Memory configuration reference

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

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

Provider selection

When provider is not set, OpenClaw uses OpenAI embeddings. Set provider explicitly to use Gemini, Voyage, Mistral, DeepInfra, Bedrock, GitHub Copilot, Ollama, 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 memorySearch.provider to a concrete remote-backed provider such as OpenAI, Gemini, Voyage, Mistral, Bedrock, GitHub Copilot, DeepInfra, Ollama, LM Studio, 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

memorySearch.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:

{  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).

Remote endpoint config

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

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

Provider-specific config

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

{  agents: {    defaults: {      memorySearch: {        provider: "bedrock",        model: "amazon.titan-embed-text-v2:0",      },    },  },}

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

arn:aws:bedrock:*::foundation-model/amazon.titan-embed-text-v2:0

openclaw memory status --deep --agent mainopenclaw memory index --force --agent main

Inline embedding timeout

Hybrid search config

All under memorySearch.query.hybrid:

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

{  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:

Supported formats: .jpg, .jpeg, .png, .webp, .gif, .heic, .heif (images); .mp3, .wav, .ogg, .opus, .m4a, .aac, .flac (audio).

Embedding cache

Prevents re-embedding unchanged text during reindex or transcript updates.

Batch indexing

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:

SQLite vector acceleration (sqlite-vec)

When sqlite-vec is unavailable, OpenClaw falls back to in-process cosine similarity automatically.

Index storage

QMD backend config

Set memory.backend = "qmd" to enable. All QMD settings live under memory.qmd:

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 are grouped together, while session transcript collections remain a separate group so source diversification still has both inputs.

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

When gateway-start QMD initialization is enabled, OpenClaw starts QMD only for eligible agents. If update.onBoot is true and no interval/embed maintenance is configured, startup uses a one-shot manager for the boot refresh and closes it. If an update or embed interval is configured, startup opens the long-lived QMD manager so it can own the watcher and interval timers; update.onBoot: false skips only the immediate boot refresh.

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

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",          },        },      },    },  },}

Related

• Configuration reference
• Memory overview
• Memory search