openclaw/docs/concepts/memory-builtin.md

summary, title, read_when

summary	title	read_when
The default SQLite-based memory backend with keyword, vector, and hybrid search	Builtin memory engine	You want to understand the default memory backend You want to configure embedding providers or hybrid search

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:

{
  memory: {
    search: {
      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:

openclaw plugins install @openclaw/llama-cpp-provider

{
  memory: {
    search: {
      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 memory.search.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

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

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 if you need reranking, query expansion, or want to index directories outside the workspace.

Consider 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:

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.

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.

Related

• Memory overview
• Memory search
• Active memory