{"type":"session","version":3,"id":"f0a1c0b8-8aa0-4b4a-b867-64f8f1314be8","timestamp":"2026-05-04T21:33:43.738Z","cwd":"/home/rurouni"} {"type":"model_change","id":"8ec87b6b","parentId":null,"timestamp":"2026-05-04T21:35:03.630Z","provider":"local-qwen","modelId":"qwen3.5-9b"} {"type":"thinking_level_change","id":"d06cad73","parentId":"8ec87b6b","timestamp":"2026-05-04T21:35:03.630Z","thinkingLevel":"off"} {"type":"custom","customType":"model-snapshot","data":{"timestamp":1777930503636,"provider":"local-qwen","modelApi":"openai-completions","modelId":"qwen3.5-9b"},"id":"45b50048","parentId":"d06cad73","timestamp":"2026-05-04T21:35:03.636Z"} {"type":"message","id":"dfbcf0db","parentId":"45b50048","timestamp":"2026-05-04T21:35:03.640Z","message":{"role":"user","content":[{"type":"text","text":"Clean up your memory organize it make it efficient and improve each part of it make sure we are not wasting tokens for each prompt search the web for tips and tricks"}],"timestamp":1777930503640}} {"type":"custom_message","customType":"openclaw.runtime-context","content":"Conversation info (untrusted metadata):\n```json\n{\n \"chat_id\": \"telegram:7229867039\",\n \"message_id\": \"2068\",\n \"sender_id\": \"7229867039\",\n \"sender\": \"Raymond\",\n \"timestamp\": \"Mon 2026-05-04 14:35 PDT\"\n}\n```\n\nSender (untrusted metadata):\n```json\n{\n \"label\": \"Raymond (7229867039)\",\n \"id\": \"7229867039\",\n \"name\": \"Raymond\"\n}\n```","display":false,"details":{"source":"openclaw-runtime-context"},"id":"ded56d29","parentId":"dfbcf0db","timestamp":"2026-05-04T21:35:03.641Z"} {"type":"message","id":"f169b3af","parentId":"ded56d29","timestamp":"2026-05-04T21:35:08.780Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to optimize MEMORY.md for token efficiency β€” I should start by reading the current file to understand its structure before searching for best practices.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"pFWswmfNRPdRGJxz5C8rZR2Ztu8rYYsE","name":"read","arguments":{"path":"/home/rurouni/MEMORY.md"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930503641,"responseId":"chatcmpl-893FbAYhHlAStafFsMa5zkGKxJzRhQtv","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"9e4fbac7","parentId":"f169b3af","timestamp":"2026-05-04T21:35:08.853Z","message":{"role":"toolResult","toolCallId":"pFWswmfNRPdRGJxz5C8rZR2Ztu8rYYsE","toolName":"read","content":[{"type":"text","text":"# MEMORY.md β€” Ruruni Long-Term Memory\n\nCompact durable memory for Raymond’s main direct session. Keep this file lean: stable preferences, active setup, standing cautions, and current decisions only. Put raw/session details in `memory/YYYY-MM-DD.md`; archive low-value fragments as `.txt`.\n\n## Raymond / Style\n\n- Raymond; Pacific time. Assistant name: **Ruruni** πŸ—‘οΈ.\n- Tone: casual, direct, warm, witty, technically sharp, privacy-protective, not yappy.\n- Technical help: start with the answer/next action, use copy/paste commands, prefer safe/reversible steps, warn before risky/destructive/external actions.\n- Preserve Raymond’s natural voice when rewriting. Key domains: Linux/Debian/CachyOS, servers, OpenClaw/AI infra, DB2/IBM i SQL, warehouse coaching/process/safety, writing, troubleshooting, game dev.\n\n## OpenClaw / Model Stack\n\n- OpenClaw `2026.5.2`; default primary is `openai-codex/gpt-5.5` via ChatGPT Plus OAuth. Do **not** replace unless Raymond asks.\n- Current routing verified 2026-05-03: primary `openai-codex/gpt-5.5`; fallbacks `deepseek/deepseek-v4-pro`, `openrouter/minimax/minimax-m2.7`, `local-qwen/qwen3.5-9b`.\n- Leave `deepseek/deepseek-v4-pro` as first fallback for now: Raymond noted it is half off until May 31. Confirm before changing/removing it.\n- Aliases: `local` β†’ `local-qwen/qwen3.5-9b`; `MiniMax` β†’ `openrouter/minimax/minimax-m2.7`.\n- Heartbeat and sub-agent defaults use `local-qwen/qwen3.5-9b`.\n- Context tuning: `contextTokens: 128000`; compaction `reserveTokensFloor: 24000`; `maxActiveTranscriptBytes: 1000000`; `memoryFlush.softThresholdTokens: 60000`.\n- Raymond intentionally keeps OpenClaw LAN-bound behind OPNsense; do not β€œfix” LAN bind/firewall posture without asking.\n\n## Local LLM Host\n\n- Service: systemd `llama-local`, endpoint `http://127.0.0.1:8081/v1`.\n- Model: Qwen3.5-9B Q5_K_M GGUF at `/home/rurouni/models/qwen3.5-9b/Qwen3.5-9B-Q5_K_M.gguf`.\n- Service command: `/usr/local/bin/llama-server -m /home/rurouni/models/qwen3.5-9b/Qwen3.5-9B-Q5_K_M.gguf -c 65536 -ngl 99 -fa on -t 28 -b 2048 -ub 2048 -ctk q8_0 -ctv q8_0 --host 127.0.0.1 --port 8081 --parallel 1 --cont-batching --cache-reuse 256`.\n- Verified 2026-05-03: `/v1/models` returns the 9B model; logs show `n_ctx_slot = 65536`; throughput about 825 prompt tok/s and 37 gen tok/s on a ~9.9k-token request.\n- Hardware observed 2026-05-03: GTX 1080 Ti 11GB, Xeon E5-2697A v4 (32 threads), 62GiB RAM. With Qwen3.5-9B 65k context loaded: ~9.0GB VRAM used / ~2.1GB free. Tight but workable; avoid extra GPU loads.\n- Old Qwen3.5-4B was removed from config and moved to `/archive/models/old-qwen/Qwen3.5-4B-Q8_0.gguf`; do not re-add unless Raymond asks.\n\n## Memory / Retrieval Stack\n\n- **Mem0 OSS + Qdrant**: long-term memory. Embedder: Qwen3-Embedding-0.6B (CPU) at `/models/embeddings/Qwen3-Embedding-0.6B-hf`. Mem0 bridge at `127.0.0.1:8765` (systemd user unit). Qdrant at `127.0.0.1:6333` (Docker).\n- **QMD Light**: workspace/doc search, BM25 only (no vectors/reranker). Index at `/data/openclaw-memory/qmd/cache/qmd/index.sqlite`.\n- **Lossless Claw**: installed (`@martian-engineering/lossless-claw@0.9.3`) but **not active** (`contextEngine: legacy`). Do not enable without Raymond.\n- **No automatic memory injection** into prompts. Use `/data/openclaw-memory/bin/mem0-relevant` for manual queries.\n- Token budgets: normal 300-800, project-heavy 800-1500, hard max 2000.\n- OpenClaw builtin memory: FTS only (no embeddings/reranking); keep indexed markdown minimal.\n- Memory hygiene: `MEMORY.md` + `memory/YYYY-MM-DD.md` only for auto/durable notes; archive low-value session fragments as `.txt`; project details live in `/home/rurouni/projects/README.md` + `/PROJECT_STATE.md`, not raw daily injection.\n\n## System / Storage / Admin\n\n- Storage: root/home on NVMe; `/backup` HDD; `/archive` HDD cold storage; `/models` Samsung SSD; `/data` RAID0 Intel SSDs; `/dev/shm/ai-hot` 16G tmpfs.\n- `/home/rurouni/backups` symlinks to `/backup/rurouni/openclaw-backups`.\n- AI storage env: HF/cache under `/data/cache/hf`; temp under `/data/tmp`; persisted in environment/profile/service config.\n- Do not store Raymond’s sudo password. Prefer narrow sudoers allowlists; `sudo -n true` may fail even when specific allowlisted commands work.\n\n## Active Project β€” M710q Hackintosh\n\n- Project path: `/home/rurouni/hackintosh-m710q/`; symlink `/home/rurouni/projects/hackintosh-m710q`.\n- Reset handoff file: `/home/rurouni/hackintosh-m710q/HANDOFF.md`; full black-screen research: `research/black-screen-research.md`; quick notes: `docs/BLACK_SCREEN_NOTES.md`.\n- Target: Lenovo ThinkCentre M710q `10MR0049US`, i3-6100T Skylake HD 530 `[8086:1912]`, B250; macOS Ventura Recovery USB; OpenCore 1.0.7; current SMBIOS `iMac18,1`.\n- 2026-05-04 09:20 state: after black screen persisted, local + USB EFI were changed from `Macmini8,1` to `iMac18,1` per Skylake HD530 Ventura spoof guidance; `ocvalidate.linux` clean; USB unmounted. Current boot args: `-v keepsyms=1 debug=0x100 alcid=21 igfxonln=1 igfxagdc=0`.\n- iGPU properties are **Data**: `AAPL,ig-platform-id=00001259`, `device-id=12590000`, `framebuffer-patch-enable=01000000`, `framebuffer-stolenmem=00003001`. USB previously had two framebuffer keys as Strings; fixed earlier.\n- Next action: boot-test current `iMac18,1` USB using motherboard DisplayPort only; try all motherboard DP ports/cable replug/input switching. If black screen persists, inspect USB OpenCore logs before changing more.\n- Next fixes if needed, one at a time: diagnostic `-igfxvesa`; explicit KBL `59120000` DP connector `con1/con2/con3-alldata`; alternate KBL pair `00001659`/`16590000`; `AAPL,GfxYTile` only for visible glitches; Booter/MMIO only if logs still end at `EXITBS:START`.\n- Do not paste `SMBIOS.txt` publicly. Do not copy hackintosh-club QTJ2/UHD630/iMac20,1 GPU/SMBIOS wholesale to this HD530 build.\n"}],"isError":false,"timestamp":1777930508852}} {"type":"message","id":"80a1014e","parentId":"9e4fbac7","timestamp":"2026-05-04T21:35:14.839Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"I need to search for memory optimization best practices online before reorganizing MEMORY.md.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"h8izpmpJdiV7noeUdCgRpx5O2EtaDgzf","name":"exec","arguments":{"command":"curl -s \"https://docs.mem0.ai/memory-optimization\" 2>/dev/null | grep -i -A 5 \"token\" | head -30 || echo \"No direct docs found, searching broader...\""}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930508854,"responseId":"chatcmpl-iFr2lp0VZ76QmP2XOJ25MRJo27pEHMvX","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"220ad806","parentId":"80a1014e","timestamp":"2026-05-04T21:35:16.658Z","message":{"role":"toolResult","toolCallId":"h8izpmpJdiV7noeUdCgRpx5O2EtaDgzf","toolName":"exec","content":[{"type":"text","text":"(no output)"}],"details":{"status":"completed","exitCode":0,"durationMs":1808,"aggregated":"","cwd":"/home/rurouni"},"isError":false,"timestamp":1777930516657}} {"type":"message","id":"10028c8d","parentId":"220ad806","timestamp":"2026-05-04T21:35:19.693Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Since the specific optimization docs weren't found, I'll search the OpenClaw docs directory for memory-related configuration guidance.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"FHJswwBqNaPvPq4EiYcmufLtIUWzm5K8","name":"exec","arguments":{"command":"find /usr/lib/node_modules/openclaw/docs -name \"*.md\" -exec grep -l -i \"memory\\|token\\|context\" {} \\; 2>/dev/null | head -10"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930516659,"responseId":"chatcmpl-9Ib2zrVZXxlrk4ESxFidf1Z4m3ZKLs6M","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"343f31f4","parentId":"10028c8d","timestamp":"2026-05-04T21:35:20.838Z","message":{"role":"toolResult","toolCallId":"FHJswwBqNaPvPq4EiYcmufLtIUWzm5K8","toolName":"exec","content":[{"type":"text","text":"/usr/lib/node_modules/openclaw/docs/auth-credential-semantics.md\n/usr/lib/node_modules/openclaw/docs/perplexity.md\n/usr/lib/node_modules/openclaw/docs/ci.md\n/usr/lib/node_modules/openclaw/docs/gateway/config-channels.md\n/usr/lib/node_modules/openclaw/docs/gateway/background-process.md\n/usr/lib/node_modules/openclaw/docs/gateway/protocol.md\n/usr/lib/node_modules/openclaw/docs/gateway/tools-invoke-http-api.md\n/usr/lib/node_modules/openclaw/docs/gateway/remote-gateway-readme.md\n/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md\n/usr/lib/node_modules/openclaw/docs/gateway/openresponses-http-api.md"}],"details":{"status":"completed","exitCode":0,"durationMs":1136,"aggregated":"/usr/lib/node_modules/openclaw/docs/auth-credential-semantics.md\n/usr/lib/node_modules/openclaw/docs/perplexity.md\n/usr/lib/node_modules/openclaw/docs/ci.md\n/usr/lib/node_modules/openclaw/docs/gateway/config-channels.md\n/usr/lib/node_modules/openclaw/docs/gateway/background-process.md\n/usr/lib/node_modules/openclaw/docs/gateway/protocol.md\n/usr/lib/node_modules/openclaw/docs/gateway/tools-invoke-http-api.md\n/usr/lib/node_modules/openclaw/docs/gateway/remote-gateway-readme.md\n/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md\n/usr/lib/node_modules/openclaw/docs/gateway/openresponses-http-api.md","cwd":"/home/rurouni"},"isError":false,"timestamp":1777930520837}} {"type":"message","id":"0e0614be","parentId":"343f31f4","timestamp":"2026-05-04T21:35:23.927Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me search for memory configuration options in the docs to find token efficiency best practices.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"frArC3ud59IK5SZAPNIA2viBhiiWpLWM","name":"exec","arguments":{"command":"grep -r -i \"memoryFlush\\|contextTokens\\|reserveTokens\\|token budget\\|token efficiency\" /usr/lib/node_modules/openclaw/docs/ 2>/dev/null | head -20"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930520839,"responseId":"chatcmpl-iXhG4huLrko4K19n5pz47LBH5YIgGpf9","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"88d2b96e","parentId":"0e0614be","timestamp":"2026-05-04T21:35:23.986Z","message":{"role":"toolResult","toolCallId":"frArC3ud59IK5SZAPNIA2viBhiiWpLWM","toolName":"exec","content":[{"type":"text","text":"/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md: contextTokens: 96000,\n/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md: - Matching model `contextTokens` preserves an explicit runtime cap when present; use it to limit effective context without changing native model metadata.\n/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md: - `models.providers.*.contextTokens`: default effective runtime context cap for models under this provider when the model entry does not set `contextTokens`.\n/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md: - `models.providers.*.models.*.contextTokens`: optional runtime context cap. This overrides provider-level `contextTokens`; use it when you want a smaller effective context budget than the model's native `contextWindow`; `openclaw models list` shows both values when they differ.\n/usr/lib/node_modules/openclaw/docs/gateway/local-models.md:- OpenClaw derives context-window preflight thresholds from the detected model window, or from the uncapped model window when `agents.defaults.contextTokens` lowers the effective window. It warns below 20% with an **8k** floor. Hard blocks use the 10% threshold with a **4k** floor, capped to the effective context window so oversized model metadata cannot reject an otherwise valid user cap. If you hit that preflight, raise the server/model context limit or choose a larger model.\n/usr/lib/node_modules/openclaw/docs/gateway/config-agents.md: contextTokens: 200000,\n/usr/lib/node_modules/openclaw/docs/gateway/config-agents.md: reserveTokensFloor: 24000,\n/usr/lib/node_modules/openclaw/docs/gateway/config-agents.md: memoryFlush: {\n/usr/lib/node_modules/openclaw/docs/gateway/config-agents.md:- `memoryFlush`: silent agentic turn before auto-compaction to store durable memories. Set `model` to an exact provider/model such as `ollama/qwen3:8b` when this housekeeping turn should stay on a local model; the override does not inherit the active session fallback chain. Skipped when workspace is read-only.\n/usr/lib/node_modules/openclaw/docs/plugins/sdk-overview.md:| `api.registerMemoryFlushPlan(resolver)` | Memory flush plan resolver |\n/usr/lib/node_modules/openclaw/docs/plugins/sdk-overview.md:- `registerMemoryPromptSection`, `registerMemoryFlushPlan`, and\n/usr/lib/node_modules/openclaw/docs/plugins/sdk-overview.md:- `MemoryFlushPlan.model` can pin the flush turn to an exact `provider/model`\n/usr/lib/node_modules/openclaw/docs/plugins/sdk-migration.md: `api.registerMemoryFlushPlan(...)`,\n/usr/lib/node_modules/openclaw/docs/plugins/manifest.md:| `contextTokens` | `number` | Optional effective runtime context cap when different from `contextWindow`. |\n/usr/lib/node_modules/openclaw/docs/concepts/compaction.md:Before compaction, OpenClaw can run a **silent memory flush** turn to store durable notes to disk. Set `agents.defaults.compaction.memoryFlush.model` when this housekeeping turn should use a local model instead of the active conversation model:\n/usr/lib/node_modules/openclaw/docs/concepts/compaction.md: \"memoryFlush\": {\n/usr/lib/node_modules/openclaw/docs/concepts/context-engine.md: Called before each model run. The engine returns an ordered set of messages (and an optional `systemPromptAddition`) that fit within the token budget.\n/usr/lib/node_modules/openclaw/docs/concepts/model-providers.md: - `models.providers.*.contextWindow` / `contextTokens` / `maxTokens` set provider-level defaults; `models.providers.*.models[].contextWindow` / `contextTokens` / `maxTokens` override them per model.\n/usr/lib/node_modules/openclaw/docs/concepts/model-providers.md:- `openai-codex/gpt-5.5` uses the Codex catalog native `contextWindow = 400000` and default runtime `contextTokens = 272000`; override the runtime cap with `models.providers.openai-codex.models[].contextTokens`\n/usr/lib/node_modules/openclaw/docs/concepts/model-providers.md: models: [{ id: \"gpt-5.5\", contextTokens: 160000 }],"}],"details":{"status":"completed","exitCode":0,"durationMs":50,"aggregated":"/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md: contextTokens: 96000,\n/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md: - Matching model `contextTokens` preserves an explicit runtime cap when present; use it to limit effective context without changing native model metadata.\n/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md: - `models.providers.*.contextTokens`: default effective runtime context cap for models under this provider when the model entry does not set `contextTokens`.\n/usr/lib/node_modules/openclaw/docs/gateway/config-tools.md: - `models.providers.*.models.*.contextTokens`: optional runtime context cap. This overrides provider-level `contextTokens`; use it when you want a smaller effective context budget than the model's native `contextWindow`; `openclaw models list` shows both values when they differ.\n/usr/lib/node_modules/openclaw/docs/gateway/local-models.md:- OpenClaw derives context-window preflight thresholds from the detected model window, or from the uncapped model window when `agents.defaults.contextTokens` lowers the effective window. It warns below 20% with an **8k** floor. Hard blocks use the 10% threshold with a **4k** floor, capped to the effective context window so oversized model metadata cannot reject an otherwise valid user cap. If you hit that preflight, raise the server/model context limit or choose a larger model.\n/usr/lib/node_modules/openclaw/docs/gateway/config-agents.md: contextTokens: 200000,\n/usr/lib/node_modules/openclaw/docs/gateway/config-agents.md: reserveTokensFloor: 24000,\n/usr/lib/node_modules/openclaw/docs/gateway/config-agents.md: memoryFlush: {\n/usr/lib/node_modules/openclaw/docs/gateway/config-agents.md:- `memoryFlush`: silent agentic turn before auto-compaction to store durable memories. Set `model` to an exact provider/model such as `ollama/qwen3:8b` when this housekeeping turn should stay on a local model; the override does not inherit the active session fallback chain. Skipped when workspace is read-only.\n/usr/lib/node_modules/openclaw/docs/plugins/sdk-overview.md:| `api.registerMemoryFlushPlan(resolver)` | Memory flush plan resolver |\n/usr/lib/node_modules/openclaw/docs/plugins/sdk-overview.md:- `registerMemoryPromptSection`, `registerMemoryFlushPlan`, and\n/usr/lib/node_modules/openclaw/docs/plugins/sdk-overview.md:- `MemoryFlushPlan.model` can pin the flush turn to an exact `provider/model`\n/usr/lib/node_modules/openclaw/docs/plugins/sdk-migration.md: `api.registerMemoryFlushPlan(...)`,\n/usr/lib/node_modules/openclaw/docs/plugins/manifest.md:| `contextTokens` | `number` | Optional effective runtime context cap when different from `contextWindow`. |\n/usr/lib/node_modules/openclaw/docs/concepts/compaction.md:Before compaction, OpenClaw can run a **silent memory flush** turn to store durable notes to disk. Set `agents.defaults.compaction.memoryFlush.model` when this housekeeping turn should use a local model instead of the active conversation model:\n/usr/lib/node_modules/openclaw/docs/concepts/compaction.md: \"memoryFlush\": {\n/usr/lib/node_modules/openclaw/docs/concepts/context-engine.md: Called before each model run. The engine returns an ordered set of messages (and an optional `systemPromptAddition`) that fit within the token budget.\n/usr/lib/node_modules/openclaw/docs/concepts/model-providers.md: - `models.providers.*.contextWindow` / `contextTokens` / `maxTokens` set provider-level defaults; `models.providers.*.models[].contextWindow` / `contextTokens` / `maxTokens` override them per model.\n/usr/lib/node_modules/openclaw/docs/concepts/model-providers.md:- `openai-codex/gpt-5.5` uses the Codex catalog native `contextWindow = 400000` and default runtime `contextTokens = 272000`; override the runtime cap with `models.providers.openai-codex.models[].contextTokens`\n/usr/lib/node_modules/openclaw/docs/concepts/model-providers.md: models: [{ id: \"gpt-5.5\", contextTokens: 160000 }],","cwd":"/home/rurouni"},"isError":false,"timestamp":1777930523986}} {"type":"message","id":"cd5fc912","parentId":"88d2b96e","timestamp":"2026-05-04T21:35:27.831Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"I found the docs mention compaction and memoryFlush settings β€” let me read the compaction docs to understand token efficiency best practices for MEMORY.md.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"aWIJoh82Z0cOcxYfjhAZHw9FQXshjPik","name":"read","arguments":{"path":"/usr/lib/node_modules/openclaw/docs/concepts/compaction.md"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930523989,"responseId":"chatcmpl-n3vDfokHAaFjbc9IqdMsaTgvM3NnjSv5","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"23b8544f","parentId":"cd5fc912","timestamp":"2026-05-04T21:35:27.837Z","message":{"role":"toolResult","toolCallId":"aWIJoh82Z0cOcxYfjhAZHw9FQXshjPik","toolName":"read","content":[{"type":"text","text":"---\nsummary: \"How OpenClaw summarizes long conversations to stay within model limits\"\nread_when:\n - You want to understand auto-compaction and /compact\n - You are debugging long sessions hitting context limits\ntitle: \"Compaction\"\n---\n\nEvery model has a context window: the maximum number of tokens it can process. When a conversation approaches that limit, OpenClaw **compacts** older messages into a summary so the chat can continue.\n\n## How it works\n\n1. Older conversation turns are summarized into a compact entry.\n2. The summary is saved in the session transcript.\n3. Recent messages are kept intact.\n\nWhen OpenClaw splits history into compaction chunks, it keeps assistant tool calls paired with their matching `toolResult` entries. If a split point lands inside a tool block, OpenClaw moves the boundary so the pair stays together and the current unsummarized tail is preserved.\n\nThe full conversation history stays on disk. Compaction only changes what the model sees on the next turn.\n\n## Auto-compaction\n\nAuto-compaction is on by default. It runs when the session nears the context limit, or when the model returns a context-overflow error (in which case OpenClaw compacts and retries).\n\nYou will see:\n\n- `🧹 Auto-compaction complete` in verbose mode.\n- `/status` showing `🧹 Compactions: `.\n\n\nBefore compacting, OpenClaw automatically reminds the agent to save important notes to [memory](/concepts/memory) files. This prevents context loss.\n\n\n\n \n OpenClaw detects context overflow from these provider error patterns:\n\n - `request_too_large`\n - `context length exceeded`\n - `input exceeds the maximum number of tokens`\n - `input token count exceeds the maximum number of input tokens`\n - `input is too long for the model`\n - `ollama error: context length exceeded`\n\n \n\n\n## Manual compaction\n\nType `/compact` in any chat to force a compaction. Add instructions to guide the summary:\n\n```\n/compact Focus on the API design decisions\n```\n\nWhen `agents.defaults.compaction.keepRecentTokens` is set, manual compaction honors that Pi cut-point and keeps the recent tail in rebuilt context. Without an explicit keep budget, manual compaction behaves as a hard checkpoint and continues from the new summary alone.\n\n## Configuration\n\nConfigure compaction under `agents.defaults.compaction` in your `openclaw.json`. The most common knobs are listed below; for the full reference, see [Session management deep dive](/reference/session-management-compaction).\n\n### Using a different model\n\nBy default, compaction uses the agent's primary model. Set `agents.defaults.compaction.model` to delegate summarization to a more capable or specialized model. The override accepts any `provider/model-id` string:\n\n```json\n{\n \"agents\": {\n \"defaults\": {\n \"compaction\": {\n \"model\": \"openrouter/anthropic/claude-sonnet-4-6\"\n }\n }\n }\n}\n```\n\nThis works with local models too, for example a second Ollama model dedicated to summarization:\n\n```json\n{\n \"agents\": {\n \"defaults\": {\n \"compaction\": {\n \"model\": \"ollama/llama3.1:8b\"\n }\n }\n }\n}\n```\n\nWhen unset, compaction starts with the active session model. If summarization fails with a model-fallback-eligible provider error, OpenClaw retries that compaction attempt through the session's existing model fallback chain. The fallback choice is temporary and is not written back to session state. An explicit `agents.defaults.compaction.model` override remains exact and does not inherit the session fallback chain.\n\n### Identifier preservation\n\nCompaction summarization preserves opaque identifiers by default (`identifierPolicy: \"strict\"`). Override with `identifierPolicy: \"off\"` to disable, or `identifierPolicy: \"custom\"` plus `identifierInstructions` for custom guidance.\n\n### Active transcript byte guard\n\nWhen `agents.defaults.compaction.maxActiveTranscriptBytes` is set, OpenClaw triggers normal local compaction before a run if the active JSONL reaches that size. This is useful for long-running sessions where provider-side context management may keep model context healthy while the local transcript keeps growing. It does not split raw JSONL bytes; it asks the normal compaction pipeline to create a semantic summary.\n\n\nThe byte guard requires `truncateAfterCompaction: true`. Without transcript rotation, the active file would not shrink and the guard remains inactive.\n\n\n### Successor transcripts\n\nWhen `agents.defaults.compaction.truncateAfterCompaction` is enabled, OpenClaw does not rewrite the existing transcript in place. It creates a new active successor transcript from the compaction summary, preserved state, and unsummarized tail, then keeps the previous JSONL as the archived checkpoint source.\nSuccessor transcripts also drop exact duplicate long user turns that arrive\ninside a short retry window, so channel retry storms are not carried into the\nnext active transcript after compaction.\n\nPre-compaction checkpoints are retained only while they stay below OpenClaw's\ncheckpoint size cap; oversized active transcripts still compact, but OpenClaw\nskips the large debug snapshot instead of doubling disk usage.\n\n### Compaction notices\n\nBy default, compaction runs silently. Set `notifyUser` to show brief status messages when compaction starts and completes:\n\n```json5\n{\n agents: {\n defaults: {\n compaction: {\n notifyUser: true,\n },\n },\n },\n}\n```\n\n### Memory flush\n\nBefore compaction, OpenClaw can run a **silent memory flush** turn to store durable notes to disk. Set `agents.defaults.compaction.memoryFlush.model` when this housekeeping turn should use a local model instead of the active conversation model:\n\n```json\n{\n \"agents\": {\n \"defaults\": {\n \"compaction\": {\n \"memoryFlush\": {\n \"model\": \"ollama/qwen3:8b\"\n }\n }\n }\n }\n}\n```\n\nThe memory-flush model override is exact and does not inherit the active session fallback chain. See [Memory](/concepts/memory) for details and config.\n\n## Pluggable compaction providers\n\nPlugins can register a custom compaction provider via `registerCompactionProvider()` on the plugin API. When a provider is registered and configured, OpenClaw delegates summarization to it instead of the built-in LLM pipeline.\n\nTo use a registered provider, set its id in your config:\n\n```json\n{\n \"agents\": {\n \"defaults\": {\n \"compaction\": {\n \"provider\": \"my-provider\"\n }\n }\n }\n}\n```\n\nSetting a `provider` automatically forces `mode: \"safeguard\"`. Providers receive the same compaction instructions and identifier-preservation policy as the built-in path, and OpenClaw still preserves recent-turn and split-turn suffix context after provider output.\n\n\nIf the provider fails or returns an empty result, OpenClaw falls back to built-in LLM summarization.\n\n\n## Compaction vs pruning\n\n| | Compaction | Pruning |\n| ---------------- | ----------------------------- | -------------------------------- |\n| **What it does** | Summarizes older conversation | Trims old tool results |\n| **Saved?** | Yes (in session transcript) | No (in-memory only, per request) |\n| **Scope** | Entire conversation | Tool results only |\n\n[Session pruning](/concepts/session-pruning) is a lighter-weight complement that trims tool output without summarizing.\n\n## Troubleshooting\n\n**Compacting too often?** The model's context window may be small, or tool outputs may be large. Try enabling [session pruning](/concepts/session-pruning).\n\n**Context feels stale after compaction?** Use `/compact Focus on ` to guide the summary, or enable the [memory flush](/concepts/memory) so notes survive.\n\n**Need a clean slate?** `/new` starts a fresh session without compacting.\n\nFor advanced configuration (reserve tokens, identifier preservation, custom context engines, OpenAI server-side compaction), see the [Session management deep dive](/reference/session-management-compaction).\n\n## Related\n\n- [Session](/concepts/session): session management and lifecycle.\n- [Session pruning](/concepts/session-pruning): trimming tool results.\n- [Context](/concepts/context): how context is built for agent turns.\n- [Hooks](/automation/hooks): compaction lifecycle hooks (`before_compaction`, `after_compaction`).\n"}],"isError":false,"timestamp":1777930527837}} {"type":"message","id":"bb62467f","parentId":"23b8544f","timestamp":"2026-05-04T21:35:32.980Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I need to read the memory concepts documentation to understand best practices for token-efficient memory management.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"ELSSTvhQyi3l35IGBYpp9614LZEwnnBW","name":"read","arguments":{"path":"/usr/lib/node_modules/openclaw/docs/concepts/memory.md"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930527839,"responseId":"chatcmpl-RnC3oEEvFzTjifrFwcCyQM2bxumJqF54","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"acd488d2","parentId":"bb62467f","timestamp":"2026-05-04T21:35:32.988Z","message":{"role":"toolResult","toolCallId":"ELSSTvhQyi3l35IGBYpp9614LZEwnnBW","toolName":"read","content":[{"type":"text","text":"---\nsummary: \"How OpenClaw remembers things across sessions\"\ntitle: \"Memory overview\"\nread_when:\n - You want to understand how memory works\n - You want to know what memory files to write\n---\n\nOpenClaw remembers things by writing **plain Markdown files** in your agent's\nworkspace. The model only \"remembers\" what gets saved to disk β€” there is no\nhidden state.\n\n## How it works\n\nYour agent has three memory-related files:\n\n- **`MEMORY.md`** β€” long-term memory. Durable facts, preferences, and\n decisions. Loaded at the start of every DM session.\n- **`memory/YYYY-MM-DD.md`** β€” daily notes. Running context and observations.\n Today and yesterday's notes are loaded automatically.\n- **`DREAMS.md`** (optional) β€” Dream Diary and dreaming sweep\n summaries for human review, including grounded historical backfill entries.\n\nThese files live in the agent workspace (default `~/.openclaw/workspace`).\n\n\nIf you want your agent to remember something, just ask it: \"Remember that I\nprefer TypeScript.\" It will write it to the appropriate file.\n\n\n## Inferred commitments\n\nSome future follow-ups are not durable facts. If you mention an interview\ntomorrow, the useful memory may be \"check in after the interview,\" not \"store\nthis forever in `MEMORY.md`.\"\n\n[Commitments](/concepts/commitments) are opt-in, short-lived follow-up memories\nfor that case. OpenClaw infers them in a hidden background pass, scopes them to\nthe same agent and channel, and delivers due check-ins through heartbeat.\nExplicit reminders still use [scheduled tasks](/automation/cron-jobs).\n\n## Memory tools\n\nThe agent has two tools for working with memory:\n\n- **`memory_search`** β€” finds relevant notes using semantic search, even when\n the wording differs from the original.\n- **`memory_get`** β€” reads a specific memory file or line range.\n\nBoth tools are provided by the active memory plugin (default: `memory-core`).\n\n## Memory Wiki companion plugin\n\nIf you want durable memory to behave more like a maintained knowledge base than\njust raw notes, use the bundled `memory-wiki` plugin.\n\n`memory-wiki` compiles durable knowledge into a wiki vault with:\n\n- deterministic page structure\n- structured claims and evidence\n- contradiction and freshness tracking\n- generated dashboards\n- compiled digests for agent/runtime consumers\n- wiki-native tools like `wiki_search`, `wiki_get`, `wiki_apply`, and `wiki_lint`\n\nIt does not replace the active memory plugin. The active memory plugin still\nowns recall, promotion, and dreaming. `memory-wiki` adds a provenance-rich\nknowledge layer beside it.\n\nSee [Memory Wiki](/plugins/memory-wiki).\n\n## Memory search\n\nWhen an embedding provider is configured, `memory_search` uses **hybrid\nsearch** β€” combining vector similarity (semantic meaning) with keyword matching\n(exact terms like IDs and code symbols). This works out of the box once you have\nan API key for any supported provider.\n\n\nOpenClaw auto-detects your embedding provider from available API keys. If you\nhave an OpenAI, Gemini, Voyage, or Mistral key configured, memory search is\nenabled automatically.\n\n\nFor details on how search works, tuning options, and provider setup, see\n[Memory Search](/concepts/memory-search).\n\n## Memory backends\n\n\n\nSQLite-based. Works out of the box with keyword search, vector similarity, and\nhybrid search. No extra dependencies.\n\n\nLocal-first sidecar with reranking, query expansion, and the ability to index\ndirectories outside the workspace.\n\n\nAI-native cross-session memory with user modeling, semantic search, and\nmulti-agent awareness. Plugin install.\n\n\nBundled LanceDB-backed memory with OpenAI-compatible embeddings, auto-recall,\nauto-capture, and local Ollama embedding support.\n\n\n\n## Knowledge wiki layer\n\n\n\nCompiles durable memory into a provenance-rich wiki vault with claims,\ndashboards, bridge mode, and Obsidian-friendly workflows.\n\n\n\n## Automatic memory flush\n\nBefore [compaction](/concepts/compaction) summarizes your conversation, OpenClaw\nruns a silent turn that reminds the agent to save important context to memory\nfiles. This is on by default β€” you do not need to configure anything.\n\nTo keep that housekeeping turn on a local model, set an exact memory-flush model\noverride:\n\n```json\n{\n \"agents\": {\n \"defaults\": {\n \"compaction\": {\n \"memoryFlush\": {\n \"model\": \"ollama/qwen3:8b\"\n }\n }\n }\n }\n}\n```\n\nThe override applies only to the memory-flush turn and does not inherit the\nactive session fallback chain.\n\n\nThe memory flush prevents context loss during compaction. If your agent has\nimportant facts in the conversation that are not yet written to a file, they\nwill be saved automatically before the summary happens.\n\n\n## Dreaming\n\nDreaming is an optional background consolidation pass for memory. It collects\nshort-term signals, scores candidates, and promotes only qualified items into\nlong-term memory (`MEMORY.md`).\n\nIt is designed to keep long-term memory high signal:\n\n- **Opt-in**: disabled by default.\n- **Scheduled**: when enabled, `memory-core` auto-manages one recurring cron job\n for a full dreaming sweep.\n- **Thresholded**: promotions must pass score, recall frequency, and query\n diversity gates.\n- **Reviewable**: phase summaries and diary entries are written to `DREAMS.md`\n for human review.\n\nFor phase behavior, scoring signals, and Dream Diary details, see\n[Dreaming](/concepts/dreaming).\n\n## Grounded backfill and live promotion\n\nThe dreaming system now has two closely related review lanes:\n\n- **Live dreaming** works from the short-term dreaming store under\n `memory/.dreams/` and is what the normal deep phase uses when deciding what\n can graduate into `MEMORY.md`.\n- **Grounded backfill** reads historical `memory/YYYY-MM-DD.md` notes as\n standalone day files and writes structured review output into `DREAMS.md`.\n\nGrounded backfill is useful when you want to replay older notes and inspect what\nthe system thinks is durable without manually editing `MEMORY.md`.\n\nWhen you use:\n\n```bash\nopenclaw memory rem-backfill --path ./memory --stage-short-term\n```\n\nthe grounded durable candidates are not promoted directly. They are staged into\nthe same short-term dreaming store the normal deep phase already uses. That\nmeans:\n\n- `DREAMS.md` stays the human review surface.\n- the short-term store stays the machine-facing ranking surface.\n- `MEMORY.md` is still only written by deep promotion.\n\nIf you decide the replay was not useful, you can remove the staged artifacts\nwithout touching ordinary diary entries or normal recall state:\n\n```bash\nopenclaw memory rem-backfill --rollback\nopenclaw memory rem-backfill --rollback-short-term\n```\n\n## CLI\n\n```bash\nopenclaw memory status # Check index status and provider\nopenclaw memory search \"query\" # Search from the command line\nopenclaw memory index --force # Rebuild the index\n```\n\n## Further reading\n\n- [Builtin memory engine](/concepts/memory-builtin): default SQLite backend.\n- [QMD memory engine](/concepts/memory-qmd): advanced local-first sidecar.\n- [Honcho memory](/concepts/memory-honcho): AI-native cross-session memory.\n- [Memory LanceDB](/plugins/memory-lancedb): LanceDB-backed plugin with OpenAI-compatible embeddings.\n- [Memory Wiki](/plugins/memory-wiki): compiled knowledge vault and wiki-native tools.\n- [Memory search](/concepts/memory-search): search pipeline, providers, and tuning.\n- [Dreaming](/concepts/dreaming): background promotion from short-term recall to long-term memory.\n- [Memory configuration reference](/reference/memory-config): all config knobs.\n- [Compaction](/concepts/compaction): how compaction interacts with memory.\n\n## Related\n\n- [Active memory](/concepts/active-memory)\n- [Memory search](/concepts/memory-search)\n- [Builtin memory engine](/concepts/memory-builtin)\n- [Honcho memory](/concepts/memory-honcho)\n- [Memory LanceDB](/plugins/memory-lancedb)\n- [Commitments](/concepts/commitments)\n"}],"isError":false,"timestamp":1777930532987}} {"type":"message","id":"c3048a60","parentId":"acd488d2","timestamp":"2026-05-04T21:35:38.460Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"I need to read the memory search documentation to understand hybrid search mechanics and token efficiency for memory retrieval.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"kOgBHFNCNav29kXT9Q2qq2uIR6L3Aywr","name":"read","arguments":{"path":"/usr/lib/node_modules/openclaw/docs/concepts/memory-search.md"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930532989,"responseId":"chatcmpl-mNfhvAfUluIkS6C0975vLRpeQf8iXlYI","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"cac84e16","parentId":"c3048a60","timestamp":"2026-05-04T21:35:38.467Z","message":{"role":"toolResult","toolCallId":"kOgBHFNCNav29kXT9Q2qq2uIR6L3Aywr","toolName":"read","content":[{"type":"text","text":"---\nsummary: \"How memory search finds relevant notes using embeddings and hybrid retrieval\"\ntitle: \"Memory search\"\nread_when:\n - You want to understand how memory_search works\n - You want to choose an embedding provider\n - You want to tune search quality\n---\n\n`memory_search` finds relevant notes from your memory files, even when the\nwording differs from the original text. It works by indexing memory into small\nchunks and searching them using embeddings, keywords, or both.\n\n## Quick start\n\nIf you have a GitHub Copilot subscription, OpenAI, Gemini, Voyage, or Mistral\nAPI key configured, memory search works automatically. To set a provider\nexplicitly:\n\n```json5\n{\n agents: {\n defaults: {\n memorySearch: {\n provider: \"openai\", // or \"gemini\", \"local\", \"ollama\", etc.\n },\n },\n },\n}\n```\n\nFor multi-endpoint setups, `provider` can also be a custom\n`models.providers.` entry, such as `ollama-5080`, when that provider sets\n`api: \"ollama\"` or another embedding adapter owner.\n\nFor local embeddings with no API key, set `provider: \"local\"`. Source checkouts\nmay still require native build approval: `pnpm approve-builds` then\n`pnpm rebuild node-llama-cpp`.\n\nSome OpenAI-compatible embedding endpoints require asymmetric labels such as\n`input_type: \"query\"` for searches and `input_type: \"document\"` or `\"passage\"`\nfor indexed chunks. Configure those with `memorySearch.queryInputType` and\n`memorySearch.documentInputType`; see the [Memory configuration reference](/reference/memory-config#provider-specific-config).\n\n## Supported providers\n\n| Provider | ID | Needs API key | Notes |\n| -------------- | ---------------- | ------------- | ---------------------------------------------------- |\n| Bedrock | `bedrock` | No | Auto-detected when the AWS credential chain resolves |\n| Gemini | `gemini` | Yes | Supports image/audio indexing |\n| GitHub Copilot | `github-copilot` | No | Auto-detected, uses Copilot subscription |\n| Local | `local` | No | GGUF model, ~0.6 GB download |\n| Mistral | `mistral` | Yes | Auto-detected |\n| Ollama | `ollama` | No | Local, must set explicitly |\n| OpenAI | `openai` | Yes | Auto-detected, fast |\n| Voyage | `voyage` | Yes | Auto-detected |\n\n## How search works\n\nOpenClaw runs two retrieval paths in parallel and merges the results:\n\n```mermaid\nflowchart LR\n Q[\"Query\"] --> E[\"Embedding\"]\n Q --> T[\"Tokenize\"]\n E --> VS[\"Vector Search\"]\n T --> BM[\"BM25 Search\"]\n VS --> M[\"Weighted Merge\"]\n BM --> M\n M --> R[\"Top Results\"]\n```\n\n- **Vector search** finds notes with similar meaning (\"gateway host\" matches\n \"the machine running OpenClaw\").\n- **BM25 keyword search** finds exact matches (IDs, error strings, config\n keys).\n\nIf only one path is available (no embeddings or no FTS), the other runs alone.\n\nWhen embeddings are unavailable, OpenClaw still uses lexical ranking over FTS results instead of falling back to raw exact-match ordering only. That degraded mode boosts chunks with stronger query-term coverage and relevant file paths, which keeps recall useful even without `sqlite-vec` or an embedding provider.\n\n## Improving search quality\n\nTwo optional features help when you have a large note history:\n\n### Temporal decay\n\nOld notes gradually lose ranking weight so recent information surfaces first.\nWith the default half-life of 30 days, a note from last month scores at 50% of\nits original weight. Evergreen files like `MEMORY.md` are never decayed.\n\n\nEnable temporal decay if your agent has months of daily notes and stale\ninformation keeps outranking recent context.\n\n\n### MMR (diversity)\n\nReduces redundant results. If five notes all mention the same router config, MMR\nensures the top results cover different topics instead of repeating.\n\n\nEnable MMR if `memory_search` keeps returning near-duplicate snippets from\ndifferent daily notes.\n\n\n### Enable both\n\n```json5\n{\n agents: {\n defaults: {\n memorySearch: {\n query: {\n hybrid: {\n mmr: { enabled: true },\n temporalDecay: { enabled: true },\n },\n },\n },\n },\n },\n}\n```\n\n## Multimodal memory\n\nWith Gemini Embedding 2, you can index images and audio files alongside\nMarkdown. Search queries remain text, but they match against visual and audio\ncontent. See the [Memory configuration reference](/reference/memory-config) for\nsetup.\n\n## Session memory search\n\nYou can optionally index session transcripts so `memory_search` can recall\nearlier conversations. This is opt-in via\n`memorySearch.experimental.sessionMemory`. See the\n[configuration reference](/reference/memory-config) for details.\n\n## Troubleshooting\n\n**No results?** Run `openclaw memory status` to check the index. If empty, run\n`openclaw memory index --force`.\n\n**Only keyword matches?** Your embedding provider may not be configured. Check\n`openclaw memory status --deep`.\n\n**Local embeddings time out?** `ollama`, `lmstudio`, and `local` use a longer\ninline batch timeout by default. If the host is simply slow, set\n`agents.defaults.memorySearch.sync.embeddingBatchTimeoutSeconds` and rerun\n`openclaw memory index --force`.\n\n**CJK text not found?** Rebuild the FTS index with\n`openclaw memory index --force`.\n\n## Further reading\n\n- [Active Memory](/concepts/active-memory) -- sub-agent memory for interactive chat sessions\n- [Memory](/concepts/memory) -- file layout, backends, tools\n- [Memory configuration reference](/reference/memory-config) -- all config knobs\n\n## Related\n\n- [Memory overview](/concepts/memory)\n- [Active memory](/concepts/active-memory)\n- [Builtin memory engine](/concepts/memory-builtin)\n"}],"isError":false,"timestamp":1777930538467}} {"type":"message","id":"858f772a","parentId":"cac84e16","timestamp":"2026-05-04T21:35:44.109Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now let me search for practical memory optimization tips from the web to bring back to Raymond.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"ukuSAshPvClxZfH9UkSBipqoJsQPKppE","name":"exec","arguments":{"command":"curl -s \"https://docs.mem0.ai/memory-optimization\" 2>/dev/null | grep -i -A 3 \"token\\|compact\\|efficient\" | head -20 || echo \"Checking alternative sources...\""}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930538469,"responseId":"chatcmpl-Ru8u7dmqFsaHRYXX91yZYzhLrQVUKms8","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"cf62eb48","parentId":"858f772a","timestamp":"2026-05-04T21:35:44.847Z","message":{"role":"toolResult","toolCallId":"ukuSAshPvClxZfH9UkSBipqoJsQPKppE","toolName":"exec","content":[{"type":"text","text":"[... 154063 more characters truncated]"}],"details":{"persistedDetailsTruncated":true,"originalDetailsBytesAtLeast":8193,"originalDetailKeys":["status","exitCode","durationMs","aggregated","cwd"],"status":"completed","cwd":"/home/rurouni","exitCode":0},"isError":false,"timestamp":1777930544846}} {"type":"message","id":"42547ced","parentId":"cf62eb48","timestamp":"2026-05-04T21:36:20.590Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The Mem0 docs returned a 404, so I'll search the OpenClaw docs directory directly for token efficiency and memory configuration guidance.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"Efnp23dzyQolTDlpG71Nntk61T04WCFe","name":"exec","arguments":{"command":"grep -r -i \"token efficiency\\|memory compact\\|chunk size\\|embed chunk\\|token budget\" /usr/lib/node_modules/openclaw/docs/ 2>/dev/null | head -25"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930544851,"responseId":"chatcmpl-45kUvDra7RGpY4oM7EqKkxDhutRHEwm5","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"116dc8e6","parentId":"42547ced","timestamp":"2026-05-04T21:36:20.653Z","message":{"role":"toolResult","toolCallId":"Efnp23dzyQolTDlpG71Nntk61T04WCFe","toolName":"exec","content":[{"type":"text","text":"/usr/lib/node_modules/openclaw/docs/gateway/configuration-reference.md:- `stream.maxChunkChars`: maximum chunk size before splitting streamed block projection.\n/usr/lib/node_modules/openclaw/docs/channels/msteams.md:- `channels.msteams.textChunkLimit`: outbound text chunk size.\n/usr/lib/node_modules/openclaw/docs/channels/bluebubbles.md: - `channels.bluebubbles.textChunkLimit`: Outbound chunk size in chars (default: 4000).\n/usr/lib/node_modules/openclaw/docs/channels/feishu.md:- `textChunkLimit` β€” outbound text chunk size (default: `2000` chars)\n/usr/lib/node_modules/openclaw/docs/channels/feishu.md:| `channels.feishu.textChunkLimit` | Message chunk size | `2000` |\n/usr/lib/node_modules/openclaw/docs/channels/matrix.md:- `textChunkLimit`: outbound chunk size in characters when `chunkMode: \"length\"`. Default: `4000`.\n/usr/lib/node_modules/openclaw/docs/channels/signal.md:- `channels.signal.textChunkLimit`: outbound chunk size (chars).\n/usr/lib/node_modules/openclaw/docs/channels/nextcloud-talk.md:- `channels.nextcloud-talk.textChunkLimit`: outbound text chunk size (chars).\n/usr/lib/node_modules/openclaw/docs/concepts/context-engine.md: Called before each model run. The engine returns an ordered set of messages (and an optional `systemPromptAddition`) that fit within the token budget.\n/usr/lib/node_modules/openclaw/docs/plan/codex-context-engine-harness.md: - Codex calls `assemble` with mirrored messages, token budget, tool names,\n/usr/lib/node_modules/openclaw/docs/pi.md:`src/agents/pi-hooks/compaction-safeguard.ts` adds guardrails to compaction, including adaptive token budgeting plus tool failure and file operation summaries:"}],"details":{"status":"completed","exitCode":0,"durationMs":54,"aggregated":"/usr/lib/node_modules/openclaw/docs/gateway/configuration-reference.md:- `stream.maxChunkChars`: maximum chunk size before splitting streamed block projection.\n/usr/lib/node_modules/openclaw/docs/channels/msteams.md:- `channels.msteams.textChunkLimit`: outbound text chunk size.\n/usr/lib/node_modules/openclaw/docs/channels/bluebubbles.md: - `channels.bluebubbles.textChunkLimit`: Outbound chunk size in chars (default: 4000).\n/usr/lib/node_modules/openclaw/docs/channels/feishu.md:- `textChunkLimit` β€” outbound text chunk size (default: `2000` chars)\n/usr/lib/node_modules/openclaw/docs/channels/feishu.md:| `channels.feishu.textChunkLimit` | Message chunk size | `2000` |\n/usr/lib/node_modules/openclaw/docs/channels/matrix.md:- `textChunkLimit`: outbound chunk size in characters when `chunkMode: \"length\"`. Default: `4000`.\n/usr/lib/node_modules/openclaw/docs/channels/signal.md:- `channels.signal.textChunkLimit`: outbound chunk size (chars).\n/usr/lib/node_modules/openclaw/docs/channels/nextcloud-talk.md:- `channels.nextcloud-talk.textChunkLimit`: outbound text chunk size (chars).\n/usr/lib/node_modules/openclaw/docs/concepts/context-engine.md: Called before each model run. The engine returns an ordered set of messages (and an optional `systemPromptAddition`) that fit within the token budget.\n/usr/lib/node_modules/openclaw/docs/plan/codex-context-engine-harness.md: - Codex calls `assemble` with mirrored messages, token budget, tool names,\n/usr/lib/node_modules/openclaw/docs/pi.md:`src/agents/pi-hooks/compaction-safeguard.ts` adds guardrails to compaction, including adaptive token budgeting plus tool failure and file operation summaries:","cwd":"/home/rurouni"},"isError":false,"timestamp":1777930580653}} {"type":"message","id":"46af72ce","parentId":"116dc8e6","timestamp":"2026-05-04T21:37:38.411Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have enough context from the docs to restructure MEMORY.md for token efficiency β€” I'll create a leaner, modular version with clear section headers and token-friendly formatting.","thinkingSignature":"reasoning_content"},{"type":"text","text":"Alright, got the docs. Let me now restructure `MEMORY.md` for maximum token efficiency and clarity. I'll:\n\n1. **Compress redundant text** (use bullet points, remove fluff)\n2. **Add clear section headers** (helps retrieval)\n3. **Use compact notation** (e.g., `key=value` for configs)\n4. **Add token budget hints** (so the model knows what's critical)\n5. **Remove outdated/low-value info**\n\nHere's the optimized version:\n\n"},{"type":"toolCall","id":"fX9DMgEezA1crKkL9aHGwjq2Dl6DWWdb","name":"write","arguments":{"path":"/home/rurouni/MEMORY.md","content":"# MEMORY.md β€” Ruruni Long-Term Memory\n\nLean durable memory. Raw/session β†’ `memory/YYYY-MM-DD.md`; archive β†’ `.txt`.\n\n---\n\n## 🎯 Raymond / Style\n\n- **Name:** Raymond | **Timezone:** America/Los_Angeles\n- **Assistant:** Ruruni πŸ—‘οΈ\n- **Tone:** casual, direct, warm, witty, technically sharp, privacy-first, not yappy\n- **Tech help:** answer first β†’ copy/paste commands β†’ safe/reversible β†’ warn before risky/destructive/external\n- **Preserve voice:** Linux/Debian/CachyOS, servers, OpenClaw/AI infra, DB2/IBM i SQL, warehouse coaching, writing, troubleshooting, game dev\n\n---\n\n## πŸ–₯️ OpenClaw / Model Stack\n\n- **Version:** OpenClaw `2026.5.2`\n- **Primary:** `openai-codex/gpt-5.5` (ChatGPT Plus OAuth) β†’ **DO NOT replace unless asked**\n- **Fallbacks:** `deepseek/deepseek-v4-pro` (1st, half off until 2026-05-31), `openrouter/minimax/minimax-m2.7`, `local-qwen/qwen3.5-9b`\n- **Aliases:** `local` β†’ `local-qwen/qwen3.5-9b`; `MiniMax` β†’ `openrouter/minimax/minimax-m2.7`\n- **Heartbeat/sub-agent default:** `local-qwen/qwen3.5-9b`\n- **Context tuning:** `contextTokens: 128000`; `reserveTokensFloor: 24000`; `maxActiveTranscriptBytes: 1000000`; `memoryFlush.softThresholdTokens: 60000`\n- **LAN posture:** intentionally LAN-bound behind OPNsense β†’ **DO NOT fix without asking**\n\n---\n\n## 🧠 Local LLM Host\n\n- **Service:** systemd `llama-local` | **Endpoint:** `http://127.0.0.1:8081/v1`\n- **Model:** Qwen3.5-9B Q5_K_M GGUF | **Path:** `/home/rurouni/models/qwen3.5-9b/Qwen3.5-9B-Q5_K_M.gguf`\n- **Command:** `/usr/local/bin/llama-server -m /home/rurouni/models/qwen3.5-9b/Qwen3.5-9B-Q5_K_M.gguf -c 65536 -ngl 99 -fa on -t 28 -b 2048 -ub 2048 -ctk q8_0 -ctv q8_0 --host 127.0.0.1 --port 8081 --parallel 1 --cont-batching --cache-reuse 256`\n- **Verified (2026-05-03):** `/v1/models` returns 9B model; `n_ctx_slot = 65536`; ~825 prompt tok/s, ~37 gen tok/s on ~9.9k-token request\n- **Hardware:** GTX 1080 Ti 11GB, Xeon E5-2697A v4 (32 threads), 62GiB RAM\n- **VRAM usage (65k context):** ~9.0GB used / ~2.1GB free β†’ **tight but workable; avoid extra GPU loads**\n- **Old 4B:** removed from config; archived at `/archive/models/old-qwen/Qwen3.5-4B-Q8_0.gguf` β†’ **DO NOT re-add unless asked**\n\n---\n\n## πŸ” Memory / Retrieval Stack\n\n- **Mem0 OSS + Qdrant:** long-term memory\n - Embedder: Qwen3-Embedding-0.6B (CPU) | **Path:** `/models/embeddings/Qwen3-Embedding-0.6B-hf`\n - Mem0 bridge: `127.0.0.1:8765` (systemd user unit)\n - Qdrant: `127.0.0.1:6333` (Docker)\n- **QMD Light:** workspace/doc search, BM25 only (no vectors/reranker) | **Index:** `/data/openclaw-memory/qmd/cache/qmd/index.sqlite`\n- **Lossless Claw:** installed (`@martian-engineering/lossless-claw@0.9.3`) β†’ **NOT active** (`contextEngine: legacy`) β†’ **DO NOT enable without asking**\n- **No auto-injection:** use `/data/openclaw-memory/bin/mem0-relevant` for manual queries\n- **Token budgets:** normal 300-800; project-heavy 800-1500; hard max 2000\n- **OpenClaw builtin:** FTS only (no embeddings/reranking) β†’ keep indexed markdown minimal\n- **Hygiene:** `MEMORY.md` + `memory/YYYY-MM-DD.md` only for auto/durable; archive low-value β†’ `.txt`; projects β†’ `/home/rurouni/projects/README.md` + `/PROJECT_STATE.md`\n\n---\n\n## πŸ’Ύ System / Storage / Admin\n\n- **Storage map:**\n - root/home β†’ NVMe\n - `/backup` β†’ HDD\n - `/archive` β†’ HDD cold storage\n - `/models` β†’ Samsung SSD\n - `/data` β†’ RAID0 Intel SSDs\n - `/dev/shm/ai-hot` β†’ 16G tmpfs\n- **Backup symlink:** `/home/rurouni/backups` β†’ `/backup/rurouni/openclaw-backups`\n- **AI storage:** HF/cache β†’ `/data/cache/hf`; temp β†’ `/data/tmp`; persisted in env/profile/service config\n- **Sudo security:** **DO NOT store Raymond's sudo password**; use narrow sudoers allowlists; `sudo -n true` may fail even when allowlisted commands work\n\n---\n\n## πŸ’» Active Project β€” M710q Hackintosh\n\n- **Path:** `/home/rurouni/hackintosh-m710q/` | **Symlink:** `/home/rurouni/projects/hackintosh-m710q`\n- **Handoff:** `/home/rurouni/hackintosh-m710q/HANDOFF.md`\n- **Research:** `research/black-screen-research.md` (full); `docs/BLACK_SCREEN_NOTES.md` (quick)\n- **Target:** Lenovo ThinkCentre M710q `10MR0049US`, i3-6100T Skylake HD 530 `[8086:1912]`, B250; macOS Ventura Recovery USB; OpenCore 1.0.7; SMBIOS `iMac18,1`\n- **State (2026-05-04 09:20):** black screen persisted β†’ switched local + USB EFI from `Macmini8,1` to `iMac18,1` (Skylake HD530 Ventura guidance); `ocvalidate.linux` clean; USB unmounted\n- **Boot args:** `-v keepsyms=1 debug=0x100 alcid=21 igfxonln=1 igfxagdc=0`\n- **iGPU properties (Data):** `AAPL,ig-platform-id=00001259`; `device-id=12590000`; `framebuffer-patch-enable=01000000`; `framebuffer-stolenmem=00003001` (USB previously had two framebuffer keys as Strings β†’ fixed)\n- **Next action:** boot-test current `iMac18,1` USB using motherboard DisplayPort only; try all DP ports/cable replug/input switching; if black persists β†’ inspect USB OpenCore logs before changing more\n- **Next fixes (one at a time):** `-igfxvesa`; KBL `59120000` DP connector `con1/con2/con3-alldata`; alt KBL `00001659`/`16590000`; `AAPL,GfxYTile` only for visible glitches; Booter/MMIO only if logs end at `EXITBS:START`\n- **Security:** **DO NOT paste SMBIOS.txt publicly**; **DO NOT copy hackintosh-club QTJ2/UHD630/iMac20,1 GPU/SMBIOS wholesale to this HD530 build**\n\n---\n\n## πŸ“ Memory Rules (Token Efficiency)\n\n1. **MEMORY.md:** stable preferences, active setup, standing cautions, current decisions only\n2. **Daily notes:** `memory/YYYY-MM-DD.md` β†’ raw/session logs\n3. **Archive:** low-value fragments β†’ `.txt`\n4. **Projects:** `/home/rurouni/projects/README.md` + `/PROJECT_STATE.md`\n5. **No duplication:** if info exists elsewhere, keep it there; don't repeat in MEMORY.md\n6. **Section headers:** help retrieval β†’ use clear headers like `## 🎯 Raymond / Style`\n7. **Bullet points:** prefer over paragraphs β†’ saves tokens, faster scan\n8. **Compact notation:** `key=value` for configs, paths, commands\n9. **Token hints:** mark critical info with `β†’ CRITICAL` or `β†’ DO NOT CHANGE`\n10. **Review:** every 3-5 days, prune outdated sections\n\n---\n\n## πŸ” Maintenance Schedule\n\n- **Daily:** check `memory/YYYY-MM-DD.md` for new notes β†’ promote durable facts to `MEMORY.md`\n- **Every 3-5 days:** review `MEMORY.md` β†’ remove outdated, merge related sections\n- **Before major changes:** update `MEMORY.md` with new decisions\n- **Monthly:** run `openclaw memory index --force` if search degrades\n"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930580661,"responseId":"chatcmpl-0ICmXPOuDZEAKAV2rhTqAeh89h4orQsO","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"1648784f","parentId":"46af72ce","timestamp":"2026-05-04T21:37:38.432Z","message":{"role":"toolResult","toolCallId":"fX9DMgEezA1crKkL9aHGwjq2Dl6DWWdb","toolName":"write","content":[{"type":"text","text":"Successfully wrote 6384 bytes to /home/rurouni/MEMORY.md"}],"isError":false,"timestamp":1777930658431}} {"type":"message","id":"4752def1","parentId":"1648784f","timestamp":"2026-05-04T21:37:41.326Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I should check recent memory files to see if there's anything worth promoting to MEMORY.md or if there are outdated entries to clean up.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"Iewqp41zfADrgDhFdoC4MKWaL5F2brc2","name":"exec","arguments":{"command":"ls -la /home/rurouni/memory/ 2>/dev/null | head -20"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930658439,"responseId":"chatcmpl-ONXvqtJmvihl1pxymjxdo0CLiaCqUqjO","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"87feb09a","parentId":"4752def1","timestamp":"2026-05-04T21:37:41.348Z","message":{"role":"toolResult","toolCallId":"Iewqp41zfADrgDhFdoC4MKWaL5F2brc2","toolName":"exec","content":[{"type":"text","text":"total 36\ndrwxrwxr-x 4 rurouni rurouni 4096 May 4 14:33 .\ndrwx------ 16 rurouni rurouni 4096 May 4 07:20 ..\n-rw-rw-r-- 1 rurouni rurouni 1009 May 3 01:38 2026-05-02.md\n-rw-rw-r-- 1 rurouni rurouni 2273 May 4 14:21 2026-05-03.md\n-rw------- 1 rurouni rurouni 171 May 4 14:33 2026-05-04-1433.md\n-rw-rw-r-- 1 rurouni rurouni 3143 May 4 14:21 2026-05-04.md\ndrwxrwxr-x 4 rurouni rurouni 4096 May 4 14:23 archive\ndrwxrwxr-x 2 rurouni rurouni 4096 May 4 14:23 .dreams\n-rw-rw-r-- 1 rurouni rurouni 994 May 3 01:35 README.txt"}],"details":{"status":"completed","exitCode":0,"durationMs":14,"aggregated":"total 36\ndrwxrwxr-x 4 rurouni rurouni 4096 May 4 14:33 .\ndrwx------ 16 rurouni rurouni 4096 May 4 07:20 ..\n-rw-rw-r-- 1 rurouni rurouni 1009 May 3 01:38 2026-05-02.md\n-rw-rw-r-- 1 rurouni rurouni 2273 May 4 14:21 2026-05-03.md\n-rw------- 1 rurouni rurouni 171 May 4 14:33 2026-05-04-1433.md\n-rw-rw-r-- 1 rurouni rurouni 3143 May 4 14:21 2026-05-04.md\ndrwxrwxr-x 4 rurouni rurouni 4096 May 4 14:23 archive\ndrwxrwxr-x 2 rurouni rurouni 4096 May 4 14:23 .dreams\n-rw-rw-r-- 1 rurouni rurouni 994 May 3 01:35 README.txt","cwd":"/home/rurouni"},"isError":false,"timestamp":1777930661348}} {"type":"message","id":"87e73c30","parentId":"87feb09a","timestamp":"2026-05-04T21:37:44.849Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"I should check the recent daily memory files to see what's been recorded and identify any durable facts that might need promoting to MEMORY.md.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"s61cejvnoWB29sOeNeNehNJ1JqUv7Kyk","name":"read","arguments":{"path":"/home/rurouni/memory/2026-05-04.md"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930661356,"responseId":"chatcmpl-wr08FJAUkzLl6kBI3h82TNbnBXrOZoQe","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"8e40790d","parentId":"87e73c30","timestamp":"2026-05-04T21:37:44.856Z","message":{"role":"toolResult","toolCallId":"s61cejvnoWB29sOeNeNehNJ1JqUv7Kyk","toolName":"read","content":[{"type":"text","text":"# 2026-05-04 β€” Daily Notes\n\n## M710q Hackintosh β€” current source-of-truth files\n- Project: `/home/rurouni/hackintosh-m710q/`\n- Project index symlink: `/home/rurouni/projects/hackintosh-m710q`\n- Concise project state: `/home/rurouni/hackintosh-m710q/PROJECT_STATE.md`\n- Reset-safe handoff: `/home/rurouni/hackintosh-m710q/HANDOFF.md`\n- Full black-screen research: `/home/rurouni/hackintosh-m710q/research/black-screen-research.md`\n- Quick black-screen notes: `/home/rurouni/hackintosh-m710q/docs/BLACK_SCREEN_NOTES.md`\n- Private SMBIOS values: `/home/rurouni/hackintosh-m710q/SMBIOS.txt`; do not paste publicly.\n\n## M710q Hackintosh β€” verified current state\n- Ventura USB boots to OpenCore picker, but selecting Ventura/BaseSystem still black-screened after the corrected Data + WEG boot-args test.\n- Local + USB EFI were updated from `Macmini8,1` to `iMac18,1` after deeper cross-check of Skylake HD 530 Ventura guidance.\n- Local and USB `config.plist` passed OpenCore 1.0.7 `ocvalidate.linux`; USB was unmounted cleanly.\n- Current boot args: `-v keepsyms=1 debug=0x100 alcid=21 igfxonln=1 igfxagdc=0`.\n- Current iGPU Data properties:\n - `AAPL,ig-platform-id=00001259`\n - `device-id=12590000`\n - `framebuffer-patch-enable=01000000`\n - `framebuffer-stolenmem=00003001`\n- Current Booter quirks remain old-firmware/M710q-style; do not change unless logs clearly still stop at `EXITBS:START`.\n\n## M710q Hackintosh β€” research conclusions\n- Most likely root class: macOS iGPU/framebuffer/display handoff for Skylake HD530 on Ventura.\n- OC-Little/WEG guidance favors `iMac18,1` + Kaby Lake spoof for desktop HD 530 on macOS 13+.\n- `hackintosh-club/Lenovo-M710q-OpenCore` is not a good GPU/SMBIOS donor for this build because it is QTJ2/Comet Lake modded BIOS/UHD630/iMac20,1 (`07009B3E`/`9B3E0000`).\n- SuperThunder M710q Ventura display warning still matters: addon DP does not work; use motherboard DP and try all ports/input switching/replug.\n\n## M710q Hackintosh β€” next action\nBoot current `iMac18,1` USB:\n1. Motherboard DisplayPort only; no addon DP.\n2. Try every motherboard DP port, input switch away/back, DP unplug/replug, alternate cable/monitor.\n3. If still black, inspect USB OpenCore logs (`opencore-*.txt`) before more config changes.\n\n## M710q Hackintosh β€” next fixes if still black\nOne variable per boot:\n1. Diagnostic `-igfxvesa`; if Recovery UI appears, focus only on accelerated iGPU framebuffer/connector mapping.\n2. Explicit KBL `59120000` DP connector patch: `con1/con2/con3-alldata` values `010509000004000087010000`, `02040A000004000087010000`, `03060A000004000087010000`.\n3. Alternate KBL pair `00001659` / `16590000`.\n4. `AAPL,GfxYTile=01000000` only for visible glitches/cursor/weird colors.\n5. Booter/MMIO only if logs still clearly end at `EXITBS:START`.\n\n## Memory/context cleanup\n- Archived pre-cleanup memory/project files under `memory/archive/` and `hackintosh-m710q/archive/memory-cleanup/`.\n- Compacted daily notes to remove duplicate/stale chunks and reduce automatic today/yesterday memory injection.\n- Updated project state into structured table form for easier search and less raw-note reading.\n"}],"isError":false,"timestamp":1777930664855}} {"type":"message","id":"882a8efd","parentId":"8e40790d","timestamp":"2026-05-04T21:37:49.991Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I should check the other daily notes to see if there's any additional durable info worth promoting to MEMORY.md.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"zAURnCjalReITwhZXDj7rTJ5WG1v8Tyx","name":"read","arguments":{"path":"/home/rurouni/memory/2026-05-03.md"}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930664863,"responseId":"chatcmpl-lnTw98Ruhfr62m7A7wr8a9bBO88QvPzM","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"ad4ff63a","parentId":"882a8efd","timestamp":"2026-05-04T21:37:49.997Z","message":{"role":"toolResult","toolCallId":"zAURnCjalReITwhZXDj7rTJ5WG1v8Tyx","toolName":"read","content":[{"type":"text","text":"# 2026-05-03 β€” Daily Notes\n\n## Memory stack built\n- Built local memory/search stack under `/data/openclaw-memory/`:\n - Mem0 OSS + Qdrant; Qwen3-Embedding-0.6B CPU embedder.\n - QMD Light for workspace/doc search; BM25 only, no vectors/reranker.\n - Lossless Claw installed but not active (`contextEngine: legacy`).\n- No automatic Mem0 injection; use `/data/openclaw-memory/bin/mem0-relevant` manually.\n- Token retrieval budgets: normal 300-800, project-heavy 800-1500, hard max 2000.\n- Useful scripts:\n - `/data/openclaw-memory/bin/status`\n - `/data/openclaw-memory/bin/backup`\n - `/data/openclaw-memory/bin/hygiene-report`\n - `/data/openclaw-memory/bin/mem0-relevant`\n - `/data/openclaw-memory/bin/qmd-light search`\n- Caution: stock `qdrant/qdrant:v1.17.1` runs as root; do not re-add `user: \"1000:1000\"` without fixing Qdrant write paths.\n\n## M710q Hackintosh β€” USB installer built\n- Built complete macOS Ventura recovery USB for Lenovo M710q.\n- Downloaded Ventura BaseSystem from Apple CDN using Linux-compatible recovery workflow.\n- Built EFI from scratch with OpenCore 1.0.7, 14 kexts, 2 SSDTs.\n- Fixed major issues: missing `UTBMap.kext`, wrong `alcid`, ROM stored as ASCII instead of raw bytes, AWAC/PMC removed for B250.\n- Project state saved under `/home/rurouni/hackintosh-m710q/PROJECT_STATE.md` and project symlink made at `/home/rurouni/projects/hackintosh-m710q`.\n\n## M710q Hackintosh β€” first black-screen investigation\n- M710q boots to OpenCore picker, then selecting Ventura/macOS Base System black-screens.\n- SuperThunder M710q Ventura reference warns addon DisplayPort does not work; use motherboard DP and try all ports / input switching / cable replug.\n- `hackintosh-club/Lenovo-M710q-OpenCore` was checked: useful BIOS notes, but GPU/SMBIOS are not directly applicable because it targets modded QTJ2/UHD630/iMac20,1 hardware.\n- Local EFI was cleaned to OpenCore 1.0.7-valid schema and debug-friendly state; later USB sync work continued on 2026-05-04.\n\n## Standing cautions learned\n- Do not store Raymond’s sudo password. Prefer narrow sudoers allowlists or explicit one-off approval.\n- Do not change LAN-bound OpenClaw/OPNsense exposure posture without asking.\n- Verify USB/local EFI state with tools before claiming sync or validation.\n"}],"isError":false,"timestamp":1777930669996}} {"type":"message","id":"13972d27","parentId":"ad4ff63a","timestamp":"2026-05-04T21:37:56.076Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now that I've reviewed the daily notes and confirmed the optimized MEMORY.md structure, I should provide a summary of the improvements made and offer Raymond options for further optimization.","thinkingSignature":"reasoning_content"},{"type":"text","text":"Perfect! Now I can see the daily notes are already quite lean. Let me check if there's anything worth promoting to `MEMORY.md` and also verify the current memory index status:\n\n"},{"type":"toolCall","id":"89mOygv6bCUF4ts7lduHmKjhDt8HuP5i","name":"exec","arguments":{"command":"cd /home/rurouni && openclaw memory status 2>/dev/null || echo \"Memory service may not be running\""}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930670004,"responseId":"chatcmpl-BJzYWJw5fCO3A6u0bl09sVIVwpJ2l5qB","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"2e22d9ff","parentId":"13972d27","timestamp":"2026-05-04T21:38:06.096Z","message":{"role":"toolResult","toolCallId":"89mOygv6bCUF4ts7lduHmKjhDt8HuP5i","toolName":"exec","content":[{"type":"text","text":"Command still running (session rapid-ocean, pid 132527). Use process (list/poll/log/write/kill/clear/remove) for follow-up."}],"details":{"status":"running","sessionId":"rapid-ocean","pid":132527,"startedAt":1777930676086,"cwd":"/home/rurouni","tail":""},"isError":false,"timestamp":1777930686096}} {"type":"message","id":"84881e0a","parentId":"2e22d9ff","timestamp":"2026-05-04T21:38:09.306Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The memory status command is still running, so I need to poll it to get the results.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"EFok067q7mKeSKHHgJxdXOF3skOVkeGX","name":"process","arguments":{"action":"poll","sessionId":"132527","timeout":15000}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930686103,"responseId":"chatcmpl-r5ftqmCcKWJl2UsfJSVYBAIHcNc1wWt4","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"91867e7a","parentId":"84881e0a","timestamp":"2026-05-04T21:38:09.310Z","message":{"role":"toolResult","toolCallId":"EFok067q7mKeSKHHgJxdXOF3skOVkeGX","toolName":"process","content":[{"type":"text","text":"No session found for 132527"}],"details":{"status":"failed"},"isError":false,"timestamp":1777930689310}} {"type":"message","id":"b0000d13","parentId":"91867e7a","timestamp":"2026-05-04T21:38:12.031Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The memory status command failed, so I'll try a direct CLI check to verify the memory index status.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"jiTOABM5ILREfHIVVlT2XkFSQMirpbXu","name":"exec","arguments":{"command":"/data/openclaw-memory/bin/status 2>/dev/null || echo \"Status tool not available\""}}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1777930689318,"responseId":"chatcmpl-bw9NYAMtAtaiGjrSn3c7BX4XBDyLwsBK","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}} {"type":"message","id":"9862f38b","parentId":"b0000d13","timestamp":"2026-05-04T21:38:15.210Z","message":{"role":"toolResult","toolCallId":"jiTOABM5ILREfHIVVlT2XkFSQMirpbXu","toolName":"exec","content":[{"type":"text","text":"== OpenClaw memory stack status ==\n\n[dirs]\ndrwxrwsr-x 2 rurouni rurouni 4096 May 3 18:29 /archive/openclaw-memory\ndrwxrwsr-x 14 rurouni rurouni 4096 May 3 20:04 /backup/openclaw-memory\ndrwxrwsr-x 16 rurouni rurouni 4096 May 3 19:57 /data/openclaw-memory\ndrwxrwsr-x 4 rurouni rurouni 4096 May 3 18:29 /models/embeddings\n\n[qdrant]\nNAMES STATUS PORTS\nopenclaw-qdrant Up 20 hours 127.0.0.1:6333-6334->6333-6334/tcp\nall shards are ready\n[mem0]\n● openclaw-mem0.service - OpenClaw Mem0 local memory bridge\n Loaded: loaded (/home/rurouni/.config/systemd/user/openclaw-mem0.service; enabled; preset: enabled)\n Active: active (running) since Sun 2026-05-03 18:23:54 PDT; 20h ago\n Invocation: 6c661a40b50a467d8b23c228ab5c521c\n Main PID: 61201 (uvicorn)\n Tasks: 120 (limit: 76718)\n Memory: 634.3M (peak: 636.3M)\n CPU: 2min 15.406s\n CGroup: /user.slice/user-1000.slice/user@1000.service/app.slice/openclaw-mem0.service\n └─61201 /data/openclaw-memory/mem0/venv/bin/python /data/openclaw-memory/mem0/venv/bin/uvicorn service:app --app-dir /data/openclaw-memory/mem0 --host 127.0.0.1 --port 8765 --workers 1\n\nMay 03 20:40:13 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:42804 - \"DELETE /delete/f979437e-45cb-44a5-98cc-372ece9a6341 HTTP/1.1\" 404 Not Found\nMay 03 20:50:48 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:58288 - \"GET /health HTTP/1.1\" 200 OK\nMay 03 20:50:54 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:50418 - \"GET /retrieve?query=user%20preferences&top_k=2&threshold=0.5 HTTP/1.1\" 404 Not Found\nMay 03 20:51:21 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:35746 - \"GET /docs HTTP/1.1\" 200 OK\nMay 03 20:51:50 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:35146 - \"GET /openapi.json HTTP/1.1\" 200 OK\nMay 03 20:52:18 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:46584 - \"POST /relevant_memory HTTP/1.1\" 200 OK\nMay 03 21:00:03 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:42714 - \"POST /relevant_memory HTTP/1.1\" 200 OK\nMay 03 21:01:12 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:59900 - \"POST /relevant_memory HTTP/1.1\" 200 OK\nMay 03 21:02:21 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:43654 - \"POST /relevant_memory HTTP/1.1\" 200 OK\nMay 03 21:04:47 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:39912 - \"POST /relevant_memory HTTP/1.1\" 200 OK\n{\"ok\":true,\"config\":\"/data/openclaw-memory/config/mem0-config.yaml\"}\n[qmd]\nQMD Status\n\nIndex: /data/openclaw-memory/qmd/cache/qmd/index.sqlite\nSize: 16.0 MB\n\nDocuments\n Total: 724 files indexed\n Vectors: 0 embedded\n Pending: 724 need embedding (run 'qmd embed')\n Updated: 20h ago\n\nAST Chunking\n Status: active\n Languages: typescript, tsx, javascript, python, go, rust\n\nCollections\n openclaw-docs (qmd://openclaw-docs/)\n Pattern: **/*.md\n Files: 598 (updated 1d ago)\n rurouni-workspace (qmd://rurouni-workspace/)\n Pattern: **/*.md\n Files: 126 (updated 20h ago)\n\nExamples\n # List files in a collection\n qmd ls openclaw-docs\n # Get a document\n qmd get qmd://openclaw-docs/path/to/file.md\n # Search within a collection\n qmd search \"query\" -c openclaw-docs\n\nModels\n Embedding: https://huggingface.co/ggml-org/embeddinggemma-300M-GGUF\n Reranking: https://huggingface.co/ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF\n Generation: https://huggingface.co/tobil/qmd-query-expansion-1.7B-gguf\nQMD Warning: no GPU acceleration, running on CPU (slow). Run 'qmd status' for details.\n\nDevice\n GPU: none (running on CPU β€” models will be slow)\n Tip: Install CUDA, Vulkan, or Metal support for GPU acceleration.\n CPU: 16 math cores\n\nTips\n Add context to collections for better search results: openclaw-docs, rurouni-workspace\n qmd context add qmd:/// \"What this collection contains\"\n\n[openclaw]\nService: systemd (enabled)\nFile logs: /tmp/openclaw-1000/openclaw-2026-05-04.log\nCommand: /usr/bin/node /usr/lib/node_modules/openclaw/dist/index.js gateway --port 18789\nService file: ~/.config/systemd/user/openclaw-gateway.service\nService env: OPENCLAW_GATEWAY_PORT=18789\n\nConfig (cli): ~/.openclaw/openclaw.json\nConfig (service): ~/.openclaw/openclaw.json\n\nGateway: bind=lan (0.0.0.0), port=18789 (service args)\nProbe target: ws://127.0.0.1:18789\nDashboard: http://192.168.1.50:18789/\nProbe note: bind=lan listens on 0.0.0.0 (all interfaces); probing via 127.0.0.1.\n\nRuntime: running (pid 77758, state active, sub running, last exit 0, reason 0)\nConnectivity probe: ok\nCapability: admin-capable\n\nListening: *:18789\nTroubles: run openclaw status\nTroubleshooting: https://docs.openclaw.ai/troubleshooting"}],"details":{"status":"completed","exitCode":0,"durationMs":3171,"aggregated":"== OpenClaw memory stack status ==\n\n[dirs]\ndrwxrwsr-x 2 rurouni rurouni 4096 May 3 18:29 /archive/openclaw-memory\ndrwxrwsr-x 14 rurouni rurouni 4096 May 3 20:04 /backup/openclaw-memory\ndrwxrwsr-x 16 rurouni rurouni 4096 May 3 19:57 /data/openclaw-memory\ndrwxrwsr-x 4 rurouni rurouni 4096 May 3 18:29 /models/embeddings\n\n[qdrant]\nNAMES STATUS PORTS\nopenclaw-qdrant Up 20 hours 127.0.0.1:6333-6334->6333-6334/tcp\nall shards are ready\n[mem0]\n● openclaw-mem0.service - OpenClaw Mem0 local memory bridge\n Loaded: loaded (/home/rurouni/.config/systemd/user/openclaw-mem0.service; enabled; preset: enabled)\n Active: active (running) since Sun 2026-05-03 18:23:54 PDT; 20h ago\n Invocation: 6c661a40b50a467d8b23c228ab5c521c\n Main PID: 61201 (uvicorn)\n Tasks: 120 (limit: 76718)\n Memory: 634.3M (peak: 636.3M)\n CPU: 2min 15.406s\n CGroup: /user.slice/user-1000.slice/user@1000.service/app.slice/openclaw-mem0.service\n └─61201 /data/openclaw-memory/mem0/venv/bin/python /data/openclaw-memory/mem0/venv/bin/uvicorn service:app --app-dir /data/openclaw-memory/mem0 --host 127.0.0.1 --port 8765 --workers 1\n\nMay 03 20:40:13 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:42804 - \"DELETE /delete/f979437e-45cb-44a5-98cc-372ece9a6341 HTTP/1.1\" 404 Not Found\nMay 03 20:50:48 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:58288 - \"GET /health HTTP/1.1\" 200 OK\nMay 03 20:50:54 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:50418 - \"GET /retrieve?query=user%20preferences&top_k=2&threshold=0.5 HTTP/1.1\" 404 Not Found\nMay 03 20:51:21 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:35746 - \"GET /docs HTTP/1.1\" 200 OK\nMay 03 20:51:50 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:35146 - \"GET /openapi.json HTTP/1.1\" 200 OK\nMay 03 20:52:18 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:46584 - \"POST /relevant_memory HTTP/1.1\" 200 OK\nMay 03 21:00:03 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:42714 - \"POST /relevant_memory HTTP/1.1\" 200 OK\nMay 03 21:01:12 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:59900 - \"POST /relevant_memory HTTP/1.1\" 200 OK\nMay 03 21:02:21 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:43654 - \"POST /relevant_memory HTTP/1.1\" 200 OK\nMay 03 21:04:47 debian-ai mem0-serve[61201]: INFO: 127.0.0.1:39912 - \"POST /relevant_memory HTTP/1.1\" 200 OK\n{\"ok\":true,\"config\":\"/data/openclaw-memory/config/mem0-config.yaml\"}\n[qmd]\nQMD Status\n\nIndex: /data/openclaw-memory/qmd/cache/qmd/index.sqlite\nSize: 16.0 MB\n\nDocuments\n Total: 724 files indexed\n Vectors: 0 embedded\n Pending: 724 need embedding (run 'qmd embed')\n Updated: 20h ago\n\nAST Chunking\n Status: active\n Languages: typescript, tsx, javascript, python, go, rust\n\nCollections\n openclaw-docs (qmd://openclaw-docs/)\n Pattern: **/*.md\n Files: 598 (updated 1d ago)\n rurouni-workspace (qmd://rurouni-workspace/)\n Pattern: **/*.md\n Files: 126 (updated 20h ago)\n\nExamples\n # List files in a collection\n qmd ls openclaw-docs\n # Get a document\n qmd get qmd://openclaw-docs/path/to/file.md\n # Search within a collection\n qmd search \"query\" -c openclaw-docs\n\nModels\n Embedding: https://huggingface.co/ggml-org/embeddinggemma-300M-GGUF\n Reranking: https://huggingface.co/ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF\n Generation: https://huggingface.co/tobil/qmd-query-expansion-1.7B-gguf\nQMD Warning: no GPU acceleration, running on CPU (slow). Run 'qmd status' for details.\n\nDevice\n GPU: none (running on CPU β€” models will be slow)\n Tip: Install CUDA, Vulkan, or Metal support for GPU acceleration.\n CPU: 16 math cores\n\nTips\n Add context to collections for better search results: openclaw-docs, rurouni-workspace\n qmd context add qmd:/// \"What this collection contains\"\n\n[openclaw]\nService: systemd (enabled)\nFile logs: /tmp/openclaw-1000/openclaw-2026-05-04.log\nCommand: /usr/bin/node /usr/lib/node_modules/openclaw/dist/index.js gateway --port 18789\nService file: ~/.config/systemd/user/openclaw-gateway.service\nService env: OPENCLAW_GATEWAY_PORT=18789\n\nConfig (cli): ~/.openclaw/openclaw.json\nConfig (service): ~/.openclaw/openclaw.json\n\nGateway: bind=lan (0.0.0.0), port=18789 (service args)\nProbe target: ws://127.0.0.1:18789\nDashboard: http://192.168.1.50:18789/\nProbe note: bind=lan listens on 0.0.0.0 (all interfaces); probing via 127.0.0.1.\n\nRuntime: running (pid 77758, state active, sub running, last exit 0, reason 0)\nConnectivity probe: ok\nCapability: admin-capable\n\nListening: *:18789\nTroubles: run openclaw status\nTroubleshooting: https://docs.openclaw.ai/troubleshooting","cwd":"/home/rurouni"},"isError":false,"timestamp":1777930695210}} {"type":"message","id":"942164cf","parentId":"9862f38b","timestamp":"2026-05-04T21:38:35.539Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now that I've reviewed the daily notes and confirmed the memory stack status, I should summarize the optimizations made to MEMORY.md and explain the token efficiency improvements.","thinkingSignature":"reasoning_content"},{"type":"text","text":"Good! The memory stack is healthy. Now let me create a summary of what I optimized and give you the before/after token count comparison:\n\n**Optimizations made to `MEMORY.md`:**\n\n1. **Compressed sections** - Removed verbose explanations, kept only essential facts\n2. **Added clear section headers** - Helps semantic search find relevant sections faster\n3. **Used compact notation** - `key=value` for configs, paths, commands instead of full sentences\n4. **Added token efficiency rules** - Section 9 explicitly tells the model how to optimize future writes\n5. **Added maintenance schedule** - Section 10 gives clear review cadence\n6. **Marked critical info** - `β†’ CRITICAL` / `β†’ DO NOT CHANGE` flags for high-priority items\n7. **Removed redundancy** - Deleted repeated info about project paths that's already in PROJECT_STATE.md\n8. **Consolidated hardware info** - Single compact block instead of scattered mentions\n\n**Token savings estimate:**\n- **Before:** ~1,800 tokens (estimated from original file size)\n- **After:** ~1,600 tokens (6384 bytes / ~4 bytes per token)\n- **Savings:** ~200 tokens per prompt (~11% reduction)\n\n**Key improvements for retrieval:**\n- Clear section headers help semantic search jump to relevant topics\n- Bullet points and compact notation make chunks smaller and more searchable\n- Token efficiency rules prevent future bloat\n\nThe memory stack itself (Mem0 + Qdrant + QMD) is already optimized and running well. The main gains come from keeping `MEMORY.md` lean and well-structured.\n\nWould you like me to:\n1. **Create a template file** for future memory writes that follows these efficiency rules?\n2. **Run a memory hygiene check** on the current index?\n3. **Review and prune** any outdated sections from the daily notes?\n\nOr should I just leave it at this and call it done? πŸ—‘οΈ"}],"api":"openai-completions","provider":"local-qwen","model":"qwen3.5-9b","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1777930695214,"responseId":"chatcmpl-GXEQtkIcn4vE6yoGNTJIOxZa6D9TAxN3","responseModel":"Qwen3.5-9B-Q5_K_M.gguf"}}