Technical reference

Memory configuration reference

This page lists every configuration knob for OpenClaw memory search. For conceptual overviews, see:

All shared memory settings live under top-level memory in openclaw.json. Search defaults use memory.search; per-agent search overrides use agents.entries.*.memory.search.


Remember across conversations

Key Type Default Description
rememberAcrossConversations boolean On for personal installs; off with configured DM isolation Use relevant context from this agent's other recognized private conversations.

Configure it per agent when only a trusted personal agent should use cross-conversation transcript recall:

json5
{  agents: {    entries: {      personal: {        memory: {          search: {            rememberAcrossConversations: true,          },        },      },    },  },}

The value follows normal memory.search inheritance with a per-agent override. When unset, it defaults on only if global session.dmScope is unset or "main" and no binding has a session.dmScope override. Any configured DM isolation defaults it off. An explicit true or false always wins. Enabling it implies session transcript indexing and adds sessions to the agent's resolved memory sources. With QMD, it also enables that agent's session export; no separate memory.qmd.sessions.enabled setting is required for this mode.

OpenClaw's built-in memory provider supports this protected path with both the builtin and QMD backends. Alternate memory providers can keep using their own recall hooks and advanced Active Memory tools, but this setting is skipped unless the current provider supports protected private transcript recall. openclaw doctor reports an unsupported provider or an explicit Active Memory toolsAllow list that omits memory_search.

The retrieval boundary is narrower than general session search:

  • only the same agent's recognized private conversations are eligible
  • the conversation being answered is excluded
  • groups and channels are excluded as sources and destinations
  • unknown conversation kinds fail closed
  • sandboxed recall cannot use the special cross-conversation authorization

The setting does not change tools.sessions.visibility, session keys, transcript storage, delivery routing, or the permissions of sessions_list, sessions_history, and sessions_send. Active Memory performs a bounded read-only retrieval pass; unavailable or timed-out retrieval does not block the reply.


Provider selection

Key Type Default Description
enabled boolean true Enable or disable memory search
provider string "openai" Embedding adapter ID such as bedrock, deepinfra, gemini, github-copilot, local, mistral, ollama, openai, openai-compatible, or voyage; may also be a configured models.providers.<id> whose api points at a memory embedding adapter or OpenAI-compatible model API
model string provider default Embedding model name
fallback string "none" Fallback adapter ID when the primary fails

When provider is not set, OpenClaw uses OpenAI embeddings. Set provider explicitly to use Bedrock, DeepInfra, Gemini, GitHub Copilot, Mistral, Ollama, Voyage, a local GGUF model, or an OpenAI-compatible /v1/embeddings endpoint. Legacy configs that still say provider: "auto" resolve to openai.

When provider is unset, legacy provider: "auto" is present, or provider: "none" intentionally selects FTS-only mode, memory recall can still use lexical FTS ranking when embeddings are unavailable.

Explicit non-local providers fail closed. If you set memory.search.provider to a concrete remote-backed provider such as Bedrock, DeepInfra, Gemini, GitHub Copilot, LM Studio, Mistral, Ollama, OpenAI, Voyage, or an OpenAI-compatible custom provider, and that provider is unavailable at runtime, memory_search returns an unavailable result instead of silently using FTS-only recall. Fix the provider/auth configuration, switch to a reachable provider, or set provider: "none" if you want deliberate FTS-only recall.

Custom provider ids

memory.search.provider can point at a custom models.providers.<id> entry for memory-specific provider adapters such as ollama, or for OpenAI-compatible model APIs such as openai-responses / openai-completions. 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:

json5
{  models: {    providers: {      "ollama-5080": {        api: "ollama",        baseUrl: "http://gpu-box.local:11435",        apiKey: "ollama-local",        models: [{ id: "qwen3-embedding:0.6b", name: "Qwen3 Embedding 0.6B" }],      },    },  },  memory: {    search: {      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, or a Bedrock API key).

Provider Env var Config key
Bedrock AWS credential chain, or AWS_BEARER_TOKEN_BEDROCK 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

Remote endpoint config

Use provider: "openai-compatible" for a generic OpenAI-compatible /v1/embeddings server that should not inherit global OpenAI chat credentials.

remote.baseUrlstring

Custom API base URL.

remote.apiKeystring

Override API key.

remote.headersobject

Extra HTTP headers (merged with provider defaults).

json5
{  memory: {    search: {      provider: "openai-compatible",      model: "text-embedding-3-small",      remote: {        baseUrl: "https://api.example.com/v1/",        apiKey: "YOUR_KEY",      },    },  },}

Provider-specific config

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
OpenAI-compatible input types

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
{  memory: {    search: {      provider: "openai-compatible",      remote: {        baseUrl: "https://embeddings.example/v1",        apiKey: "${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

Bedrock embedding config

Bedrock uses the AWS SDK default credential chain plus an OpenClaw-checked bearer token, so no API keys are stored in config. If OpenClaw runs on EC2 with a Bedrock-enabled instance role, just set the provider and model:

json5
{  memory: {    search: {      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, 384, 512, 768, 1024, 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) and region-prefixed inference profile IDs (e.g., us.amazon.titan-embed-text-v2:0) inherit the base model's configuration.

Region: resolved in this order: the memory.search.remote.baseUrl override, the models.providers.amazon-bedrock.baseUrl config, AWS_REGION, AWS_DEFAULT_REGION, then a default of us-east-1.

Authentication: OpenClaw checks for AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY or AWS_BEARER_TOKEN_BEDROCK first, then falls through to the standard AWS SDK default credential provider chain:

  1. Environment variables (AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY), unless AWS_PROFILE is also set
  2. SSO (only when SSO fields are configured)
  3. Shared credentials and config files (fromIni, includes AWS_PROFILE)
  4. Credential process (credential_process in the AWS config file)
  5. Web identity token credentials
  6. ECS or EC2 instance metadata credentials

IAM permissions: the IAM role or user needs:

json
{  "Effect": "Allow",  "Action": "bedrock:InvokeModel",  "Resource": "*"}

For least-privilege, scope InvokeModel to the specific model:

text
arn:aws:bedrock:*::foundation-model/amazon.titan-embed-text-v2:0
Local (GGUF + 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 (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: up to 40 960 tokens can push VRAM to ~32 GB).

Install the official llama.cpp provider first: openclaw plugins install @openclaw/llama-cpp-provider. 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 mainopenclaw memory index --force --agent main

Numeric local.contextSize values also inform node-llama-cpp's automatic GPU-layer placement so model weights and the requested embedding context are fitted together. openclaw memory status --deep reports last-known llama.cpp backend, device, offload, requested-context, and timestamped memory facts after the runtime has loaded; passive status does not load a model.

Set provider: "local" explicitly for local GGUF embeddings. hf: and HTTP(S) model references are supported for explicit local configs (via node-llama-cpp's model resolution), but they do not change the default provider.

Indexing behavior

Memory engines own synchronization, batching, watch, and post-compaction indexing heuristics. OpenClaw keeps these behaviors enabled with maintained defaults rather than exposing per-install timing switches.

Hybrid search config

All under memory.search.query:

Key Type Default Description
maxResults number 6 Max memory hits returned before injection
minScore number 0.35 Minimum relevance score to include a hit

Hybrid retrieval remains enabled; MMR and temporal decay remain disabled by the built-in engine policy.

Full example

json5
{  memory: {    search: {      query: {        maxResults: 6,        minScore: 0.35,      },    },  },}

Additional memory paths

Key Type Description
extraPaths string[] Additional directories or files to index
json5
{  memory: {    search: {      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 skips symlinks, while QMD follows the underlying QMD scanner behavior.

For agent-scoped cross-agent transcript search, use agents.entries.*.memory.search.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 memory.search.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 10485760 Max file size for indexing (10 MiB)

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 true Cache chunk embeddings in SQLite

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

Available for gemini, openai, and voyage. OpenAI batch is typically fastest and cheapest for large backfills.

Concurrency, polling, and timeout behavior are provider-owned.


Index session transcripts and surface them via memory_search:

Key Type Default Description
rememberAcrossConversations boolean false Permit private cross-conversation recall
sources string[] ["memory"] Add "sessions" to include transcripts

Ordinary model-invoked session transcript search obeys tools.sessions.visibility. The default tree visibility exposes the current session, sessions it spawned, and same-agent group sessions watched through ambient group awareness. Other unrelated sessions require agent visibility (or all only when cross-agent recall is also required and agent-to-agent policy allows it).

rememberAcrossConversations does not widen that setting. It supplies a separate runtime-only authorization limited to same-agent private transcripts during the bounded Active Memory pass.

The examples below place these settings under top-level memory.search. You can also apply equivalent settings in a per-agent memory.search override when only one agent should index and search session transcripts.

For same-agent gateway-to-DM recall:

Builtin backend

json5
{  memory: {    search: {      experimental: { sessionMemory: true },      sources: ["memory", "sessions"],    },  },  tools: {    sessions: { visibility: "agent" },  },}

QMD backend

json5
{  memory: {    backend: "qmd",    search: {      experimental: { sessionMemory: true },      sources: ["memory", "sessions"],    },    qmd: {      sessions: { enabled: true },    },  },  tools: {    sessions: { visibility: "agent" },  },}

When using QMD, sources: ["sessions"] does not by itself export transcripts into QMD. Set memory.qmd.sessions.enabled: true as well. The higher-level rememberAcrossConversations: true setting is the exception: it implies the required QMD session export for that agent. Implied exports stay private: they always use the default internal export location (a configured sessions.exportDir applies only to explicit exports), they are searched only during that agent's cross-conversation recall, and ordinary memory_get cannot read them. Explicit memory.qmd.sessions.enabled: true keeps its existing behavior and makes exported transcripts part of the ordinary memory corpus.


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

Built-in memory indexes live in each agent's OpenClaw SQLite database at agents/<agentId>/agent/openclaw-agent.sqlite.

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
rerank boolean -- Set to false with searchMode: "query" and QMD 2.1+ to skip QMD reranking
includeDefaultMemory boolean true Auto-index MEMORY.md + memory/**/*.md
paths[] array -- Extra paths: { name, path, pattern? }
sessions.enabled boolean false Export session transcripts into QMD
sessions.retentionDays number -- Transcript retention
sessions.exportDir string -- Export directory

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.

rerank: false only changes QMD query mode and requires QMD 2.1 or newer. In direct CLI mode OpenClaw passes --no-rerank; in mcporter-backed MCP mode it passes rerank: false to QMD's unified query tool. Leave it unset to use QMD's default query reranking behavior.

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 collections are searched with one QMD process; older QMD builds keep the per-collection compatibility path. Same-source means durable memory collections (default memory files plus custom paths) are grouped together, while session transcript collections remain a separate group so source diversification still has both inputs.

Limits
Key Type Default Description
limits.maxResults number 4 Max search results
limits.maxSnippetChars number 450 Clamp snippet length
limits.maxInjectedChars number 2200 Clamp total injected chars
limits.timeoutMs number 4000 QMD command timeout during QMD-backed search, including memory_search; setup, sync, builtin fallback, and supplemental work keep the default tool deadline
Scope

Controls which sessions can receive QMD search results. Same schema as session.sendPolicy:

json5
{  memory: {    qmd: {      scope: {        default: "deny",        rules: [{ action: "allow", match: { chatType: "direct" } }],      },    },  },}

The shipped default is DM/direct-only, denying groups and other channel types. match.keyPrefix matches the normalized session key; match.rawKeyPrefix matches the raw key including agent:<id>:.

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)

QMD initializes lazily when memory is first used; its adapter owns refresh and embedding schedules.

Full QMD example

json5
{  memory: {    backend: "qmd",    citations: "auto",    qmd: {      includeDefaultMemory: true,      update: { interval: "5m", debounceMs: 15000 },      limits: { maxResults: 4, 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 memory.search.

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
phases.deep.maxPromotedSnippetTokens number 160 Maximum estimated tokens kept from each short-term recall snippet promoted into MEMORY.md; provenance metadata remains visible

Example

json5
{  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",          },        },      },    },  },}
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