openclaw/docs/reference/memory-config.md

summary, title, sidebarTitle, read_when

summary	title	sidebarTitle	read_when
All configuration knobs for memory search, embedding providers, QMD, hybrid search, and multimodal indexing	Memory configuration reference	Memory config	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:

How memory works. Default SQLite backend. Local-first sidecar. Search pipeline and tuning. Memory sub-agent for interactive sessions.

All memory search settings live under agents.defaults.memorySearch in openclaw.json unless noted otherwise.

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 for the activation model, plugin-owned config, transcript persistence, and safe rollout pattern.

---

Provider selection

Key	Type	Default	Description
provider	string	auto-detected	Embedding adapter ID such as bedrock, deepinfra, gemini, github-copilot, local, mistral, ollama, openai, or voyage; may also be a configured models.providers.<id> whose api points at one of those adapters
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:

Selected if `memorySearch.local.modelPath` is configured and the file exists. Selected if a GitHub Copilot token can be resolved (env var or auth profile). Selected if an OpenAI key can be resolved. Selected if a Gemini key can be resolved. Selected if a Voyage key can be resolved. Selected if a Mistral key can be resolved. Selected if a DeepInfra key can be resolved. Selected if the AWS SDK credential chain resolves (instance role, access keys, profile, SSO, web identity, or shared config).

ollama is supported but not auto-detected (set it explicitly).

Custom provider ids

memorySearch.provider can point at a custom models.providers.<id> entry. OpenClaw resolves that provider's api owner for the embedding adapter while preserving the custom provider id for endpoint, auth, and model-prefix handling. This lets multi-GPU or multi-host setups dedicate memory embeddings to a specific local endpoint:

{
  models: {
    providers: {
      "ollama-5080": {
        api: "ollama",
        baseUrl: "http://gpu-box.local:11435",
        apiKey: "ollama-local",
        models: [{ id: "qwen3-embedding:0.6b" }],
      },
    },
  },
  agents: {
    defaults: {
      memorySearch: {
        provider: "ollama-5080",
        model: "qwen3-embedding:0.6b",
      },
    },
  },
}

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
DeepInfra	DEEPINFRA_API_KEY	models.providers.deepinfra.apiKey
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

Codex OAuth covers chat/completions only and does not satisfy embedding requests.

---

Remote endpoint config

For custom OpenAI-compatible endpoints or overriding provider defaults:

Custom API base URL. Override API key. Extra HTTP headers (merged with provider defaults).

{
  agents: {
    defaults: {
      memorySearch: {
        provider: "openai",
        model: "text-embedding-3-small",
        remote: {
          baseUrl: "https://api.example.com/v1/",
          apiKey: "YOUR_KEY",
        },
      },
    },
  },
}

---

Provider-specific config

| 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>

OpenAI-compatible embedding endpoints can opt into provider-specific `input_type` request fields. This is useful for asymmetric embedding models that require different labels for query and document embeddings.

| Key                 | Type     | Default | Description                                             |
| ------------------- | -------- | ------- | ------------------------------------------------------- |
| `inputType`         | `string` | unset   | Shared `input_type` for query and document embeddings   |
| `queryInputType`    | `string` | unset   | Query-time `input_type`; overrides `inputType`          |
| `documentInputType` | `string` | unset   | Index/document `input_type`; overrides `inputType`      |

```json5
{
  agents: {
    defaults: {
      memorySearch: {
        provider: "openai",
        remote: {
          baseUrl: "https://embeddings.example/v1",
          apiKey: "env:EMBEDDINGS_API_KEY",
        },
        model: "asymmetric-embedder",
        queryInputType: "query",
        documentInputType: "passage",
      },
    },
  },
}
```

Changing these values affects embedding cache identity for provider batch indexing and should be followed by a memory reindex when the upstream model treats the labels differently.

### Bedrock embedding config

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
```

| 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 (128–512 tokens) while bounding non-weight VRAM. Lower to 1024–2048 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). Source checkouts still require native build approval: `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.

Inline embedding timeout

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.

---

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

| Key | Type | Default | Description | | ------------- | --------- | ------- | ------------------------------------ | | `mmr.enabled` | `boolean` | `false` | Enable MMR re-ranking | | `mmr.lambda` | `number` | `0.7` | 0 = max diversity, 1 = max relevance | | 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.

Full example

{
  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

{
  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

Only applies to files in `extraPaths`. Default memory roots stay Markdown-only. Requires `gemini-embedding-2-preview`. `fallback` must be `"none"`.

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.nonBatchConcurrency	number	4	Parallel inline embeddings
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.

remote.nonBatchConcurrency controls inline embedding calls used by local/self-hosted providers and hosted providers when provider batch APIs are not active. Ollama defaults to 1 for non-batch indexing to avoid overwhelming smaller local hosts; set a higher value on larger machines.

This is separate from sync.embeddingBatchTimeoutSeconds, which controls the timeout for inline embedding calls.

---

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

Session indexing is opt-in and runs asynchronously. Results can be slightly stale. Runtime transcripts live in SQLite; legacy transcript files are doctor migration inputs only.

---

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

The builtin memory index is stored in each agent's openclaw-agent.sqlite database.

Key	Type	Default	Description
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; set an absolute path when service PATH differs from your shell
searchMode	string	search	Search command: search, vsearch, query
includeDefaultMemory	boolean	true	Auto-index MEMORY.md + memory/**/*.md
paths[]	array	--	Extra paths: { name, path, pattern? }

searchMode: "search" is lexical/BM25-only. OpenClaw does not run semantic vector readiness probes or QMD embedding maintenance for that mode, including during memory status --deep; vsearch and query continue to require QMD vector readiness and embeddings.

OpenClaw prefers current QMD collection and MCP query shapes, but keeps older QMD releases working by trying compatible collection pattern flags and older MCP tool names when needed. When QMD advertises support for multiple collection filters, same-source durable-memory collections are searched with one QMD process; older QMD builds keep the per-collection compatibility path.

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. | Key | Type | Default | Description | | ------------------------- | --------- | ------- | ------------------------------------- | | `update.interval` | `string` | `5m` | Refresh interval | | `update.debounceMs` | `number` | `15000` | Debounce file changes | | `update.onBoot` | `boolean` | `true` | Refresh when the long-lived QMD manager opens; also gates opt-in startup refresh | | `update.startup` | `string` | `off` | Optional gateway-start refresh: `off`, `idle`, or `immediate` | | `update.startupDelayMs` | `number` | `120000` | Delay before `startup: "idle"` refresh runs | | `update.waitForBootSync` | `boolean` | `false` | Block manager opening until its initial 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 | | 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 | 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>:`.

`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) |

QMD boot refreshes use a one-shot subprocess path during gateway startup. The long-lived QMD manager still owns the regular file watcher and interval timers when memory search is opened for interactive use.

Full QMD example

{
  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.

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
model	string	default model	Optional Dream Diary subagent model override

Example

{
  plugins: {
    entries: {
      "memory-core": {
        subagent: {
          allowModelOverride: true,
          allowedModels: ["anthropic/claude-sonnet-4-6"],
        },
        config: {
          dreaming: {
            enabled: true,
            frequency: "0 3 * * *",
            model: "anthropic/claude-sonnet-4-6",
          },
        },
      },
    },
  },
}

- Dreaming writes machine state to `memory/.dreams/`. - Dreaming writes human-readable narrative output to `DREAMS.md` (or existing `dreams.md`). - `dreaming.model` uses the existing plugin subagent trust gate; set `plugins.entries.memory-core.subagent.allowModelOverride: true` before enabling it. - Dream Diary retries once with the session default model when the configured model is unavailable. Trust or allowlist failures are logged and are not silently retried. - The light/deep/REM phase policy and thresholds are internal behavior, not user-facing config.

Related

• Configuration reference
• Memory overview
• Memory search