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2026-10-11 06:43:16 +08:00 · 2026-04-26 08:34:33 +08:00 · 2026-04-26 08:34:33 +08:00 · dbbe3add6a
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+---
+title: "Memory Search"
+summary: "How memory search finds relevant notes using embeddings and hybrid retrieval"
+read_when:
+  - You want to understand how memory_search works
+  - You want to choose an embedding provider
+  - You want to tune search quality
+---
+
+# Memory Search
+
+`memory_search` finds relevant notes from your memory files, even when the
+wording differs from the original text. It works by indexing memory into small
+chunks and searching them using embeddings, keywords, or both.
+
+## Quick start
+
+If you have a GitHub Copilot subscription, OpenAI, Gemini, Voyage, or Mistral
+API key configured, memory search works automatically. To set a provider
+explicitly:
+
+```json5
+{
+  agents: {
+    defaults: {
+      memorySearch: {
+        provider: "openai", // or "gemini", "local", "ollama", etc.
+      },
+    },
+  },
+}
+```
+
+For local embeddings with no API key, use `provider: "local"` (requires
+node-llama-cpp).
+
+## Supported providers
+
+| Provider       | ID               | Needs API key | Notes                                                |
+| -------------- | ---------------- | ------------- | ---------------------------------------------------- |
+| Bedrock        | `bedrock`        | No            | Auto-detected when the AWS credential chain resolves |
+| Gemini         | `gemini`         | Yes           | Supports image/audio indexing                        |
+| GitHub Copilot | `github-copilot` | No            | Auto-detected, uses Copilot subscription             |
+| Local          | `local`          | No            | GGUF model, ~0.6 GB download                         |
+| Mistral        | `mistral`        | Yes           | Auto-detected                                        |
+| Ollama         | `ollama`         | No            | Local, must set explicitly                           |
+| OpenAI         | `openai`         | Yes           | Auto-detected, fast                                  |
+| Voyage         | `voyage`         | Yes           | Auto-detected                                        |
+
+## How search works
+
+OpenClaw runs two retrieval paths in parallel and merges the results:
+
+```mermaid
+flowchart LR
+    Q["Query"] --> E["Embedding"]
+    Q --> T["Tokenize"]
+    E --> VS["Vector Search"]
+    T --> BM["BM25 Search"]
+    VS --> M["Weighted Merge"]
+    BM --> M
+    M --> R["Top Results"]
+```
+
+- **Vector search** finds notes with similar meaning ("gateway host" matches
+  "the machine running OpenClaw").
+- **BM25 keyword search** finds exact matches (IDs, error strings, config
+  keys).
+
+If only one path is available (no embeddings or no FTS), the other runs alone.
+
+When embeddings are unavailable, OpenClaw still uses lexical ranking over FTS results instead of falling back to raw exact-match ordering only. That degraded mode boosts chunks with stronger query-term coverage and relevant file paths, which keeps recall useful even without `sqlite-vec` or an embedding provider.
+
+## Improving search quality
+
+Two optional features help when you have a large note history:
+
+### Temporal decay
+
+Old notes gradually lose ranking weight so recent information surfaces first.
+With the default half-life of 30 days, a note from last month scores at 50% of
+its original weight. Evergreen files like `MEMORY.md` are never decayed.
+
+<Tip>
+Enable temporal decay if your agent has months of daily notes and stale
+information keeps outranking recent context.
+</Tip>
+
+### MMR (diversity)
+
+Reduces redundant results. If five notes all mention the same router config, MMR
+ensures the top results cover different topics instead of repeating.
+
+<Tip>
+Enable MMR if `memory_search` keeps returning near-duplicate snippets from
+different daily notes.
+</Tip>
+
+### Enable both
+
+```json5
+{
+  agents: {
+    defaults: {
+      memorySearch: {
+        query: {
+          hybrid: {
+            mmr: { enabled: true },
+            temporalDecay: { enabled: true },
+          },
+        },
+      },
+    },
+  },
+}
+```
+
+## Multimodal memory
+
+With Gemini Embedding 2, you can index images and audio files alongside
+Markdown. Search queries remain text, but they match against visual and audio
+content. See the [Memory configuration reference](/reference/memory-config) for
+setup.
+
+## Session memory search
+
+You can optionally index session transcripts so `memory_search` can recall
+earlier conversations. This is opt-in via
+`memorySearch.experimental.sessionMemory`. See the
+[configuration reference](/reference/memory-config) for details.
+
+## Troubleshooting
+
+**No results?** Run `openclaw memory status` to check the index. If empty, run
+`openclaw memory index --force`.
+
+**Only keyword matches?** Your embedding provider may not be configured. Check
+`openclaw memory status --deep`.
+
+**CJK text not found?** Rebuild the FTS index with
+`openclaw memory index --force`.
+
+## Further reading
+
+- [Active Memory](/concepts/active-memory) -- sub-agent memory for interactive chat sessions
+- [Memory](/concepts/memory) -- file layout, backends, tools
+- [Memory configuration reference](/reference/memory-config) -- all config knobs