> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wednesdayai.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompt Caching

# Prompt caching

Prompt caching means the model provider can reuse unchanged prompt prefixes (usually system/developer instructions and other stable context) across turns instead of re-processing them every time. The first matching request writes cache tokens (`cacheWrite`), and later matching requests can read them back (`cacheRead`).

Why this matters: lower token cost, faster responses, and more predictable performance for long-running sessions. Without caching, repeated prompts pay the full prompt cost on every turn even when most input did not change.

This page covers all cache-related knobs that affect prompt reuse and token cost.

For Anthropic pricing details, see:
[https://docs.anthropic.com/docs/build-with-claude/prompt-caching](https://docs.anthropic.com/docs/build-with-claude/prompt-caching)

## Primary knobs

### `cacheRetention` (model and per-agent)

Set cache retention on model params:

```yaml theme={"dark"}
agents:
  defaults:
    models:
      "anthropic/claude-opus-4-6":
        params:
          cacheRetention: "short" # none | short | long
```

Per-agent override:

```yaml theme={"dark"}
agents:
  list:
    - id: "alerts"
      params:
        cacheRetention: "none"
```

Config merge order:

1. `agents.defaults.models["provider/model"].params`
2. `agents.list[].params` (matching agent id; overrides by key)

### Legacy `cacheControlTtl`

Legacy values are still accepted and mapped:

* `5m` -> `short`
* `1h` -> `long`

Prefer `cacheRetention` for new config.

### `contextPruning.mode: "cache-ttl"`

Prunes old tool-result context after cache TTL windows so post-idle requests do not re-cache oversized history.

```yaml theme={"dark"}
agents:
  defaults:
    contextPruning:
      mode: "cache-ttl"
      ttl: "1h"
```

See [Session Pruning](/concepts/session-pruning) for full behavior.

### Heartbeat keep-warm

Heartbeat can keep cache windows warm and reduce repeated cache writes after idle gaps.

```yaml theme={"dark"}
agents:
  defaults:
    heartbeat:
      every: "55m"
```

Per-agent heartbeat is supported at `agents.list[].heartbeat`.

## System Prompt Shape

Prompt caching works best when stable shared content appears before volatile content. OpenClaw keeps identity, generated tool and skill indexes, stable workspace guidance, and stable Project Context before the cache boundary. Runtime details, channel-specific owner context, reply mechanics, heartbeat state, and other per-run context appear after it.

The boundary is controlled by `agents.defaults.systemPrompt.cache.boundary` and can be overridden per agent at `agents.list[].systemPrompt.cache.boundary`:

* `auto` (default): insert the cache boundary between stable and volatile prompt sections, regardless of preset.
* `off`: render one continuous system prompt without the boundary marker.

The boundary marker is a structural ordering aid: it groups stable content before volatile content so providers that cache prompt prefixes get longer reusable prefixes. It is not itself a provider cache-control breakpoint.

Use `agents.defaults.systemPrompt.sections.projectContext.files` for stable, ordered workspace context. Keep high-churn files out of the stable prefix by using `manifest` mode, `runKind` mode, or per-agent overrides for agents that truly need that content inline. A good cache-friendly shape is: stable identity and policy first, compact tool and skill listings next, stable project files next, then volatile runtime/session information.

Use `/context list` to confirm the effective preset, cache boundary, and Project Context file modes for the current session. Use cache trace diagnostics when you need provider-level cache read and write evidence.

## Provider behavior

### Anthropic (direct API)

* `cacheRetention` is supported.
* With Anthropic API-key auth profiles, OpenClaw seeds `cacheRetention: "short"` for Anthropic model refs when unset.

### Amazon Bedrock

* Anthropic Claude model refs (`amazon-bedrock/*anthropic.claude*`) support explicit `cacheRetention` pass-through.
* Non-Anthropic Bedrock models are forced to `cacheRetention: "none"` at runtime.

### OpenRouter Anthropic models

For `openrouter/anthropic/*` model refs, OpenClaw injects Anthropic `cache_control` on system/developer prompt blocks to improve prompt-cache reuse.

### Other providers

If the provider does not support this cache mode, `cacheRetention` has no effect.

## Tuning patterns

### Mixed traffic (recommended default)

Keep a long-lived baseline on your main agent, disable caching on bursty notifier agents:

```yaml theme={"dark"}
agents:
  defaults:
    model:
      primary: "anthropic/claude-opus-4-6"
    models:
      "anthropic/claude-opus-4-6":
        params:
          cacheRetention: "long"
  list:
    - id: "research"
      default: true
      heartbeat:
        every: "55m"
    - id: "alerts"
      params:
        cacheRetention: "none"
```

### Cost-first baseline

* Set baseline `cacheRetention: "short"`.
* Enable `contextPruning.mode: "cache-ttl"`.
* Keep heartbeat below your TTL only for agents that benefit from warm caches.

## Cache diagnostics

OpenClaw exposes dedicated cache-trace diagnostics for embedded agent runs.

### `diagnostics.cacheTrace` config

```yaml theme={"dark"}
diagnostics:
  cacheTrace:
    enabled: true
    filePath: "~/.openclaw/logs/cache-trace.jsonl" # optional
    includeMessages: false # default true
    includePrompt: false # default true
    includeSystem: false # default true
```

Defaults:

* `filePath`: `$OPENCLAW_STATE_DIR/logs/cache-trace.jsonl`
* `includeMessages`: `true`
* `includePrompt`: `true`
* `includeSystem`: `true`

### Env toggles (one-off debugging)

* `OPENCLAW_CACHE_TRACE=1` enables cache tracing.
* `OPENCLAW_CACHE_TRACE_FILE=/path/to/cache-trace.jsonl` overrides output path.
* `OPENCLAW_CACHE_TRACE_MESSAGES=0|1` toggles full message payload capture.
* `OPENCLAW_CACHE_TRACE_PROMPT=0|1` toggles prompt text capture.
* `OPENCLAW_CACHE_TRACE_SYSTEM=0|1` toggles system prompt capture.

### What to inspect

* Cache trace events are JSONL and include staged snapshots like `session:loaded`, `prompt:before`, `stream:context`, and `session:after`.
* Per-turn cache token impact is visible in normal usage surfaces via `cacheRead` and `cacheWrite` (for example `/usage full` and session usage summaries).

## Quick troubleshooting

* High `cacheWrite` on most turns: check for volatile system-prompt inputs and verify model/provider supports your cache settings.
* No effect from `cacheRetention`: confirm model key matches `agents.defaults.models["provider/model"]`.
* Bedrock Nova/Mistral requests with cache settings: expected runtime force to `none`.

Related docs:

* [Anthropic](/providers/anthropic)
* [Token Use and Costs](/reference/token-use)
* [Session Pruning](/concepts/session-pruning)
* [Gateway Configuration Reference](/gateway/configuration-reference)