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docs: restructure cli index and active memory pages

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Vincent Koc
2026-04-23 00:41:46 -07:00
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@@ -20,10 +20,11 @@ have made the reply feel natural has already passed.
Active memory gives the system one bounded chance to surface relevant memory
before the main reply is generated.
## Paste This Into Your Agent
## Quick start
Paste this into your agent if you want it to enable Active Memory with a
self-contained, safe-default setup:
Paste this into `openclaw.json` for a safe-default setup — plugin on, scoped to
the `main` agent, direct-message sessions only, inherits the session model
when available:
```json5
{
@@ -49,12 +50,7 @@ self-contained, safe-default setup:
}
```
This turns the plugin on for the `main` agent, keeps it limited to direct-message
style sessions by default, lets it inherit the current session model first, and
uses the configured fallback model only if no explicit or inherited model is
available.
After that, restart the gateway:
Then restart the gateway:
```bash
openclaw gateway
@@ -67,54 +63,15 @@ To inspect it live in a conversation:
/trace on
```
## Turn active memory on
The safest setup is:
1. enable the plugin
2. target one conversational agent
3. keep logging on only while tuning
Start with this in `openclaw.json`:
```json5
{
plugins: {
entries: {
"active-memory": {
enabled: true,
config: {
agents: ["main"],
allowedChatTypes: ["direct"],
modelFallback: "google/gemini-3-flash",
queryMode: "recent",
promptStyle: "balanced",
timeoutMs: 15000,
maxSummaryChars: 220,
persistTranscripts: false,
logging: true,
},
},
},
},
}
```
Then restart the gateway:
```bash
openclaw gateway
```
What this means:
What the key fields do:
- `plugins.entries.active-memory.enabled: true` turns the plugin on
- `config.agents: ["main"]` opts only the `main` agent into active memory
- `config.allowedChatTypes: ["direct"]` keeps active memory on for direct-message style sessions only by default
- if `config.model` is unset, active memory inherits the current session model first
- `config.modelFallback` optionally provides your own fallback provider/model for recall
- `config.promptStyle: "balanced"` uses the default general-purpose prompt style for `recent` mode
- active memory still runs only on eligible interactive persistent chat sessions
- `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.promptStyle: "balanced"` is the default for `recent` mode
- Active memory still runs only for eligible interactive persistent chat sessions
## Speed recommendations
@@ -123,83 +80,45 @@ the same model you already use for normal replies. That is the safest default
because it follows your existing provider, auth, and model preferences.
If you want Active Memory to feel faster, use a dedicated inference model
instead of borrowing the main chat model.
instead of borrowing the main chat model. Recall quality matters, but latency
matters more than for the main answer path, and Active Memory's tool surface
is narrow (it only calls `memory_search` and `memory_get`).
Example fast-provider setup:
Good fast-model options:
```json5
models: {
providers: {
cerebras: {
baseUrl: "https://api.cerebras.ai/v1",
apiKey: "${CEREBRAS_API_KEY}",
api: "openai-completions",
models: [{ id: "gpt-oss-120b", name: "GPT OSS 120B (Cerebras)" }],
},
},
},
plugins: {
entries: {
"active-memory": {
enabled: true,
config: {
model: "cerebras/gpt-oss-120b",
},
},
},
}
```
Fast-model options worth considering:
- `cerebras/gpt-oss-120b` for a fast dedicated recall model with a narrow tool surface
- `cerebras/gpt-oss-120b` for a dedicated low-latency recall model
- `google/gemini-3-flash` as a low-latency fallback without changing your primary chat model
- your normal session model, by leaving `config.model` unset
- a low-latency fallback model such as `google/gemini-3-flash` when you want a separate recall model without changing your primary chat model
Why Cerebras is a strong speed-oriented option for Active Memory:
- the Active Memory tool surface is narrow: it only calls `memory_search` and `memory_get`
- recall quality matters, but latency matters more than for the main answer path
- a dedicated fast provider avoids tying memory recall latency to your primary chat provider
If you do not want a separate speed-optimized model, leave `config.model` unset
and let Active Memory inherit the current session model.
### Cerebras setup
Add a provider entry like this:
Add a Cerebras provider and point Active Memory at it:
```json5
models: {
providers: {
cerebras: {
baseUrl: "https://api.cerebras.ai/v1",
apiKey: "${CEREBRAS_API_KEY}",
api: "openai-completions",
models: [{ id: "gpt-oss-120b", name: "GPT OSS 120B (Cerebras)" }],
{
models: {
providers: {
cerebras: {
baseUrl: "https://api.cerebras.ai/v1",
apiKey: "${CEREBRAS_API_KEY}",
api: "openai-completions",
models: [{ id: "gpt-oss-120b", name: "GPT OSS 120B (Cerebras)" }],
},
},
},
}
```
Then point Active Memory at it:
```json5
plugins: {
entries: {
"active-memory": {
enabled: true,
config: {
model: "cerebras/gpt-oss-120b",
plugins: {
entries: {
"active-memory": {
enabled: true,
config: { model: "cerebras/gpt-oss-120b" },
},
},
},
}
```
Caveat:
- make sure the Cerebras API key actually has model access for the model you choose, because `/v1/models` visibility alone does not guarantee `chat/completions` access
Make sure the Cerebras API key actually has `chat/completions` access for the
chosen model — `/v1/models` visibility alone does not guarantee it.
## How to see it
@@ -396,7 +315,70 @@ If the connection is weak, it should return `NONE`.
## Query modes
`config.queryMode` controls how much conversation the blocking memory sub-agent sees.
`config.queryMode` controls how much conversation the blocking memory sub-agent
sees. Pick the smallest mode that still answers follow-up questions well;
timeout budgets should grow with context size (`message` < `recent` < `full`).
<Tabs>
<Tab title="message">
Only the latest user message is sent.
```text
Latest user message only
```
Use this when:
- you want the fastest behavior
- you want the strongest bias toward stable preference recall
- follow-up turns do not need conversational context
Start around `3000` to `5000` ms for `config.timeoutMs`.
</Tab>
<Tab title="recent">
The latest user message plus a small recent conversational tail is sent.
```text
Recent conversation tail:
user: ...
assistant: ...
user: ...
Latest user message:
...
```
Use this when:
- you want a better balance of speed and conversational grounding
- follow-up questions often depend on the last few turns
Start around `15000` ms for `config.timeoutMs`.
</Tab>
<Tab title="full">
The full conversation is sent to the blocking memory sub-agent.
```text
Full conversation context:
user: ...
assistant: ...
user: ...
...
```
Use this when:
- the strongest recall quality matters more than latency
- the conversation contains important setup far back in the thread
Start around `15000` ms or higher depending on thread size.
</Tab>
</Tabs>
## Prompt styles
@@ -490,75 +472,6 @@ Prompt customization is not recommended unless you are deliberately testing a
different recall contract. The default prompt is tuned to return either `NONE`
or compact user-fact context for the main model.
### `message`
Only the latest user message is sent.
```text
Latest user message only
```
Use this when:
- you want the fastest behavior
- you want the strongest bias toward stable preference recall
- follow-up turns do not need conversational context
Recommended timeout:
- start around `3000` to `5000` ms
### `recent`
The latest user message plus a small recent conversational tail is sent.
```text
Recent conversation tail:
user: ...
assistant: ...
user: ...
Latest user message:
...
```
Use this when:
- you want a better balance of speed and conversational grounding
- follow-up questions often depend on the last few turns
Recommended timeout:
- start around `15000` ms
### `full`
The full conversation is sent to the blocking memory sub-agent.
```text
Full conversation context:
user: ...
assistant: ...
user: ...
...
```
Use this when:
- the strongest recall quality matters more than latency
- the conversation contains important setup far back in the thread
Recommended timeout:
- increase it substantially compared with `message` or `recent`
- start around `15000` ms or higher depending on thread size
In general, timeout should increase with context size:
```text
message < recent < full
```
## Transcript persistence
Active memory blocking memory sub-agent runs create a real `session.jsonl`
@@ -702,181 +615,38 @@ If active memory is too slow:
## Common issues
### Embedding provider changed unexpectedly
Active Memory rides on the normal `memory_search` pipeline under
`agents.defaults.memorySearch`, so most recall surprises are embedding-provider
problems, not Active Memory bugs.
Active Memory uses the normal `memory_search` pipeline under
`agents.defaults.memorySearch`. That means embedding-provider setup is only a
requirement when your `memorySearch` setup requires embeddings for the behavior
you want.
<AccordionGroup>
<Accordion title="Embedding provider switched or stopped working">
If `memorySearch.provider` is unset, OpenClaw auto-detects the first
available embedding provider. A new API key, quota exhaustion, or a
rate-limited hosted provider can change which provider resolves between
runs. If no provider resolves, `memory_search` may degrade to lexical-only
retrieval; runtime failures after a provider is already selected do not
fall back automatically.
In practice:
Pin the provider (and an optional fallback) explicitly to make selection
deterministic. See [Memory Search](/concepts/memory-search) for the full
list of providers and pinning examples.
- explicit provider setup is **required** if you want a provider that is not
auto-detected, such as `ollama`
- explicit provider setup is **required** if auto-detection does not resolve
any usable embedding provider for your environment
- explicit provider setup is **highly recommended** if you want deterministic
provider selection instead of "first available wins"
- explicit provider setup is usually **not required** if auto-detection already
resolves the provider you want and that provider is stable in your deployment
</Accordion>
If `memorySearch.provider` is unset, OpenClaw auto-detects the first available
embedding provider.
That can be confusing in real deployments:
- a newly available API key can change which provider memory search uses
- one command or diagnostics surface may make the selected provider look
different from the path you are actually hitting during live memory sync or
search bootstrap
- hosted providers can fail with quota or rate-limit errors that only show up
once Active Memory starts issuing recall searches before each reply
Active Memory can still run without embeddings when `memory_search` can operate
in degraded lexical-only mode, which typically happens when no embedding
provider can be resolved.
Do not assume the same fallback on provider runtime failures such as quota
exhaustion, rate limits, network/provider errors, or missing local/remote
models after a provider has already been selected.
In practice:
- if no embedding provider can be resolved, `memory_search` may degrade to
lexical-only retrieval
- if an embedding provider is resolved and then fails at runtime, OpenClaw does
not currently guarantee a lexical fallback for that request
- if you need deterministic provider selection, pin
`agents.defaults.memorySearch.provider`
- if you need provider failover on runtime errors, configure
`agents.defaults.memorySearch.fallback` explicitly
If you depend on embedding-backed recall, multimodal indexing, or a specific
local/remote provider, pin the provider explicitly instead of relying on
auto-detection.
Common pinning examples:
OpenAI:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "openai",
model: "text-embedding-3-small",
},
},
},
}
```
Gemini:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "gemini",
model: "gemini-embedding-001",
},
},
},
}
```
Ollama:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "ollama",
model: "nomic-embed-text",
},
},
},
}
```
If you expect provider failover on runtime errors such as quota exhaustion,
pinning a provider alone is not enough. Configure an explicit fallback too:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "openai",
fallback: "gemini",
},
},
},
}
```
### Debugging provider issues
If Active Memory is slow, empty, or appears to switch providers unexpectedly:
- watch the gateway logs while reproducing the problem; look for lines such as
`active-memory: ... start|done`, `memory sync failed (search-bootstrap)`, or
provider-specific embedding errors
- turn on `/trace on` to surface the plugin-owned Active Memory debug summary in
the session
- turn on `/verbose on` if you also want the normal `🧩 Active Memory: ...`
status line after each reply
- run `openclaw memory status --deep` to inspect the current memory-search
backend and index health
- check `agents.defaults.memorySearch.provider` and related auth/config to make
sure the provider you expect is actually the one that can resolve at runtime
- if you use `ollama`, verify the configured embedding model is installed, for
example `ollama list`
Example debugging loop:
```text
1. Start the gateway and watch its logs
2. In the chat session, run /trace on
3. Send one message that should trigger Active Memory
4. Compare the chat-visible debug line with the gateway log lines
5. If provider choice is ambiguous, pin agents.defaults.memorySearch.provider explicitly
```
Example:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "ollama",
model: "nomic-embed-text",
},
},
},
}
```
Or, if you want Gemini embeddings:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "gemini",
},
},
},
}
```
After changing the provider, restart the gateway and run a fresh test with
`/trace on` so the Active Memory debug line reflects the new embedding path.
<Accordion title="Recall feels slow, empty, or inconsistent">
- 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 memory status --deep` to inspect the memory-search backend
and index health.
- If you use `ollama`, confirm the embedding model is installed
(`ollama list`).
</Accordion>
</AccordionGroup>
## Related pages