> ## Documentation Index
> Fetch the complete documentation index at: https://docs2.openclaw.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Builtin memory engine

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.
* **CJK support** via trigram tokenization for Chinese, Japanese, and Korean.
* **sqlite-vec acceleration** for in-database vector queries (optional).

## 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 theme={"theme":{"light":"min-light","dark":"min-dark"}}
{
  agents: {
    defaults: {
      memorySearch: {
        provider: "openai",
      },
    },
  },
}
```

Without an embedding provider, only keyword search is available.

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

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
openclaw plugins install @openclaw/llama-cpp-provider
```

```json5 theme={"theme":{"light":"min-light","dark":"min-dark"}}
{
  agents: {
    defaults: {
      memorySearch: {
        provider: "local",
        fallback: "none",
        local: {
          modelPath: "~/.node-llama-cpp/models/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/llama-cpp-provider`      |
| 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 `memorySearch.provider` to switch away from OpenAI.

## How indexing works

OpenClaw indexes `MEMORY.md` and `memory/*.md` into chunks (400 tokens with
80-token overlap by default) and stores them in a per-agent SQLite database.

* **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).
* **Auto-reindex:** the index rebuilds automatically when the embedding
  provider, model, chunking config, configured sources, or scope change.
* **Reindex on demand:** `openclaw memory index --force`

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

## 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.

Consider switching to [QMD](/concepts/memory-qmd) if you need reranking, query
expansion, or want to index directories outside the workspace.

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?** Confirm the local path exists and run:

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
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 `memorySearch.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.

## Configuration

For embedding provider setup, hybrid search tuning (weights, MMR, temporal
decay), 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)