mirror of https://github.com/openclaw/openclaw.git
66 lines
1.9 KiB
Markdown
66 lines
1.9 KiB
Markdown
---
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title: "Builtin Memory Engine"
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summary: "The default SQLite-based memory backend with keyword, vector, and hybrid search"
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read_when:
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- You want to understand the default memory backend
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- You want to configure embedding providers or hybrid search
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---
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# Builtin Memory Engine
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The builtin engine is the default memory backend. It stores your memory index in
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a per-agent SQLite database and needs no extra dependencies to get started.
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## What it provides
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- **Keyword search** via FTS5 full-text indexing (BM25 scoring).
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- **Vector search** via embeddings from OpenAI, Gemini, Voyage, Mistral, Ollama,
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or a local GGUF model.
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- **Hybrid search** that combines both for best results.
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- **CJK support** via trigram tokenization.
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- **sqlite-vec acceleration** for in-database vector queries (optional, falls
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back to in-process cosine similarity).
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## Getting started
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If you have an API key for OpenAI, Gemini, Voyage, or Mistral, the builtin
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engine auto-detects it and enables vector search. No config needed.
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To set a provider explicitly:
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```json5
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{
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agents: {
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defaults: {
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memorySearch: {
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provider: "openai",
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},
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},
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},
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}
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```
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Without an embedding provider, only keyword search is available.
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## Index location
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The index lives at `~/.openclaw/memory/<agentId>.sqlite`. Reindex anytime with:
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```bash
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openclaw memory index --force
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```
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## When to use
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The builtin engine is the right choice for most users. It works out of the box,
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has no external dependencies, and handles keyword + vector search well.
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Consider switching to [QMD](/concepts/memory-qmd) if you need reranking, query
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expansion, or want to index directories outside the workspace.
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## Configuration
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For embedding provider setup, hybrid search tuning (weights, MMR, temporal
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decay), batch indexing, multimodal memory, sqlite-vec, and all other config
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knobs, see the [Memory configuration reference](/reference/memory-config).
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