moltbot/docs/concepts/memory.md

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summary	title	read_when
How OpenClaw remembers things across sessions	Memory overview	You want to understand how memory works You want to know what memory files to write

OpenClaw remembers things by writing plain Markdown files in your agent's workspace. The model only "remembers" what gets saved to disk — there is no hidden state.

How it works

Your agent has three memory-related files:

• MEMORY.md — long-term memory. Durable facts, preferences, and decisions. Loaded at the start of every DM session.
• memory/YYYY-MM-DD.md — daily notes. Running context and observations. Today and yesterday's notes are loaded automatically.
• DREAMS.md (optional) — Dream Diary and dreaming sweep summaries for human review, including grounded historical backfill entries.

These files live in the agent workspace (default ~/.openclaw/workspace).

What goes where

MEMORY.md is the compact, curated layer. Use it for durable facts, preferences, standing decisions, and short summaries that should be available at the start of a main private session. It is not meant to be a raw transcript, daily log, or exhaustive archive.

memory/YYYY-MM-DD.md files are the working layer. Use them for detailed daily notes, observations, session summaries, and raw context that may still be useful later. These files are indexed for memory_search and memory_get, but they are not injected into the normal bootstrap prompt on every turn.

Over time, the agent is expected to distill useful material from daily notes into MEMORY.md and remove stale long-term entries. The generated workspace instructions and heartbeat flow can do that periodically; you do not need to manually edit MEMORY.md for every remembered detail.

If MEMORY.md grows past the bootstrap file budget, OpenClaw keeps the file on disk intact but truncates the copy injected into the model context. Treat that as a signal to move detailed material back into memory/*.md, keep only the durable summary in MEMORY.md, or raise the bootstrap limits if you explicitly want to spend more prompt budget. Use /context list, /context detail, or openclaw doctor to see raw vs injected sizes and truncation status.

If you want your agent to remember something, just ask it: "Remember that I prefer TypeScript." It will write it to the appropriate file.

Inferred commitments

Some future follow-ups are not durable facts. If you mention an interview tomorrow, the useful memory may be "check in after the interview," not "store this forever in MEMORY.md."

Commitments are opt-in, short-lived follow-up memories for that case. OpenClaw infers them in a hidden background pass, scopes them to the same agent and channel, and delivers due check-ins through heartbeat. Explicit reminders still use scheduled tasks.

Memory tools

The agent has two tools for working with memory:

• memory_search — finds relevant notes using semantic search, even when the wording differs from the original.
• memory_get — reads a specific memory file or line range.

Both tools are provided by the active memory plugin (default: memory-core).

Memory Wiki companion plugin

If you want durable memory to behave more like a maintained knowledge base than just raw notes, use the bundled memory-wiki plugin.

memory-wiki compiles durable knowledge into a wiki vault with:

• deterministic page structure
• structured claims and evidence
• contradiction and freshness tracking
• generated dashboards
• compiled digests for agent/runtime consumers
• wiki-native tools like wiki_search, wiki_get, wiki_apply, and wiki_lint

It does not replace the active memory plugin. The active memory plugin still owns recall, promotion, and dreaming. memory-wiki adds a provenance-rich knowledge layer beside it.

See Memory Wiki.

Memory search

When an embedding provider is configured, memory_search uses hybrid search — combining vector similarity (semantic meaning) with keyword matching (exact terms like IDs and code symbols). This works out of the box once you have an API key for any supported provider.

OpenClaw auto-detects your embedding provider from available API keys. If you have an OpenAI, Gemini, Voyage, or Mistral key configured, memory search is enabled automatically.

For details on how search works, tuning options, and provider setup, see Memory Search.

Memory backends

SQLite-based. Works out of the box with keyword search, vector similarity, and hybrid search. No extra dependencies. Local-first sidecar with reranking, query expansion, and the ability to index directories outside the workspace. AI-native cross-session memory with user modeling, semantic search, and multi-agent awareness. Plugin install. Bundled LanceDB-backed memory with OpenAI-compatible embeddings, auto-recall, auto-capture, and local Ollama embedding support.

Knowledge wiki layer

Compiles durable memory into a provenance-rich wiki vault with claims, dashboards, bridge mode, and Obsidian-friendly workflows.

Automatic memory flush

Before compaction summarizes your conversation, OpenClaw runs a silent turn that reminds the agent to save important context to memory files. This is on by default — you do not need to configure anything.

To keep that housekeeping turn on a local model, set an exact memory-flush model override:

{
  "agents": {
    "defaults": {
      "compaction": {
        "memoryFlush": {
          "model": "ollama/qwen3:8b"
        }
      }
    }
  }
}

The override applies only to the memory-flush turn and does not inherit the active session fallback chain.

The memory flush prevents context loss during compaction. If your agent has important facts in the conversation that are not yet written to a file, they will be saved automatically before the summary happens.

Dreaming

Dreaming is an optional background consolidation pass for memory. It collects short-term signals, scores candidates, and promotes only qualified items into long-term memory (MEMORY.md).

It is designed to keep long-term memory high signal:

• Opt-in: disabled by default.
• Scheduled: when enabled, memory-core auto-manages one recurring cron job for a full dreaming sweep.
• Thresholded: promotions must pass score, recall frequency, and query diversity gates.
• Reviewable: phase summaries and diary entries are written to DREAMS.md for human review.

For phase behavior, scoring signals, and Dream Diary details, see Dreaming.

Grounded backfill and live promotion

The dreaming system now has two closely related review lanes:

• Live dreaming works from the short-term dreaming store under memory/.dreams/ and is what the normal deep phase uses when deciding what can graduate into MEMORY.md.
• Grounded backfill reads historical memory/YYYY-MM-DD.md notes as standalone day files and writes structured review output into DREAMS.md.

Grounded backfill is useful when you want to replay older notes and inspect what the system thinks is durable without manually editing MEMORY.md.

When you use:

openclaw memory rem-backfill --path ./memory --stage-short-term

the grounded durable candidates are not promoted directly. They are staged into the same short-term dreaming store the normal deep phase already uses. That means:

• DREAMS.md stays the human review surface.
• the short-term store stays the machine-facing ranking surface.
• MEMORY.md is still only written by deep promotion.

If you decide the replay was not useful, you can remove the staged artifacts without touching ordinary diary entries or normal recall state:

openclaw memory rem-backfill --rollback
openclaw memory rem-backfill --rollback-short-term

CLI

openclaw memory status          # Check index status and provider
openclaw memory search "query"  # Search from the command line
openclaw memory index --force   # Rebuild the index

Further reading

• Builtin memory engine: default SQLite backend.
• QMD memory engine: advanced local-first sidecar.
• Honcho memory: AI-native cross-session memory.
• Memory LanceDB: LanceDB-backed plugin with OpenAI-compatible embeddings.
• Memory Wiki: compiled knowledge vault and wiki-native tools.
• Memory search: search pipeline, providers, and tuning.
• Dreaming: background promotion from short-term recall to long-term memory.
• Memory configuration reference: all config knobs.
• Compaction: how compaction interacts with memory.

Related

• Active memory
• Memory search
• Builtin memory engine
• Honcho memory
• Memory LanceDB
• Commitments