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Active memory is an optional bundled plugin that runs a blocking memory recall sub-agent before the main reply, for eligible conversational sessions. It exists because most memory systems are reactive: the main agent has to decide to search memory, or the user has to say “remember this.” By then the moment for the recalled fact to feel natural has passed. Active memory gives the system one bounded chance to surface relevant memory before the main reply is generated.

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Quick start

Paste into openclaw.json for a safe default: plugin on, scoped to main, direct-message sessions only, model inherited from the session.
plugins.entries.* (including active-memory.config) is in the no-restart config category: the Gateway reloads the plugin runtime automatically and no manual restart is needed. If you want to force a full restart anyway, run:
To inspect it live in a conversation:
What the key fields do:
  • plugins.entries.active-memory.enabled: true turns the plugin on
  • config.agents: ["main"] opts only the main agent in
  • config.allowedChatTypes: ["direct"] scopes it to direct-message sessions (opt in groups/channels explicitly)
  • config.model (optional) pins a dedicated recall model; unset inherits the current session model
  • config.modelFallback is used only when no explicit or inherited model resolves
  • config.fastMode optionally overrides fast mode for recall without changing the main agent
  • config.promptStyle: "balanced" is the default for recent mode
  • active memory still runs only for eligible interactive persistent chat sessions (see When it runs)

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How it works

The blocking sub-agent can call only the configured memory recall tools (see Memory tools). If the connection between the query and available memory is weak, it returns NONE and the main reply proceeds without extra context. Active memory is a conversational enrichment feature, not a platform-wide inference feature: Use it when the session is persistent and user-facing, the agent has meaningful long-term memory to search, and continuity/personalization matter more than raw prompt determinism: stable preferences, recurring habits, long-term context that should surface naturally. It is a poor fit for automation, internal workers, one-shot API tasks, or anywhere hidden personalization would be surprising.

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When it runs

Two gates must both pass:
  1. Config opt-in — the plugin is enabled and the current agent id is in config.agents.
  2. Runtime eligibility — the session is an eligible interactive persistent chat session, its chat type is allowed, and its conversation id is not filtered out.
If any condition fails, active memory does not run for that turn (and the main reply is unaffected).

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Session types

config.allowedChatTypes controls which kinds of conversations may run active memory. Default:
Valid values: direct, group, channel, explicit (portal-style sessions with an opaque session id, for example agent:main:explicit:portal-123). Direct-message sessions run by default; group, channel, and explicit sessions need to be opted in:
For narrower rollout inside an allowed chat type, add config.allowedChatIds and config.deniedChatIds:
  • allowedChatIds is an allowlist of resolved conversation ids. When non-empty, active memory only runs for sessions whose conversation id is in the list — this narrows every allowed chat type at once, including direct messages. To keep all direct messages while narrowing only groups, add the direct peer ids to allowedChatIds too, or keep allowedChatTypes scoped to the group/channel rollout you are testing.
  • deniedChatIds is a denylist that always wins over allowedChatTypes and allowedChatIds.
Ids come from the persistent channel session key (for example Feishu chat_id/open_id, Telegram chat id, Slack channel id). Matching is case-insensitive. If allowedChatIds is non-empty and OpenClaw cannot resolve a conversation id for the session, active memory skips the turn instead of guessing.

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Session toggle

Pause or resume active memory for the current chat session without editing config:
This only affects the current session; it does not change plugins.entries.active-memory.config.enabled or other global configuration. To pause/resume for all sessions instead, use the global form (requires owner or operator.admin):
The global form writes plugins.entries.active-memory.config.enabled but leaves plugins.entries.active-memory.enabled on, so the command stays available to turn active memory back on later.

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How to see it

By default, active memory injects a hidden untrusted prompt prefix that is not shown in the normal reply. Turn on the session toggles that match the output you want:
With those on, OpenClaw appends diagnostic lines after the normal reply (as a follow-up, so channel clients do not flash a separate pre-reply bubble):
  • /verbose on adds a status line: 🧩 Active Memory: status=ok elapsed=842ms query=recent summary=34 chars
  • /trace on adds a debug summary: 🔎 Active Memory Debug: Lemon pepper wings with blue cheese.
Example flow:
With /trace raw, the traced Model Input (User Role) block shows the raw hidden prefix:
By default the blocking sub-agent’s transcript is temporary and deleted after the run completes; see Transcript persistence to keep it.

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Query modes

config.queryMode controls how much conversation the blocking sub-agent sees. Pick the smallest mode that still answers follow-ups well; grow timeoutMs as context size grows, from message to recent to full.
Only the latest user message is sent.
Use when you want the fastest behavior, the strongest bias toward stable preference recall, and follow-up turns do not need conversational context. Start around 3000-5000 ms for config.timeoutMs.

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Prompt styles

config.promptStyle controls how eager or strict the sub-agent is about returning memory: Default mapping when config.promptStyle is unset:
An explicit config.promptStyle always overrides the mapping.

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Model fallback policy

If config.model is unset, active memory resolves a model in this order:
If nothing in that chain resolves, active memory skips recall for the turn. config.modelFallbackPolicy is a deprecated compatibility field kept for older configs; it no longer changes runtime behavior — modelFallback is strictly the last resort in the chain above, not a runtime failover that swaps in another model when the resolved one errors.

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Speed recommendations

Leaving config.model unset (inherit the session model) is the safest default: it follows your existing provider, auth, and model preferences. For lower latency, use a dedicated fast model instead — recall quality matters, but latency matters more here than on the main answer path, and the tool surface is narrow (only memory recall tools). Good fast-model options:
  • cerebras/gpt-oss-120b, a dedicated low-latency recall model
  • google/gemini-3-flash, a low-latency fallback without changing your primary chat model
  • your normal session model, by leaving config.model unset

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Cerebras setup

Confirm the Cerebras API key has chat/completions access for the chosen model — /v1/models visibility alone does not guarantee it.

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Memory tools

config.toolsAllow sets the concrete tool names the blocking sub-agent may call. Defaults depend on the active memory provider: If none of the configured tools are available, or the sub-agent run fails, active memory skips recall for that turn and the main reply continues without memory context. For custom recall tools, non-empty model-visible tool output counts as recall evidence unless structured result fields explicitly report an empty result or failure. toolsAllow only accepts concrete memory tool names: wildcards, group:* entries, and core agent tools (read, exec, message, web_search, and similar) are silently filtered out before the hidden sub-agent starts.

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Built-in memory-core

No explicit toolsAllow needed:

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LanceDB memory

Selecting the memory slot is enough for active memory to use memory_recall:

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Lossless Claw

Lossless Claw is an external context-engine plugin (openclaw plugins install @martian-engineering/lossless-claw) with its own recall tools. Set it up as a context engine first; see Context engine. Then point active memory at its tools:
Do not add lcm_expand to toolsAllow here; Lossless Claw uses it as a lower-level tool for delegated expansion, not meant for the top-level active-memory sub-agent.

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Advanced escape hatches

Not part of the recommended setup. config.thinking overrides the sub-agent’s thinking level (default "off", since active memory runs in the reply path and extra thinking time directly adds user-visible latency):
config.fastMode overrides fast mode only for the blocking memory sub-agent. Use true, false, or "auto"; leave it unset to inherit the normal agent, session, and model defaults. "auto" uses the recall model’s configured fastAutoOnSeconds cutoff:
config.promptAppend adds operator instructions after the default prompt and before the conversation context — pair it with a custom toolsAllow when a non-core memory plugin needs specific tool order or query shaping:
config.promptOverride replaces the default prompt entirely (conversation context is still appended afterward). Not recommended unless deliberately testing a different recall contract — the default prompt is tuned to return either NONE or compact user-fact context for the main model:

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Transcript persistence

Blocking sub-agent runs create a real session.jsonl transcript during the call. By default it is written to a temp directory and deleted immediately after the run finishes. To keep those transcripts on disk for debugging:
Persisted transcripts go under the target agent’s sessions folder, in a separate directory from the main user conversation transcript:
Change the relative subdirectory with config.transcriptDir. Use this carefully: transcripts can accumulate quickly on busy sessions, full query mode duplicates a lot of conversation context, and these transcripts contain hidden prompt context plus recalled memories.

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Configuration

All active memory configuration lives under plugins.entries.active-memory. Useful tuning fields: Start with recent:
Use /verbose on for the status line and /trace on for the debug summary while tuning — both are sent as a follow-up after the main reply, not before. Then move to message for lower latency, or full if extra context is worth the slower sub-agent run.

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Cold-start grace

Before v2026.5.2 the plugin silently extended timeoutMs by an extra 30000 ms during cold start, so model warm-up, embedding-index load, and the first recall could share one larger budget. v2026.5.2 moved that grace behind an explicit setupGraceTimeoutMs config: timeoutMs is now the recall-work budget by default unless you opt in. The blocking hook wraps that budget in two fixed phases: up to 1500 ms for session/config preflight before recall starts, then a separate fixed 1500 ms for abort settlement and transcript recovery after recall work stops. Neither allowance extends model or tool execution. If you upgraded from v2026.4.x and tuned timeoutMs for the old implicit-grace world (the recommended starter timeoutMs: 15000 is one example), set setupGraceTimeoutMs: 30000 to restore the pre-v5.2 effective budget:
Worst-case blocking time is timeoutMs + setupGraceTimeoutMs + 3000 ms (the configured recall-work budget, plus up to 1500 ms preflight, plus a fixed 1500 ms post-recall completion allowance). The embedded recall runner uses the same effective timeout budget, so setupGraceTimeoutMs covers both the outer prompt-build watchdog and the inner blocking recall run. For resource-tight gateways where cold-start latency is an accepted trade-off, lower values (5000-15000 ms) work too — the trade-off is a higher chance of the very first recall after a gateway restart returning empty while warm-up finishes.

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Debugging

If active memory is not showing up where you expect:
  1. Confirm the plugin is enabled under plugins.entries.active-memory.enabled.
  2. Confirm the current agent id is listed in config.agents.
  3. Confirm you are testing through an interactive persistent chat session.
  4. Turn on config.logging: true and watch the gateway logs.
  5. Verify memory search itself works with openclaw status --deep.
If memory hits are noisy, tighten maxSummaryChars. If active memory is too slow, lower queryMode, lower timeoutMs, or reduce recent turn counts and per-turn char caps.

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Common issues

Active memory rides on the configured memory plugin’s recall pipeline, so most recall surprises are embedding-provider problems, not active-memory bugs. The default memory-core path uses memory_search and memory_get; the memory-lancedb slot uses memory_recall. If you use another memory plugin, confirm config.toolsAllow names the tools that plugin actually registers.
If memorySearch.provider is unset, OpenClaw uses OpenAI embeddings. Set memorySearch.provider explicitly for Bedrock, DeepInfra, Gemini, GitHub Copilot, LM Studio, local, Mistral, Ollama, Voyage, or OpenAI-compatible embeddings. If the configured provider cannot run, memory_search may degrade to lexical-only retrieval; runtime failures after a provider is already selected do not fall back automatically.Set an optional memorySearch.fallback only when you want a deliberate single fallback. See Memory Search for the full list of providers and examples.
  • Turn on /trace on to surface the plugin-owned Active Memory debug summary in the session.
  • Turn on /verbose on to also see the 🧩 Active Memory: ... status line after each reply.
  • Watch gateway logs for active-memory: ... start|done, memory sync failed (search-bootstrap), or provider embedding errors.
  • Run openclaw status --deep to inspect the memory-search backend and index health.
  • If you use ollama, confirm the embedding model is installed (ollama list).
On v2026.5.2 and later, if cold-start setup (model warm-up + embedding index load) has not finished by the time the first recall fires, the run can hit the configured timeoutMs budget and return status=timeout with empty output. Gateway logs show active-memory timeout after Nms around the first eligible reply after a restart.See Cold-start grace under Recommended setup for the recommended setupGraceTimeoutMs value.