[telegram] se estan corriendo memory sweeps de todas las sesiones recientes?


**Inspecting memory sweeps**

I 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.

bash

ps -ef | grep -i 'memory sweep\|session analysis\|agents-database\|ingest' | grep -v grep

ls -1dt /home/sebas/.pi/sessions/* 2>/dev/null | head -20


root        1505    1273  0 May05 ?        00:00:00 sudo -n -u sebas /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data
sebas       1511    1505  0 May05 ?        00:00:01 /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data
sebas      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
sebas      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


(no output)


**Searching for sweeps configuration**

I 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!

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

find /home/sebas -maxdepth 3 \( -name '*session*' -o -name '*memory*' \) 2>/dev/null | head -200


/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}}
/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"}}
/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}}
/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"}}
/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"}}
/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"}}
/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}}
/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"}}
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/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"}}
/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"}}
/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"}}
/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"}}
/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}}
/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"}}
/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}}
/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"}}
/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"}}
/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"}}
/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}}
/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"}}
/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"}}
/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"}}
/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"}}
/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"}}
/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}}
/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}}

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/home/sebas/pi-config/bin/pi-telegram-new-session
/home/sebas/pi-config/sessions
/home/sebas/pi-config/sessions/spec-memory-layer-agents.md
/home/sebas/pi-config/sessions/RFC-memory-layer-agents.md
/home/sebas/pi-config/sessions/session-memory-jobs
/home/sebas/pi-config/sessions/RFC-memory-layer-agents.html
/home/sebas/pi-config/sessions/memory-techniques-review.html
/home/sebas/pi-config/sessions/memory-techniques-review.md
/home/sebas/pi-config/scripts/session-memory-sweeper.service
/home/sebas/pi-config/scripts/session-memory-sweeper.timer
/home/sebas/pi-config/scripts/install-session-memory-dispatcher.service
/home/sebas/pi-config/scripts/session-memory-sweeper.py
/home/sebas/pi-config/scripts/session-memory-analyzer.py
/home/sebas/pi-config/scripts/session-memory-dispatcher.py
/home/sebas/pi-config/docs/agents-database-memory-contract.md
/home/sebas/pi-config/runtime/session-memory-sweeper-state.json
/home/sebas/pi-config/extensions/session-switch
/home/sebas/pi-config/extensions/session-memory-dispatcher.ts
/home/sebas/.xsession-errors.old
/home/sebas/.claude/session-env
/home/sebas/.claude/statsig/statsig.session_id.2656274335
/home/sebas/.pi/agent/sessions
/home/sebas/.pi/pi-acp/session-map.json
/home/sebas/.codex/sessions
/home/sebas/.config/copyparty/sessions.db.lock
/home/sebas/.config/copyparty/sessions.db
/home/sebas/.agents/skills/memory-read
/home/sebas/.agents/skills/memory-write
/home/sebas/.agents/skills.bck/personal-memory-search
/home/sebas/.xsession-errors
/home/sebas/outputs/reports/memory-audit-report.html
/home/sebas/agents-database/docs/shared-agent-memory-rfc.md
/home/sebas/agents-database/docs/memory-consolidation-rfc.md
/home/sebas/agents-database/tests/test_memory_service.py
/home/sebas/agents-database/src/shared_agent_memory
/home/sebas/agents-database/data/shared-agent-memory.sqlite3


**Inspecting service states**

I 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!

systemctl --user status session-memory-sweeper.timer --no-pager -n 20

systemctl --user status session-memory-sweeper.service --no-pager -n 40


● session-memory-sweeper.timer - Run Pi session memory sweeper periodically
     Loaded: loaded (/home/sebas/.config/systemd/user/session-memory-sweeper.timer; enabled; preset: enabled)
     Active: active (waiting) since Sun 2026-05-24 06:14:10 -03; 1 week 4 days ago
 Invocation: 6c24261dbf5f45c6a1b23dc5a0193bd3
    Trigger: Thu 2026-06-04 12:28:57 -03; 3h 51min left
   Triggers: ● session-memory-sweeper.service

Notice: journal has been rotated since unit was started, output may be incomplete.


○ session-memory-sweeper.service - Pi session memory sweeper
     Loaded: loaded (/home/sebas/.config/systemd/user/session-memory-sweeper.service; static)
     Active: inactive (dead) since Thu 2026-06-04 06:28:57 -03; 2h 8min ago
 Invocation: dccf76edf26e4b0c997b6e764247a8bf
TriggeredBy: ● session-memory-sweeper.timer
    Process: 510149 ExecStart=/usr/bin/env python3 /home/sebas/pi-config/scripts/session-memory-sweeper.py --once (code=exited, status=0/SUCCESS)
   Main PID: 510149 (code=exited, status=0/SUCCESS)
        CPU: 226ms

Jun 04 06:28:57 ballbox-first systemd[1289]: Starting session-memory-sweeper.service - Pi session memory sweeper...
Jun 04 06:28:57 ballbox-first env[510149]: {"processed_now": 0, "results": [], "maintenance": null, "notifications": []}
Jun 04 06:28:57 ballbox-first systemd[1289]: Finished session-memory-sweeper.service - Pi session memory sweeper.

Command exited with code 3


**Checking recent sessions**

I 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!

find /home/sebas/pi-config/sessions -type f -name '*.jsonl' | sort | tail -20

python3 - <<'PY'
import json,glob,os
state=json.load(open('/home/sebas/pi-config/runtime/session-memory-sweeper-state.json'))
processed=set(state['processed'])
files=sorted(glob.glob('/home/sebas/pi-config/sessions/**/*.jsonl', recursive=True))
recent=files[-20:]
for f in recent:
    print(('OK ' if f in processed else 'MISS'), f)
PY


MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T00-40-43-848Z_019da855-1e48-73f6-9fb3-3e7af0384b1a.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-20T20-38-09-531Z_019dac9d-657a-7261-bcfe-1e8aba68425f.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-32-15-820Z_019db52d-43cb-7714-b600-a8e0da2d5b72.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T12-35-06-723Z_019db52f-df63-716e-b5fc-10f13fd9de1b.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T13-09-51-204Z_019db54f-ade4-70ca-bccf-03ddd7fc60d9.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-31-46-594Z_019db59a-aea2-71dd-aca1-e14a4a32409b.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T14-38-27-079Z_019db5a0-cb07-701e-b971-284bf7a60070.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-22T18-10-19-700Z_019db662-c5b3-70c9-a569-597a7f9d15cf.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-23T13-38-40-507Z_019dba90-6cfb-70df-8ba6-034822682371.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-23T19-52-56-556Z_019dbbe7-13ec-73a6-9faf-d597676a0900.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-28T21-03-55-211Z_019dd5e7-db4a-76e2-8ccd-e4e657863880.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-29T20-32-52-925Z_019ddaf1-ccbc-77cd-af19-d9a9c2666da1.jsonl
MISS /home/sebas/pi-config/sessions/--mnt-rpi-repos-ballbox--/2026-04-30T19-17-03-147Z_019ddfd2-bc2a-76e9-a0f2-9cdd36ca3335.jsonl
OK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-34-306Z_019dfbb6-7e21-74de-a57e-8c42b778991b.jsonl
OK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-41-291Z_019dfbb6-996a-744c-a2ab-a1e71dc26506.jsonl
OK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test--/2026-05-06T05-15-48-674Z_019dfbb6-b641-700d-b562-ac63d9b7d0cb.jsonl
OK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-25-240Z_019dfbb8-2f77-7021-8f8b-950f41ddcae8.jsonl
OK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-31-864Z_019dfbb8-4957-77ab-8b81-ac7e4734bbe1.jsonl
OK  /home/sebas/pi-config/sessions/--tmp-pi-loop-test-2--/2026-05-06T05-17-38-831Z_019dfbb8-648e-7115-8f04-27ca57c09fc6.jsonl
OK  /home/sebas/pi-config/sessions/--tmp-pi-tau-mux-test-project-a--/2026-04-29T16-49-04-845Z_019dda24-e74d-72aa-8f07-5b90064db8f0.jsonl


**Looking into file sorting**

I 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!

python3 - <<'PY'
import json,glob,os
from pathlib import Path
state=json.load(open('/home/sebas/pi-config/runtime/session-memory-sweeper-state.json'))
processed=set(state['processed'])
files=sorted(glob.glob('/home/sebas/pi-config/sessions/**/*.jsonl', recursive=True))
missing=[f for f in files if f not in processed]
print('total_files', len(files))
print('processed', len(processed))
print('missing', len(missing))
print('latest_missing:')
for f in missing[-15:]:
    print(f)
print('latest_processed:')
for f in sorted(processed)[-10:]:
    print(f)
PY


**Analyzing recent sessions**

I 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.

python3 - <<'PY'
import json,glob,os,re
state=json.load(open('/home/sebas/pi-config/runtime/session-memory-sweeper-state.json'))
processed=set(state['processed'])
files=sorted(glob.glob('/home/sebas/pi-config/sessions/**/*.jsonl', recursive=True))
recent=[f for f in files if '/2026-06-' in f]
print('june files', len(recent))
for f in recent:
    print(('OK ' if f in processed else 'MISS'), f)
PY


june files 10
OK  /home/sebas/pi-config/sessions/--home-sebas--/2026-06-01T12-33-34-483Z_019e832c-d712-76b7-9953-0074f47c6d77.jsonl
OK  /home/sebas/pi-config/sessions/--home-sebas--/2026-06-01T20-22-11-303Z_019e84d9-de67-704a-81ce-f506986a41f0.jsonl
OK  /home/sebas/pi-config/sessions/--home-sebas--/2026-06-02T14-45-26-796Z_019e88cb-ee8c-70d7-990a-2cd779f85ec1.jsonl
OK  /home/sebas/pi-config/sessions/--home-sebas--/2026-06-02T19-08-10-186Z_019e89bc-764a-7995-bbce-27de9b19c956.jsonl
MISS /home/sebas/pi-config/sessions/--home-sebas--/2026-06-04T11-36-30-837Z_019e926b-ad75-7aee-8e2a-e659e86435b9.jsonl
MISS /home/sebas/pi-config/sessions/--home-sebas--/2026-06-04T11-37-20-619Z_019e926c-6feb-7773-8e97-5645482738b0.jsonl
OK  /home/sebas/pi-config/sessions/--home-sebas-pi-config--/2026-06-01T07-30-39-919Z_019e8217-84ac-71b8-91fc-1fa48ecce09a.jsonl
OK  /home/sebas/pi-config/sessions/--home-sebas-pi-config--/2026-06-01T07-31-31-861Z_019e8218-4f95-71e8-9dc6-33b2c06dd0aa.jsonl
OK  /home/sebas/pi-config/sessions/--home-sebas-pi-config--/2026-06-01T07-38-42-520Z_019e821e-e1d8-7429-a663-9fef969f67e8.jsonl
OK  /home/sebas/pi-config/sessions/--home-sebas-pi-config--/2026-06-01T07-40-11-504Z_019e8220-3d70-7779-8f73-11bb956240c7.jsonl


No.

State now:
- sweeper timer: running
- last run: `2026-06-04 06:28 -03`
- last run result: success
- processed this run: `0`

Coverage:
- processed sessions: `97`
- total sessions: `201`
- missing: `104`

For truly recent ones:
- June 1–2 sessions: swept
- two new June 4 sessions: not swept yet
- next scheduled run: ~`12:28 -03`

Also:
- many older `--mnt-rpi-repos-ballbox--` sessions were never swept.

If you want, I can inspect why backlog coverage is partial and fix it.


[telegram] viejas y muy recientes tiene sentido que no se hayan corrido. solo quiero saber si ahora se van a correr eventualmente para todas


Sí, en principio sí.

Ahora:
- el timer está activo
- el sweeper corre periódico
- las nuevas sesiones deberían entrar solas en próximas corridas

Pero no puedo decir “todas” con certeza total porque:
- hoy hay backlog viejo sin procesar
- la última corrida hizo `0`, o sea no agarró nada nuevo todavía

Conclusión:
- las recientes: sí, deberían correrse eventualmente
- “todas todas”, incluyendo backlog viejo: no garantizado con lo visto

Si querés, reviso la regla exacta de selección y te digo si el diseño excluye viejas a propósito o si hay bug.


[telegram] esta bien asi. promotions/demotions/conflicts hubieron las ultimas dos semanas?


**Evaluating memory maintenance tasks**

I 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.

python3 - <<'PY'
import sqlite3, datetime
p='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'
con=sqlite3.connect(p)
con.row_factory=sqlite3.Row
cur=con.cursor()
for table in ['maintenance_runs','memory_conflicts']:
    print('\nTABLE', table)
    cols=cur.execute(f'PRAGMA table_info({table})').fetchall()
    print([c['name'] for c in cols])
    rows=cur.execute(f'SELECT * FROM {table} ORDER BY rowid DESC LIMIT 5').fetchall()
    for r in rows:
        print(dict(r))
PY


TABLE maintenance_runs
['id', 'job_id', 'status', 'started_at', 'completed_at', 'summary', 'stats_json', 'error_message']
{'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}
{'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}
{'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}
{'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}
{'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}

TABLE memory_conflicts
['id', 'memory_id', 'conflicting_memory_id', 'reason', 'created_at', 'metadata_json']


python3 - <<'PY'
import sqlite3, json, datetime
p='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'
con=sqlite3.connect(p)
con.row_factory=sqlite3.Row
cur=con.cursor()
now=datetime.datetime.fromisoformat('2026-06-04T11:41:08+00:00')
cutoff=now-datetime.timedelta(days=14)
rows=cur.execute("SELECT * FROM maintenance_runs WHERE started_at >= ? ORDER BY started_at", (cutoff.isoformat(),)).fetchall()
prom=dem=conf=arch=0
runs=0
byjob={}
for r in rows:
    runs+=1
    stats=json.loads(r['stats_json'] or '{}')
    p_=int(stats.get('promoted',0) or 0)
    a_=int(stats.get('archived',0) or 0)
    c_=int(stats.get('conflicts',0) or 0)
    prom+=p_; arch+=a_; conf+=c_
    byjob.setdefault(r['job_id'], {'runs':0,'promoted':0,'archived':0,'conflicts':0})
    byjob[r['job_id']]['runs']+=1
    byjob[r['job_id']]['promoted']+=p_
    byjob[r['job_id']]['archived']+=a_
    byjob[r['job_id']]['conflicts']+=c_
print('cutoff', cutoff.isoformat())
print('runs', runs)
print('promoted', prom)
print('archived', arch)
print('conflicts_stats', conf)
print('conflict_rows', cur.execute('SELECT COUNT(*) FROM memory_conflicts WHERE created_at >= ?', (cutoff.isoformat(),)).fetchone()[0])
print('jobs', json.dumps(byjob, indent=2, sort_keys=True))
PY


cutoff 2026-05-21T11:41:08+00:00
runs 30
promoted 1434
archived 0
conflicts_stats 0
conflict_rows 0
jobs {
  "job_consolidation_daily_10": {
    "archived": 0,
    "conflicts": 0,
    "promoted": 714,
    "runs": 14
  },
  "job_consolidation_daily_15": {
    "archived": 0,
    "conflicts": 0,
    "promoted": 647,
    "runs": 14
  },
  "job_consolidation_weekly": {
    "archived": 0,
    "conflicts": 0,
    "promoted": 73,
    "runs": 2
  }
}


**Considering promotions and demotions**

I 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.

Sí.

Últimas 2 semanas:
- promotions: `1434`
- demotions/archives: `0`
- conflicts: `0`

Detalle:
- corridas de consolidación: `30`
- daily_10: `714` promotions
- daily_15: `647` promotions
- weekly: `73` promotions

O sea: hubo muchas promotions, ninguna demotion, ningún conflict.


[telegram] okk. como ves las memorias en su totalidad moviendose?


**Considering promotional strategies**

I’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.

Se ven vivas, pero desbalanceadas.

Patrón:
- entra material nuevo
- consolidación promueve bastante
- casi no poda
- no aparecen conflictos

Lectura:
- el sistema está moviendo memorias hacia arriba
- pero no está haciendo casi trabajo de depurar,
  competir, fusionar, o bajar cosas

Eso puede significar dos cosas:
1. todavía está en fase de acumulación
2. los criterios de archive/conflict están flojos
   y casi todo pasa como “promote”

Mi lectura corta:
- movimiento sí
- metabolismo incompleto

Más simple:
- está creciendo
- no está adelgazando

Si querés, después te hago un juicio más duro:
- sano
- inflado
- estancado
- o mintiendo métricas


[telegram] hace un dry run con un criterio mas estricto de conflicto/consolidacion a ver que salta


**Inspecting scripts**

I 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!

todo

Dry run stricter memory consolidation/conflict detection

Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.


Created #1: Dry run stricter memory consolidation/conflict detection (pending)


Updated #1 (pending → in_progress)


bash

grep -Rni "promoted\|archived\|conflict\|consolid" /home/sebas/agents-database/src/shared_agent_memory /home/sebas/pi-config/scripts 2>/dev/null | head -200


#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
import os
import shlex
import subprocess
import sys
import time
from pathlib import Path
from typing import Any

BASE_DIR = Path(__file__).resolve().parent.parent
SESSIONS_DIR = Path(os.environ.get("PI_SESSION_DIR", BASE_DIR / "sessions"))
JOB_DIR = Path(os.environ.get("PI_SESSION_MEMORY_JOB_DIR", SESSIONS_DIR / "session-memory-jobs"))
STATE_FILE = Path(os.environ.get("PI_SESSION_MEMORY_SWEEP_STATE", BASE_DIR / "runtime" / "session-memory-sweeper-state.json"))
ANALYZER = Path(os.environ.get("PI_SESSION_MEMORY_ANALYZER", BASE_DIR / "scripts" / "session-memory-analyzer.py"))
LOOKBACK_DAYS = int(os.environ.get("PI_SESSION_MEMORY_LOOKBACK_DAYS", "21"))
MAX_SESSIONS_PER_RUN = int(os.environ.get("PI_SESSION_MEMORY_MAX_SESSIONS_PER_RUN", "12"))
MIN_TOTAL_MESSAGES = int(os.environ.get("PI_SESSION_MEMORY_MIN_TOTAL_MESSAGES", "6"))
MIN_ASSISTANT_TEXT = int(os.environ.get("PI_SESSION_MEMORY_MIN_ASSISTANT_TEXT", "120"))
TELEGRAM_NOTIFY_BIN = Path(os.environ.get("TELEGRAM_NOTIFY_BIN", str(Path.home() / ".agents" / "skills" / "telegram-notify" / "telegram-notify")))
MAINTENANCE_SCRIPT = Path(os.environ.get("AGENTS_DB_MAINTENANCE_SCRIPT", "/home/sebas/agents-database/scripts/run-maintenance-daemon.sh"))
DB_PATH = os.environ.get("DB_PATH", "/home/sebas/agents-database/data/shared-agent-memory.sqlite3")

SKIP_PREFIXES = (
    "you are iteration ",
    "continue the bounded ralph-loop",
)

def load_state() -> dict[str, Any]:
    if not STATE_FILE.exists():
        return {"processed": {}, "last_run_at": None}
    try:
        return json.loads(STATE_FILE.read_text(encoding="utf-8"))
    except Exception:
        return {"processed": {}, "last_run_at": None}

def save_state(state: dict[str, Any]) -> None:
    STATE_FILE.parent.mkdir(parents=True, exist_ok=True)
    STATE_FILE.write_text(json.dumps(state, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")

def iter_session_files() -> list[Path]:
    cutoff = time.time() - LOOKBACK_DAYS * 86400
    paths = [p for p in SESSIONS_DIR.rglob("*.jsonl") if p.is_file() and p.stat().st_mtime >= cutoff]
    return sorted(paths, key=lambda p: p.stat().st_mtime)

def parse_session(path: Path) -> dict[str, Any]:
    session_id = None
    cwd = None
    branch: list[dict[str, Any]] = []
    user_messages = 0
    assistant_messages = 0
    last_user_text = ""
    last_assistant_text = ""
    first_user_text = ""

    with path.open("r", encoding="utf-8", errors="ignore") as handle:
        for line in handle:
            try:
                entry = json.loads(line)
            except Exception:
                continue
            if entry.get("type") == "session":
                session_id = entry.get("id") or session_id
                cwd = entry.get("cwd") or cwd
                continue
            if entry.get("type") != "message":
                continue
            message = entry.get("message") or {}
            role = message.get("role")
            if role not in {"user", "assistant"}:
                continue
            text_parts = []
            for part in message.get("content") or []:
                if part.get("type") == "text" and isinstance(part.get("text"), str):
                    text_parts.append(part["text"])
            text = "\n".join(text_parts).strip()
            branch.append(entry)
            if role == "user":
                user_messages += 1
                if text and not first_user_text:
                    first_user_text = text
                if text:
                    last_user_text = text
            elif role == "assistant":
                assistant_messages += 1
                if text:
                    last_assistant_text = text

    return {
        "session_id": session_id,
        "session_file": str(path),
        "cwd": cwd or str(Path.home()),
        "scope": infer_scope(cwd or str(Path.home())),
        "reason": "periodic_sweep",
        "trigger": "periodic_sweeper",
        "branch": branch,
        "user_messages": user_messages,
        "assistant_messages": assistant_messages,
        "last_user_text": truncate(last_user_text, 2000),
        "last_assistant_text": truncate(last_assistant_text, 2000),
        "first_user_text": truncate(first_user_text, 400),
    }

def infer_scope(cwd: str) -> str:
    home = str(Path.home())
    try:
        resolved = str(Path(cwd).resolve())
    except Exception:
        resolved = cwd
    if resolved == home or resolved.startswith(str(Path(home) / ".pi")):
        return "global"
    return "project"

def truncate(text: str, max_len: int) -> str:
    text = " ".join(text.split())
    if len(text) <= max_len:
        return text
    return text[: max_len - 1] + "…"

def should_skip(meta: dict[str, Any]) -> tuple[bool, str | None]:
    if meta["user_messages"] <= 0 or meta["assistant_messages"] <= 0:
        return True, "no_dialogue"
    first = (meta.get("first_user_text") or "").strip().lower()
    if first.startswith(SKIP_PREFIXES):
        return True, "loop_session"
    total_messages = meta["user_messages"] + meta["assistant_messages"]
    if total_messages < MIN_TOTAL_MESSAGES and len(meta.get("last_assistant_text") or "") < MIN_ASSISTANT_TEXT:
        return True, "too_small"
    return False, None

def existing_job_for_session(session_file: str) -> bool:
    for path in JOB_DIR.glob("*.json"):
        try:
            payload = json.loads(path.read_text(encoding="utf-8"))
        except Exception:
            continue
        if payload.get("payload", {}).get("session_file") == session_file:
            return True
    return False

def write_job(payload: dict[str, Any]) -> Path:
    JOB_DIR.mkdir(parents=True, exist_ok=True)
    job_id = time.strftime("%Y%m%dT%H%M%S", time.gmtime()) + f"{int(time.time_ns()%1_000_000):06d}Z"
    job_file = JOB_DIR / f"{job_id}.json"
    job = {
        "job_id": job_id,
        "queued_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
        "status": "queued",
        "job_type": "session_memory_analysis",
        "payload": payload,
        "analysis": {
            "status": "queued",
            "save": None,
            "memory_id": None,
        },
    }
    job_file.write_text(json.dumps(job, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
    return job_file

def run_analyzer(job_file: Path) -> int:
    proc = subprocess.run([sys.executable, str(ANALYZER), "--job-file", str(job_file)], capture_output=True, text=True)
    log_file = job_file.with_suffix(".log")
    log_file.write_text((proc.stdout or "") + (proc.stderr or ""), encoding="utf-8")
    return proc.returncode

def classify_agents_candidates(analysis: dict[str, Any]) -> list[str]:
    items = []
    for memory in (analysis.get("result") or {}).get("memories") or []:
        if not isinstance(memory, dict):
            continue
        if memory.get("scope") != "global":
            continue
        if memory.get("type") not in {"profile", "decision", "procedural"}:
            continue
        if float(memory.get("confidence") or 0) < 0.85 or float(memory.get("importance") or 0) < 0.8:
            continue
        text = f"{memory.get('title','')} {memory.get('content','')}".lower()
        if any(token in text for token in ["always", "default", "prefer", "should", "avoid", "never", "every conversation", "working rule"]):
            items.append(memory.get("title") or "untitled")
    return items

def maybe_send_telegram(message: str) -> None:
    if not message.strip() or not TELEGRAM_NOTIFY_BIN.exists():
        return
    subprocess.run([str(TELEGRAM_NOTIFY_BIN), message], check=False, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)

def run_maintenance_once() -> dict[str, Any] | None:
    if not MAINTENANCE_SCRIPT.exists():
        return None
    env = dict(os.environ)
    env["DB_PATH"] = DB_PATH
    proc = subprocess.run([str(MAINTENANCE_SCRIPT), "--once"], capture_output=True, text=True, env=env)
    raw = (proc.stdout or "").strip().splitlines()
    if proc.returncode != 0 or not raw:
        return None
    try:
        payload = json.loads(raw[-1])
    except Exception:
        return None
    promoted = 0
    archived = 0
    linked = 0
    conflicts = 0
    for item in payload:
        stats = item.get("stats") or {}
        promoted += int(stats.get("promoted") or 0)
        archived += int(stats.get("archived") or 0)
        linked += int(stats.get("linked") or 0)
        conflicts += int(stats.get("conflicts") or 0)
    return {"promoted": promoted, "archived": archived, "linked": linked, "conflicts": conflicts}

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--once", action="store_true")
    args = parser.parse_args()
    state = load_state()
    processed = state.setdefault("processed", {})
    results = []
    notifications: list[str] = []

    for session_file in iter_session_files():
        if len(results) >= MAX_SESSIONS_PER_RUN:
            break
        stat = session_file.stat()
        key = str(session_file)
        marker = f"{stat.st_mtime_ns}:{stat.st_size}"
        if processed.get(key) == marker:
            continue
        meta = parse_session(session_file)
        skip, reason = should_skip(meta)
        if existing_job_for_session(key):
            processed[key] = marker
            results.append({"session_file": key, "status": "already_queued"})
            continue
        if skip:
            processed[key] = marker
            results.append({"session_file": key, "status": "skipped", "reason": reason})
            continue
        job_file = write_job(meta)
        exit_code = run_analyzer(job_file)
        processed[key] = marker
        job = json.loads(job_file.read_text(encoding="utf-8"))
        analysis = job.get("analysis") or {}

[33 more lines in file. Use offset=261 to continue.]


from __future__ import annotations

import json
import math
import subprocess
import uuid
from dataclasses import dataclass
from datetime import UTC, datetime, timedelta
from typing import Any
from urllib.parse import urlparse

from .schema import SCHEMA_VERSION
from .store import SQLiteStore

def utcnow() -> str:
    return datetime.now(UTC).isoformat()

def make_id(prefix: str) -> str:
    return f"{prefix}_{uuid.uuid4().hex}"

def normalize_text(value: str) -> str:
    return " ".join(value.lower().split())

def _line_value(content: str, label: str) -> str:
    prefix = f"{label}:"
    for line in content.splitlines():
        if line.startswith(prefix):
            return line[len(prefix) :].strip()
    return ""

@dataclass(slots=True)
class SearchResult:
    memory: dict[str, Any]
    score: float
    explanation: str
    facet_score: float
    lexical_score: float
    semantic_score: float

    def as_dict(self) -> dict[str, Any]:
        return {
            "memory": self.memory,
            "score": self.score,
            "score_breakdown": {
                "facet": self.facet_score,
                "lexical": self.lexical_score,
                "semantic": self.semantic_score,
                "confidence": self.memory["confidence"],
                "freshness": self.memory["freshness"],
            },
            "matched_scope": self.memory["scope"],
            "evidence_ref": self.memory["evidence_ref"],
            "explanation": self.explanation,
        }

class MemoryService:
    def __init__(self, db_path: str) -> None:
        self.store = SQLiteStore(db_path)
        self._maintenance_check_running = False
        self.store.initialize()
        self.migrate_v2()
        self._ensure_default_jobs()
        self._run_opportunistic_maintenance(limit=10)

    def create_memory(self, memory_input: dict[str, Any]) -> dict[str, Any]:
        now = utcnow()
        metadata = dict(memory_input.get("metadata") or {})
        subtype = memory_input.get("subtype")
        run_id = memory_input.get("run_id")
        task_id = memory_input.get("task_id")
        url = memory_input.get("url")
        domain = memory_input.get("domain") or (urlparse(url).netloc if url else None)
        origin_agent = memory_input.get("origin_agent") or metadata.get("origin_agent") or memory_input.get("agent_id")
        record = {
            "id": memory_input.get("id", make_id("mem")),
            "schema_version": memory_input.get("schema_version", SCHEMA_VERSION),
            "type": memory_input["type"],
            "subtype": subtype,
            "scope": memory_input["scope"],
            "status": memory_input.get("status", "active"),
            "project_id": memory_input.get("project_id"),
            "repo_id": memory_input.get("repo_id"),
            "agent_id": memory_input.get("agent_id"),
            "origin_agent": origin_agent,
            "run_id": run_id,
            "task_id": task_id,
            "url": url,
            "domain": domain,
            "source_kind": memory_input.get("source_kind", "manual"),
            "title": memory_input["title"],
            "content": memory_input["content"],
            "summary": memory_input.get("summary") or self._make_summary(memory_input["content"]),
            "confidence": float(memory_input.get("confidence", 1.0)),
            "freshness": float(memory_input.get("freshness", 1.0)),
            "created_at": memory_input.get("created_at", now),
            "updated_at": now,
            "observed_at": memory_input.get("observed_at", now),
            "source_ref": memory_input.get("source_ref"),
            "evidence_ref": memory_input.get("evidence_ref"),
            "embedding_json": self._encode_embedding(memory_input.get("embedding")),
            "metadata_json": self.store.dumps(metadata),
        }
        with self.store.connection() as conn:
            conn.execute(
                """
                INSERT OR REPLACE INTO memories (
                    id, schema_version, type, subtype, scope, status, project_id, repo_id, agent_id, origin_agent,
                    run_id, task_id, url, domain, source_kind, title, content, summary, confidence, freshness,
                    created_at, updated_at, observed_at, source_ref, evidence_ref, embedding_json, metadata_json
                ) VALUES (
                    :id, :schema_version, :type, :subtype, :scope, :status, :project_id, :repo_id, :agent_id, :origin_agent,
                    :run_id, :task_id, :url, :domain, :source_kind, :title, :content, :summary, :confidence, :freshness,
                    :created_at, :updated_at, :observed_at, :source_ref, :evidence_ref, :embedding_json, :metadata_json
                )
                """,
                record,
            )
            self._upsert_fts(conn, record)
        self._run_opportunistic_maintenance(limit=1)
        return self.get_memory(record["id"])

    def ingest(self, memory_input: dict[str, Any]) -> dict[str, Any]:
        return self.create_memory(memory_input)

    def capture_session(self, source_ref: str, scope: str, content: str, **metadata: Any) -> dict[str, Any]:
        return self._capture_source(
            trigger_type="session_close",
            source_kind="run",
            source_ref=source_ref,
            scope=scope,
            content=content,
            metadata=metadata,
        )

    def capture_conversation(
        self, source_ref: str, scope: str, content: str, capture_mode: str = "manual_import", **metadata: Any
    ) -> dict[str, Any]:
        metadata = {"capture_mode": capture_mode, **metadata}
        return self._capture_source(
            trigger_type="conversation_capture",
            source_kind="conversation",
            source_ref=source_ref,
            scope=scope,
            content=content,
            metadata=metadata,
        )

    def search(
        self,
        query: str,
        scopes: list[str] | None = None,
        filters: dict[str, Any] | None = None,
        limit: int = 10,
        include_inbox: bool = False,
    ) -> dict[str, Any]:
        filters = filters or {}
        query_terms = [term for term in normalize_text(query).split() if term]
        sql = ["SELECT * FROM memories WHERE 1=1"]
        params: list[Any] = []

        if scopes:
            sql.append(f"AND scope IN ({','.join('?' for _ in scopes)})")
            params.extend(scopes)
        if not include_inbox:
            sql.append("AND status = ?")
            params.append("active")
        params.extend(self._memory_filter_sql(sql, filters))

        with self.store.connection() as conn:
            rows = [self._row_to_memory(row) for row in conn.execute(" ".join(sql), params)]

        results: list[SearchResult] = []
        query_embedding = self._text_embedding(query) if query_terms else []
        for row in rows:
            haystack = normalize_text(" ".join([row["title"], row["summary"], row["content"]]))
            lexical = self._lexical_score(query_terms, haystack)
            semantic = self._semantic_score(query_embedding, row.get("embedding")) if query_terms else 0.0
            facet = self._facet_score(row, filters)
            if query_terms and lexical == 0 and semantic == 0 and facet == 0:
                continue
            score = facet * 0.45 + lexical * 0.3 + semantic * 0.15 + row["confidence"] * 0.06 + row["freshness"] * 0.04
            explanation = (
                f"facets={facet:.2f} lexical={lexical:.2f} semantic={semantic:.2f} "
                f"confidence={row['confidence']:.2f} freshness={row['freshness']:.2f}"
            )
            results.append(SearchResult(row, score, explanation, facet, lexical, semantic))

        ordered = sorted(
            results,
            key=lambda item: (
                item.facet_score,
                item.lexical_score,
                item.semantic_score,
                item.memory["confidence"],
                item.memory["freshness"],
                item.memory["updated_at"],
            ),
            reverse=True,
        )[:limit]
        payload = [item.as_dict() for item in ordered]

        retrieval_id = make_id("ret")
        with self.store.connection() as conn:
            conn.execute(
                "INSERT INTO retrieval_logs (id, query_text, filters_json, results_json, created_at) VALUES (?, ?, ?, ?, ?)",
                (
                    retrieval_id,
                    query,
                    self.store.dumps({"scopes": scopes, "filters": filters, "include_inbox": include_inbox}),
                    self.store.dumps(payload),
                    utcnow(),
                ),
            )
        return {"retrieval_id": retrieval_id, "results": payload}

    def context_for(
        self, project: str | None = None, repo: str | None = None, agent: str | None = None, task: str | None = None
    ) -> dict[str, Any]:
        profile_facts = self.list_memories(status="active", scope="global", memory_type="profile", limit=8)
        project_filters = {"project_id": project, "repo_id": repo}
        active_decisions = self.list_memories(
            status="active",
            scope="project" if project else None,
            memory_type="decision",
            project_id=project,
            repo_id=repo,
            limit=8,
        )
        project_memories = self.list_memories(
            status="active",
            project_id=project,
            repo_id=repo,
            limit=12,
        )
        project_memories = [item for item in project_memories if item["type"] in {"project", "decision", "artifact", "episode"}][:12]
        recent_episodes = self.list_memories(
            status="active",
            memory_type="episode",
            project_id=project,
            repo_id=repo,
            limit=10,
        )
        task_relevant_artifacts = self.list_memories(
            status="active",
            memory_type="artifact",
            project_id=project,
            repo_id=repo,
            task_id=task,
            limit=10,
        )
        if task and not task_relevant_artifacts:
            task_relevant_artifacts = self.search(task, filters={**project_filters, "type": "artifact"}, limit=5)["results"]
        citations = [
            item["id"]
            for item in [*profile_facts, *project_memories, *active_decisions, *recent_episodes]
            if isinstance(item, dict) and "id" in item
        ]
        return {
            "profile_facts": profile_facts,
            "project_memories": project_memories,
            "active_decisions": active_decisions,
            "recent_episodes": recent_episodes,
            "task_relevant_artifacts": task_relevant_artifacts,
            "citations": citations,
            "agent_id": agent,
        }

    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:
        promoted: list[str] = []
        linked: list[dict[str, str]] = []
        with self.store.connection() as conn:
            for memory_id in inbox_items:
                row = conn.execute("SELECT * FROM memories WHERE id = ?", (memory_id,)).fetchone()
                if row is None:
                    continue
                memory = self._row_to_memory(row)
                duplicate = conn.execute(
                    """
                    SELECT id FROM memories
                    WHERE id != ?
                      AND status = 'active'
                      AND type = ?
                      AND scope = ?
                      AND content = ?
                    LIMIT 1
                    """,
                    (memory_id, memory["type"], memory["scope"], memory["content"]),
                ).fetchone()
                if duplicate:
                    link_id = make_id("lnk")
                    conn.execute(
                        """
                        INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)
                        VALUES (?, ?, ?, ?, ?, ?)
                        """,
                        (
                            link_id,
                            memory_id,
                            duplicate["id"],
                            "related_to",
                            utcnow(),
                            self.store.dumps({"reason": "exact_duplicate"}),
                        ),
                    )
                    conn.execute("UPDATE memories SET status = ?, updated_at = ? WHERE id = ?", ("archived", utcnow(), memory_id))
                    linked.append({"from": memory_id, "to": duplicate["id"], "relation": "related_to"})
                    continue
                conn.execute("UPDATE memories SET status = ?, updated_at = ? WHERE id = ?", ("active", utcnow(), memory_id))
                promoted.append(memory_id)
        return {"promoted": promoted, "linked": linked}

    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:
        stats = {
            "candidates": 0,
            "promoted": 0,
            "archived": 0,
            "linked": 0,
            "conflicts": 0,
            "llm_summaries": 0,
        }
        age_days = int(config.get("candidate_age_days", 7))
        max_candidates = int(config.get("max_candidates", 200))
        high_threshold = float(config.get("dedupe_high_threshold", 0.9))
        mid_threshold = float(config.get("dedupe_mid_threshold", 0.75))
        promote_threshold = float(config.get("promote_confidence", 0.7))
        conflict_threshold = float(config.get("conflict_threshold", 0.55))
        llm_enabled = bool(config.get("llm_enabled", False))
        llm_provider = config.get("llm_provider") or "opencode"
        llm_command = config.get("llm_command") or ["opencode", "-m", "MiniMax M2.5 Free"]
        cutoff = (datetime.now(UTC) - timedelta(days=age_days)).isoformat()

        with self.store.connection() as conn:
            rows = conn.execute(
                """

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