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112 lines
4.5 KiB
Markdown
112 lines
4.5 KiB
Markdown
---
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title: "Memory"
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summary: "How OpenClaw memory works (workspace files + automatic memory flush)"
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read_when:
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- You want the memory file layout and workflow
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- You want to tune the automatic pre-compaction memory flush
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---
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# Memory
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OpenClaw memory is **plain Markdown in the agent workspace**. The files are the
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source of truth; the model only "remembers" what gets written to disk.
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Memory search tools are provided by the active memory plugin (default:
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`memory-core`). Disable memory plugins with `plugins.slots.memory = "none"`.
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## Memory files (Markdown)
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The default workspace layout uses two memory layers:
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- `memory/YYYY-MM-DD.md`
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- Daily log (append-only).
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- Read today + yesterday at session start.
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- `MEMORY.md` (optional)
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- Curated long-term memory.
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- If both `MEMORY.md` and `memory.md` exist at the workspace root, OpenClaw loads both (deduplicated by realpath so symlinks pointing to the same file are not injected twice).
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- **Only load in the main, private session** (never in group contexts).
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These files live under the workspace (`agents.defaults.workspace`, default
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`~/.openclaw/workspace`). See [Agent workspace](/concepts/agent-workspace) for the full layout.
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## Memory tools
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OpenClaw exposes two agent-facing tools for these Markdown files:
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- `memory_search` -- semantic recall over indexed snippets.
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- `memory_get` -- targeted read of a specific Markdown file/line range.
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`memory_get` now **degrades gracefully when a file doesn't exist** (for example,
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today's daily log before the first write). Both the builtin manager and the QMD
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backend return `{ text: "", path }` instead of throwing `ENOENT`, so agents can
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handle "nothing recorded yet" and continue their workflow without wrapping the
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tool call in try/catch logic.
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## When to write memory
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- Decisions, preferences, and durable facts go to `MEMORY.md`.
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- Day-to-day notes and running context go to `memory/YYYY-MM-DD.md`.
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- If someone says "remember this," write it down (do not keep it in RAM).
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- This area is still evolving. It helps to remind the model to store memories; it will know what to do.
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- If you want something to stick, **ask the bot to write it** into memory.
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## Automatic memory flush (pre-compaction ping)
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When a session is **close to auto-compaction**, OpenClaw triggers a **silent,
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agentic turn** that reminds the model to write durable memory **before** the
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context is compacted. The default prompts explicitly say the model _may reply_,
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but usually `NO_REPLY` is the correct response so the user never sees this turn.
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The active memory plugin owns the prompt/path policy for that flush; the
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default `memory-core` plugin writes to the canonical daily file under
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`memory/YYYY-MM-DD.md`.
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This is controlled by `agents.defaults.compaction.memoryFlush`:
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```json5
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{
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agents: {
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defaults: {
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compaction: {
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reserveTokensFloor: 20000,
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memoryFlush: {
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enabled: true,
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softThresholdTokens: 4000,
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systemPrompt: "Session nearing compaction. Store durable memories now.",
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prompt: "Write any lasting notes to memory/YYYY-MM-DD.md; reply with NO_REPLY if nothing to store.",
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},
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},
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},
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},
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}
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```
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Details:
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- **Soft threshold**: flush triggers when the session token estimate crosses
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`contextWindow - reserveTokensFloor - softThresholdTokens`.
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- **Silent** by default: prompts include `NO_REPLY` so nothing is delivered.
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- **Two prompts**: a user prompt plus a system prompt append the reminder.
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- **One flush per compaction cycle** (tracked in `sessions.json`).
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- **Workspace must be writable**: if the session runs sandboxed with
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`workspaceAccess: "ro"` or `"none"`, the flush is skipped.
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For the full compaction lifecycle, see
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[Session management + compaction](/reference/session-management-compaction).
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## Vector memory search
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OpenClaw can build a small vector index over `MEMORY.md` and `memory/*.md` so
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semantic queries can find related notes even when wording differs. Hybrid search
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(BM25 + vector) is available for combining semantic matching with exact keyword
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lookups.
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Memory search adapter ids come from the active memory plugin. The default
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`memory-core` plugin ships built-ins for OpenAI, Gemini, Voyage, Mistral,
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Ollama, and local GGUF models, plus an optional QMD sidecar backend for
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advanced retrieval and post-processing features like MMR diversity re-ranking
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and temporal decay.
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For the full configuration reference -- including embedding provider setup, QMD
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backend, hybrid search tuning, multimodal memory, and all config knobs -- see
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[Memory configuration reference](/reference/memory-config).
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