31CtU2zMx7HyYZk9vGbe9QDyuvSSWIecEGb2s9oLFHjM-nzUr8OgdP7RQz5RyhjBQXP5FLx6JqFI4Jtu_GsCOID-SuD71WTMZREQGLz57EOe6QKDFwya5vPP5jkwbZR9G_UdWrzY7V2070jMAe4PRU7jMRHRg5FR69isRkzLYRewryWwlIJnSDh1DBcpwqrQoaA85l_eO5P0KF_a35YETxC9K6pDpWX7SRtAZecpHYVLKIkzCs3eU398YVNuMY6DV_6Sog5eznJKPrsntd54SbOhQqbyxMzNF2jDxTPmbrjUBEEAhxA4tiAzERV1F1EeT2uL2AK62SEcy2G_18UyfWHay98NQmQzsJRfuOw4hGkfA5HXHjqBpA8knYi3QPf0qhM2m5-iamGsLZYUXw-ibfm5pr7PJfZc3nDXwyjoNbP1Pkxl13MObFtnDIHh6kkkCrE9CRhghzhTVNCeGe4-L1bLV91Kahka0SQNOvYwv70L4a2desyK4XCtxYB40jRmLIAWgTdI9l2uTbzNZDkf9TqCq6cTWXgfFMBBUosI_PHDGbSPD6mLoCObwpzoiYpWCJVt1owwd5uVrTd3bj9wi9R0-pwPiiZwCEp7Abj35YZPT7xWRxElU8FInlYUT8u1Zn4MEecdMPbC_rtOMH4reoTd7jEmEihpnvvDhHY2vTSwtaEd3YrI8AkhHZ2H56Zu6jmoZd5T7rUw0U1mIPvOFeILQpgFQ3BK06kg2SRNXM0XTu6L-0W-sbjlK0AnXYF9vtk2SGGgxE5lq2mh1h5hIm-dYJPDdKumnQ==\",\"summary\":[]}"},{"type":"text","text":"Cambio mínimo seguro primero:\n\nArchivos a tocar:\n- `/home/sebas/pi-config/scripts/session-memory-analyzer.py`\n- `/home/sebas/agents-database/src/shared_agent_memory/service.py`\n\nReglas exactas:\n\n1. filtro de ingreso\nNo guardar memoria si contenido/título matchea:\n- `You are running one deterministic`\n- `Subagent intake task_`\n- `Task ID: task_`\n- `Payload: {`\n- `route\\\": {`\n- `phase-machine pass`\n- prompts internos de loop/iteration\n\n2. filtro por forma\nNo guardar si:\n- casi todo el contenido es boilerplate\n- domina JSON/routing/payload\n- no hay user fact, decision, artifact,\n  o resultado reusable claro\n\n3. dedupe normalizado\nAntes de consolidar, comparar versión\nnormalizada sin:\n- `[telegram]`\n- urls\n- task ids / run ids\n- timestamps\n- blobs JSON\n- espacios/puntuación extra\n\n4. promotions honestas\nNo contar `episode active` como\npromotion automática.\nSolo promotion real si:\n- venía `inbox` y sube a `active`\n- o pasa filtro durable explícito\n\n5. consolidación más dura para episodios\nPara `episode`:\n- lower dedupe thresholds\n- si es near-duplicate operativo:\n  link + archive\n\nImplementación mínima:\n- en analyzer:\n  - skip de memorias candidatas ruidosas\n- en service:\n  - helper `normalize_for_dedupe()`\n  - usar eso en duplicate/high-similarity\n  - sacar conteo inflado de promoted\n\nOrden de trabajo:\n1. meter filtros de ruido\n2. meter dedupe normalizado\n3. corregir métricas de promotion\n4. correr dry run de nuevo\n5. recién después decidir si activarlo full\n\nEsperado:\n- baja inmediata de ruido nuevo\n- menos “promotions” falsas\n- más links/archives útiles\n\nSi querés, siguiente mensaje:\nhago el cambio.","textSignature":"{\"v\":1,\"id\":\"msg_08205193eac5c3af016a216b4b29a08191ad4862626e84faf5\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":882,"output":438,"cacheRead":58368,"cacheWrite":0,"totalTokens":59688,"cost":{"input":0.0022050000000000004,"output":0.00657,"cacheRead":0.014591999999999999,"cacheWrite":0,"total":0.023367}},"stopReason":"stop","timestamp":1780575048755,"responseId":"resp_08205193eac5c3af016a216b49c9288191bbb20e92e51fbab7"}}
{"type":"message","id":"f8f7a2ea","parentId":"27107b1d","timestamp":"2026-06-04T12:15:11.575Z","message":{"role":"user","content":[{"type":"text","text":"[telegram] dale hacelo"}],"timestamp":1780575311572}}
{"type":"message","id":"90e78848","parentId":"f8f7a2ea","timestamp":"2026-06-04T12:15:14.726Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Modifying files**\n\nI need to modify some files, which means I'll focus on inspecting the relevant sections with the analyzer. I'm thinking it’s crucial to ensure I'm checking the right parts and making needed changes effectively. I need to take a closer look at what needs to be adjusted. I'll ensure I'm thorough in this inspection, as I want to get everything just right without missing anything important. So, let's dive into those sections!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216c5064d88191a613ad3299821fd1\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWxS1L03J8gP5CBzB_4psVM2mpc011VGPwWGL2BabhOz72o2LtsjdgKdIXyLcB0xbBoe5ePn9OJGv15e-Ma2r5qWKQhNIs9dUR1YQRBsr1gAaNjCpa3oSfpiELlYPt5UjCDj0Zc_UlcUFThcoNGACfxpdTCHR7gmzv5NUkG4JSUyXecnzbm7nwPG0LgxB5DXcSa_beh2TPBK4bvbZCvvGFWu23ZJMBnZ5kBDhiFTg930OkGe5Nk44BYw7YdNS0u3euZEgvlcDGl1bOzd5IvEYkRyz8nt3XmY9V1npmMbOTObcOuHbobMat4lp-_z1-hFpWSHnfc2Ezzx9dgSmsBn7FlGjYBHftV2RFec99Az0Y23Htve1WmX__DPJakmc6knvsT8fJ90aj3CIlkrfuWBg4fds4BZWFairaHLg9h4PteHUygbnDyw5ksheKLFjMCXGLwoUN2iIA_tWS6JrmIuaVEdMuyDxdndJKRDeF2gRCQ_YDcHUYBluhijzDHR65l710UkPIV8bWwV21X351-2y0DraGuLTFNsDwBJelfJHt2O7dpWi8ZlmAcNcLtvXY9XMFIYgaRVj9H1RW-J4pqQvqoUjV_rmjpVGx7QuhlQsgmoNU3uC-1gWmnNPpvN9SQFrNhsG4P0Qb2Tt3a5uarI4h12RUMGxYjtfoCdTfV84_968uPpf5Vx7DxgDu6hcY5T5vVNoCxw5DbB1wbGi6BuuDtNcPDgSA9W7lct8M5v8uyUXdDSsiwEisfYWwtoHTf3kBm_9gvbeGBCM-8JXPmub9305-KJK6sPDDa9gudpAM9lySxs3UBSZduKm3IX9LavkRFb2uj-zlDXOxmSosLL2OcBVzSr95-bZGqk7WsSBhD1AWv1_Jm1TZ7jzFUTRhb1KMYUD16B6W6v2olnP-B74O8k2--TGa_QNXm00bSeFFK040Z7Vu4HWx287Gv9vRH55-a1kyK98O82WntTNaJJM3UcX_LgnVHfAnLGm7IZtp6ipEw=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Modifying files**\\n\\nI need to modify some files, which means I'll focus on inspecting the relevant sections with the analyzer. I'm thinking it’s crucial to ensure I'm checking the right parts and making needed changes effectively. I need to take a closer look at what needs to be adjusted. I'll ensure I'm thorough in this inspection, as I want to get everything just right without missing anything important. So, let's dive into those sections!\"}]}"},{"type":"toolCall","id":"call_ewc5hhVdYNLMAQxcgaAVC0hJ|fc_08205193eac5c3af016a216c5237988191b4d134dbf9806763","name":"todo","arguments":{"action":"create","subject":"Implement memory noise filtering and normalized consolidation","description":"Apply minimum safe changes to reduce system-noise memories, normalize dedupe, and make promotion metrics honest; validate with dry run.","activeForm":"implementing memory cleanup rules","status":"in_progress"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":806,"output":84,"cacheRead":58880,"cacheWrite":0,"totalTokens":59770,"cost":{"input":0.0020150000000000003,"output":0.00126,"cacheRead":0.014719999999999999,"cacheWrite":0,"total":0.017994999999999997}},"stopReason":"toolUse","timestamp":1780575311576,"responseId":"resp_08205193eac5c3af016a216c4faef481919f4f930089917d24"}}
{"type":"message","id":"6f0f670c","parentId":"90e78848","timestamp":"2026-06-04T12:15:14.729Z","message":{"role":"toolResult","toolCallId":"call_ewc5hhVdYNLMAQxcgaAVC0hJ|fc_08205193eac5c3af016a216c5237988191b4d134dbf9806763","toolName":"todo","content":[{"type":"text","text":"Created #2: Implement memory noise filtering and normalized consolidation (pending)"}],"details":{"action":"create","params":{"action":"create","subject":"Implement memory noise filtering and normalized consolidation","description":"Apply minimum safe changes to reduce system-noise memories, normalize dedupe, and make promotion metrics honest; validate with dry run.","activeForm":"implementing memory cleanup rules","status":"in_progress"},"tasks":[{"id":1,"subject":"Dry run stricter memory consolidation/conflict detection","status":"completed","description":"Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.","activeForm":"running stricter memory dry run"},{"id":2,"subject":"Implement memory noise filtering and normalized consolidation","status":"pending","description":"Apply minimum safe changes to reduce system-noise memories, normalize dedupe, and make promotion metrics honest; validate with dry run.","activeForm":"implementing memory cleanup rules"}],"nextId":3},"isError":false,"timestamp":1780575314729}}
{"type":"message","id":"ebf2371f","parentId":"6f0f670c","timestamp":"2026-06-04T12:15:16.183Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_lcGgYecbaOtjOuy1bWyUdpEE|fc_08205193eac5c3af016a216c53b50881918b258cd6f64a650c","name":"todo","arguments":{"action":"update","id":2,"status":"in_progress"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":402,"output":26,"cacheRead":59392,"cacheWrite":0,"totalTokens":59820,"cost":{"input":0.001005,"output":0.00039,"cacheRead":0.014848,"cacheWrite":0,"total":0.016243}},"stopReason":"toolUse","timestamp":1780575314732,"responseId":"resp_08205193eac5c3af016a216c52dcbc8191ada7bd7640a758f0"}}
{"type":"message","id":"a3ffa0d6","parentId":"ebf2371f","timestamp":"2026-06-04T12:15:16.184Z","message":{"role":"toolResult","toolCallId":"call_lcGgYecbaOtjOuy1bWyUdpEE|fc_08205193eac5c3af016a216c53b50881918b258cd6f64a650c","toolName":"todo","content":[{"type":"text","text":"Updated #2 (pending → in_progress)"}],"details":{"action":"update","params":{"action":"update","id":2,"status":"in_progress"},"tasks":[{"id":1,"subject":"Dry run stricter memory consolidation/conflict detection","status":"completed","description":"Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.","activeForm":"running stricter memory dry run"},{"id":2,"subject":"Implement memory noise filtering and normalized consolidation","status":"in_progress","description":"Apply minimum safe changes to reduce system-noise memories, normalize dedupe, and make promotion metrics honest; validate with dry run.","activeForm":"implementing memory cleanup rules"}],"nextId":3},"isError":false,"timestamp":1780575316183}}
{"type":"message","id":"dd271f3b","parentId":"a3ffa0d6","timestamp":"2026-06-04T12:15:20.184Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_1qA9Tat7Hzo8ZbXfc9hAjjgl|fc_08205193eac5c3af016a216c581f5c81918e0b8045c351c1b1","name":"read","arguments":{"path":"/home/sebas/pi-config/scripts/session-memory-analyzer.py","offset":1,"limit":320}},{"type":"toolCall","id":"call_gXUyabpK2u6alaBRyyXNz6HH|fc_08205193eac5c3af016a216c581f6c8191b4a2424f1aab187d","name":"read","arguments":{"path":"/home/sebas/agents-database/src/shared_agent_memory/service.py","offset":318,"limit":190}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":447,"output":86,"cacheRead":59392,"cacheWrite":0,"totalTokens":59925,"cost":{"input":0.0011175,"output":0.0012900000000000001,"cacheRead":0.014848,"cacheWrite":0,"total":0.0172555}},"stopReason":"toolUse","timestamp":1780575316185,"responseId":"resp_08205193eac5c3af016a216c55ebd48191b0d75e08b11b1c08"}}
{"type":"message","id":"8b9a59fc","parentId":"dd271f3b","timestamp":"2026-06-04T12:15:20.194Z","message":{"role":"toolResult","toolCallId":"call_1qA9Tat7Hzo8ZbXfc9hAjjgl|fc_08205193eac5c3af016a216c581f5c81918e0b8045c351c1b1","toolName":"read","content":[{"type":"text","text":"#!/usr/bin/env python3\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport shutil\nimport subprocess\nimport sys\nfrom pathlib import Path\nfrom typing import Any\nfrom urllib import request\n\nFALLBACK_TITLE_MAX = 72\n\nAGENTS_DB_API = os.environ.get(\"AGENTS_DB_API\", \"http://100.116.176.16:8091\")\nPI_MODEL = os.environ.get(\"PI_SESSION_MEMORY_MODEL\", \"gpt-5.4-mini\")\nPI_THINKING = os.environ.get(\"PI_SESSION_MEMORY_THINKING\", \"off\")\nPI_BIN = os.environ.get(\"PI_SESSION_MEMORY_PI_BIN\")\nMAX_CANDIDATES = int(os.environ.get(\"PI_SESSION_MEMORY_MAX_CANDIDATES\", \"6\"))\nACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD\", \"0.92\"))\nACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD\", \"0.9\"))\nDURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\n\n\ndef resolve_pi_bin() -> str:\n    candidates = [\n        PI_BIN,\n        shutil.which(\"pi\"),\n        str(Path.home() / \".npm-global\" / \"bin\" / \"pi\"),\n        \"/usr/local/bin/pi\",\n        \"/usr/bin/pi\",\n    ]\n    for candidate in candidates:\n        if not candidate:\n            continue\n        if Path(candidate).exists():\n            return candidate\n    raise FileNotFoundError(\"pi\")\n\n\ndef load_job(job_file: Path) -> dict[str, Any]:\n    return json.loads(job_file.read_text(encoding=\"utf-8\"))\n\n\ndef save_job(job_file: Path, payload: dict[str, Any]) -> None:\n    job_file.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + \"\\n\", encoding=\"utf-8\")\n\n\ndef call_pi(prompt: str) -> str:\n    cmd = [\n        resolve_pi_bin(),\n        \"-p\",\n        \"--provider\",\n        \"openai-codex\",\n        \"--model\",\n        PI_MODEL,\n        \"--thinking\",\n        PI_THINKING,\n        \"--no-tools\",\n        \"--no-session\",\n        prompt,\n    ]\n    proc = subprocess.run(cmd, capture_output=True, text=True)\n    if proc.returncode != 0:\n        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f\"pi failed ({proc.returncode})\")\n    return proc.stdout.strip()\n\n\ndef fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:\n    meta = session_meta(job)\n    last_user = (meta.get(\"last_user_text\") or \"\").strip()\n    last_assistant = (meta.get(\"last_assistant_text\") or \"\").strip()\n    first_user = (meta.get(\"first_user_text\") or last_user or \"\").strip()\n    if not first_user or not last_assistant:\n        return {\"memories\": [], \"reason\": f\"fallback-no-summary:{error}\"}\n    title_base = first_user.splitlines()[0].strip()\n    if len(title_base) > FALLBACK_TITLE_MAX:\n        title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + \"…\"\n    cwd = meta.get(\"cwd\") or \"\"\n    cwd_name = Path(cwd).name or \"session\"\n    return {\n        \"memories\": [\n            {\n                \"type\": \"episode\",\n                \"subtype\": \"session_summary\",\n                \"scope\": meta.get(\"scope\") or \"project\",\n                \"title\": f\"{cwd_name}: {title_base}\",\n                \"content\": f\"User asked: {first_user}\\nResult: {last_assistant}\",\n                \"source_ref\": f\"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}\",\n                \"evidence_ref\": meta.get(\"session_file\"),\n                \"confidence\": 0.72,\n                \"freshness\": 0.95,\n                \"importance\": 0.56,\n                \"reason\": f\"Deterministic fallback because model analysis was unavailable: {error}\",\n            }\n        ],\n        \"reason\": f\"fallback-analysis:{error}\",\n    }\n\n\ndef ingest(memory: dict[str, Any]) -> dict[str, Any]:\n    req = request.Request(\n        f\"{AGENTS_DB_API}/api/ingest\",\n        method=\"POST\",\n        data=json.dumps(memory).encode(),\n        headers={\"Content-Type\": \"application/json\"},\n    )\n    with request.urlopen(req, timeout=30) as resp:\n        return json.loads(resp.read().decode(\"utf-8\"))\n\n\ndef extract_json(text: str) -> dict[str, Any]:\n    raw = text.strip()\n    if raw.startswith(\"```\"):\n        raw = raw.strip(\"`\")\n        raw = raw.split(\"\\n\", 1)[-1] if \"\\n\" in raw else raw\n    start = raw.find(\"{\")\n    end = raw.rfind(\"}\")\n    if start >= 0 and end > start:\n        raw = raw[start : end + 1]\n    return json.loads(raw)\n\n\ndef session_meta(job: dict[str, Any]) -> dict[str, Any]:\n    payload = job.get(\"payload\")\n    if isinstance(payload, dict):\n        return payload\n    return job\n\n\ndef build_prompt(job: dict[str, Any]) -> str:\n    meta = session_meta(job)\n    return f\"\"\"You are extracting reusable memory candidates from a Pi session.\n\nGoal:\n- Prefer broad candidate capture over premature rejection.\n- Return 0 to {MAX_CANDIDATES} memories from this session.\n- Include strong durable memories and weaker candidate memories that may be worth later consolidation.\n\nRules:\n- Session metadata alone is not enough.\n- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.\n- Favor stable user preferences, enduring project facts, reusable external artifacts, decisions, procedures, blockers, and compact episode summaries with future reuse value.\n- It is OK to include lower-confidence candidates if they could become useful after consolidation.\n- Avoid trivial chatter, one-off execution noise, and duplicates of the same point unless they capture materially different memories.\n- Prefer at most one memory per distinct insight.\n- Output JSON only. No markdown. No extra text.\n\nReturn this schema:\n{{\n  \"memories\": [\n    {{\n      \"type\": \"profile\" | \"decision\" | \"project\" | \"artifact\" | \"episode\" | \"procedural\" | \"semantic\",\n      \"subtype\": string | null,\n      \"scope\": \"global\" | \"project\",\n      \"title\": string,\n      \"content\": string,\n      \"source_ref\": string,\n      \"evidence_ref\": string | null,\n      \"confidence\": number,\n      \"freshness\": number,\n      \"importance\": number,\n      \"reason\": string\n    }}\n  ],\n  \"reason\": string\n}}\n\nScoring guidance:\n- confidence: 0 to 1, how well supported the memory is.\n- freshness: 0 to 1, how current it is.\n- importance: 0 to 1, how much future value it has.\n- evidence_ref: cite the strongest source in the session or branch excerpt when possible.\n- Use lower confidence/importance for tentative candidates rather than omitting them.\n\nSession metadata:\n- session_id: {meta.get('session_id')}\n- session_file: {meta.get('session_file')}\n- cwd: {meta.get('cwd')}\n- scope: {meta.get('scope')}\n- reason: {meta.get('reason')}\n- user_messages: {meta.get('user_messages')}\n- assistant_messages: {meta.get('assistant_messages')}\n- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}\n- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}\n- trigger: {meta.get('trigger')}\n\nBranch excerpt JSON:\n{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}\n\"\"\"\n\n\ndef normalize_memories(analysis: dict[str, Any]) -> list[dict[str, Any]]:\n    memories = analysis.get(\"memories\")\n    if isinstance(memories, list):\n        return [item for item in memories if isinstance(item, dict)]\n    if bool(analysis.get(\"save\")):\n        return [analysis]\n    return []\n\n\ndef choose_status(memory_type: str, confidence: float, importance: float) -> str:\n    if memory_type in DURABLE_TYPES and confidence >= ACTIVE_CONFIDENCE_THRESHOLD and importance >= ACTIVE_IMPORTANCE_THRESHOLD:\n        return \"active\"\n    return \"inbox\"\n\n\ndef build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n    memory_type = candidate.get(\"type\") or \"episode\"\n    confidence = float(candidate.get(\"confidence\") or 0.6)\n    importance = float(candidate.get(\"importance\") or 0.5)\n    status = choose_status(memory_type, confidence, importance)\n    return {\n        \"type\": memory_type,\n        \"subtype\": candidate.get(\"subtype\"),\n        \"scope\": candidate.get(\"scope\") or meta.get(\"scope\") or \"global\",\n        \"status\": status,\n        \"source_kind\": \"manual\",\n        \"title\": candidate.get(\"title\") or f\"Session insight {index}\",\n        \"content\": candidate.get(\"content\") or \"\",\n        \"confidence\": confidence,\n        \"freshness\": float(candidate.get(\"freshness\") or 0.9),\n        \"importance\": importance,\n        \"source_ref\": candidate.get(\"source_ref\") or f\"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}\",\n        \"evidence_ref\": candidate.get(\"evidence_ref\"),\n        \"origin_agent\": \"pi-session-memory-analyzer\",\n        \"metadata\": {\n            \"job_file\": str(job_file),\n            \"session_id\": meta.get(\"session_id\"),\n            \"session_file\": meta.get(\"session_file\"),\n            \"cwd\": meta.get(\"cwd\"),\n            \"reason\": meta.get(\"reason\"),\n            \"user_messages\": meta.get(\"user_messages\"),\n            \"assistant_messages\": meta.get(\"assistant_messages\"),\n            \"analysis_reason\": analysis_reason,\n            \"candidate_reason\": candidate.get(\"reason\"),\n            \"trigger\": meta.get(\"trigger\"),\n            \"promotion_candidate\": status == \"inbox\",\n            \"session_candidate_index\": index,\n        },\n    }\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--job-file\", required=True)\n    args = parser.parse_args()\n\n    job_file = Path(args.job_file)\n    job = load_job(job_file)\n    meta = session_meta(job)\n    job[\"status\"] = \"running\"\n    job[\"analysis\"] = {\n        \"status\": \"running\",\n        \"save\": None,\n        \"saved\": None,\n        \"memory_id\": None,\n        \"memory_ids\": [],\n        \"saved_count\": 0,\n    }\n    save_job(job_file, job)\n\n    try:\n        fallback_error = None\n        try:\n            raw = call_pi(build_prompt(job))\n            analysis = extract_json(raw)\n        except Exception as exc:\n            fallback_error = exc\n            analysis = fallback_analysis(job, exc)\n            raw = json.dumps(analysis, ensure_ascii=False)\n        candidates = normalize_memories(analysis)[:MAX_CANDIDATES]\n        saved_items: list[dict[str, Any]] = []\n\n        for index, candidate in enumerate(candidates, start=1):\n            memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n            if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n                continue\n            saved_items.append(ingest(memory))\n\n        job[\"analysis\"][\"raw\"] = raw\n        job[\"analysis\"][\"result\"] = analysis\n        job[\"analysis\"][\"status\"] = \"completed\"\n        job[\"status\"] = \"completed\"\n        if fallback_error is not None:\n            job[\"analysis\"][\"fallback_error\"] = str(fallback_error)\n        job[\"analysis\"][\"save\"] = bool(saved_items)\n        job[\"analysis\"][\"saved\"] = bool(saved_items)\n        job[\"analysis\"][\"saved_count\"] = len(saved_items)\n        job[\"analysis\"][\"memory_ids\"] = [item.get(\"id\") for item in saved_items if item.get(\"id\")]\n        job[\"analysis\"][\"memory_id\"] = job[\"analysis\"][\"memory_ids\"][0] if job[\"analysis\"][\"memory_ids\"] else None\n\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False))\n        return 0\n    except Exception as exc:\n        job[\"status\"] = \"failed\"\n        job[\"analysis\"][\"status\"] = \"failed\"\n        job[\"analysis\"][\"error\"] = str(exc)\n        save_job(job_file, job)\n        print(json.dumps(job[\"analysis\"], ensure_ascii=False), file=sys.stderr)\n        return 1\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n"}],"isError":false,"timestamp":1780575320194}}
{"type":"message","id":"88fd4a28","parentId":"8b9a59fc","timestamp":"2026-06-04T12:15:20.194Z","message":{"role":"toolResult","toolCallId":"call_gXUyabpK2u6alaBRyyXNz6HH|fc_08205193eac5c3af016a216c581f6c8191b4a2424f1aab187d","toolName":"read","content":[{"type":"text","text":"    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:\n        stats = {\n            \"candidates\": 0,\n            \"promoted\": 0,\n            \"archived\": 0,\n            \"linked\": 0,\n            \"conflicts\": 0,\n            \"llm_summaries\": 0,\n        }\n        age_days = int(config.get(\"candidate_age_days\", 7))\n        max_candidates = int(config.get(\"max_candidates\", 200))\n        high_threshold = float(config.get(\"dedupe_high_threshold\", 0.9))\n        mid_threshold = float(config.get(\"dedupe_mid_threshold\", 0.75))\n        promote_threshold = float(config.get(\"promote_confidence\", 0.7))\n        conflict_threshold = float(config.get(\"conflict_threshold\", 0.55))\n        llm_enabled = bool(config.get(\"llm_enabled\", False))\n        llm_provider = config.get(\"llm_provider\") or \"opencode\"\n        llm_command = config.get(\"llm_command\") or [\"opencode\", \"-m\", \"MiniMax M2.5 Free\"]\n        cutoff = (datetime.now(UTC) - timedelta(days=age_days)).isoformat()\n\n        with self.store.connection() as conn:\n            rows = conn.execute(\n                \"\"\"\n                SELECT * FROM memories\n                WHERE (\n                    status = 'inbox'\n                    OR (type = 'episode' AND status = 'active')\n                )\n                  AND created_at <= ?\n                ORDER BY created_at ASC\n                LIMIT ?\n                \"\"\",\n                (cutoff, max_candidates),\n            ).fetchall()\n\n            candidates = [self._row_to_memory(row) for row in rows]\n            stats[\"candidates\"] = len(candidates)\n\n            for candidate in candidates:\n                related = conn.execute(\n                    \"\"\"\n                    SELECT * FROM memories\n                    WHERE status = 'active'\n                      AND id != ?\n                      AND type = ?\n                      AND scope = ?\n                    ORDER BY updated_at DESC\n                    LIMIT 50\n                    \"\"\",\n                    (candidate[\"id\"], candidate[\"type\"], candidate[\"scope\"]),\n                ).fetchall()\n                related_memories = [self._row_to_memory(row) for row in related]\n\n                best_match = None\n                best_score = 0.0\n                for other in related_memories:\n                    lexical = self._lexical_score(\n                        normalize_text(candidate[\"content\"]).split(),\n                        normalize_text(other[\"content\"]),\n                    )\n                    semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n                    if score > best_score:\n                        best_score = score\n                        best_match = other\n\n                if best_match and candidate[\"content\"] == best_match[\"content\"]:\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"exact_duplicate\")\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"archived\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"archived\"] += 1\n                    stats[\"linked\"] += 1\n                    continue\n\n                if best_match and best_score >= high_threshold:\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"high_similarity\")\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"archived\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"archived\"] += 1\n                    stats[\"linked\"] += 1\n                    continue\n\n                if best_match and best_score >= mid_threshold:\n                    if llm_enabled:\n                        synthesized = self._llm_summarize_pair(\n                            candidate,\n                            best_match,\n                            provider=llm_provider,\n                            command=llm_command,\n                        )\n                        if synthesized:\n                            conn.execute(\n                                \"UPDATE memories SET summary = ?, updated_at = ?, metadata_json = ? WHERE id = ?\",\n                                (\n                                    synthesized,\n                                    utcnow(),\n                                    self.store.dumps(\n                                        {\n                                            **candidate.get(\"metadata\", {}),\n                                            \"synthesis_sources\": [candidate[\"id\"], best_match[\"id\"]],\n                                            \"synthesis_provider\": llm_provider,\n                                            \"synthesis_command\": llm_command,\n                                        }\n                                    ),\n                                    candidate[\"id\"],\n                                ),\n                            )\n                            stats[\"llm_summaries\"] += 1\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"mid_similarity\")\n                    stats[\"linked\"] += 1\n\n                if best_match and best_score >= conflict_threshold and self._conflict_signal(candidate, best_match):\n                    self._record_conflict(conn, candidate[\"id\"], best_match[\"id\"], \"potential_contradiction\")\n                    self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"contradicts\", \"heuristic\")\n                    stats[\"conflicts\"] += 1\n\n                if candidate[\"status\"] == \"inbox\" and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"active\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"promoted\"] += 1\n                elif candidate[\"status\"] == \"active\" and candidate[\"type\"] in {\"episode\", \"artifact\", \"decision\", \"project\", \"procedural\", \"semantic\"} and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    # Normalize qualifying active records as already-promoted durable memories.\n                    stats[\"promoted\"] += 1\n\n        return stats\n\n    def upsert_profile_fact(\n        self, title: str, content: str, evidence_ref: str | None = None, source_ref: str | None = None\n    ) -> dict[str, Any]:\n        return self.create_memory(\n            {\n                \"type\": \"profile\",\n                \"scope\": \"global\",\n                \"title\": title,\n                \"content\": content,\n                \"summary\": self._make_summary(content),\n                \"source_ref\": source_ref,\n                \"evidence_ref\": evidence_ref,\n                \"metadata\": {\"kind\": \"profile_fact\"},\n            }\n        )\n\n    def explain(self, identifier: str) -> dict[str, Any]:\n        with self.store.connection() as conn:\n            retrieval = conn.execute(\"SELECT * FROM retrieval_logs WHERE id = ?\", (identifier,)).fetchone()\n            if retrieval:\n                return {\n                    \"kind\": \"retrieval\",\n                    \"id\": retrieval[\"id\"],\n                    \"query_text\": retrieval[\"query_text\"],\n                    \"filters\": self.store.loads(retrieval[\"filters_json\"]),\n                    \"results\": self.store.loads(retrieval[\"results_json\"]),\n                }\n            memory = conn.execute(\"SELECT * FROM memories WHERE id = ?\", (identifier,)).fetchone()\n            if memory:\n                mem = self._row_to_memory(memory)\n                sources = conn.execute(\"SELECT * FROM sources WHERE source_ref = ? OR id = ?\", (mem[\"source_ref\"], mem[\"source_ref\"])).fetchall()\n                return {\"kind\": \"memory\", \"memory\": mem, \"sources\": [dict(source) for source in sources]}\n        raise KeyError(f\"Unknown identifier: {identifier}\")\n\n    def export(self) -> dict[str, Any]:\n        tables = [\n            \"projects\",\n            \"repos\",\n            \"sources\",\n            \"ingestion_events\",\n            \"memories\",\n            \"memory_links\",\n            \"memory_conflicts\",\n            \"retrieval_logs\",\n            \"maintenance_jobs\",\n            \"maintenance_runs\",\n            \"profiles\",\n            \"tasks\",\n            \"task_runs\",\n            \"artifacts\",\n        ]\n        exported: dict[str, list[dict[str, Any]]] = {}\n        with self.store.connection() as conn:\n            for table in tables:\n                rows = conn.execute(f\"SELECT * FROM {table}\").fetchall()\n                exported[table] = [dict(row) for row in rows]\n        return exported\n\n\n[1202 more lines in file. Use offset=508 to continue.]"}],"isError":false,"timestamp":1780575320194}}
{"type":"message","id":"4b9d0dab","parentId":"88fd4a28","timestamp":"2026-06-04T12:15:36.404Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Editing for clarity**\n\nI need to make some edits. First, I should add filter helpers in the analyzer and consider importing regex. It seems like it makes sense to import the `re` package in the service section. I'll want to find a suitable spot for that, likely near where I normalize the text. It's time to get these updates implemented, so let's dive in and make everything fit together nicely!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216c597cdc8191b6293def76d0175d\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWxb0RCVr4I5WG1oI2UuFy0ssqs5Il3OWwtH5uPAFGcsBZ3FUi_OCsc9lY98ixwekED78WOsP608YRQtLsfhsaqQtxm1Y6-qCcGgbuhf4u8VofHr8UTg1AHckPo0TJsPDsbnZP2_FAJKDgbwfOLGFFgpcol1QYBBl2Gx9vvuPiNZhnMhmZkYAWfqZd8cZA3pamZjHZDXejSl8dlHMBPzXFEj-7HlS2_111pfuJ78Ymw7G10buNtHk9E5ScqmWdY2z8Zci8pr-RcFJMJTFftZAAQ6O-FWCgfZoXrywzs6rEONMdytI_Ey4-0I8qAnfFcqz85zPGhaHsUm3YdDlkxbcC68-Rih09Ek-TzWZANj___f0hAiQ2FU8bHBWwNtv2ysuClANPrDJPs9kqQCsllbPvfX6AHhq93cruj3E3MMpYYPupVfrMhvjIXYGOh6CdpAHR3TY5_CAnMhP2UsJyWtg0qqGYSpc4YtczRvDhcjvF38eLSdaEXJ8jp2IS1_8Ti628LHla8iYgdEmIt-IcVAn_7qizAFROKh7CI6ucI81TbBBQ-wwpDk28TEZOs1mzAXbm_sB5rctmUFl5KWFuirb2spqQqIiGXLf7nPlOAt8exL4BuSd2_b1MvIJmRPDK2d_yz9SBwO6g-UAJjIXegav1YLx2SpXVwcAjOV9uFgyLU5KZbg--pMmoG1D3oYijwVRXJAL7R90XKuAtIbeVy6hhHZ3_SEEfKub7ck5OQkzDBbX41mKVlmng6UvBeFNjle0b65tR7naWiUFDT8FP_xHl0Fj2j4Cg-jzI6CEHT-Ri5YrpNLz216_uqafwoCLahh_VV3AqJFaC9SvuUxYJeDXM8j-xUhZLcnAg1qohjky90xnj7Zluo9R6dip2mf_9QO9CZ_v9PhQyNrXvmai0sW5ABsF_AxxbyViPVFyfGSYOp9_nHuhCpVwUSVhvShg6gPo3SFNOzFAnq8cyqkG36dTD8eSkDRl1HLB7i0hsQESUeHHsjLrEicZ4IXHX-scOGWbE2Pa7aGITw5ddPNzux4p73IvcyVo5zTlqJU3-cqe535taHkWpj9fgFOxv5E25t9tOjYVgAEtJc5xuYjXjQOUZgx830hwLmUwcPiycSlwAqkCpM=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Editing for clarity**\\n\\nI need to make some edits. First, I should add filter helpers in the analyzer and consider importing regex. It seems like it makes sense to import the `re` package in the service section. I'll want to find a suitable spot for that, likely near where I normalize the text. It's time to get these updates implemented, so let's dive in and make everything fit together nicely!\"}]}"},{"type":"toolCall","id":"call_XesWx1kYu0f6lbjRk6G6YKcO|fc_08205193eac5c3af016a216c5b731c8191ac545f4efec77ab4","name":"edit","arguments":{"path":"/home/sebas/pi-config/scripts/session-memory-analyzer.py","edits":[{"oldText":"import argparse\nimport json\nimport os\nimport shutil\nimport subprocess\nimport sys\nfrom pathlib import Path\nfrom typing import Any\nfrom urllib import request\n","newText":"import argparse\nimport json\nimport os\nimport re\nimport shutil\nimport subprocess\nimport sys\nfrom pathlib import Path\nfrom typing import Any\nfrom urllib import request\n"},{"oldText":"DURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\n","newText":"DURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\nNOISE_PATTERNS = [\n    re.compile(pattern, re.IGNORECASE)\n    for pattern in (\n        r\"you are running one deterministic\",\n        r\"phase-machine pass\",\n        r\"subagent intake task_\",\n        r\"^task id:\\s*task_\",\n        r\"^payload:\\s*\\{\",\n        r'\"route\"\\s*:\\s*\\{',\n        r\"you are iteration \",\n        r\"continue the bounded ralph-loop\",\n    )\n]\n"},{"oldText":"def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n","newText":"def is_noise_memory(candidate: dict[str, Any]) -> bool:\n    title = str(candidate.get(\"title\") or \"\")\n    content = str(candidate.get(\"content\") or \"\")\n    blob = f\"{title}\\n{content}\".strip()\n    if not blob:\n        return True\n    lowered = blob.lower()\n    if any(pattern.search(blob) for pattern in NOISE_PATTERNS):\n        return True\n    if lowered.count(\"task_\") >= 2:\n        return True\n    if lowered.count(\"payload:\") >= 1 and lowered.count(\"route\") >= 1:\n        return True\n    lines = [line.strip() for line in content.splitlines() if line.strip()]\n    if lines:\n        jsonish = sum(1 for line in lines if line.startswith((\"Payload:\", \"{\", \"}\", '\"')) or '\"' in line)\n        if jsonish / len(lines) >= 0.45:\n            return True\n    return False\n\n\ndef build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n"},{"oldText":"        for index, candidate in enumerate(candidates, start=1):\n            memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n            if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n                continue\n            saved_items.append(ingest(memory))\n","newText":"        for index, candidate in enumerate(candidates, start=1):\n            if is_noise_memory(candidate):\n                continue\n            memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n            if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n                continue\n            saved_items.append(ingest(memory))\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":64361,"output":820,"cacheRead":0,"cacheWrite":0,"totalTokens":65181,"cost":{"input":0.1609025,"output":0.0123,"cacheRead":0,"cacheWrite":0,"total":0.1732025}},"stopReason":"toolUse","timestamp":1780575320196,"responseId":"resp_08205193eac5c3af016a216c584e44819192310d0f82bb05dd"}}
{"type":"message","id":"356d2c11","parentId":"4b9d0dab","timestamp":"2026-06-04T12:15:36.471Z","message":{"role":"toolResult","toolCallId":"call_XesWx1kYu0f6lbjRk6G6YKcO|fc_08205193eac5c3af016a216c5b731c8191ac545f4efec77ab4","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 4 block(s) in /home/sebas/pi-config/scripts/session-memory-analyzer.py."}],"details":{"diff":"     ...\n   3 \n   4 import argparse\n   5 import json\n   6 import os\n+  7 import re\n   7 import shutil\n   8 import subprocess\n   9 import sys\n  10 from pathlib import Path\n     ...\n  20 MAX_CANDIDATES = int(os.environ.get(\"PI_SESSION_MEMORY_MAX_CANDIDATES\", \"6\"))\n  21 ACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD\", \"0.92\"))\n  22 ACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD\", \"0.9\"))\n  23 DURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\n+ 25 NOISE_PATTERNS = [\n+ 26     re.compile(pattern, re.IGNORECASE)\n+ 27     for pattern in (\n+ 28         r\"you are running one deterministic\",\n+ 29         r\"phase-machine pass\",\n+ 30         r\"subagent intake task_\",\n+ 31         r\"^task id:\\s*task_\",\n+ 32         r\"^payload:\\s*\\{\",\n+ 33         r'\"route\"\\s*:\\s*\\{',\n+ 34         r\"you are iteration \",\n+ 35         r\"continue the bounded ralph-loop\",\n+ 36     )\n+ 37 ]\n  24 \n  25 \n  26 def resolve_pi_bin() -> str:\n  27     candidates = [\n     ...\n 205         return \"active\"\n 206     return \"inbox\"\n 207 \n 208 \n+223 def is_noise_memory(candidate: dict[str, Any]) -> bool:\n+224     title = str(candidate.get(\"title\") or \"\")\n+225     content = str(candidate.get(\"content\") or \"\")\n+226     blob = f\"{title}\\n{content}\".strip()\n+227     if not blob:\n+228         return True\n+229     lowered = blob.lower()\n+230     if any(pattern.search(blob) for pattern in NOISE_PATTERNS):\n+231         return True\n+232     if lowered.count(\"task_\") >= 2:\n+233         return True\n+234     if lowered.count(\"payload:\") >= 1 and lowered.count(\"route\") >= 1:\n+235         return True\n+236     lines = [line.strip() for line in content.splitlines() if line.strip()]\n+237     if lines:\n+238         jsonish = sum(1 for line in lines if line.startswith((\"Payload:\", \"{\", \"}\", '\"')) or '\"' in line)\n+239         if jsonish / len(lines) >= 0.45:\n+240             return True\n+241     return False\n+242 \n+243 \n 209 def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n 210     memory_type = candidate.get(\"type\") or \"episode\"\n 211     confidence = float(candidate.get(\"confidence\") or 0.6)\n 212     importance = float(candidate.get(\"importance\") or 0.5)\n     ...\n 273         candidates = normalize_memories(analysis)[:MAX_CANDIDATES]\n 274         saved_items: list[dict[str, Any]] = []\n 275 \n 276         for index, candidate in enumerate(candidates, start=1):\n+312             if is_noise_memory(candidate):\n+313                 continue\n 277             memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n 278             if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n 279                 continue\n 280             saved_items.append(ingest(memory))\n     ...","patch":"--- /home/sebas/pi-config/scripts/session-memory-analyzer.py\n+++ /home/sebas/pi-config/scripts/session-memory-analyzer.py\n@@ -3,8 +3,9 @@\n \n import argparse\n import json\n import os\n+import re\n import shutil\n import subprocess\n import sys\n from pathlib import Path\n@@ -20,8 +21,21 @@\n MAX_CANDIDATES = int(os.environ.get(\"PI_SESSION_MEMORY_MAX_CANDIDATES\", \"6\"))\n ACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD\", \"0.92\"))\n ACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD\", \"0.9\"))\n DURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\n+NOISE_PATTERNS = [\n+    re.compile(pattern, re.IGNORECASE)\n+    for pattern in (\n+        r\"you are running one deterministic\",\n+        r\"phase-machine pass\",\n+        r\"subagent intake task_\",\n+        r\"^task id:\\s*task_\",\n+        r\"^payload:\\s*\\{\",\n+        r'\"route\"\\s*:\\s*\\{',\n+        r\"you are iteration \",\n+        r\"continue the bounded ralph-loop\",\n+    )\n+]\n \n \n def resolve_pi_bin() -> str:\n     candidates = [\n@@ -205,8 +219,29 @@\n         return \"active\"\n     return \"inbox\"\n \n \n+def is_noise_memory(candidate: dict[str, Any]) -> bool:\n+    title = str(candidate.get(\"title\") or \"\")\n+    content = str(candidate.get(\"content\") or \"\")\n+    blob = f\"{title}\\n{content}\".strip()\n+    if not blob:\n+        return True\n+    lowered = blob.lower()\n+    if any(pattern.search(blob) for pattern in NOISE_PATTERNS):\n+        return True\n+    if lowered.count(\"task_\") >= 2:\n+        return True\n+    if lowered.count(\"payload:\") >= 1 and lowered.count(\"route\") >= 1:\n+        return True\n+    lines = [line.strip() for line in content.splitlines() if line.strip()]\n+    if lines:\n+        jsonish = sum(1 for line in lines if line.startswith((\"Payload:\", \"{\", \"}\", '\"')) or '\"' in line)\n+        if jsonish / len(lines) >= 0.45:\n+            return True\n+    return False\n+\n+\n def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n     memory_type = candidate.get(\"type\") or \"episode\"\n     confidence = float(candidate.get(\"confidence\") or 0.6)\n     importance = float(candidate.get(\"importance\") or 0.5)\n@@ -273,8 +308,10 @@\n         candidates = normalize_memories(analysis)[:MAX_CANDIDATES]\n         saved_items: list[dict[str, Any]] = []\n \n         for index, candidate in enumerate(candidates, start=1):\n+            if is_noise_memory(candidate):\n+                continue\n             memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n             if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n                 continue\n             saved_items.append(ingest(memory))\n","firstChangedLine":7},"isError":false,"timestamp":1780575336471}}
{"type":"message","id":"eaf49ab7","parentId":"356d2c11","timestamp":"2026-06-04T12:15:54.500Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_5yQ5qbQLIl66QXudlOTb5eX3|fc_08205193eac5c3af016a216c6970c88191a7721e1ec7340b5e","name":"edit","arguments":{"path":"/home/sebas/agents-database/src/shared_agent_memory/service.py","edits":[{"oldText":"import json\nimport math\nimport subprocess\nimport uuid\n","newText":"import json\nimport math\nimport re\nimport subprocess\nimport uuid\n"},{"oldText":"def normalize_text(value: str) -> str:\n    return \" \".join(value.lower().split())\n","newText":"def normalize_text(value: str) -> str:\n    return \" \".join(value.lower().split())\n\n\ndef normalize_for_dedupe(value: str) -> str:\n    text = value.lower()\n    text = re.sub(r\"\\[telegram\\]\", \" \", text)\n    text = re.sub(r\"https?://\\S+\", \" URL \", text)\n    text = re.sub(r\"\\b(task|run|job|session)_[a-z0-9_-]+\\b\", \" ID \", text)\n    text = re.sub(r\"\\b\\d{4}-\\d{2}-\\d{2}[t ][^\\s]+\\b\", \" TS \", text)\n    text = re.sub(r\"payload:\\s*\\{.*\", \" payload_json \", text, flags=re.IGNORECASE | re.DOTALL)\n    text = re.sub(r'\"route\"\\s*:\\s*\\{.*', \" route_json \", text, flags=re.IGNORECASE | re.DOTALL)\n    text = re.sub(r\"[^\\w\\s]\", \" \", text)\n    return \" \".join(text.split())\n"},{"oldText":"                best_match = None\n                best_score = 0.0\n                for other in related_memories:\n                    lexical = self._lexical_score(\n                        normalize_text(candidate[\"content\"]).split(),\n                        normalize_text(other[\"content\"]),\n                    )\n                    semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n                    if score > best_score:\n                        best_score = score\n                        best_match = other\n\n                if best_match and candidate[\"content\"] == best_match[\"content\"]:\n","newText":"                candidate_norm = normalize_for_dedupe(candidate[\"content\"])\n                best_match = None\n                best_score = 0.0\n                for other in related_memories:\n                    other_norm = normalize_for_dedupe(other[\"content\"])\n                    lexical = self._lexical_score(candidate_norm.split(), other_norm)\n                    semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n                    if score > best_score:\n                        best_score = score\n                        best_match = other\n\n                if best_match and candidate_norm == normalize_for_dedupe(best_match[\"content\"]):\n"},{"oldText":"                if candidate[\"status\"] == \"inbox\" and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"active\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"promoted\"] += 1\n                elif candidate[\"status\"] == \"active\" and candidate[\"type\"] in {\"episode\", \"artifact\", \"decision\", \"project\", \"procedural\", \"semantic\"} and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    # Normalize qualifying active records as already-promoted durable memories.\n                    stats[\"promoted\"] += 1\n","newText":"                if candidate[\"status\"] == \"inbox\" and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n                    conn.execute(\n                        \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                        (\"active\", utcnow(), candidate[\"id\"]),\n                    )\n                    stats[\"promoted\"] += 1\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1212,"output":947,"cacheRead":64000,"cacheWrite":0,"totalTokens":66159,"cost":{"input":0.00303,"output":0.014205,"cacheRead":0.016,"cacheWrite":0,"total":0.033235}},"stopReason":"toolUse","timestamp":1780575336473,"responseId":"resp_08205193eac5c3af016a216c68908881918ea4743cd8f7b3ce"}}
{"type":"message","id":"d97daf70","parentId":"eaf49ab7","timestamp":"2026-06-04T12:15:54.584Z","message":{"role":"toolResult","toolCallId":"call_5yQ5qbQLIl66QXudlOTb5eX3|fc_08205193eac5c3af016a216c6970c88191a7721e1ec7340b5e","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 4 block(s) in /home/sebas/agents-database/src/shared_agent_memory/service.py."}],"details":{"diff":"    1 from __future__ import annotations\n    2 \n    3 import json\n    4 import math\n+   5 import re\n    5 import subprocess\n    6 import uuid\n    7 from dataclasses import dataclass\n    8 from datetime import UTC, datetime, timedelta\n      ...\n   24 def normalize_text(value: str) -> str:\n   25     return \" \".join(value.lower().split())\n   26 \n   27 \n+  29 def normalize_for_dedupe(value: str) -> str:\n+  30     text = value.lower()\n+  31     text = re.sub(r\"\\[telegram\\]\", \" \", text)\n+  32     text = re.sub(r\"https?://\\S+\", \" URL \", text)\n+  33     text = re.sub(r\"\\b(task|run|job|session)_[a-z0-9_-]+\\b\", \" ID \", text)\n+  34     text = re.sub(r\"\\b\\d{4}-\\d{2}-\\d{2}[t ][^\\s]+\\b\", \" TS \", text)\n+  35     text = re.sub(r\"payload:\\s*\\{.*\", \" payload_json \", text, flags=re.IGNORECASE | re.DOTALL)\n+  36     text = re.sub(r'\"route\"\\s*:\\s*\\{.*', \" route_json \", text, flags=re.IGNORECASE | re.DOTALL)\n+  37     text = re.sub(r\"[^\\w\\s]\", \" \", text)\n+  38     return \" \".join(text.split())\n+  39 \n+  40 \n   28 def _line_value(content: str, label: str) -> str:\n   29     prefix = f\"{label}:\"\n   30     for line in content.splitlines():\n   31         if line.startswith(prefix):\n      ...\n  367                     (candidate[\"id\"], candidate[\"type\"], candidate[\"scope\"]),\n  368                 ).fetchall()\n  369                 related_memories = [self._row_to_memory(row) for row in related]\n  370 \n+ 384                 candidate_norm = normalize_for_dedupe(candidate[\"content\"])\n  371                 best_match = None\n  372                 best_score = 0.0\n  373                 for other in related_memories:\n- 374                     lexical = self._lexical_score(\n- 375                         normalize_text(candidate[\"content\"]).split(),\n- 376                         normalize_text(other[\"content\"]),\n- 377                     )\n+ 388                     other_norm = normalize_for_dedupe(other[\"content\"])\n+ 389                     lexical = self._lexical_score(candidate_norm.split(), other_norm)\n  378                     semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n  379                     score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n  380                     if score > best_score:\n  381                         best_score = score\n  382                         best_match = other\n  383 \n- 384                 if best_match and candidate[\"content\"] == best_match[\"content\"]:\n+ 396                 if best_match and candidate_norm == normalize_for_dedupe(best_match[\"content\"]):\n  385                     self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"exact_duplicate\")\n  386                     conn.execute(\n  387                         \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n  388                         (\"archived\", utcnow(), candidate[\"id\"]),\n      ...\n  440                         \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n  441                         (\"active\", utcnow(), candidate[\"id\"]),\n  442                     )\n  443                     stats[\"promoted\"] += 1\n- 444                 elif candidate[\"status\"] == \"active\" and candidate[\"type\"] in {\"episode\", \"artifact\", \"decision\", \"project\", \"procedural\", \"semantic\"} and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n- 445                     # Normalize qualifying active records as already-promoted durable memories.\n- 446                     stats[\"promoted\"] += 1\n  447 \n  448         return stats\n  449 \n  450     def upsert_profile_fact(\n      ...","patch":"--- /home/sebas/agents-database/src/shared_agent_memory/service.py\n+++ /home/sebas/agents-database/src/shared_agent_memory/service.py\n@@ -1,8 +1,9 @@\n from __future__ import annotations\n \n import json\n import math\n+import re\n import subprocess\n import uuid\n from dataclasses import dataclass\n from datetime import UTC, datetime, timedelta\n@@ -24,8 +25,20 @@\n def normalize_text(value: str) -> str:\n     return \" \".join(value.lower().split())\n \n \n+def normalize_for_dedupe(value: str) -> str:\n+    text = value.lower()\n+    text = re.sub(r\"\\[telegram\\]\", \" \", text)\n+    text = re.sub(r\"https?://\\S+\", \" URL \", text)\n+    text = re.sub(r\"\\b(task|run|job|session)_[a-z0-9_-]+\\b\", \" ID \", text)\n+    text = re.sub(r\"\\b\\d{4}-\\d{2}-\\d{2}[t ][^\\s]+\\b\", \" TS \", text)\n+    text = re.sub(r\"payload:\\s*\\{.*\", \" payload_json \", text, flags=re.IGNORECASE | re.DOTALL)\n+    text = re.sub(r'\"route\"\\s*:\\s*\\{.*', \" route_json \", text, flags=re.IGNORECASE | re.DOTALL)\n+    text = re.sub(r\"[^\\w\\s]\", \" \", text)\n+    return \" \".join(text.split())\n+\n+\n def _line_value(content: str, label: str) -> str:\n     prefix = f\"{label}:\"\n     for line in content.splitlines():\n         if line.startswith(prefix):\n@@ -367,22 +380,21 @@\n                     (candidate[\"id\"], candidate[\"type\"], candidate[\"scope\"]),\n                 ).fetchall()\n                 related_memories = [self._row_to_memory(row) for row in related]\n \n+                candidate_norm = normalize_for_dedupe(candidate[\"content\"])\n                 best_match = None\n                 best_score = 0.0\n                 for other in related_memories:\n-                    lexical = self._lexical_score(\n-                        normalize_text(candidate[\"content\"]).split(),\n-                        normalize_text(other[\"content\"]),\n-                    )\n+                    other_norm = normalize_for_dedupe(other[\"content\"])\n+                    lexical = self._lexical_score(candidate_norm.split(), other_norm)\n                     semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n                     score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n                     if score > best_score:\n                         best_score = score\n                         best_match = other\n \n-                if best_match and candidate[\"content\"] == best_match[\"content\"]:\n+                if best_match and candidate_norm == normalize_for_dedupe(best_match[\"content\"]):\n                     self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"exact_duplicate\")\n                     conn.execute(\n                         \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                         (\"archived\", utcnow(), candidate[\"id\"]),\n@@ -440,11 +452,8 @@\n                         \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n                         (\"active\", utcnow(), candidate[\"id\"]),\n                     )\n                     stats[\"promoted\"] += 1\n-                elif candidate[\"status\"] == \"active\" and candidate[\"type\"] in {\"episode\", \"artifact\", \"decision\", \"project\", \"procedural\", \"semantic\"} and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n-                    # Normalize qualifying active records as already-promoted durable memories.\n-                    stats[\"promoted\"] += 1\n \n         return stats\n \n     def upsert_profile_fact(\n","firstChangedLine":5},"isError":false,"timestamp":1780575354583}}
{"type":"message","id":"84454354","parentId":"d97daf70","timestamp":"2026-06-04T12:15:58.007Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering Python compilation**\n\nI’m thinking about needing `py_compile`, which is a module for compiling Python source files to bytecode. Maybe I should also consider a dry run, which helps test the code without actually executing it, just to check for errors. I want to ensure I'm prepared for whatever the user needs. It seems like having this toolset could streamline my process and make it easier to troubleshoot any issues that come up.","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216c7b49288191a63984b7d95139cc\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWx9p2lZs_A0QVIx5fgSqCDqrX9h3HtS3Np9ORPCW-n28XwPoiWsIaxlSysNEt5uLND3WuydykNSvSuoB1hREIVzSKKMErzLIQBd6gNvPne-WBHTQsWFaejCpRWjBs7oM4EENyu0kIlBFStT7shrahvHBdQY5HBlUfVbXkcsdbPEpLWD95RAlGrGz-wzyd0y4TuRK5ABy7ZIELO6-Z_XLQXNohnTumdUVFBkQGBwVWsrFCRDoQRoJx60GYqfVmrMQAVimNgC4sc48vIHrSZFpVRz7GFtfcjLOkKLn1uAAxYpx5aYUI5JwlwWQxeKrXzuHS6dheg4cAYqA7Bzl2HbXYT6A5AduSETql9jsdUKwINwqfgT6hG9-fFUjb-9-yWlNZIWl1Sl5adEUFVgeNw3HNM8ZB04RfNrXQ8YRJgfMJZSyOlDzpG56eqsMX8LPM_1A8GsfJT-L-WLif5v0TtbjWrFf8AgW-05o4IwEEYvIft85PXzeMrsnGrcImjnwe-8H5xqfQMRj4N-x9A6FFivYJ874BR4uzESWQLqkx20mo0ZdjoDbgkxhQw_ismLo8kb_e8cHzbQW2WDazytDqkvGkKWIjQ9boPvoFXMHWCiS9BRQCIoyt8NgRLxcaw_b7IIl0Wy4U6rUPgbu_L4b7g2BvoDCgMmJqOkp_5RqruOAbnh7zQYysJhC0zdsVXIWwDtPcYgiKpdDrR0YSox__vUe1_pspvxpZ6oOI1GLGjDYuTAsEKK-ddc-vTU7-L2_3fDeFUSwI5wd6mcuJM-77xMH7r68NY3EYIBxnc_NO6LkddsR_ey8jMFtAo4yrFv5afaf-t4Co_RhBXAGG9bYJnqeCcKh-wo3YYRIIRDoNa3xJ2_qVlvR5gjq7MYGSIOUWamnwEfW8MTWQmLnCrBOoSWSDFAdEmX3-xXykSTRSzHAs4zJ9QCJ2tkAzpTiTX3eToDylSF2Zie-ETpvXmDvZQxNansqQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering Python compilation**\\n\\nI’m thinking about needing `py_compile`, which is a module for compiling Python source files to bytecode. Maybe I should also consider a dry run, which helps test the code without actually executing it, just to check for errors. I want to ensure I'm prepared for whatever the user needs. It seems like having this toolset could streamline my process and make it easier to troubleshoot any issues that come up.\"}]}"},{"type":"toolCall","id":"call_2vfS3LXzInKok8dPG5CKw4w8|fc_08205193eac5c3af016a216c7df0c8819198be99f2e5769fa8","name":"bash","arguments":{"command":"python3 -m py_compile /home/sebas/pi-config/scripts/session-memory-analyzer.py /home/sebas/agents-database/src/shared_agent_memory/service.py","timeout":20}},{"type":"toolCall","id":"call_hLQ7apYH9kms8tkrZQfuGv9d|fc_08205193eac5c3af016a216c7df0e481918c886610975db8e3","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport runpy\nns=runpy.run_path('/home/sebas/pi-config/scripts/session-memory-analyzer.py')\nprint('loaded analyzer ok')\nPY","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":1168,"output":142,"cacheRead":65024,"cacheWrite":0,"totalTokens":66334,"cost":{"input":0.0029200000000000003,"output":0.00213,"cacheRead":0.016256,"cacheWrite":0,"total":0.021306}},"stopReason":"toolUse","timestamp":1780575354585,"responseId":"resp_08205193eac5c3af016a216c7ab1b481919fd04707490f657f"}}
{"type":"message","id":"aeec36a6","parentId":"84454354","timestamp":"2026-06-04T12:15:58.142Z","message":{"role":"toolResult","toolCallId":"call_2vfS3LXzInKok8dPG5CKw4w8|fc_08205193eac5c3af016a216c7df0c8819198be99f2e5769fa8","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1780575358142}}
{"type":"message","id":"3a08d0d4","parentId":"aeec36a6","timestamp":"2026-06-04T12:15:58.142Z","message":{"role":"toolResult","toolCallId":"call_hLQ7apYH9kms8tkrZQfuGv9d|fc_08205193eac5c3af016a216c7df0e481918c886610975db8e3","toolName":"bash","content":[{"type":"text","text":"loaded analyzer ok\n"}],"isError":false,"timestamp":1780575358142}}
{"type":"message","id":"f2648e27","parentId":"3a08d0d4","timestamp":"2026-06-04T12:16:20.576Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Testing dry run logic**\n\nI think I need to test the updated logic live, but without applying it just yet. Maybe I can run a dry run with a script to import the service and call against a copy. That feels safer! I'll mimic the new normalize_for_dedupe process and avoid using any active-promoted inflation in this dry run. Custom code from the service might also be a good option for this setup. It’s all about ensuring accuracy!","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216c813d808191a4719b6aeee207b0\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWyEpQfa05uUCHZ8qHFDIuwAjP5GOGPioouAfGKLJzk72jJAR1Li3voYgercb5ADG6FNmtQ0eWum0ORwPtSvL99qxtCk6TjgU8SViIZzpj07khAPlRsw8Gy76bor_DlLLHhVjJzoA1lXLqHs1Fj5m_Rb_L4oOl72TZz7Qk3jVaQnSFE4xVokkbSFD9THHM8REUURNCDxlBgXoHPdSPHE4AEr5YoyhhgdRZiab6zDjZXnzVQJOd4Lpher6iN0CoN9ofAH_WrmJJeMAJTd-09BAOCw6UZ-E0ip2S1pBN9lSbEgXCyK3nMdBSt3S5VdAeAR1OLC6XB1t5AR9ZhR2Jj-GuYQq270tcFCk0MhFoLleUchTl_mNPjaVAYzTUINWaLC28gBFmNwE-GXplzNk0ZcysE6HwiE6_mzO_m8xB73s1QVEnxCRQ3swOpZkDk-fXkvJYl_BjlJeBdR3fSGOnQD3zrz8evBjV_l6VdImrPaChWXOIrUk8MVCLiHSzduTObs0z8-L4aR1MsN0sJYCE_JS3W9p3sGr7iHmdYGeeYxhHspHnKSbfA2tiD_LgPXqZjpRAmoE_zXexJnkaE6LNVNILtgO4Wu9BqDIPxkqYUgGuTEBIWhQaS4bD9V4R4GdngwFf4vBYdLKFE106fwXsswKSVlawkU_i-qWfnLyBMu1d83WdDcFOXOT23r2U7-5L3MbLWz8Ez9hKhiqM6aW7NuOVDIVh2Npl9wjhhewFflPH-xgkL62cQINPDGcOUNhseIqSkwkGMPF3JQsfn9bqpL1vFwMsG65EwN-_N2Ua2w7LrZrJrGc-Jc-b-7BJQsq98pzXSBUVvVBe7gDQRlmukKZUUYrovIAAVCa6hS_GVIgDNnWH7ikv5Ynk9YVVHIXhc7yiOUTXSgUWUIssYj6M8xBeFkuj9bO-vlj-jSUr7OucubSZPvHF6ObBQnIGsm4A4TkNPjuMX6GRkSvRkz3EscCeYJ0OdHmk5CYc6a8ZTU6M1ct-OU0D_fOpxyW51KKb0f_VkaPe8t4vb522Yh7Z3b4xmJMgfCk35yHugR58Pjcl618N-4Od2dv4BHB2tZOjhTT2dQr0BcDcDsCtWnfexaY8RoT8tqIpZuNz6DewyZAUE_TQSswu4InibiHVdU07tdNyz8pvcRkfCGvkyJqp-TBbz0bh-Nalhdpx9tbJ8TMmB7X5jPCo6YKsY1Uvltthj54JqmSy0JTJ7S0XT10t6GXLjfbio-VjC-JuDnHxJraqC7WMziP79sp8JaEp0kJ8HmftAv\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Testing dry run logic**\\n\\nI think I need to test the updated logic live, but without applying it just yet. Maybe I can run a dry run with a script to import the service and call against a copy. That feels safer! I'll mimic the new normalize_for_dedupe process and avoid using any active-promoted inflation in this dry run. Custom code from the service might also be a good option for this setup. It’s all about ensuring accuracy!\"}]}"},{"type":"toolCall","id":"call_UhoN7vdsSJFAHjRE5sPrxZG1|fc_08205193eac5c3af016a216c84c0108191875ff311af189766","name":"bash","arguments":{"command":"python3 - <<'PY'\nimport json, sqlite3, sys\nfrom datetime import UTC, datetime, timedelta\nsys.path.insert(0, '/home/sebas/agents-database/src')\nfrom shared_agent_memory.service import normalize_for_dedupe, MemoryService\n\np='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'\nsvc=MemoryService(p)\ncon=sqlite3.connect(p)\ncon.row_factory=sqlite3.Row\ncur=con.cursor()\nconfig={\n    'candidate_age_days':0,\n    'max_candidates':200,\n    'dedupe_high_threshold':0.9,\n    'dedupe_mid_threshold':0.75,\n    'promote_confidence':0.7,\n    'conflict_threshold':0.55,\n    'llm_enabled':False,\n}\ncutoff=(datetime.now(UTC)-timedelta(days=int(config['candidate_age_days']))).isoformat()\nrows=cur.execute(\"\"\"\nSELECT * FROM memories\nWHERE (status='inbox' OR (type='episode' AND status='active'))\n  AND created_at <= ?\nORDER BY created_at ASC\nLIMIT ?\n\"\"\", (cutoff, int(config['max_candidates']))).fetchall()\ncandidates=[svc._row_to_memory(r) for r in rows]\nstats={'candidates':len(candidates),'promoted':0,'archived':0,'linked':0,'conflicts':0}\nexamples={'archived':[],'linked':[]}\nfor candidate in candidates:\n    related=cur.execute(\"\"\"\n    SELECT * FROM memories WHERE status='active' AND id != ? AND type = ? AND scope = ?\n    ORDER BY updated_at DESC LIMIT 50\n    \"\"\", (candidate['id'], candidate['type'], candidate['scope'])).fetchall()\n    related=[svc._row_to_memory(r) for r in related]\n    candidate_norm=normalize_for_dedupe(candidate['content'])\n    best_match=None; best_score=0.0\n    for other in related:\n        other_norm=normalize_for_dedupe(other['content'])\n        lexical=svc._lexical_score(candidate_norm.split(), other_norm)\n        semantic=svc._semantic_score(candidate.get('embedding') or [], other.get('embedding'))\n        score=lexical if semantic==0.0 else (lexical*0.6+semantic*0.4)\n        if score>best_score:\n            best_score=score; best_match=other\n    if best_match and candidate_norm == normalize_for_dedupe(best_match['content']):\n        stats['archived'] += 1; stats['linked'] += 1\n        if len(examples['archived'])<5:\n            examples['archived'].append((candidate['id'], best_match['id'], candidate['title'][:80], best_match['title'][:80]))\n        continue\n    if best_match and best_score >= float(config['dedupe_high_threshold']):\n        stats['archived'] += 1; stats['linked'] += 1\n        if len(examples['archived'])<5:\n            examples['archived'].append((candidate['id'], best_match['id'], candidate['title'][:80], best_match['title'][:80]))\n        continue\n    if best_match and best_score >= float(config['dedupe_mid_threshold']):\n        stats['linked'] += 1\n        if len(examples['linked'])<5:\n            examples['linked'].append((candidate['id'], best_match['id'], round(best_score,3), candidate['title'][:80], best_match['title'][:80]))\n    if best_match and best_score >= float(config['conflict_threshold']) and svc._conflict_signal(candidate, best_match):\n        stats['conflicts'] += 1\n    if candidate['status']=='inbox' and candidate.get('confidence',0.0) >= float(config['promote_confidence']):\n        stats['promoted'] += 1\nprint(json.dumps(stats, indent=2))\nprint('archived_examples', json.dumps(examples['archived'], ensure_ascii=False, indent=2))\nprint('linked_examples', json.dumps(examples['linked'], ensure_ascii=False, indent=2))\nPY","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":6969,"output":1024,"cacheRead":59392,"cacheWrite":0,"totalTokens":67385,"cost":{"input":0.0174225,"output":0.01536,"cacheRead":0.014848,"cacheWrite":0,"total":0.0476305}},"stopReason":"toolUse","timestamp":1780575358144,"responseId":"resp_08205193eac5c3af016a216c7e66a08191ae730c16eedfdbda"}}
{"type":"message","id":"47f1ac93","parentId":"f2648e27","timestamp":"2026-06-04T12:16:21.212Z","message":{"role":"toolResult","toolCallId":"call_UhoN7vdsSJFAHjRE5sPrxZG1|fc_08205193eac5c3af016a216c84c0108191875ff311af189766","toolName":"bash","content":[{"type":"text","text":"{\n  \"candidates\": 67,\n  \"promoted\": 0,\n  \"archived\": 4,\n  \"linked\": 9,\n  \"conflicts\": 0\n}\narchived_examples [\n  [\n    \"legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7\",\n    \"legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff\",\n    \"Research run: Source 50+ inch China displays for Ballbox advertising in padel ve\",\n    \"Research run: Find China-sourced 50+ inch displays for Ballbox advertising in co\"\n  ],\n  [\n    \"mem_04dc02a90cad40cc826ed1e8f93aff36\",\n    \"mem_17eea5c0658f4cd3beece90a93d35e36\",\n    \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\",\n    \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\"\n  ],\n  [\n    \"mem_17eea5c0658f4cd3beece90a93d35e36\",\n    \"mem_04dc02a90cad40cc826ed1e8f93aff36\",\n    \"Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726\",\n    \"Subagent intake task_ed6a38c1b49f4e829e067d49d206148b\"\n  ],\n  [\n    \"mem_89d7f9db9cb04d0da7539c33e3ccc024\",\n    \"mem_e80d2be0aecc4eed84d5aa179f83bfdd\",\n    \"pi-config: You are running one deterministic phase-machine pass. Task folder: /h\",\n    \"pi-config: You are running one deterministic phase-machine pass. Task folder: /h\"\n  ]\n]\nlinked_examples [\n  [\n    \"legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb\",\n    \"legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7\",\n    0.888,\n    \"Research run: Drive Ballbox payment integration toward Volvox-style demo on vend\",\n    \"Research run: Drive ballbox-payment-integration to a demoable vending-machine QR\"\n  ],\n  [\n    \"legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7\",\n    \"legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7\",\n    0.768,\n    \"Research run: Drive ballbox-payment-integration to a demoable vending-machine QR\",\n    \"Research run: Analyze the best next steps to make ballbox-payment-integration pr\"\n  ],\n  [\n    \"legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54\",\n    \"legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f\",\n    0.852,\n    \"Research run: Production-readiness plan for ballbox-payment-integration\",\n    \"Research run: Produce a production-readiness plan for ballbox-payment-integratio\"\n  ],\n  [\n    \"mem_a986376af1554bec8e3909c656585bb6\",\n    \"mem_e80d2be0aecc4eed84d5aa179f83bfdd\",\n    0.882,\n    \"pi-config: You are running one deterministic phase-machine pass. Task folder: /h\",\n    \"pi-config: You are running one deterministic phase-machine pass. Task folder: /h\"\n  ],\n  [\n    \"mem_e80d2be0aecc4eed84d5aa179f83bfdd\",\n    \"mem_a986376af1554bec8e3909c656585bb6\",\n    0.878,\n    \"pi-config: You are running one deterministic phase-machine pass. Task folder: /h\",\n    \"pi-config: You are running one deterministic phase-machine pass. Task folder: /h\"\n  ]\n]\n"}],"isError":false,"timestamp":1780575381211}}
{"type":"message","id":"20ef9160","parentId":"47f1ac93","timestamp":"2026-06-04T12:16:26.493Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216c95db848191bf3f654ecb3f4b47\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWyaE0Xphr01aCLfxSfCeVg18DhVZ_NtNuKi6oyqGzCL7uxJmqZWaw6krgSGc0xwmUJC1CQgK2oVCeY3ZsdtoHZtzA-AzaKL1EZuehFjZI29wp7Jmcn7PFJ9kNLrkR91OEPx_7PdWyMvtSIDkGdVlQGj5-E_Hxku5wXBq_xNzXTCfXyAjgNwMBUhKFs0kDfS9S3fuD2P7_NAxrqIM_rXA2Dn0LoMLk0i5tsdX-spROKUPym5-xcrPfmO2m6-I2SPDZ98PV9aMO1sDCZ2PSn1AddZKuESSNFkcqaNVsSvTBiExAuKmPiBZPalH3d0m5qRIlgg0d2-OHjVHfzLfIUOeQh7AIFgoSs6wKIkaeX6V8Zz2m_59V3Ivd3ma8TWs8eBc36-nHPtfG82dy46Yem28nX37HxkUFGwfmTHeI8XK5znYNJVHqK2vVz0QKBAcklRHx610NAJJXnky3ZOWeyQV8A1PMUU5kuGh5NvaUmY4f_PhHfiHSwSwagvvAnJ2iy02oHm4u3PT5LSQWZ2Cioe_A0Y6z1p_cBOOsg3ElLf9PTWCkRQZVnZ1F-4mZH5TCyZ1SM_l017dZeIRQ2y_V40ZNtVGHLJSNJeSL5WbathLPgKEOLGWgUrV1AAYbytZwlZBMeZzh5FvqaOUx-sqYxrkIXf4yczybTTuIdxBa2uzWBaE4cm-wnp0g6J5lW1xDK7XHlZ6mMT_Z6baYmUl6-oNWU5BKt63tOlHpidC3XBOrpflG5bcTH1q0COlYYEDA6qt-XOXwYVJkpRm_9nLt7ld8ZgPweFplhq_qEQ93B9GrQrnWhCBsPdNFtwW42XphYTZO9Z-B8xHLC-HTvIiCBw81YIv27MT2xgx3U2sVShGZmYnhd666ETEKe_KaudBgOQpu6Z1MGPKUP9RaTai480rjnnB1kusDVGZfOWgQBOQtIxC4k6vmwrMihIcarni2jQd0Nr_nFTNvTdsMhpUYpMcHpdGLdaApWszdP8vKH27s4CLDZWKVtCl_6IAyfbkmz0MKvNVpmS9Ewt3oCxkf9_4aL8pCnRvshvjIP7-SKV1oLHbYpOtbcjPIanEMa5oKCHabzTkkgdZeT_2yWOT98Fxp9K9osUMCNhjlEcReiIl3UfYeZx69BGCQDi0D4rrlr0lkS5sXUr2wUEJTuUkevKurkJSpM-JR9y5XUBZK2b165FoS74iSwC8FCj8QtnLZToBr2VWPUTarcUEEJMFQFlYGWF-jV4JMYhuDTJxUHQebTPvHqwcynKUmTiFzbDybw1IuQYcat_fLd030rFRVebR33rVwDJtsrWqxlOSmIC0Sj0TZ9ZIVIEL3EQGvm6VW-Tmid84ptFlKj8ed1aQhKmB52K9_zPJtr3vfoJNB-63tm6UF3qBkBZhK47fcNconqUIXlcJpAzj88Yruy-q3CfnTQWLavIAH8KuzYDcPPxoYyGb-9o7m6najAL_6bqUHqT_OPHS7PlitHoO6osGHgjQbt1kFx_TEce8kMBZhww0o-NOSnRhbnnAw0tNtKxZXthJ2QC8MY_aJYert8VKbBwgGnTZ0z1-bD3X2D-yqIgPbYPg7PBvr95yhGKOpaaaYKZ1O_085nJmUsNJUA-dK4pwo9D8BaysIWfPWhIYN2pVMIb4h6rParyWu1ebp1ScDF1LhDpzJTYmvtnLZuCFKMKDPEJTsb8zKMOKlwez1VD6GO_dUuX-OJGx5V-6otDEuK_bnQ2\",\"summary\":[]}"},{"type":"toolCall","id":"call_3wonFkVdQostR0jVWFFN2GlA|fc_08205193eac5c3af016a216c9a63e88191a5ba06900187a686","name":"bash","arguments":{"command":"systemctl --user list-units --type=service --all | grep 'agents-database\\|session-memory'","timeout":20}},{"type":"toolCall","id":"call_ve1CBXZt2YTPqM304MZ4V11W|fc_08205193eac5c3af016a216c9a63f881918d24a026b51b8f5c","name":"bash","arguments":{"command":"cd /home/sebas/agents-database && git diff -- src/shared_agent_memory/service.py && echo '---' && cd /home/sebas/pi-config && git diff -- scripts/session-memory-analyzer.py","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":2309,"output":233,"cacheRead":66048,"cacheWrite":0,"totalTokens":68590,"cost":{"input":0.005772500000000001,"output":0.0034950000000000003,"cacheRead":0.016512,"cacheWrite":0,"total":0.0257795}},"stopReason":"toolUse","timestamp":1780575381213,"responseId":"resp_08205193eac5c3af016a216c954e208191880fb0907e6fdd84"}}
{"type":"message","id":"890162ab","parentId":"20ef9160","timestamp":"2026-06-04T12:16:26.543Z","message":{"role":"toolResult","toolCallId":"call_3wonFkVdQostR0jVWFFN2GlA|fc_08205193eac5c3af016a216c9a63e88191a5ba06900187a686","toolName":"bash","content":[{"type":"text","text":"  agents-database-http.service                 loaded    active   running Agents Database HTTP API\n  agents-database-maintenance.service          loaded    active   running Agents Database Maintenance Daemon\n  install-session-memory-dispatcher.service    loaded    active   running Pi session memory dispatcher\n  session-memory-sweeper.service               loaded    inactive dead    Pi session memory sweeper\n"}],"isError":false,"timestamp":1780575386543}}
{"type":"message","id":"5889df1c","parentId":"890162ab","timestamp":"2026-06-04T12:16:26.543Z","message":{"role":"toolResult","toolCallId":"call_ve1CBXZt2YTPqM304MZ4V11W|fc_08205193eac5c3af016a216c9a63f881918d24a026b51b8f5c","toolName":"bash","content":[{"type":"text","text":"diff --git a/src/shared_agent_memory/service.py b/src/shared_agent_memory/service.py\nindex 1d4c1c4..d3d4961 100644\n--- a/src/shared_agent_memory/service.py\n+++ b/src/shared_agent_memory/service.py\n@@ -2,6 +2,7 @@ from __future__ import annotations\n \n import json\n import math\n+import re\n import subprocess\n import uuid\n from dataclasses import dataclass\n@@ -25,6 +26,18 @@ def normalize_text(value: str) -> str:\n     return \" \".join(value.lower().split())\n \n \n+def normalize_for_dedupe(value: str) -> str:\n+    text = value.lower()\n+    text = re.sub(r\"\\[telegram\\]\", \" \", text)\n+    text = re.sub(r\"https?://\\S+\", \" URL \", text)\n+    text = re.sub(r\"\\b(task|run|job|session)_[a-z0-9_-]+\\b\", \" ID \", text)\n+    text = re.sub(r\"\\b\\d{4}-\\d{2}-\\d{2}[t ][^\\s]+\\b\", \" TS \", text)\n+    text = re.sub(r\"payload:\\s*\\{.*\", \" payload_json \", text, flags=re.IGNORECASE | re.DOTALL)\n+    text = re.sub(r'\"route\"\\s*:\\s*\\{.*', \" route_json \", text, flags=re.IGNORECASE | re.DOTALL)\n+    text = re.sub(r\"[^\\w\\s]\", \" \", text)\n+    return \" \".join(text.split())\n+\n+\n def _line_value(content: str, label: str) -> str:\n     prefix = f\"{label}:\"\n     for line in content.splitlines():\n@@ -368,20 +381,19 @@ class MemoryService:\n                 ).fetchall()\n                 related_memories = [self._row_to_memory(row) for row in related]\n \n+                candidate_norm = normalize_for_dedupe(candidate[\"content\"])\n                 best_match = None\n                 best_score = 0.0\n                 for other in related_memories:\n-                    lexical = self._lexical_score(\n-                        normalize_text(candidate[\"content\"]).split(),\n-                        normalize_text(other[\"content\"]),\n-                    )\n+                    other_norm = normalize_for_dedupe(other[\"content\"])\n+                    lexical = self._lexical_score(candidate_norm.split(), other_norm)\n                     semantic = self._semantic_score(candidate.get(\"embedding\") or [], other.get(\"embedding\"))\n                     score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)\n                     if score > best_score:\n                         best_score = score\n                         best_match = other\n \n-                if best_match and candidate[\"content\"] == best_match[\"content\"]:\n+                if best_match and candidate_norm == normalize_for_dedupe(best_match[\"content\"]):\n                     self._link_memories(conn, candidate[\"id\"], best_match[\"id\"], \"related_to\", \"exact_duplicate\")\n                     conn.execute(\n                         \"UPDATE memories SET status = ?, updated_at = ? WHERE id = ?\",\n@@ -441,9 +453,6 @@ class MemoryService:\n                         (\"active\", utcnow(), candidate[\"id\"]),\n                     )\n                     stats[\"promoted\"] += 1\n-                elif candidate[\"status\"] == \"active\" and candidate[\"type\"] in {\"episode\", \"artifact\", \"decision\", \"project\", \"procedural\", \"semantic\"} and candidate.get(\"confidence\", 0.0) >= promote_threshold:\n-                    # Normalize qualifying active records as already-promoted durable memories.\n-                    stats[\"promoted\"] += 1\n \n         return stats\n \n---\ndiff --git a/scripts/session-memory-analyzer.py b/scripts/session-memory-analyzer.py\nindex 2a195ea..5afad10 100644\n--- a/scripts/session-memory-analyzer.py\n+++ b/scripts/session-memory-analyzer.py\n@@ -4,6 +4,7 @@ from __future__ import annotations\n import argparse\n import json\n import os\n+import re\n import shutil\n import subprocess\n import sys\n@@ -21,6 +22,19 @@ MAX_CANDIDATES = int(os.environ.get(\"PI_SESSION_MEMORY_MAX_CANDIDATES\", \"6\"))\n ACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD\", \"0.92\"))\n ACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get(\"PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD\", \"0.9\"))\n DURABLE_TYPES = {\"profile\", \"decision\", \"project\", \"artifact\", \"procedural\", \"semantic\"}\n+NOISE_PATTERNS = [\n+    re.compile(pattern, re.IGNORECASE)\n+    for pattern in (\n+        r\"you are running one deterministic\",\n+        r\"phase-machine pass\",\n+        r\"subagent intake task_\",\n+        r\"^task id:\\s*task_\",\n+        r\"^payload:\\s*\\{\",\n+        r'\"route\"\\s*:\\s*\\{',\n+        r\"you are iteration \",\n+        r\"continue the bounded ralph-loop\",\n+    )\n+]\n \n \n def resolve_pi_bin() -> str:\n@@ -206,6 +220,27 @@ def choose_status(memory_type: str, confidence: float, importance: float) -> str\n     return \"inbox\"\n \n \n+def is_noise_memory(candidate: dict[str, Any]) -> bool:\n+    title = str(candidate.get(\"title\") or \"\")\n+    content = str(candidate.get(\"content\") or \"\")\n+    blob = f\"{title}\\n{content}\".strip()\n+    if not blob:\n+        return True\n+    lowered = blob.lower()\n+    if any(pattern.search(blob) for pattern in NOISE_PATTERNS):\n+        return True\n+    if lowered.count(\"task_\") >= 2:\n+        return True\n+    if lowered.count(\"payload:\") >= 1 and lowered.count(\"route\") >= 1:\n+        return True\n+    lines = [line.strip() for line in content.splitlines() if line.strip()]\n+    if lines:\n+        jsonish = sum(1 for line in lines if line.startswith((\"Payload:\", \"{\", \"}\", '\"')) or '\"' in line)\n+        if jsonish / len(lines) >= 0.45:\n+            return True\n+    return False\n+\n+\n def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:\n     memory_type = candidate.get(\"type\") or \"episode\"\n     confidence = float(candidate.get(\"confidence\") or 0.6)\n@@ -274,6 +309,8 @@ def main() -> int:\n         saved_items: list[dict[str, Any]] = []\n \n         for index, candidate in enumerate(candidates, start=1):\n+            if is_noise_memory(candidate):\n+                continue\n             memory = build_memory_payload(candidate, meta, job_file, analysis.get(\"reason\"), index)\n             if not memory[\"content\"].strip() or not memory[\"title\"].strip():\n                 continue\n"}],"isError":false,"timestamp":1780575386543}}
{"type":"message","id":"e0182f67","parentId":"5889df1c","timestamp":"2026-06-04T12:16:30.452Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_08205193eac5c3af016a216c9bb76481918b5a487b14b9860b\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqIWyeNNEpSlGV3M4ejygIa1awwCAFQfep5DuPNncw88Ak5jRb-v4_PWZ9y3Bzf5RPjqcD2ByDVRoIrRD9_uQIwDneRycEZuMG40ajRqjQQawi0proQ-zOwd7rADdbjd3BVYzmED9OfFAmFtjsI0jbQnGgHZpyPNhv36MJHLCTe6QqZwHmfHU-SNAtsgLksfU9ZtfiRh6_rZcnUuTJyo1ZoWFIeEnBbU7DW1IB6YU-gvUNwoK_fNa2Y4uj5P1wPOdHmuvsQQluy5eHewMRj9CWyys9pBoB54SyOgT9nqPbzzX_4eRSrzBIOtnAPioQc_BXAImTH_fsR0NRL3tNwwiKqlk_BVUgBJy8kq8TqKqiptJ8MQIbsgX1qRmtqahbBxyq_jU-R71Ry6b6MgODnFwJNAAnGyCSFj14w6cbUHIeNL_jEYOW8Uq6dyQF02o4f5TIXiSh7ePGXMokl0a4GXP8UtO-FQoYiy0rXwyVoikBn_3QJ58lAHYSw_cfFUqx0SFCKcFns6yYtQfs6Q4MefCpVjbHMDriczRVDWRXiot4Eh1WMEsrL5edD-k4mYHAYOHKmrBfviLU9ahwGcv04ALOwBa8V3C-fcYUegvfevnctlOOtKZR4QdmUQxdDQ4uqsBe1dafLY8ntkECwHaKyEEXZqs5RNtZNRAv2Ij11OKKKYejIkBrmDADIFWIPbhcDKrjmPg9yi9BCjgFHw86BYdfePGdxhAwr6WzsbYVjXm1CIU_8OEqP108cbOu6H7wqBgkwcKnJv5ZqacnNBA0PMyR4OdP3r8ZmRPBMv3Xy4gWUMr8Hdb3oIHGv3XeTEOrvVGvotPvH08iff8fo5iB_ZSvCIJMD7aZb8Yn94HhBTp6PUNuQ56Zjlol1BRizTWh5LiNqRbTawijtamIQ1qptTtLfDOxuZ20QlaroHHi6VfLJcI3q5rv8j-bgFBr76pSpRU0ukJ_\",\"summary\":[]}"},{"type":"toolCall","id":"call_y2OArm6GYzqwST7WQeRLvf7R|fc_08205193eac5c3af016a216c9e1c8081919219c796f4a2fb98","name":"bash","arguments":{"command":"systemctl --user restart agents-database-http.service agents-database-maintenance.service && sleep 2 && systemctl --user --no-pager --full status agents-database-http.service agents-database-maintenance.service | sed -n '1,80p'","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.4","usage":{"input":4146,"output":85,"cacheRead":66048,"cacheWrite":0,"totalTokens":70279,"cost":{"input":0.010365000000000001,"output":0.001275,"cacheRead":0.016512,"cacheWrite":0,"total":0.028152}},"stopReason":"toolUse","timestamp":1780575386545,"responseId":"resp_08205193eac5c3af016a216c9ab30c8191a96a02509e4a44ae"}}
{"type":"message","id":"295bf624","parentId":"e0182f67","timestamp":"2026-06-04T12:16:32.505Z","message":{"role":"toolResult","toolCallId":"call_y2OArm6GYzqwST7WQeRLvf7R|fc_08205193eac5c3af016a216c9e1c8081919219c796f4a2fb98","toolName":"bash","content":[{"type":"text","text":"● agents-database-http.service - Agents Database HTTP API\n     Loaded: loaded (/home/sebas/.config/systemd/user/agents-database-http.service; enabled; preset: enabled)\n     Active: active (running) since Thu 2026-06-04 09:16:30 -03; 2s ago\n Invocation: 19e87166e4d94e6cad31282239602fa1\n   Main PID: 599703 (python3)\n      Tasks: 1 (limit: 9575)\n        CPU: 136ms\n     CGroup: /user.slice/user-1000.slice/user@1000.service/app.slice/agents-database-http.service\n             └─599703 python3 -m shared_agent_memory.cli --db /home/sebas/agents-database/data/shared-agent-memory.sqlite3 serve-http --host 0.0.0.0 --port 8091\n\nJun 04 09:16:30 ballbox-first systemd[1289]: Started agents-database-http.service - Agents Database HTTP API.\nJun 04 09:16:30 ballbox-first run-http-api.sh[599703]: agents-database listening on http://0.0.0.0:8091\n\n● agents-database-maintenance.service - Agents Database Maintenance Daemon\n     Loaded: loaded (/home/sebas/.config/systemd/user/agents-database-maintenance.service; enabled; preset: enabled)\n     Active: active (running) since Thu 2026-06-04 09:16:30 -03; 2s ago\n Invocation: 3682570e768d43b580b7450a037ebe83\n   Main PID: 599704 (python)\n      Tasks: 1 (limit: 9575)\n        CPU: 118ms\n     CGroup: /user.slice/user-1000.slice/user@1000.service/app.slice/agents-database-maintenance.service\n             └─599704 /home/sebas/agents-database/.venv/bin/python -m shared_agent_memory.cli --db /home/sebas/agents-database/data/shared-agent-memory.sqlite3 maintenance-daemon --interval-seconds 900 --jitter-seconds 60\n\nJun 04 09:16:30 ballbox-first systemd[1289]: Started agents-database-maintenance.service - Agents Database Maintenance Daemon.\n"}],"isError":false,"timestamp":1780575392505}}
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