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              "text": "we were updating linux, can you move some things to /mnt/rpi to make space?"
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              "thinking": "**Planning symlink migrations**\n\nI'm looking to set up some low-risk symlink migrations for various directories to point to my home cache on the Raspberry Pi. I have a list of directories, including \"go,\" \".bun,\" and \"downloads\" that I want to link. I'll need to check a few things, like the existing directories, and consider using rsync for backup and handling any conflicts with existing targets. Once I execute this plan, I’ll be sure to report back on the results.",
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                "command": "set -euo pipefail\nmkdir -p /mnt/rpi/home-cache\nmove_link() {\n  src=\"$1\"\n  dest=\"$2\"\n  if [ -L \"$src\" ]; then\n    echo \"skip symlink $src -> $(readlink \"$src\")\"\n    return\n  fi\n  if [ ! -e \"$src\" ]; then\n    echo \"skip missing $src\"\n    return\n  fi\n  mkdir -p \"$(dirname \"$dest\")\"\n  echo \"moving $src -> $dest\"\n  rsync -a \"$src\"/ \"$dest\"/\n  mv \"$src\" \"${src}.bak-move-20260504\"\n  ln -s \"$dest\" \"$src\"\n  rm -rf \"${src}.bak-move-20260504\"\n}\nmove_link /home/sebas/go /mnt/rpi/home-cache/go\nmove_link /home/sebas/.bun /mnt/rpi/home-cache/.bun\nmove_link /home/sebas/.zed_server /mnt/rpi/home-cache/.zed_server\nmove_link /home/sebas/venvs /mnt/rpi/home-cache/venvs\nmove_link /home/sebas/downloads /mnt/rpi/home-cache/downloads\nif [ -d /home/sebas/.pnpm-store ] && [ ! -L /home/sebas/.pnpm-store ]; then\n  mkdir -p /mnt/rpi/.pnpm-store\n  rsync -a /home/sebas/.pnpm-store/ /mnt/rpi/.pnpm-store/\n  mv /home/sebas/.pnpm-store /home/sebas/.pnpm-store.bak-move-20260504\n  ln -s /mnt/rpi/.pnpm-store /home/sebas/.pnpm-store\n  rm -rf /home/sebas/.pnpm-store.bak-move-20260504\n  echo \"moving /home/sebas/.pnpm-store -> /mnt/rpi/.pnpm-store\"\nfi\n\necho '--- post-move ---'\ndf -h ~ /mnt/rpi /\ndu -sh /home/sebas/go /home/sebas/.bun /home/sebas/.zed_server /home/sebas/venvs /home/sebas/downloads /home/sebas/.pnpm-store 2>/dev/null\n",
                "timeout": 1200
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          "usage": {
            "input": 1796,
            "output": 929,
            "cacheRead": 5632,
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            "totalTokens": 8357,
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          "timestamp": 1777929536936,
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        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Assessing symlink behavior**\n\nI need to assess the partial state of the system. It seems that something may be symlinked, and I'm unsure if the rest will run due to the `set -e` option. This could lead to a fail after the first move when I try to remove something. It's crucial that I inspect everything closely first to ensure I understand what's going on before proceeding.",
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                "command": "ls -ld /home/sebas/go /home/sebas/go.bak-move-20260504 /mnt/rpi/home-cache/go 2>/dev/null; df -h ~ /mnt/rpi /; du -sh /home/sebas/go* /mnt/rpi/home-cache/go 2>/dev/null",
                "timeout": 120
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            {
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              "arguments": {
                "command": "for p in /home/sebas/.bun /home/sebas/.zed_server /home/sebas/venvs /home/sebas/downloads /home/sebas/.pnpm-store; do [ -e \"$p\" ] && ls -ld \"$p\"; done",
                "timeout": 60
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          "model": "gpt-5.4",
          "usage": {
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            "cacheRead": 7168,
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              "cacheRead": 0.001792,
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          "stopReason": "toolUse",
          "timestamp": 1777929594589,
          "responseId": "resp_0056cf023ec1dd460169f90d7af75881949de169cfc574ae90"
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      {
        "type": "message",
        "id": "56cea859",
        "parentId": "9171a68b",
        "timestamp": "2026-05-04T21:20:05.064Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Inspecting file permissions**\n\nI need to create a clean backup, and I should check the chmod settings and permissions first. It seems like I could use `rm -rf` to remove write-protected files, but I might run into a permission denied error if they're marked immutable. I should definitely inspect the ownership and permissions of those files to see what's going on. Let's clarify these settings before proceeding!",
              "thinkingSignature": "{\"id\":\"rs_0056cf023ec1dd460169f90d810f5c8194b07cc2b1d70f34a7\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-Q2Ce3ChNtqN3yTPyl4mSiDelz_kZ5zq40aS7a5-_fjnchN7bWjO6BkgpT_d5T56akeVzMlJsPn2IR05_f0ooowrdF35kQy3S2uzCGsE55nN4nqmb-JWXmKiqFqVwkyVQuSFIvYjfRUr8Dkh0IAtd9U8MB0QFU17QR5fWxVGmvctdEN2FQ9z2Q0NTWImbdwlwqOTHwVK0hz0HjgWLIM-Us1zBTSGuF_GdiPWYQ2BPlW2IUu8RAP6mKU3rdvBkgobgb09tGESDFdw4dZVI0bnzp8ghKBZhmmk3y6mxirCElM4DtxtDfkyh42vs3vsAyD1xcsfrFFflRn_Hlk_nB1hrjyG-AAD2wWtnkwlxAw0NRwRizWCcrnxGXPDGY51tOmLd882HeGdVu4bKK2eIKkcW9k83FvmsfJap8QyO-o6aCJUk9QQtFIoRO2ryU8VOgNrUfhgcJn_KFpCRi8bxCtH0lVlLC-mrysZkkus5qB8eLZJqNJFDXSGDWjg08ku1AU0jRYD87df9WsSlV45yTbHoSnalC-NjJgJT2FZ9FbXQcYejAAskwbSQ4xH-E_1lddJQD8iE9enkP5Nm895AL0XrZxpWzmKj04QjD3ty1-3vQ9e5la7A3jTV2ksPgUTQGavsW2J7qM3rU1PBj-uzITEvRSDaqDoFfetJit6cQs_Ytv79tBe5wOZBUk32uOwobMyChmmrQDxIf8lhAKYHoXIt2Hk8GfRvZ8KH3aIUC87haLWBqVg9nlKvN5rx-HMGymjwCTnhWb_IdONks8uVtWQmgZdApJkzWojRXkA7xoW2Puw41YttfBmij1PJius03AOTpC9IBWp8ZA6SxpI3El5EsD9GujV727gVmr0O0ILLEmWRRswVcDgykI6jPVs-8glzsbCXOeHf37n7FsvGAT1_jzALcU2Hoeta0lDTyC6e7IG_wUDML0bffWcfDm_kkkgOGxv2ZRjdFPiFp3AsOne1OAUwOcBmVpBBmajjg4X5o_1cc4KCc1IYRvd1jG2YrYeGvkltXgJnKqzg_6pTBTn2WJhtU33xXHVPWM1_YZzfA9kSPTVbF9-Mj4qHEeCYWqK8Jqr8BHXsg1dx-d3ZwpywCtQ0-ZO5tg2jmh-J7Ff7B6wWJMmKLqzAbhsVCVpletmWa_h\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting file permissions**\\n\\nI need to create a clean backup, and I should check the chmod settings and permissions first. It seems like I could use `rm -rf` to remove write-protected files, but I might run into a permission denied error if they're marked immutable. I should definitely inspect the ownership and permissions of those files to see what's going on. Let's clarify these settings before proceeding!\"}]}"
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              "name": "bash",
              "arguments": {
                "command": "find /home/sebas/go.bak-move-20260504 -maxdepth 4 \\( ! -user sebas -o ! -writable \\) | sed -n '1,80p' && stat -c '%U %G %A %n' /home/sebas/go.bak-move-20260504/pkg/mod/golang.org/toolchain@v0.0.1-go1.26.2.linux-arm64 2>/dev/null && find /home/sebas/go.bak-move-20260504/pkg/mod/golang.org/toolchain@v0.0.1-go1.26.2.linux-arm64 -maxdepth 2 | sed -n '1,10p'",
                "timeout": 120
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          "api": "openai-codex-responses",
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            "cacheRead": 25088,
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            "totalTokens": 26359,
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          "stopReason": "toolUse",
          "timestamp": 1777929599997,
          "responseId": "resp_0056cf023ec1dd460169f90d80485c8194a09b2f8b20e3263d"
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      {
        "type": "message",
        "id": "21c2cc24",
        "parentId": "a697a785",
        "timestamp": "2026-05-04T21:20:18.981Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Considering safer backup methods**\n\nI’m thinking about using `chmod -R u+w backup` so I can allow write permissions on the backup. After that, I’ll remove unnecessary files, but I want to be cautious about it. I need to ensure I don’t remove anything if the previous steps fail. So, I'll use a safer method moving forward to prevent any mishaps. It’s all about minimizing risks here!",
              "thinkingSignature": "{\"id\":\"rs_0056cf023ec1dd460169f90d864c208194a9e6170f611a5d9b\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-Q2IjVIvU40gbChKQZBRGPlhydESVrZGxPJ-KlDbFitV5VGm8IyyWfvgTzLZH6lZ4gIKKFDSUAjmqFr8lFQFbEmHLJ9B6ofF_wuVstHJbyaQOHPCpDQOFhZo83eS_JUHI05ho-KzIS27YZqPReayyd6oR9P8AUSEfg-uVR2F-cdWXZWf1q7SYIKqjQB8ik7fCD4XnGCakS2jQ5GQQZzpEMES2Nsw9FjsQYO2zTd1l0LQs0vqM-FHFbrVpCDGN8kN94i0AUYCar4FT4A77eqPkHHRqtTbNSaxElJHOnzXf0Zf60cYI3aQ-OoAAUxQ3WiWaL5uRFpARs4ARwgtTx099gDla4BeufGcBly68GhUGQp33obBvjWe9C5VCr4YCcVhlxcwKjR_M76Eb86TELKQgvq6DADF1d8RFBHbEa3aw3EG8Y3ti3X__Zn6BA9q60AbYH5GtsQXlMnjm4byDlFvKS9-j2fQhlM7BT3sHm-QLxg9qrUFaAf-fBLLrdhZU836L87YoW32tkIEXkhZY0K02Xcd2kGyk4pWFFuXUWm30N_jOsfenHv8aDPELwlhvGZUSIBISx8Xh5gSHoyT-0iizxtmO1yR3cwdHteFSJlbURxLhMdbpqsQvtSpiz8ALxAC-RYr7VECujYtP2qYtV5jmf2lRhq3iOtg9R6gvZSIcIYd8W0vaU9bOBiEIoqZlJwf7P7TXhw_o4qjX5xnVLyoEKvR8gcMcddANB53ocalpFxYzkSy9q6T2KFhMjPWzeRgOCQMLqT12n_VKJJH0aJVIQfaUXE9toW827Js7Cq3fI12yWzNiebJGF_BL6udIWHNDD6dmw0PXAKlG-tPaKlYyOvfQ-AHMqTXWXozSnRyAwed-8borFyKFFaVuVMJ6iAGN1QGwrnvKnPzmaAFafP7gaciulz6RSx5UceEvlaLlb3l0QPCZA5SfU3Kshz8gQ2kZY8MCVs1Ft-jFoutMp-fJ6hTOkPo5JAvwRQQMFJ_C_oLHQzaSD60VqYxOrPdLEpcKgdoRshn_cjE8aVAkpxyOHva0g==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering safer backup methods**\\n\\nI’m thinking about using `chmod -R u+w backup` so I can allow write permissions on the backup. After that, I’ll remove unnecessary files, but I want to be cautious about it. I need to ensure I don’t remove anything if the previous steps fail. So, I'll use a safer method moving forward to prevent any mishaps. It’s all about minimizing risks here!\"}]}"
            },
            {
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              "id": "call_FXPRup90jnYryHNTaXkWypxr|fc_0056cf023ec1dd460169f90d889830819488e58b4f2d1b20f5",
              "name": "bash",
              "arguments": {
                "command": "set -euo pipefail\n# clean leftover backup from go move\nif [ -d /home/sebas/go.bak-move-20260504 ]; then\n  chmod -R u+w /home/sebas/go.bak-move-20260504 || true\n  rm -rf /home/sebas/go.bak-move-20260504\nfi\nmove_link() {\n  src=\"$1\"\n  dest=\"$2\"\n  if [ -L \"$src\" ]; then\n    echo \"skip symlink $src -> $(readlink \"$src\")\"\n    return\n  fi\n  if [ ! -e \"$src\" ]; then\n    echo \"skip missing $src\"\n    return\n  fi\n  mkdir -p \"$(dirname \"$dest\")\"\n  echo \"moving $src -> $dest\"\n  rsync -a \"$src\"/ \"$dest\"/\n  mv \"$src\" \"${src}.bak-move-20260504\"\n  ln -s \"$dest\" \"$src\"\n  chmod -R u+w \"${src}.bak-move-20260504\" || true\n  rm -rf \"${src}.bak-move-20260504\"\n}\nmove_link /home/sebas/.bun /mnt/rpi/home-cache/.bun\nmove_link /home/sebas/.zed_server /mnt/rpi/home-cache/.zed_server\nmove_link /home/sebas/venvs /mnt/rpi/home-cache/venvs\nmove_link /home/sebas/downloads /mnt/rpi/home-cache/downloads\nif [ -d /home/sebas/.pnpm-store ] && [ ! -L /home/sebas/.pnpm-store ]; then\n  echo \"moving /home/sebas/.pnpm-store -> /mnt/rpi/.pnpm-store\"\n  mkdir -p /mnt/rpi/.pnpm-store\n  rsync -a /home/sebas/.pnpm-store/ /mnt/rpi/.pnpm-store/\n  mv /home/sebas/.pnpm-store /home/sebas/.pnpm-store.bak-move-20260504\n  ln -s /mnt/rpi/.pnpm-store /home/sebas/.pnpm-store\n  chmod -R u+w /home/sebas/.pnpm-store.bak-move-20260504 || true\n  rm -rf /home/sebas/.pnpm-store.bak-move-20260504\nfi\n\necho '--- post-move ---'\ndf -h ~ /mnt/rpi /\ndu -sh /mnt/rpi/home-cache/go /mnt/rpi/home-cache/.bun /mnt/rpi/home-cache/.zed_server /mnt/rpi/home-cache/venvs /mnt/rpi/home-cache/downloads /mnt/rpi/.pnpm-store 2>/dev/null\nls -ld /home/sebas/go /home/sebas/.bun /home/sebas/.zed_server /home/sebas/venvs /home/sebas/downloads /home/sebas/.pnpm-store\n",
                "timeout": 1200
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 1360,
            "output": 690,
            "cacheRead": 25600,
            "cacheWrite": 0,
            "totalTokens": 27650,
            "cost": {
              "input": 0.0034000000000000002,
              "output": 0.01035,
              "cacheRead": 0.0063999999999999994,
              "cacheWrite": 0,
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            }
          },
          "stopReason": "toolUse",
          "timestamp": 1777929605084,
          "responseId": "resp_0056cf023ec1dd460169f90d8581b081949f75164f863396ca"
        }
      },
      {
        "type": "message",
        "id": "82056887",
        "parentId": "19834f5f",
        "timestamp": "2026-05-04T21:22:23.314Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Considering changes and updates**\n\nI need to mention that I've made some moves to create space, as the user requested. But, I also want to note that some data, particularly the zed data, hasn't been moved yet. I'll keep that on my to-do list for completion. I think being clear about these details will be important to keep everything organized and ensure the user knows what's happening! Alright, I'll wrap this up!",
              "thinkingSignature": "{\"id\":\"rs_0056cf023ec1dd460169f90e0c5950819496fec8876777b9d3\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-Q4P67LKrYDostdkmmBVmm1DgvD059x1fPVmK0zLp9Q4xlXR_AuAcURwtlEkbhI0M6fnDsfAQDKt4a3K1nrydu2Urgk47IJZwiiGYo3wAU2T8trhOrtlJL0-redTuqHxoS0X9Gnc_sLwtN4ja2YxcJheUdLjOqXpVhOscBsksqU2Fbuu3pc3kC943jqIxF0LF-Qbh-sI3qY1kJd79QNvFCuqdPgrxZ9xqeqMS1bsTTEJ9LXOKKEgg7amEtZoiLEDCmlW5kxv6VJVGNMxdpaOnF9VZGvm7kGisHhbheIk4IMcy36KbN1vH7pdemVPhLnoNk3oKwpqUqnOQoYvKVYybucdo6v5ZmdD-iaWsbTUrm7KLpWzcUw1GoirmjkGh85e9Jqyvb2ipT82dbwsJu49EyjUQjKn4ei2HOzUhR6LXX6KWJZCGQobzNN54bC4PaX3MB-7SSNmaSFfy6zKZ7Ci6nlxNUIm5CHFb1gN8USmP7fWRNSK6skJSeoeZbTiFUn8RdMxxBQq77PBanuxSm0b_UMDrG94lKQBRauHTcYFy9LvD7ZSaGT9NjmIZhgUf1B0m3X35bS4BCGuSnAIW-gHl7Jd94qbYv6NOaOUpf6L51suDTQsXASJFo86GL56uE--MtsvBAAKMPTSb1MCDfYJDIiJorvBfx9qag7pWkLmmsZfnIWozVZZNT0yL0kwrHeCh6CYq6uWAY9C3gVUd_aXZ4yV-MOGnPmB_0tyv6cHmDFQo9emw5srYzY8MXqjPkCEUdkjz7cPcuFUsrI0ZOo18zcngtBcbcMSiIJPbte3lvN6Ok_C9JkAIzMezqWSqjaQK6dvUahCz4Wj6BV9oOVGNO7n5a3W-y1BWtCMVXU37UfQ3HjPiaADCpjwCnwricE7cFb7QJZkirBsAO-BQltC0ZZchocfoHj_MJwrmBKAjgX4PQ_XBEukOU2PjzRiMHrXeiCj2r9LQvOIBNnODy2KucuFAMDwYWDp3Lq959W2LdLSA2dzySt_BoEidy-I2nZbu9Qf30TMxc_zm8y2ki9Isbths_qbvOJOZbun2qRyYOreOm9CA7ALeHbqJnRuk4lvDMIuBUp7ZTyAzEiV7Ke0q_lOQsgHFTH556IKIqzJX6lznoA=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering changes and updates**\\n\\nI need to mention that I've made some moves to create space, as the user requested. But, I also want to note that some data, particularly the zed data, hasn't been moved yet. I'll keep that on my to-do list for completion. I think being clear about these details will be important to keep everything organized and ensure the user knows what's happening! Alright, I'll wrap this up!\"}]}"
            },
            {
              "type": "toolCall",
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              "name": "todo",
              "arguments": {
                "action": "update",
                "id": 1,
                "status": "completed"
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 1528,
            "output": 65,
            "cacheRead": 26624,
            "cacheWrite": 0,
            "totalTokens": 28217,
            "cost": {
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              "output": 0.0009750000000000001,
              "cacheRead": 0.006656,
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          },
          "stopReason": "toolUse",
          "timestamp": 1777929737289,
          "responseId": "resp_0056cf023ec1dd460169f90e0997288194b474cbcc8c2434b8"
        }
      },
      {
        "type": "message",
        "id": "999aa631",
        "parentId": "5ddb3a02",
        "timestamp": "2026-05-04T21:22:29.623Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "text",
              "text": "Yes. Moved safe big paths off root and symlinked them back.\n\nSpace now:\n- root `/`: `88%` used\n- free: `1.6G`\n- before: `494M` free\n\nMoved to `/mnt/rpi`:\n- `~/go` → `/mnt/rpi/home-cache/go`\n- `~/.bun` → `/mnt/rpi/home-cache/.bun`\n- `~/.zed_server` → `/mnt/rpi/home-cache/.zed_server`\n- `~/venvs` → `/mnt/rpi/home-cache/venvs`\n- `~/downloads` → `/mnt/rpi/home-cache/downloads`\n- `~/.pnpm-store` → `/mnt/rpi/.pnpm-store`\n\nBiggest wins:\n- `~/go` `721M`\n- `~/.pnpm-store` `668M`\n- `~/.bun` `142M`\n\nI did not touch `~/.local/share/zed` (`1.5G`). More risky while Zed-related processes exist.\n\nIf you want, next I can:\n1. check if Linux update can run now\n2. free more by moving `~/.local/share/zed` too\n3. clean apt/cache space instead of moving more user data",
              "textSignature": "{\"v\":1,\"id\":\"msg_0056cf023ec1dd460169f90e1090c88194968052f6563f8546\",\"phase\":\"final_answer\"}"
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 588,
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            "cacheRead": 27648,
            "cacheWrite": 0,
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            }
          },
          "stopReason": "stop",
          "timestamp": 1777929743321,
          "responseId": "resp_0056cf023ec1dd460169f90e0fb05881949f6aa2ca84848e1a"
        }
      }
    ],
    "user_messages": 4,
    "assistant_messages": 15,
    "last_user_text": "we were updating linux, can you move some things to /mnt/rpi to make space?",
    "last_assistant_text": "Yes. Moved safe big paths off root and symlinked them back. Space now: - root `/`: `88%` used - free: `1.6G` - before: `494M` free Moved to `/mnt/rpi`: - `~/go` → `/mnt/rpi/home-cache/go` - `~/.bun` → `/mnt/rpi/home-cache/.bun` - `~/.zed_server` → `/mnt/rpi/home-cache/.zed_server` - `~/venvs` → `/mnt/rpi/home-cache/venvs` - `~/downloads` → `/mnt/rpi/home-cache/downloads` - `~/.pnpm-store` → `/mnt/rpi/.pnpm-store` Biggest wins: - `~/go` `721M` - `~/.pnpm-store` `668M` - `~/.bun` `142M` I did not touch `~/.local/share/zed` (`1.5G`). More risky while Zed-related processes exist. If you want, next I can: 1. check if Linux update can run now 2. free more by moving `~/.local/share/zed` too 3. clean apt/cache space instead of moving more user data",
    "first_user_text": "clear /tmp"
  },
  "analysis": {
    "status": "completed",
    "save": true,
    "saved": true,
    "memory_id": "mem_c6371cbb4e92425ca123bbc8b1f304f3",
    "memory_ids": [
      "mem_c6371cbb4e92425ca123bbc8b1f304f3",
      "mem_3ff27df7bb94403c915ebcee665e338a",
      "mem_dcaa730515ae48b39eda5dcc5e38edd0",
      "mem_541c5ade3b7141d28217b30d48da4e83",
      "mem_250c2415e6cb4560a45ffc165052aed6"
    ],
    "saved_count": 5,
    "raw": "{\"memories\":[{\"type\":\"project\",\"subtype\":\"disk_space_management\",\"scope\":\"global\",\"title\":\"Use /mnt/rpi for large user caches when root is tight\",\"content\":\"On ballbox-first, root/home can fill fast during updates; safe large user/tool caches were moved to /mnt/rpi and symlinked back to free space. Moved: ~/go, ~/.bun, ~/.zed_server, ~/venvs, ~/downloads, ~/.pnpm-store. ~/.local/share/zed was intentionally left in place because it was larger and riskier while Zed-related processes existed.\",\"source_ref\":\"session:019df4da-3522-776c-986b-78e057101a55\",\"evidence_ref\":\"branch excerpt message 47d2bdb6 and last_assistant_text\",\"confidence\":0.98,\"freshness\":0.99,\"importance\":0.88,\"reason\":\"Durable machine-specific space-saving pattern and concrete paths moved.\"},{\"type\":\"procedural\",\"subtype\":\"disk_cleanup\",\"scope\":\"global\",\"title\":\"Safe /tmp cleanup policy\",\"content\":\"For /tmp cleanup on this machine, prefer bin/tmp-cleanup to delete only top-level files older than 3 days. Full /tmp wipes are treated as risky and require explicit scope choice.\",\"source_ref\":\"session:019df4da-3522-776c-986b-78e057101a55\",\"evidence_ref\":\"branch excerpt messages 653400fb, e5be1b7c, 31d2eadd\",\"confidence\":0.97,\"freshness\":0.98,\"importance\":0.74,\"reason\":\"Reusable cleanup rule with direct operational value.\"},{\"type\":\"project\",\"subtype\":\"disk_usage_fact\",\"scope\":\"global\",\"title\":\"Root partition nearly full during Linux update\",\"content\":\"The root filesystem backing ~ was 14G total, 13G used, 494M free, 97% full. /mnt/rpi was around 40% used. This was the blocker while updating Linux.\",\"source_ref\":\"session:019df4da-3522-776c-986b-78e057101a55\",\"evidence_ref\":\"branch excerpt message 8e674418\",\"confidence\":0.96,\"freshness\":0.95,\"importance\":0.72,\"reason\":\"Useful host state and update blocker context.\"},{\"type\":\"artifact\",\"subtype\":\"filesystem_paths\",\"scope\":\"global\",\"title\":\"High-value home paths found on root\",\"content\":\"Largest paths under home/root during the cleanup were ~/.local (~1.7G), ~/.cache (~160M), ~/.bun (~142M), ~/.zed_server (~85M), ~/.venvs (~19M), and ~/.pnpm-store (~17M). ~/.local was the main remaining candidate for deeper cleanup.\",\"source_ref\":\"session:019df4da-3522-776c-986b-78e057101a55\",\"evidence_ref\":\"branch excerpt message 8e674418\",\"confidence\":0.94,\"freshness\":0.95,\"importance\":0.66,\"reason\":\"Reusable sizing snapshot for future space work.\"},{\"type\":\"episode\",\"subtype\":\"maintenance\",\"scope\":\"global\",\"title\":\"Linux update space-recovery pass\",\"content\":\"During a Linux update, disk space was recovered by moving safe large user data to /mnt/rpi, then validating that /tmp cleanup found nothing older than 3 days. Root space improved from 494M free to 1.6G free.\",\"source_ref\":\"session:019df4da-3522-776c-986b-78e057101a55\",\"evidence_ref\":\"last_assistant_text and branch excerpt message 31d2eadd\",\"confidence\":0.95,\"freshness\":0.98,\"importance\":0.7,\"reason\":\"Compact handoff summary with measurable outcome.\"}],\"reason\":\"Captured durable machine/storage procedures, a blocker state, and one reusable episode summary. Excluded session-local mechanics.\"}",
    "result": {
      "memories": [
        {
          "type": "project",
          "subtype": "disk_space_management",
          "scope": "global",
          "title": "Use /mnt/rpi for large user caches when root is tight",
          "content": "On ballbox-first, root/home can fill fast during updates; safe large user/tool caches were moved to /mnt/rpi and symlinked back to free space. Moved: ~/go, ~/.bun, ~/.zed_server, ~/venvs, ~/downloads, ~/.pnpm-store. ~/.local/share/zed was intentionally left in place because it was larger and riskier while Zed-related processes existed.",
          "source_ref": "session:019df4da-3522-776c-986b-78e057101a55",
          "evidence_ref": "branch excerpt message 47d2bdb6 and last_assistant_text",
          "confidence": 0.98,
          "freshness": 0.99,
          "importance": 0.88,
          "reason": "Durable machine-specific space-saving pattern and concrete paths moved."
        },
        {
          "type": "procedural",
          "subtype": "disk_cleanup",
          "scope": "global",
          "title": "Safe /tmp cleanup policy",
          "content": "For /tmp cleanup on this machine, prefer bin/tmp-cleanup to delete only top-level files older than 3 days. Full /tmp wipes are treated as risky and require explicit scope choice.",
          "source_ref": "session:019df4da-3522-776c-986b-78e057101a55",
          "evidence_ref": "branch excerpt messages 653400fb, e5be1b7c, 31d2eadd",
          "confidence": 0.97,
          "freshness": 0.98,
          "importance": 0.74,
          "reason": "Reusable cleanup rule with direct operational value."
        },
        {
          "type": "project",
          "subtype": "disk_usage_fact",
          "scope": "global",
          "title": "Root partition nearly full during Linux update",
          "content": "The root filesystem backing ~ was 14G total, 13G used, 494M free, 97% full. /mnt/rpi was around 40% used. This was the blocker while updating Linux.",
          "source_ref": "session:019df4da-3522-776c-986b-78e057101a55",
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          "confidence": 0.96,
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          "importance": 0.72,
          "reason": "Useful host state and update blocker context."
        },
        {
          "type": "artifact",
          "subtype": "filesystem_paths",
          "scope": "global",
          "title": "High-value home paths found on root",
          "content": "Largest paths under home/root during the cleanup were ~/.local (~1.7G), ~/.cache (~160M), ~/.bun (~142M), ~/.zed_server (~85M), ~/.venvs (~19M), and ~/.pnpm-store (~17M). ~/.local was the main remaining candidate for deeper cleanup.",
          "source_ref": "session:019df4da-3522-776c-986b-78e057101a55",
          "evidence_ref": "branch excerpt message 8e674418",
          "confidence": 0.94,
          "freshness": 0.95,
          "importance": 0.66,
          "reason": "Reusable sizing snapshot for future space work."
        },
        {
          "type": "episode",
          "subtype": "maintenance",
          "scope": "global",
          "title": "Linux update space-recovery pass",
          "content": "During a Linux update, disk space was recovered by moving safe large user data to /mnt/rpi, then validating that /tmp cleanup found nothing older than 3 days. Root space improved from 494M free to 1.6G free.",
          "source_ref": "session:019df4da-3522-776c-986b-78e057101a55",
          "evidence_ref": "last_assistant_text and branch excerpt message 31d2eadd",
          "confidence": 0.95,
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          "importance": 0.7,
          "reason": "Compact handoff summary with measurable outcome."
        }
      ],
      "reason": "Captured durable machine/storage procedures, a blocker state, and one reusable episode summary. Excluded session-local mechanics."
    }
  }
}

[Showing lines 198-858 of 858 (50.0KB limit). Full output: /tmp/pi-bash-a30fe3f7a03bc91c.log]


bash

python3 - <<'PY'
import json, pathlib
paths=sorted(pathlib.Path('/home/sebas/pi-config/sessions/session-memory-jobs').glob('20260524T*.json'))
for p in paths[-8:]:
 d=json.loads(p.read_text())
 a=d.get('analysis',{})
 print(p.name, a.get('status'), a.get('saved_count'), a.get('fallback_error'))
PY


{
  "job_id": "20260524T091449627951Z",
  "queued_at": "2026-05-24T09:14:49Z",
  "status": "queued",
  "job_type": "session_memory_analysis",
  "payload": {
    "session_id": "019df92c-e0ad-77b9-8651-27b7657bd979",
    "session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T17-26-01-134Z_019df92c-e0ad-77b9-8651-27b7657bd979.jsonl",
    "cwd": "/home/sebas",
    "scope": "global",
    "reason": "periodic_sweep",
    "trigger": "periodic_sweeper",
    "branch": [
      {
        "type": "message",
        "id": "1602bef2",
        "parentId": "ac6b0192",
        "timestamp": "2026-05-05T17:27:54.319Z",
        "message": {
          "role": "user",
          "content": [
            {
              "type": "text",
              "text": "En otra sesión vimos que el servicio de memoria prácticamente no usa inbox sino que promueve automáticamente todas las memorias que ingresan y esto de por sí no está mal pero parecería que estamos ingresando pocas memorias de buena calidad y perdiendo una mayor cantidad de memorias de inicialmente menor calidad que podrían ser promovidas y generar más insights. Este claim de que hay pocas memorias igual también puede estar relacionado al hecho de que uno estaba andando el hook de session end de la gente y recientemente se arregló así que deberían haber corrido unos cinco o seis eventos de estos. ¿Quieres extrapolar si de haber estado andando esto hubieran sido muchas más memorias o no tantas?\nEmpecemos por ahí el análisis y si es el caso de que igual no hubieran sido tantas memorias podemos repensar la manera en que se ingiren memorias para obtener más a pesar de que puedan ser de menor calidad."
            }
          ],
          "timestamp": 1778002074309
        }
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              "thinking": "**Analyzing memory service**\n\nI need to analyze the memory service and possibly inspect the agents-database data. The user is asking if the session-end hook could have created more memories, so I should maybe query the service stats and check the current counts in the memory database. It might be helpful to look into recent events too. I’ll also consider using the skill memory-read, as that could be useful for this inspection. Let’s see what I find!",
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[1540 more lines in file. Use offset=221 to continue.]


20260524T091412473113Z.json queued None None
20260524T091412728379Z.json queued None None
20260524T091412893766Z.json queued None None
20260524T091413807808Z.json queued None None
20260524T091449627951Z.json completed 5 None
20260524T091505352462Z.json completed 2 None
20260524T091516493814Z.json completed 3 None
20260524T091527541638Z.json completed 5 None


**Considering job status adjustments**

I think I need to adjust the job status at the top level, though maybe it's not that important. I could leave it as is, but I really want to avoid marking old jobs as processed before they've succeeded. It seems like a failure might linger. Maybe setting job status analysis states would help? The top-level "queued" status is a bit odd. I wonder if I could set it in the analyzer once it's completed. Okay, let's patch this up quickly!

edit

    job["status"] = "running"
    job["analysis"] = {
        "status": "running",
        "save": None,
        "saved": None,
        "memory_id": None,
        "memory_ids": [],
        "saved_count": 0,
    }

        job["analysis"]["raw"] = raw
        job["analysis"]["result"] = analysis
        job["analysis"]["status"] = "completed"
        if fallback_error is not None:
            job["analysis"]["fallback_error"] = str(fallback_error)
        job["analysis"]["save"] = bool(saved_items)

        job["analysis"]["raw"] = raw
        job["analysis"]["result"] = analysis
        job["analysis"]["status"] = "completed"
        job["status"] = "completed"
        if fallback_error is not None:
            job["analysis"]["fallback_error"] = str(fallback_error)
        job["analysis"]["save"] = bool(saved_items)

        job["status"] = "failed"
        job["analysis"]["status"] = "failed"
        job["analysis"]["error"] = str(exc)


Successfully replaced 3 block(s) in /home/sebas/pi-config/scripts/session-memory-analyzer.py.

     ...
 249
 250     job_file = Path(args.job_file)
 251     job = load_job(job_file)
 252     meta = session_meta(job)
+253     job["status"] = "running"
 253     job["analysis"] = {
 254         "status": "running",
 255         "save": None,
 256         "saved": None,
     ...
 280
 281         job["analysis"]["raw"] = raw
 282         job["analysis"]["result"] = analysis
 283         job["analysis"]["status"] = "completed"
+285         job["status"] = "completed"
 284         if fallback_error is not None:
 285             job["analysis"]["fallback_error"] = str(fallback_error)
 286         job["analysis"]["save"] = bool(saved_items)
 287         job["analysis"]["saved"] = bool(saved_items)
     ...
 292         save_job(job_file, job)
 293         print(json.dumps(job["analysis"], ensure_ascii=False))
 294         return 0
 295     except Exception as exc:
+298         job["status"] = "failed"
 296         job["analysis"]["status"] = "failed"
 297         job["analysis"]["error"] = str(exc)
 298         save_job(job_file, job)
 299         print(json.dumps(job["analysis"], ensure_ascii=False), file=sys.stderr)
     ...


bash

python3 -m py_compile /home/sebas/pi-config/scripts/session-memory-analyzer.py /home/sebas/pi-config/scripts/session-memory-sweeper.py && python3 /home/sebas/pi-config/scripts/session-memory-analyzer.py --job-file /home/sebas/pi-config/sessions/session-memory-jobs/20260524T091449627951Z.json >/tmp/sma.out && tail -n 1 /tmp/sma.out && python3 - <<'PY'
import json
p='/home/sebas/pi-config/sessions/session-memory-jobs/20260524T091449627951Z.json'
d=json.load(open(p))
print(d['status'], d['analysis']['status'])
PY


{"status": "completed", "save": true, "saved": true, "memory_id": "mem_3b089c54e76d4a79a5bb8836551e9571", "memory_ids": ["mem_3b089c54e76d4a79a5bb8836551e9571", "mem_d8269f8eb0104dc3961fd2fb9762a0f0", "mem_b7c29c94c9aa44448e1a043e2740ab87", "mem_7241420239534c22be6b71d3588ca81f", "mem_7bd9f85afc174694af2ab2653786ca8e"], "saved_count": 5, "raw": "{\"memories\":[{\"type\":\"episode\",\"subtype\":\"analysis\",\"scope\":\"global\",\"title\":\"Memory ingestion likely undercounts low-quality candidates\",\"content\":\"The memory service promotes ingested memories automatically and may be losing useful lower-quality candidates because too few high-quality memories are being ingested up front. Session-end hook failures may also have reduced observed volume; fixing the hook should be checked before changing ingestion policy.\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"branch excerpt: user message id 1602bef2 and assistant analysis on session-end hook behavior\",\"confidence\":0.78,\"freshness\":0.78,\"importance\":0.83,\"reason\":\"Durable product insight about ingestion strategy and observed undercount risk.\"},{\"type\":\"project\",\"subtype\":\"system_behavior\",\"scope\":\"project\",\"title\":\"Agents-database memory service auto-promotes ingested memories\",\"content\":\"The shared agent-memory service does not appear to rely on an inbox flow; memories entering the service are promoted automatically. Any quality-control strategy must happen before or at ingestion time, not via deferred inbox triage.\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"branch excerpt: user message id 1602bef2\",\"confidence\":0.7,\"freshness\":0.72,\"importance\":0.79,\"reason\":\"Useful durable project fact for future memory pipeline work.\"},{\"type\":\"decision\",\"subtype\":\"debugging_blocker\",\"scope\":\"project\",\"title\":\"Fix provider auth before validating memory ingest end-to-end\",\"content\":\"The current blocker is provider auth for `openai-codex` (`401 refresh_token_reused` / no API key), not the dispatcher hook itself. Validate auth first, then rerun the simulated and real ingest path.\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"last assistant text\",\"confidence\":0.95,\"freshness\":0.97,\"importance\":0.88,\"reason\":\"Concrete blocker and next step likely to matter in follow-up sessions.\"},{\"type\":\"artifact\",\"subtype\":\"service_url\",\"scope\":\"global\",\"title\":\"agents-database status endpoint\",\"content\":\"agents-database status endpoint: http://100.116.176.16:8091/api/status\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"branch excerpt / server details\",\"confidence\":0.99,\"freshness\":0.99,\"importance\":0.62,\"reason\":\"Reusable operational reference for future checks.\"},{\"type\":\"procedural\",\"subtype\":\"analysis_method\",\"scope\":\"project\",\"title\":\"Session-memory analysis should inspect job logs plus SQLite tables\",\"content\":\"For memory-ingest audits, inspect the session-memory-jobs files, the SQLite schema (`ingestion_events`, `memories`, job tables), and code paths in `session-memory-analyzer.py` / `session-memory-dispatcher.py` to correlate observed memory counts with actual pipeline behavior.\",\"source_ref\":\"session:019df92c-e0ad-77b9-8651-27b7657bd979\",\"evidence_ref\":\"branch excerpt: assistant tool calls reading session-memory-analyzer.py, dispatcher, and SQLite schema\",\"confidence\":0.81,\"freshness\":0.8,\"importance\":0.7,\"reason\":\"Reusable procedure for future audits of the memory pipeline.\"}],\"reason\":\"Session mostly contained investigation of memory ingestion volume and a blocker in provider auth. Kept the durable product insight, one concrete decision/blocker, one reusable procedure, and one operational artifact.\"}", "result": {"memories": [{"type": "episode", "subtype": "analysis", "scope": "global", "title": "Memory ingestion likely undercounts low-quality candidates", "content": "The memory service promotes ingested memories automatically and may be losing useful lower-quality candidates because too few high-quality memories are being ingested up front. Session-end hook failures may also have reduced observed volume; fixing the hook should be checked before changing ingestion policy.", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "branch excerpt: user message id 1602bef2 and assistant analysis on session-end hook behavior", "confidence": 0.78, "freshness": 0.78, "importance": 0.83, "reason": "Durable product insight about ingestion strategy and observed undercount risk."}, {"type": "project", "subtype": "system_behavior", "scope": "project", "title": "Agents-database memory service auto-promotes ingested memories", "content": "The shared agent-memory service does not appear to rely on an inbox flow; memories entering the service are promoted automatically. Any quality-control strategy must happen before or at ingestion time, not via deferred inbox triage.", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "branch excerpt: user message id 1602bef2", "confidence": 0.7, "freshness": 0.72, "importance": 0.79, "reason": "Useful durable project fact for future memory pipeline work."}, {"type": "decision", "subtype": "debugging_blocker", "scope": "project", "title": "Fix provider auth before validating memory ingest end-to-end", "content": "The current blocker is provider auth for `openai-codex` (`401 refresh_token_reused` / no API key), not the dispatcher hook itself. Validate auth first, then rerun the simulated and real ingest path.", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "last assistant text", "confidence": 0.95, "freshness": 0.97, "importance": 0.88, "reason": "Concrete blocker and next step likely to matter in follow-up sessions."}, {"type": "artifact", "subtype": "service_url", "scope": "global", "title": "agents-database status endpoint", "content": "agents-database status endpoint: http://100.116.176.16:8091/api/status", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "branch excerpt / server details", "confidence": 0.99, "freshness": 0.99, "importance": 0.62, "reason": "Reusable operational reference for future checks."}, {"type": "procedural", "subtype": "analysis_method", "scope": "project", "title": "Session-memory analysis should inspect job logs plus SQLite tables", "content": "For memory-ingest audits, inspect the session-memory-jobs files, the SQLite schema (`ingestion_events`, `memories`, job tables), and code paths in `session-memory-analyzer.py` / `session-memory-dispatcher.py` to correlate observed memory counts with actual pipeline behavior.", "source_ref": "session:019df92c-e0ad-77b9-8651-27b7657bd979", "evidence_ref": "branch excerpt: assistant tool calls reading session-memory-analyzer.py, dispatcher, and SQLite schema", "confidence": 0.81, "freshness": 0.8, "importance": 0.7, "reason": "Reusable procedure for future audits of the memory pipeline."}], "reason": "Session mostly contained investigation of memory ingestion volume and a blocker in provider auth. Kept the durable product insight, one concrete decision/blocker, one reusable procedure, and one operational artifact."}}
completed completed


Done.

What changed:
- added one periodic catch-up path:
  `scripts/session-memory-sweeper.py`
- installed systemd timer:
  `session-memory-sweeper.timer`
- cadence:
  - boot +10 min
  - then every 6h
- it scans recent sessions,
  skips obvious loop noise,
  and analyzes anything not yet processed

Also fixed analyzer behavior:
- before: if `pi`/provider auth failed,
  job just failed silently for memory purposes
- now:
  - tries cheap `pi` analysis first
  - if unavailable, falls back to a
    deterministic session-summary candidate
- so pipeline no longer fully depends on
  working provider auth

Docs:
- updated `pi-config/README.md`
  to reflect periodic sweeper +
  fallback behavior

What stays:
- session-end dispatcher still exists
- but periodic sweeper is now the
  reliable catch-up path
- this covers Telegram-heavy periods

Current status:
- timer active
- sweeper service installed and ran
- new jobs are getting analyzed
- some sessions were skipped as too small
- some were ingested

Not done yet:
- AGENTS-promotion suggestion flow
  as requested for later

Bluntly:
this is now much simpler:
1. opportunistic session-end queue
2. periodic sweeper catch-up
3. analyzer never hard-fails just because
   model auth is broken

If you want next, I can do one more pass:
remove/deprecate old confusing paths even
more aggressively.


[telegram] do agent promotion now and in case of memory upgrades downgrade on the periodic session also notify via telegram


**Looking into maintenance upgrades**

I’m considering whether maintenance can help with upgrading or downgrading something. Maybe I could notify users when the sweeper provides active vs. inbox counts and AGENTS candidates. That seems more straightforward! I should also inspect the telegram-notify skill to see how it can assist with these notifications. I want to ensure everything is clear and direct for the users. Let’s explore what options work best!

cd /home/sebas/agents-database && rg -n "consolidate|status == \"inbox\"|update_maintenance_job|memory_conflicts|promotion" src/shared_agent_memory -S | head -240

cd /home/sebas/pi-config && rg -n "telegram-notify|telegram" scripts session-memory* extensions -S | head -200


---
name: telegram-notify
description: Send Telegram notifications via bot
---

# telegram-notify

## Workflow

1. Ensure environment variables are set (or stored in `~/.agents/skills/telegram-notify/.env`):
   - `TELEGRAM_BOT_TOKEN`
   - `TELEGRAM_CHAT_ID`
   - fallback: `TELEGRAM_USER_ID` also works as chat id
2. Execute:
   - `~/.agents/skills/telegram-notify/telegram-notify "<message>"`
3. Confirm that the message was sent.

## Rules

- never print the token or chat id in outputs
- keep messages concise and action-oriented


src/shared_agent_memory/schema.py:129:CREATE TABLE IF NOT EXISTS memory_conflicts (
src/shared_agent_memory/schema.py:224:CREATE INDEX IF NOT EXISTS idx_memory_conflicts_mem ON memory_conflicts(memory_id);
src/shared_agent_memory/engine.py:49:                self.service.update_maintenance_job(
src/shared_agent_memory/engine.py:65:                self.service.update_maintenance_job(
src/shared_agent_memory/engine.py:77:            return self.service.consolidate_candidates(config=job.get("metadata") or {})
src/shared_agent_memory/cli.py:99:    consolidate = subparsers.add_parser("consolidate")
src/shared_agent_memory/cli.py:100:    consolidate.add_argument("memory_ids", nargs="+")
src/shared_agent_memory/cli.py:200:        updated = service.update_maintenance_job(
src/shared_agent_memory/cli.py:238:    elif args.command == "consolidate":
src/shared_agent_memory/cli.py:239:        print(json.dumps(service.consolidate(args.memory_ids), indent=2))
src/shared_agent_memory/service.py:274:    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:
src/shared_agent_memory/service.py:318:    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:
src/shared_agent_memory/service.py:492:            "memory_conflicts",
src/shared_agent_memory/service.py:516:            "memory_conflicts",
src/shared_agent_memory/service.py:1371:    def update_maintenance_job(
src/shared_agent_memory/service.py:1662:            INSERT INTO memory_conflicts (id, memory_id, conflicting_memory_id, reason, created_at, metadata_json)


rg: session-memory*: No such file or directory (os error 2)


            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(
                """
                SELECT * FROM memories
                WHERE (
                    status = 'inbox'
                    OR (type = 'episode' AND status = 'active')
                )
                  AND created_at <= ?
                ORDER BY created_at ASC
                LIMIT ?
                """,
                (cutoff, max_candidates),
            ).fetchall()

            candidates = [self._row_to_memory(row) for row in rows]
            stats["candidates"] = len(candidates)

            for candidate in candidates:
                related = conn.execute(
                    """
                    SELECT * FROM memories
                    WHERE status = 'active'
                      AND id != ?
                      AND type = ?
                      AND scope = ?
                    ORDER BY updated_at DESC
                    LIMIT 50
                    """,
                    (candidate["id"], candidate["type"], candidate["scope"]),
                ).fetchall()
                related_memories = [self._row_to_memory(row) for row in related]

                best_match = None
                best_score = 0.0
                for other in related_memories:
                    lexical = self._lexical_score(
                        normalize_text(candidate["content"]).split(),
                        normalize_text(other["content"]),
                    )
                    semantic = self._semantic_score(candidate.get("embedding") or [], other.get("embedding"))
                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)
                    if score > best_score:
                        best_score = score
                        best_match = other

                if best_match and candidate["content"] == best_match["content"]:
                    self._link_memories(conn, candidate["id"], best_match["id"], "related_to", "exact_duplicate")
                    conn.execute(
                        "UPDATE memories SET status = ?, updated_at = ? WHERE id = ?",
                        ("archived", utcnow(), candidate["id"]),
                    )

[1320 more lines in file. Use offset=390 to continue.]


    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(
                """
                SELECT * FROM memories
                WHERE (
                    status = 'inbox'
                    OR (type = 'episode' AND status = 'active')
                )
                  AND created_at <= ?
                ORDER BY created_at ASC
                LIMIT ?
                """,
                (cutoff, max_candidates),
            ).fetchall()

            candidates = [self._row_to_memory(row) for row in rows]
            stats["candidates"] = len(candidates)

            for candidate in candidates:
                related = conn.execute(
                    """
                    SELECT * FROM memories
                    WHERE status = 'active'
                      AND id != ?
                      AND type = ?
                      AND scope = ?
                    ORDER BY updated_at DESC
                    LIMIT 50
                    """,
                    (candidate["id"], candidate["type"], candidate["scope"]),
                ).fetchall()
                related_memories = [self._row_to_memory(row) for row in related]

                best_match = None
                best_score = 0.0
                for other in related_memories:
                    lexical = self._lexical_score(
                        normalize_text(candidate["content"]).split(),
                        normalize_text(other["content"]),
                    )
                    semantic = self._semantic_score(candidate.get("embedding") or [], other.get("embedding"))
                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)
                    if score > best_score:
                        best_score = score
                        best_match = other

                if best_match and candidate["content"] == best_match["content"]:
                    self._link_memories(conn, candidate["id"], best_match["id"], "related_to", "exact_duplicate")
                    conn.execute(
                        "UPDATE memories SET status = ?, updated_at = ? WHERE id = ?",
                        ("archived", utcnow(), candidate["id"]),
                    )
                    stats["archived"] += 1
                    stats["linked"] += 1
                    continue

                if best_match and best_score >= high_threshold:
                    self._link_memories(conn, candidate["id"], best_match["id"], "related_to", "high_similarity")
                    conn.execute(
                        "UPDATE memories SET status = ?, updated_at = ? WHERE id = ?",
                        ("archived", utcnow(), candidate["id"]),
                    )
                    stats["archived"] += 1
                    stats["linked"] += 1
                    continue

                if best_match and best_score >= mid_threshold:
                    if llm_enabled:
                        synthesized = self._llm_summarize_pair(
                            candidate,
                            best_match,
                            provider=llm_provider,
                            command=llm_command,
                        )
                        if synthesized:
                            conn.execute(
                                "UPDATE memories SET summary = ?, updated_at = ?, metadata_json = ? WHERE id = ?",
                                (
                                    synthesized,
                                    utcnow(),
                                    self.store.dumps(
                                        {
                                            **candidate.get("metadata", {}),
                                            "synthesis_sources": [candidate["id"], best_match["id"]],
                                            "synthesis_provider": llm_provider,
                                            "synthesis_command": llm_command,
                                        }
                                    ),
                                    candidate["id"],
                                ),
                            )
                            stats["llm_summaries"] += 1
                    self._link_memories(conn, candidate["id"], best_match["id"], "related_to", "mid_similarity")
                    stats["linked"] += 1

                if best_match and best_score >= conflict_threshold and self._conflict_signal(candidate, best_match):
                    self._record_conflict(conn, candidate["id"], best_match["id"], "potential_contradiction")
                    self._link_memories(conn, candidate["id"], best_match["id"], "contradicts", "heuristic")
                    stats["conflicts"] += 1

[1272 more lines in file. Use offset=438 to continue.]


from __future__ import annotations

from typing import Any

from .service import MemoryService, make_id, utcnow

class MemoryEngine:
    def __init__(self, service: MemoryService) -> None:
        self.service = service

    def process_pending(self, limit: int | None = None) -> list[dict[str, Any]]:
        events = self.service.list_pending_events()
        if limit is not None:
            events = events[:limit]

        processed: list[dict[str, Any]] = []
        for event in events:
            try:
                processed.append(self._process_event(event))
                self.service.mark_event(event["id"], "completed", outcome="extracted")
            except Exception as exc:  # pragma: no cover - defensive
                self.service.mark_event(event["id"], "failed", error_message=str(exc))
        return processed

    def run_due_maintenance(self, *, limit: int | None = None, job_id: str | None = None) -> list[dict[str, Any]]:
        if job_id:
            jobs = [self.service.get_maintenance_job(job_id)]
        else:
            jobs = self.service.list_due_maintenance_jobs(limit=limit)
        results: list[dict[str, Any]] = []
        for job in jobs:
            run = self.service.create_maintenance_run(job["id"])
            try:
                stats = self._run_maintenance_job(job)
                self.service.finish_maintenance_run(
                    run["id"],
                    status="completed",
                    summary=f"{job['job_type']} completed",
                    stats=stats,
                )
                next_due = self.service._compute_next_due(
                    utcnow(),
                    job["cadence"],
                    job.get("interval_minutes"),
                    job.get("window_start"),
                    job.get("window_end"),
                )
                self.service.update_maintenance_job(
                    job["id"],
                    next_due_at=next_due,
                    last_run_at=utcnow(),
                    last_status="completed",
                    last_summary=f"{job['job_type']} completed",
                )
                results.append({"job_id": job["id"], "status": "completed", "stats": stats})
            except Exception as exc:
                self.service.finish_maintenance_run(
                    run["id"],
                    status="failed",
                    summary=f"{job['job_type']} failed",
                    stats={},
                    error_message=str(exc),
                )
                self.service.update_maintenance_job(
                    job["id"],
                    last_run_at=utcnow(),
                    last_status="failed",
                    last_summary=str(exc),
                )
                results.append({"job_id": job["id"], "status": "failed", "error": str(exc)})
        return results

    def _run_maintenance_job(self, job: dict[str, Any]) -> dict[str, Any]:
        job_type = job.get("job_type")
        if job_type == "consolidation":
            return self.service.consolidate_candidates(config=job.get("metadata") or {})
        return {"skipped": True, "reason": f"unknown job_type {job_type}"}

    def _process_event(self, event: dict[str, Any]) -> dict[str, Any]:
        with self.service.store.connection() as conn:
            source = conn.execute("SELECT * FROM sources WHERE id = ?", (event["source_id"],)).fetchone()
            if source is None:
                raise KeyError(event["source_id"])
            source_dict = dict(source)

        content = source_dict["content"]
        summary = self.service._make_summary(content)
        memory = self.service.ingest(
            {
                "id": make_id("mem"),
                "type": "episode",
                "scope": self._normalize_scope(event["requested_scope"]),
                "status": "inbox",
                "source_kind": source_dict["source_kind"],
                "title": source_dict["title"],
                "content": content,
                "summary": summary,
                "confidence": 0.6,
                "freshness": 1.0,
                "observed_at": source_dict["created_at"],
                "source_ref": source_dict["source_ref"],
                "evidence_ref": source_dict["id"],
                "embedding": self.service._text_embedding(content),
                "metadata": {
                    "ingested_by": "memory-engine",
                    "trigger_type": event["trigger_type"],
                    "captured_at": utcnow(),
                },
            }
        )
        return {"event_id": event["id"], "memory_id": memory["id"]}

    def _normalize_scope(self, requested_scope: str) -> str:
        if requested_scope in {"global", "project", "repo", "agent", "session"}:
            return requested_scope
        return "session"