---
title: "Memory"
summary: "How OpenClaw memory works (workspace files + automatic memory flush)"
read_when:
  - You want the memory file layout and workflow
  - You want to tune the automatic pre-compaction memory flush
---

# Memory

OpenClaw memory is **plain Markdown in the agent workspace**. The files are the
source of truth; the model only "remembers" what gets written to disk.

Memory search tools are provided by the active memory plugin (default:
`memory-core`). Disable memory plugins with `plugins.slots.memory = "none"`.

## Memory files (Markdown)

The default workspace layout uses two memory layers:

- `memory/YYYY-MM-DD.md`
  - Daily log (append-only).
  - Read today + yesterday at session start.
- `MEMORY.md` (optional)
  - Curated long-term memory.
  - 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).
  - **Only load in the main, private session** (never in group contexts).

These files live under the workspace (`agents.defaults.workspace`, default
`~/.openclaw/workspace`). See [Agent workspace](/concepts/agent-workspace) for the full layout.

## Memory tools

OpenClaw exposes two agent-facing tools for these Markdown files:

- `memory_search` -- semantic recall over indexed snippets.
- `memory_get` -- targeted read of a specific Markdown file/line range.

`memory_get` now **degrades gracefully when a file doesn't exist** (for example,
today's daily log before the first write). Both the builtin manager and the QMD
backend return `{ text: "", path }` instead of throwing `ENOENT`, so agents can
handle "nothing recorded yet" and continue their workflow without wrapping the
tool call in try/catch logic.

## When to write memory

- Decisions, preferences, and durable facts go to `MEMORY.md`.
- Day-to-day notes and running context go to `memory/YYYY-MM-DD.md`.
- If someone says "remember this," write it down (do not keep it in RAM).
- This area is still evolving. It helps to remind the model to store memories; it will know what to do.
- If you want something to stick, **ask the bot to write it** into memory.

## Automatic memory flush (pre-compaction ping)

When a session is **close to auto-compaction**, OpenClaw triggers a **silent,
agentic turn** that reminds the model to write durable memory **before** the
context is compacted. The default prompts explicitly say the model _may reply_,
but usually `NO_REPLY` is the correct response so the user never sees this turn.

This is controlled by `agents.defaults.compaction.memoryFlush`:

```json5
{
  agents: {
    defaults: {
      compaction: {
        reserveTokensFloor: 20000,
        memoryFlush: {
          enabled: true,
          softThresholdTokens: 4000,
          systemPrompt: "Session nearing compaction. Store durable memories now.",
          prompt: "Write any lasting notes to memory/YYYY-MM-DD.md; reply with NO_REPLY if nothing to store.",
        },
      },
    },
  },
}
```

Details:

- **Soft threshold**: flush triggers when the session token estimate crosses
  `contextWindow - reserveTokensFloor - softThresholdTokens`.
- **Silent** by default: prompts include `NO_REPLY` so nothing is delivered.
- **Two prompts**: a user prompt plus a system prompt append the reminder.
- **One flush per compaction cycle** (tracked in `sessions.json`).
- **Workspace must be writable**: if the session runs sandboxed with
  `workspaceAccess: "ro"` or `"none"`, the flush is skipped.

For the full compaction lifecycle, see
[Session management + compaction](/reference/session-management-compaction).

## Vector memory search

OpenClaw can build a small vector index over `MEMORY.md` and `memory/*.md` so
semantic queries can find related notes even when wording differs. Hybrid search
(BM25 + vector) is available for combining semantic matching with exact keyword
lookups.

Memory search supports multiple embedding providers (OpenAI, Gemini, Voyage,
Mistral, Ollama, and local GGUF models), an optional QMD sidecar backend for
advanced retrieval, and post-processing features like MMR diversity re-ranking
and temporal decay.

For the full configuration reference -- including embedding provider setup, QMD
backend, hybrid search tuning, multimodal memory, and all config knobs -- see
[Memory configuration reference](/reference/memory-config).
