openclaw/docs/concepts/memory-builtin.md

Multi-channel AI gateway with extensible messaging integrations

---
doc-schema-version: 1
summary: "The default SQLite-based memory backend with keyword, vector, and hybrid search"
title: "Builtin memory engine"
read_when:
  - You want to understand the default memory backend
  - You want to configure embedding providers or hybrid search
  - You are migrating from the removed QMD memory backend
---

The builtin engine is the default memory backend. It stores your memory index
in a per-agent SQLite database and needs no extra dependencies to get
started.

## What it provides

- **Keyword search** via FTS5 full-text indexing (BM25 scoring).
- **Vector search** via embeddings from any supported provider.
- **Hybrid search** that combines both for best results.
- **Deterministic ranking** by relevance, recency, and write-time importance.
- **Diversity-aware ordering** with MMR enabled on hybrid results by default.
- **Trusted trigger recall** for bounded pre-reply context without a recall model.
- **CJK support** via trigram tokenization for Chinese, Japanese, and Korean.
- **sqlite-vec acceleration** for in-database vector queries (optional).

Native sqlite-vec queries run in a separate, read-only process so a slow query
does not block the Gateway event loop. Cancelling a search terminates its query
process; OpenClaw does not retry that native query on the Gateway thread.

## Getting started

By default, the builtin engine uses OpenAI embeddings. If `OPENAI_API_KEY` or
`models.providers.openai.apiKey` is already configured, vector search works
with no extra memory config.

To set a provider explicitly:

```json5
{
  memory: {
    search: {
      provider: "openai",
    },
  },
}
```

Without an embedding provider, only keyword search is available.

To force local GGUF embeddings, install and configure the official llama.cpp
provider, then point `local.modelPath` at a GGUF file:

```bash
openclaw plugins install @openclaw/llama-cpp-provider
```

```json5
{
  memory: {
    search: {
      provider: "local",
      fallback: "none",
      local: {
        modelPath: "~/.openclaw/models/llama.cpp/hf_ggml-org_embeddinggemma-300m-qat-Q8_0.gguf",
      },
    },
  },
}
```

## Supported embedding providers

| Provider          | ID                  | Notes                               |
| ----------------- | ------------------- | ----------------------------------- |
| Bedrock           | `bedrock`           | Uses the AWS credential chain       |
| DeepInfra         | `deepinfra`         | Default: `BAAI/bge-m3`              |
| Gemini            | `gemini`            | Supports multimodal (image + audio) |
| GitHub Copilot    | `github-copilot`    | Uses your Copilot subscription      |
| LM Studio         | `lmstudio`          | Local/self-hosted                   |
| Local             | `local`             | OpenClaw-managed llama.cpp server   |
| Mistral           | `mistral`           |                                     |
| Ollama            | `ollama`            | Local/self-hosted                   |
| OpenAI            | `openai`            | Default: `text-embedding-3-small`   |
| OpenAI-compatible | `openai-compatible` | Generic `/v1/embeddings` endpoint   |
| Voyage            | `voyage`            |                                     |

Set `memory.search.provider` to switch away from OpenAI.

## How indexing works

OpenClaw indexes `MEMORY.md`, an existing root `USER.md`, and `memory/*.md` into
chunks (400 tokens with 80-token overlap by default) and stores them in a
per-agent SQLite database. OpenClaw does not create `USER.md` automatically.

Each chunk can carry nullable importance and trigger metadata. Null values are
neutral, so older indexes remain usable. Search combines hybrid relevance,
recency decay, and importance before applying MMR diversity; trigger recall
only injects curated or promoted-trusted entries.

Each indexed chunk also has SQLite-owned provenance: origin class (`owner`,
`agent`, `untrusted`, or `system`), session kind, observation time, and an
optional supersession key. This metadata is stored separately from Markdown
so recalled prose cannot rewrite its own trust classification. Automatic
session ingestion also records source-session origins for its staged entries,
which support selective deletion after promotion. For coverage and limits, see
[Memory provenance and deletion](/concepts/memory-provenance).

- **Index location:** the owning agent database at
  `~/.openclaw/agents/<agentId>/agent/openclaw-agent.sqlite`
- **Storage maintenance:** SQLite WAL sidecars are bounded with periodic and
  shutdown checkpoints.
- **File watching:** changes to memory files trigger a debounced reindex
  (1.5s default).
- **Index compatibility:** changing the embedding provider, model, settings,
  configured sources, or scope can pause search until you explicitly rebuild.
  See [provider selection](/reference/memory-config#provider-selection).
- **Reindex on demand:** `openclaw memory index --force --agent <id>`

Search-triggered maintenance applies pending memory and session changes
incrementally while searches remain available. A failed full rebuild retains
its full-retry state; ordinary dirty content does not itself force a rebuild.

Full reindexes build a replacement in a temporary database and publish the
memory tables atomically. Concurrent searches and status reads keep using the
published index; a failed rebuild leaves that index intact. The embedding cache
is bounded before publication, not after copying excess entries into the
shared database.

Other agent state, including sessions and transcripts in the same database,
is retained. Use the [memory index command](/cli/memory#memory-index) for
memory-only repair.

`openclaw memory status` reports stored chunk text and JSON embedding bytes
for each source (`sourceCounts[].chunkBytes` in JSON). These are payload sizes,
not total disk usage: embedding cache, FTS/vector tables, SQLite overhead, and
WAL/free pages are excluded.

After an upgrade, automatic project and trigger recall may need to repair
legacy provenance. That repair runs in the background. Replies continue while
automatic recall stays empty until the affected sources have been reclassified.

<Info>
You can also index Markdown files outside the workspace with
`memory.search.extraPaths`. See the
[configuration reference](/reference/memory-config#additional-memory-paths).
</Info>

## Migrating from QMD

QMD has been removed; builtin is the only memory engine. After upgrading, run:

```bash
openclaw doctor --fix
```

Doctor removes the retired `memory.backend`, `memory.qmd`, and
`memory.search.qmd` settings, including agent-scoped `memory.search.qmd`
forms. It preserves QMD paths and extra collections as the corresponding
`memory.search.extraPaths` entries, including `{ path, pattern }` globs. When
QMD session indexing was enabled, Doctor also enables builtin session indexing
and adds `sessions` to `memory.search.sources` without enabling broader
cross-conversation recall. Retained session-reset transcripts remain in the
agent's sessions directory and are indexed from those original artifacts.

When Memory Core finds a retired per-agent QMD workspace under
`~/.openclaw/agents/<agentId>/qmd/`, Doctor also offers to remove its derived
indexes, model downloads, collection metadata, and session exports.

Canonical memory remains in `MEMORY.md`, `USER.md`, `memory/*.md`, and the
migrated extra paths. Builtin indexes those same Markdown sources on its next
sync. The cutover is lossless by construction: no canonical memory content is
copied or deleted; only derived state is rebuilt.

Builtin now covers most QMD use cases with:

- hybrid BM25 and vector retrieval by default, followed by temporal decay,
  importance, and project affinity before MMR diversity,
- bounded lexical query expansion for conversational searches,
- string or `{ path, pattern }` entries in `memory.search.extraPaths`, and
- optional image and audio indexing under `extraPaths` only.

QMD query mode's learned cross-encoder reranking and HyDE generation are not
part of builtin memory. MMR reduces duplicate results but is not a learned
relevance reranker. To replace QMD's in-process, zero-key GGUF embeddings,
install the [llama.cpp provider](/plugins/llama-cpp) and set
`memory.search.provider: "local"`; without an embedding provider, builtin uses
BM25 keyword search only.

## When to use

The builtin engine is the right choice for most users:

- Works out of the box with no extra dependencies.
- Handles keyword and vector search well.
- Supports all embedding providers.
- Hybrid search combines the best of both retrieval approaches.

The builtin engine can index directories outside the workspace with
`memory.search.extraPaths`. It uses bounded lexical query expansion to improve
conversational recall, but it does not provide a learned or model-based relevance
reranking stage. Its MMR pass is deterministic and local.

Consider [Honcho](/concepts/memory-honcho) if you want cross-session memory
with automatic user modeling.

## Troubleshooting

**Memory search disabled?** Check `openclaw memory status`. If no provider is
detected, set one explicitly or add an API key.

**Local provider not detected?** Run interactive llama.cpp setup once, confirm
the local path exists, and run:

```bash
openclaw memory status --deep --agent main
openclaw memory index --force --agent main
```

Both standalone CLI commands and the Gateway use the same `local` provider id.
Set `memory.search.provider: "local"` when you want local embeddings.

**Stale results?** Run `openclaw memory index --force` to rebuild. The watcher
may miss changes in rare edge cases.

**sqlite-vec not loading?** OpenClaw falls back to in-process cosine
similarity automatically. `openclaw memory status --deep` reports the local
vector store separately from the embedding provider, so `Vector store:
unavailable` points at sqlite-vec loading while `Embeddings: unavailable`
points at provider/auth or model readiness. Check logs for the specific load
error.

### Safe index recovery

To rebuild after stale results or an embedding-provider change, select the
affected agent explicitly:

```bash
openclaw memory status --agent <agent-id> --deep
openclaw memory index --agent <agent-id> --force --verbose
openclaw memory status --agent <agent-id> --deep
```

<Warning>
The index shares `openclaw-agent.sqlite` with canonical sessions, transcripts,
and other durable agent state. Never delete that database or its `-wal`, `-shm`,
or `-journal` sidecars to reset memory. Memory indexing cannot reconstruct
conversation history lost this way.
</Warning>

To discard the derived index and embedding cache before rebuilding, use
[`memory reset`](/cli/memory#memory-reset):

```bash
openclaw memory reset --agent <agent-id>
openclaw memory index --agent <agent-id>
```

Reset asks for confirmation; add `--yes` for non-interactive use. It clears only
memory-owned derived tables, preserving non-memory database tables, including
sessions and transcripts, and memory source files. It coordinates with
existing memory maintenance without restarting the Gateway, which can reindex
retained sources afterward. If indexing is busy, let it finish and retry reset.
Reset does not shrink the database file or recover already deleted data.

If indexing fails or the database grows unexpectedly, keep the database and
its sidecars, retain the verbose error, and [create and verify a backup](/cli/backup)
before manual recovery. A large database alone does not show which tables are
responsible. Reindexing is not a session-history restore: if history is missing
after moving or deleting the database, recover from a verified backup using
the [restore workflow](/install/backups#restore-a-full-archive).

### Reclaim disk space

Start with `openclaw memory status --agent <agent-id> --json`. Compare the
database and WAL sizes, reusable bytes, retained embedding-cache payload, and
per-source chunk payloads. Reusable bytes are pages already free inside SQLite;
they are not additional data. Cache and chunk payloads exclude indexes and
SQLite overhead, so they do not explain every byte in the shared file.

If the derived index needs to be discarded, create and verify a
[backup](/cli/backup), then stop the Gateway through its deployment owner and
stop other writers. Keep them stopped through reset and compaction so background
indexing cannot refill the cache between commands:

```bash
openclaw memory reset --agent <agent-id> --yes
openclaw doctor --session-sqlite compact --session-sqlite-agent <agent-id>
openclaw memory index --agent <agent-id>
openclaw memory status --agent <agent-id>
```

If only unused pages need reclaiming, skip reset and preserve the existing index.
Doctor compacts the whole agent database, verifies integrity, and reports the
before/after database and WAL sizes. Compaction needs temporary disk space; on a
full volume, free space or move a verified backup to a volume with sufficient
capacity before attempting it. Rebuilding can call the embedding provider and
incur cost. Restart the Gateway through its deployment owner after verification.
Neither reset nor compaction removes canonical sessions or changes retention.

## Configuration

For embedding provider setup, search result limits and thresholds, batch
indexing, multimodal memory, sqlite-vec, extra paths, and all other config
knobs, see the
[Memory configuration reference](/reference/memory-config).

## Related

- [Memory overview](/concepts/memory)
- [Memory search](/concepts/memory-search)
- [Active memory](/concepts/active-memory)