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---
summary: "All configuration knobs for memory search, embedding providers, QMD, hybrid search, and multimodal indexing"
title: "Memory configuration reference"
sidebarTitle: "Memory config"
read_when:
- You want to configure memory search providers or embedding models
- You want to set up the QMD backend
- You want to tune hybrid search, MMR, or temporal decay
- You want to enable multimodal memory indexing
---
This page lists every configuration knob for OpenClaw memory search. For conceptual overviews, see:
<CardGroup cols={2}>
<Card title="Memory overview" href="/concepts/memory">
How memory works.
</Card>
<Card title="Builtin engine" href="/concepts/memory-builtin">
Default SQLite backend.
</Card>
<Card title="QMD engine" href="/concepts/memory-qmd">
Local-first sidecar.
</Card>
<Card title="Memory search" href="/concepts/memory-search">
Search pipeline and tuning.
</Card>
<Card title="Active memory" href="/concepts/active-memory">
Memory sub-agent for interactive sessions.
</Card>
</CardGroup>
All memory search settings live under `agents.defaults.memorySearch` in `openclaw.json` unless noted otherwise.
<Note>
If you are looking for the **active memory** feature toggle and sub-agent config, that lives under `plugins.entries.active-memory` instead of `memorySearch`.
Active memory uses a two-gate model:
1. the plugin must be enabled and target the current agent id
2. the request must be an eligible interactive persistent chat session
See [Active Memory](/concepts/active-memory) for the activation model, plugin-owned config, transcript persistence, and safe rollout pattern.
</Note>
---
## Provider selection
| Key | Type | Default | Description |
| ---------- | --------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
| `provider` | `string` | auto-detected | Embedding adapter ID: `bedrock`, `gemini`, `github-copilot`, `local`, `mistral`, `ollama`, `openai`, `voyage` |
| `model` | `string` | provider default | Embedding model name |
| `fallback` | `string` | `"none"` | Fallback adapter ID when the primary fails |
| `enabled` | `boolean` | `true` | Enable or disable memory search |
### Auto-detection order
When `provider` is not set, OpenClaw selects the first available:
<Steps>
<Step title="local">
Selected if `memorySearch.local.modelPath` is configured and the file exists.
</Step>
<Step title="github-copilot">
Selected if a GitHub Copilot token can be resolved (env var or auth profile).
</Step>
<Step title="openai">
Selected if an OpenAI key can be resolved.
</Step>
<Step title="gemini">
Selected if a Gemini key can be resolved.
</Step>
<Step title="voyage">
Selected if a Voyage key can be resolved.
</Step>
<Step title="mistral">
Selected if a Mistral key can be resolved.
</Step>
<Step title="bedrock">
Selected if the AWS SDK credential chain resolves (instance role, access keys, profile, SSO, web identity, or shared config).
</Step>
</Steps>
`ollama` is supported but not auto-detected (set it explicitly).
### API key resolution
Remote embeddings require an API key. Bedrock uses the AWS SDK default credential chain instead (instance roles, SSO, access keys).
| Provider | Env var | Config key |
| -------------- | -------------------------------------------------- | --------------------------------- |
| Bedrock | AWS credential chain | No API key needed |
| Gemini | `GEMINI_API_KEY` | `models.providers.google.apiKey` |
| GitHub Copilot | `COPILOT_GITHUB_TOKEN`, `GH_TOKEN`, `GITHUB_TOKEN` | Auth profile via device login |
| Mistral | `MISTRAL_API_KEY` | `models.providers.mistral.apiKey` |
| Ollama | `OLLAMA_API_KEY` (placeholder) | -- |
| OpenAI | `OPENAI_API_KEY` | `models.providers.openai.apiKey` |
| Voyage | `VOYAGE_API_KEY` | `models.providers.voyage.apiKey` |
<Note>
Codex OAuth covers chat/completions only and does not satisfy embedding requests.
</Note>
---
## Remote endpoint config
For custom OpenAI-compatible endpoints or overriding provider defaults:
<ParamField path="remote.baseUrl" type="string">
Custom API base URL.
</ParamField>
<ParamField path="remote.apiKey" type="string">
Override API key.
</ParamField>
<ParamField path="remote.headers" type="object">
Extra HTTP headers (merged with provider defaults).
</ParamField>
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "openai",
model: "text-embedding-3-small",
remote: {
baseUrl: "https://api.example.com/v1/",
apiKey: "YOUR_KEY",
},
},
},
},
}
```
---
## Provider-specific config
<AccordionGroup>
<Accordion title="Gemini">
| Key | Type | Default | Description |
| ---------------------- | -------- | ---------------------- | ------------------------------------------ |
| `model` | `string` | `gemini-embedding-001` | Also supports `gemini-embedding-2-preview` |
| `outputDimensionality` | `number` | `3072` | For Embedding 2: 768, 1536, or 3072 |
<Warning>
Changing model or `outputDimensionality` triggers an automatic full reindex.
</Warning>
</Accordion>
<Accordion title="Bedrock">
Bedrock uses the AWS SDK default credential chain — no API keys needed. If OpenClaw runs on EC2 with a Bedrock-enabled instance role, just set the provider and model:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "bedrock",
model: "amazon.titan-embed-text-v2:0",
},
},
},
}
```
| Key | Type | Default | Description |
| ---------------------- | -------- | ------------------------------ | ------------------------------- |
| `model` | `string` | `amazon.titan-embed-text-v2:0` | Any Bedrock embedding model ID |
| `outputDimensionality` | `number` | model default | For Titan V2: 256, 512, or 1024 |
**Supported models** (with family detection and dimension defaults):
| Model ID | Provider | Default Dims | Configurable Dims |
| ------------------------------------------ | ---------- | ------------ | -------------------- |
| `amazon.titan-embed-text-v2:0` | Amazon | 1024 | 256, 512, 1024 |
| `amazon.titan-embed-text-v1` | Amazon | 1536 | -- |
| `amazon.titan-embed-g1-text-02` | Amazon | 1536 | -- |
| `amazon.titan-embed-image-v1` | Amazon | 1024 | -- |
| `amazon.nova-2-multimodal-embeddings-v1:0` | Amazon | 1024 | 256, 384, 1024, 3072 |
| `cohere.embed-english-v3` | Cohere | 1024 | -- |
| `cohere.embed-multilingual-v3` | Cohere | 1024 | -- |
| `cohere.embed-v4:0` | Cohere | 1536 | 256-1536 |
| `twelvelabs.marengo-embed-3-0-v1:0` | TwelveLabs | 512 | -- |
| `twelvelabs.marengo-embed-2-7-v1:0` | TwelveLabs | 1024 | -- |
Throughput-suffixed variants (e.g., `amazon.titan-embed-text-v1:2:8k`) inherit the base model's configuration.
**Authentication:** Bedrock auth uses the standard AWS SDK credential resolution order:
1. Environment variables (`AWS_ACCESS_KEY_ID` + `AWS_SECRET_ACCESS_KEY`)
2. SSO token cache
3. Web identity token credentials
4. Shared credentials and config files
5. ECS or EC2 metadata credentials
Region is resolved from `AWS_REGION`, `AWS_DEFAULT_REGION`, the `amazon-bedrock` provider `baseUrl`, or defaults to `us-east-1`.
**IAM permissions:** the IAM role or user needs:
```json
{
"Effect": "Allow",
"Action": "bedrock:InvokeModel",
"Resource": "*"
}
```
For least-privilege, scope `InvokeModel` to the specific model:
```
arn:aws:bedrock:*::foundation-model/amazon.titan-embed-text-v2:0
```
</Accordion>
<Accordion title="Local (GGUF + node-llama-cpp)">
| Key | Type | Default | Description |
| --------------------- | ------------------ | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `local.modelPath` | `string` | auto-downloaded | Path to GGUF model file |
| `local.modelCacheDir` | `string` | node-llama-cpp default | Cache dir for downloaded models |
| `local.contextSize` | `number \| "auto"` | `4096` | Context window size for the embedding context. 4096 covers typical chunks (128512 tokens) while bounding non-weight VRAM. Lower to 10242048 on constrained hosts. `"auto"` uses the model's trained maximum — not recommended for 8B+ models (Qwen3-Embedding-8B: 40 960 tokens → ~32 GB VRAM vs ~8.8 GB at 4096). |
Default model: `embeddinggemma-300m-qat-Q8_0.gguf` (~0.6 GB, auto-downloaded). Requires native build: `pnpm approve-builds` then `pnpm rebuild node-llama-cpp`.
Use the standalone CLI to verify the same provider path the Gateway uses:
```bash
openclaw memory status --deep --agent main
openclaw memory index --force --agent main
```
If `provider` is `auto`, `local` is selected only when `local.modelPath` points to an existing local file. `hf:` and HTTP(S) model references can still be used explicitly with `provider: "local"`, but they do not make `auto` select local before the model is available on disk.
</Accordion>
</AccordionGroup>
### Inline embedding timeout
<ParamField path="sync.embeddingBatchTimeoutSeconds" type="number">
Override the timeout for inline embedding batches during memory indexing.
Unset uses the provider default: 600 seconds for local/self-hosted providers such as `local`, `ollama`, and `lmstudio`, and 120 seconds for hosted providers. Increase this when local CPU-bound embedding batches are healthy but slow.
</ParamField>
---
## Hybrid search config
All under `memorySearch.query.hybrid`:
| Key | Type | Default | Description |
| --------------------- | --------- | ------- | ---------------------------------- |
| `enabled` | `boolean` | `true` | Enable hybrid BM25 + vector search |
| `vectorWeight` | `number` | `0.7` | Weight for vector scores (0-1) |
| `textWeight` | `number` | `0.3` | Weight for BM25 scores (0-1) |
| `candidateMultiplier` | `number` | `4` | Candidate pool size multiplier |
<Tabs>
<Tab title="MMR (diversity)">
| Key | Type | Default | Description |
| ------------- | --------- | ------- | ------------------------------------ |
| `mmr.enabled` | `boolean` | `false` | Enable MMR re-ranking |
| `mmr.lambda` | `number` | `0.7` | 0 = max diversity, 1 = max relevance |
</Tab>
<Tab title="Temporal decay (recency)">
| Key | Type | Default | Description |
| ---------------------------- | --------- | ------- | ------------------------- |
| `temporalDecay.enabled` | `boolean` | `false` | Enable recency boost |
| `temporalDecay.halfLifeDays` | `number` | `30` | Score halves every N days |
Evergreen files (`MEMORY.md`, non-dated files in `memory/`) are never decayed.
</Tab>
</Tabs>
### Full example
```json5
{
agents: {
defaults: {
memorySearch: {
query: {
hybrid: {
vectorWeight: 0.7,
textWeight: 0.3,
mmr: { enabled: true, lambda: 0.7 },
temporalDecay: { enabled: true, halfLifeDays: 30 },
},
},
},
},
},
}
```
---
## Additional memory paths
| Key | Type | Description |
| ------------ | ---------- | ---------------------------------------- |
| `extraPaths` | `string[]` | Additional directories or files to index |
```json5
{
agents: {
defaults: {
memorySearch: {
extraPaths: ["../team-docs", "/srv/shared-notes"],
},
},
},
}
```
Paths can be absolute or workspace-relative. Directories are scanned recursively for `.md` files. Symlink handling depends on the active backend: the builtin engine ignores symlinks, while QMD follows the underlying QMD scanner behavior.
For agent-scoped cross-agent transcript search, use `agents.list[].memorySearch.qmd.extraCollections` instead of `memory.qmd.paths`. Those extra collections follow the same `{ path, name, pattern? }` shape, but they are merged per agent and can preserve explicit shared names when the path points outside the current workspace. If the same resolved path appears in both `memory.qmd.paths` and `memorySearch.qmd.extraCollections`, QMD keeps the first entry and skips the duplicate.
---
## Multimodal memory (Gemini)
Index images and audio alongside Markdown using Gemini Embedding 2:
| Key | Type | Default | Description |
| ------------------------- | ---------- | ---------- | -------------------------------------- |
| `multimodal.enabled` | `boolean` | `false` | Enable multimodal indexing |
| `multimodal.modalities` | `string[]` | -- | `["image"]`, `["audio"]`, or `["all"]` |
| `multimodal.maxFileBytes` | `number` | `10000000` | Max file size for indexing |
<Note>
Only applies to files in `extraPaths`. Default memory roots stay Markdown-only. Requires `gemini-embedding-2-preview`. `fallback` must be `"none"`.
</Note>
Supported formats: `.jpg`, `.jpeg`, `.png`, `.webp`, `.gif`, `.heic`, `.heif` (images); `.mp3`, `.wav`, `.ogg`, `.opus`, `.m4a`, `.aac`, `.flac` (audio).
---
## Embedding cache
| Key | Type | Default | Description |
| ------------------ | --------- | ------- | -------------------------------- |
| `cache.enabled` | `boolean` | `false` | Cache chunk embeddings in SQLite |
| `cache.maxEntries` | `number` | `50000` | Max cached embeddings |
Prevents re-embedding unchanged text during reindex or transcript updates.
---
## Batch indexing
| Key | Type | Default | Description |
| ----------------------------- | --------- | ------- | -------------------------- |
| `remote.batch.enabled` | `boolean` | `false` | Enable batch embedding API |
| `remote.batch.concurrency` | `number` | `2` | Parallel batch jobs |
| `remote.batch.wait` | `boolean` | `true` | Wait for batch completion |
| `remote.batch.pollIntervalMs` | `number` | -- | Poll interval |
| `remote.batch.timeoutMinutes` | `number` | -- | Batch timeout |
Available for `openai`, `gemini`, and `voyage`. OpenAI batch is typically fastest and cheapest for large backfills.
This is separate from `sync.embeddingBatchTimeoutSeconds`, which controls inline embedding calls used by local/self-hosted providers and hosted providers when provider batch APIs are not active.
---
## Session memory search (experimental)
Index session transcripts and surface them via `memory_search`:
| Key | Type | Default | Description |
| ----------------------------- | ---------- | ------------ | --------------------------------------- |
| `experimental.sessionMemory` | `boolean` | `false` | Enable session indexing |
| `sources` | `string[]` | `["memory"]` | Add `"sessions"` to include transcripts |
| `sync.sessions.deltaBytes` | `number` | `100000` | Byte threshold for reindex |
| `sync.sessions.deltaMessages` | `number` | `50` | Message threshold for reindex |
<Warning>
Session indexing is opt-in and runs asynchronously. Results can be slightly stale. Session logs live on disk, so treat filesystem access as the trust boundary.
</Warning>
---
## SQLite vector acceleration (sqlite-vec)
| Key | Type | Default | Description |
| ---------------------------- | --------- | ------- | --------------------------------- |
| `store.vector.enabled` | `boolean` | `true` | Use sqlite-vec for vector queries |
| `store.vector.extensionPath` | `string` | bundled | Override sqlite-vec path |
When sqlite-vec is unavailable, OpenClaw falls back to in-process cosine similarity automatically.
---
## Index storage
| Key | Type | Default | Description |
| --------------------- | -------- | ------------------------------------- | ------------------------------------------- |
| `store.path` | `string` | `~/.openclaw/memory/{agentId}.sqlite` | Index location (supports `{agentId}` token) |
| `store.fts.tokenizer` | `string` | `unicode61` | FTS5 tokenizer (`unicode61` or `trigram`) |
---
## QMD backend config
Set `memory.backend = "qmd"` to enable. All QMD settings live under `memory.qmd`:
| Key | Type | Default | Description |
| ------------------------ | --------- | -------- | -------------------------------------------- |
| `command` | `string` | `qmd` | QMD executable path |
| `searchMode` | `string` | `search` | Search command: `search`, `vsearch`, `query` |
| `includeDefaultMemory` | `boolean` | `true` | Auto-index `MEMORY.md` + `memory/**/*.md` |
| `paths[]` | `array` | -- | Extra paths: `{ name, path, pattern? }` |
| `sessions.enabled` | `boolean` | `false` | Index session transcripts |
| `sessions.retentionDays` | `number` | -- | Transcript retention |
| `sessions.exportDir` | `string` | -- | Export directory |
OpenClaw prefers the current QMD collection and MCP query shapes, but keeps older QMD releases working by falling back to legacy `--mask` collection flags and older MCP tool names when needed.
<Note>
QMD model overrides stay on the QMD side, not OpenClaw config. If you need to override QMD's models globally, set environment variables such as `QMD_EMBED_MODEL`, `QMD_RERANK_MODEL`, and `QMD_GENERATE_MODEL` in the gateway runtime environment.
</Note>
<AccordionGroup>
<Accordion title="Update schedule">
| Key | Type | Default | Description |
| ------------------------- | --------- | ------- | ------------------------------------- |
| `update.interval` | `string` | `5m` | Refresh interval |
| `update.debounceMs` | `number` | `15000` | Debounce file changes |
| `update.onBoot` | `boolean` | `true` | Refresh on startup |
| `update.waitForBootSync` | `boolean` | `false` | Block startup until refresh completes |
| `update.embedInterval` | `string` | -- | Separate embed cadence |
| `update.commandTimeoutMs` | `number` | -- | Timeout for QMD commands |
| `update.updateTimeoutMs` | `number` | -- | Timeout for QMD update operations |
| `update.embedTimeoutMs` | `number` | -- | Timeout for QMD embed operations |
</Accordion>
<Accordion title="Limits">
| Key | Type | Default | Description |
| ------------------------- | -------- | ------- | -------------------------- |
| `limits.maxResults` | `number` | `6` | Max search results |
| `limits.maxSnippetChars` | `number` | -- | Clamp snippet length |
| `limits.maxInjectedChars` | `number` | -- | Clamp total injected chars |
| `limits.timeoutMs` | `number` | `4000` | Search timeout |
</Accordion>
<Accordion title="Scope">
Controls which sessions can receive QMD search results. Same schema as [`session.sendPolicy`](/gateway/config-agents#session):
```json5
{
memory: {
qmd: {
scope: {
default: "deny",
rules: [{ action: "allow", match: { chatType: "direct" } }],
},
},
},
}
```
The shipped default allows direct and channel sessions, while still denying groups.
Default is DM-only. `match.keyPrefix` matches the normalized session key; `match.rawKeyPrefix` matches the raw key including `agent:<id>:`.
</Accordion>
<Accordion title="Citations">
`memory.citations` applies to all backends:
| Value | Behavior |
| ---------------- | --------------------------------------------------- |
| `auto` (default) | Include `Source: <path#line>` footer in snippets |
| `on` | Always include footer |
| `off` | Omit footer (path still passed to agent internally) |
</Accordion>
</AccordionGroup>
### Full QMD example
```json5
{
memory: {
backend: "qmd",
citations: "auto",
qmd: {
includeDefaultMemory: true,
update: { interval: "5m", debounceMs: 15000 },
limits: { maxResults: 6, timeoutMs: 4000 },
scope: {
default: "deny",
rules: [{ action: "allow", match: { chatType: "direct" } }],
},
paths: [{ name: "docs", path: "~/notes", pattern: "**/*.md" }],
},
},
}
```
---
## Dreaming
Dreaming is configured under `plugins.entries.memory-core.config.dreaming`, not under `agents.defaults.memorySearch`.
Dreaming runs as one scheduled sweep and uses internal light/deep/REM phases as an implementation detail.
For conceptual behavior and slash commands, see [Dreaming](/concepts/dreaming).
### User settings
| Key | Type | Default | Description |
| ----------- | --------- | ----------- | ------------------------------------------------- |
| `enabled` | `boolean` | `false` | Enable or disable dreaming entirely |
| `frequency` | `string` | `0 3 * * *` | Optional cron cadence for the full dreaming sweep |
### Example
```json5
{
plugins: {
entries: {
"memory-core": {
config: {
dreaming: {
enabled: true,
frequency: "0 3 * * *",
},
},
},
},
},
}
```
<Note>
- Dreaming writes machine state to `memory/.dreams/`.
- Dreaming writes human-readable narrative output to `DREAMS.md` (or existing `dreams.md`).
- The light/deep/REM phase policy and thresholds are internal behavior, not user-facing config.
</Note>
## Related
- [Configuration reference](/gateway/configuration-reference)
- [Memory overview](/concepts/memory)
- [Memory search](/concepts/memory-search)