Memory configuration reference

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

Memory Overview — how memory works
Builtin Engine — default SQLite backend
QMD Engine — local-first sidecar
Memory Search — search pipeline and tuning
Active Memory — enabling the memory sub-agent for interactive sessions

All memory search settings live under agents.defaults.memorySearch in velaclaw.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:

the plugin must be enabled and target the current agent id
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: `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, Velaclaw selects the first available:

local — if memorySearch.local.modelPath is configured and the file exists.
github-copilot — if a GitHub Copilot token can be resolved (env var or auth profile).
openai — if an OpenAI key can be resolved.
gemini — if a Gemini key can be resolved.
voyage — if a Voyage key can be resolved.
mistral — if a Mistral key can be resolved.
bedrock — 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).

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`

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

Remote endpoint config

For custom OpenAI-compatible endpoints or overriding provider defaults:

Key	Type	Description
`remote.baseUrl`	`string`	Custom API base URL
`remote.apiKey`	`string`	Override API key
`remote.headers`	`object`	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",
        },
      },
    },
  },
}

Gemini-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

Changing model or outputDimensionality triggers an automatic full reindex.

Bedrock embedding config

Bedrock uses the AWS SDK default credential chain — no API keys needed. If Velaclaw runs on EC2 with a Bedrock-enabled instance role, just set the provider and model:

{
  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

The following models are supported (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:

Environment variables (AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY)
SSO token cache
Web identity token credentials
Shared credentials and config files
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:

{
  "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

Local embedding config

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

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.

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

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

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.

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.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.

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. Session logs live on disk, so treat filesystem access as the trust boundary.

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, Velaclaw falls back to in-process cosine similarity automatically.

Index storage

Key	Type	Default	Description
`store.path`	`string`	`~/.velaclaw/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

Velaclaw 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. QMD model overrides stay on the QMD side, not Velaclaw 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.

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

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

Scope

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

{
  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>:.

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)

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

Example

{
  plugins: {
    entries: {
      "memory-core": {
        config: {
          dreaming: {
            enabled: true,
            frequency: "0 3 * * *",
          },
        },
      },
    },
  },
}

Notes:

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.

CLI commands

RPC and API

Templates

Technical reference

Concept internals

Project

Release policy

Memory configuration reference

Memory configuration reference

Provider selection

Auto-detection order

API key resolution

Remote endpoint config

Gemini-specific config

Bedrock embedding config

Supported models

Authentication

IAM permissions

Local embedding config

Hybrid search config

MMR (diversity)

Temporal decay (recency)

Full example

Additional memory paths

Multimodal memory (Gemini)

Embedding cache

Batch indexing

Session memory search (experimental)

SQLite vector acceleration (sqlite-vec)

Index storage

QMD backend config

Update schedule

Limits

Scope

Citations

Full QMD example

Dreaming

User settings

Example

​Memory configuration reference

​Provider selection

​Auto-detection order

​API key resolution

​Remote endpoint config

​Gemini-specific config

​Bedrock embedding config

​Supported models

​Authentication

​IAM permissions

​Local embedding config

​Hybrid search config

​MMR (diversity)

​Temporal decay (recency)

​Full example

​Additional memory paths

​Multimodal memory (Gemini)

​Embedding cache

​Batch indexing

​Session memory search (experimental)

​SQLite vector acceleration (sqlite-vec)

​Index storage

​QMD backend config

​Update schedule

​Limits

​Scope

​Citations

​Full QMD example

​Dreaming

​User settings

​Example

Memory configuration reference

Provider selection

Auto-detection order

API key resolution

Remote endpoint config

Gemini-specific config

Bedrock embedding config

Supported models

Authentication

IAM permissions

Local embedding config

Hybrid search config

MMR (diversity)

Temporal decay (recency)

Full example

Additional memory paths

Multimodal memory (Gemini)

Embedding cache

Batch indexing

Session memory search (experimental)

SQLite vector acceleration (sqlite-vec)

Index storage

QMD backend config

Update schedule

Limits

Scope

Citations

Full QMD example

Dreaming

User settings

Example