---
summary: "Prompt caching knobs, merge order, provider behavior, and tuning patterns"
title: "Prompt caching"
read_when:
  - You want to reduce prompt token costs with cache retention
  - You need per-agent cache behavior in multi-agent setups
  - You are tuning heartbeat and cache-ttl pruning together
---

Prompt caching lets a model provider reuse an unchanged prompt prefix (system/developer instructions, tool definitions, other stable context) across turns instead of reprocessing it every request. This cuts token cost and latency on long-running sessions with repeated context.

OpenClaw normalizes provider usage into `cacheRead` and `cacheWrite` wherever the upstream API exposes those counters. Usage summaries (`/status` and similar) fall back to the last transcript usage entry when the live session snapshot lacks cache counters; a nonzero live value always wins over the fallback.

Provider references:

- [Anthropic prompt caching](https://platform.claude.com/docs/en/build-with-claude/prompt-caching)
- [OpenAI prompt caching](https://developers.openai.com/api/docs/guides/prompt-caching)

## Keep model settings stable

Prompt-cache reuse depends on provider request configuration as well as prompt
text. Changing the model always starts a different cache lineage. Changing the
thinking or reasoning level can also invalidate reuse even when the prompt and
model stay the same. In particular, OpenAI reasoning-effort changes alter the
reusable request state and can force the next turn to process the full prefix or
conversation again. Anthropic likewise documents cache invalidation when its
thinking budget, effort, or mode changes.

If cache continuity matters, choose the model and thinking level when creating
the session and keep both stable. Start a new session for a planned change.
Invalidating reuse means the next request misses that cached state; it does not
necessarily delete the provider's older cache entry before its normal expiry.

## Primary knobs

### `cacheRetention`

Values: `"none" | "short" | "long"`. Configurable as a global default, per model, and per agent.
`"standard"` is not an alias; use `"short"` for the provider's default cache window. Invalid values are ignored with a warning.

```yaml
agents:
  defaults:
    params:
      cacheRetention: "long" # none | short | long
    models:
      "anthropic/claude-opus-4-6":
        params:
          cacheRetention: "short" # overrides the global default for this model
  list:
    - id: "alerts"
      params:
        cacheRetention: "none" # overrides both defaults for this agent
```

Merge order (later wins):

1. `agents.defaults.params` - global default for all models
2. `agents.defaults.models["provider/model"].params` - per-model override
3. `agents.entries.*.params` - per-agent override, matched by agent id

Source: `src/agents/embedded-agent-runner/extra-params.ts` (`resolveExtraParams`).

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

Prunes old tool-result context after the cache TTL window elapses, so a post-idle request does not re-cache oversized history.

```yaml
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. Configurable globally (`agents.defaults.heartbeat`) or per agent (`agents.entries.*.heartbeat`).

```yaml
agents:
  defaults:
    heartbeat:
      every: "55m"
```

## Provider behavior

### Anthropic (direct API and Vertex AI)

- `cacheRetention` is supported for `anthropic` and `anthropic-vertex` providers, and for Claude models on `amazon-bedrock` and custom `anthropic-messages`-compatible endpoints when `cacheRetention` is set explicitly.
- When unset, OpenClaw seeds `cacheRetention: "short"` for direct Anthropic (`anthropic` and `anthropic-vertex` providers only; other Anthropic-family routes require an explicit value).
- Native Anthropic Messages responses expose `cache_read_input_tokens` and `cache_creation_input_tokens`, mapped to `cacheRead` and `cacheWrite`.
- `cacheRetention: "short"` maps to the default 5-minute ephemeral cache. `cacheRetention: "long"` requests the 1-hour TTL (`cache_control: { type: "ephemeral", ttl: "1h" }`) when set explicitly. An implicit/env-driven long retention (`OPENCLAW_CACHE_RETENTION=long` with no explicit `cacheRetention`) only upgrades to the 1-hour TTL on `api.anthropic.com` or Vertex AI (`aiplatform.googleapis.com` / `*-aiplatform.googleapis.com`) hosts; other hosts keep the 5-minute cache.

Source: `packages/ai/src/transports/anthropic-payload-policy.ts` (`resolveAnthropicEphemeralCacheControl`, `isLongTtlEligibleEndpoint`).

### OpenAI (direct API)

- Prompt caching is automatic on supported recent models; OpenClaw does not inject block-level cache markers.
- OpenClaw sends `prompt_cache_key` to keep cache routing stable across turns. Direct `api.openai.com` hosts get this automatically. OpenAI-compatible proxies (oMLX, llama.cpp, custom endpoints) need `compat.supportsPromptCacheKey: true` in model config to opt in - this is never auto-detected for a proxy.
- `prompt_cache_retention: "24h"` is added only when `cacheRetention: "long"` is selected and the resolved endpoint supports both the cache key and long retention (`compat.supportsLongCacheRetention`, true by default; Together AI and Cloudflare compat profiles disable it). `cacheRetention: "none"` suppresses both fields.
- Cache hits surface via `usage.prompt_tokens_details.cached_tokens` (Chat Completions) or `input_tokens_details.cached_tokens` (Responses API), mapped to `cacheRead`.
- Responses API payloads can also expose `input_tokens_details.cache_write_tokens`, mapped to `cacheWrite` and priced at the model's cache-write rate; Responses payloads that omit the field keep `cacheWrite` at `0`. OpenAI's Chat Completions API does not document or emit a `cache_write_tokens` counter, but OpenClaw still reads `prompt_tokens_details.cache_write_tokens` there for OpenRouter-compatible and DeepSeek-style proxies that report a separate write count.
- In practice, OpenAI behaves more like an initial-prefix cache than Anthropic's moving full-history reuse - see [OpenAI live expectations](#openai-live-expectations) below.

### Amazon Bedrock

- Anthropic Claude model refs (`amazon-bedrock/*anthropic.claude*`, plus AWS system inference profile prefixes `us.`/`eu.`/`global.anthropic.claude*`) support explicit `cacheRetention` pass-through.
- Non-Anthropic Bedrock models (for example `amazon.nova-*`) resolve to no cache retention at runtime, regardless of any configured `cacheRetention` value.
- Opaque Bedrock application inference profile ARNs (profile IDs that do not contain `claude`) also resolve to no cache retention unless `cacheRetention` is set explicitly, since the model family cannot be inferred from the ARN alone.

### OpenRouter

For `openrouter/anthropic/*` model refs, OpenClaw injects Anthropic `cache_control` markers on system/developer prompt blocks, but only when the request still targets a verified OpenRouter route (`openrouter` on its default endpoint, or any provider/base URL that resolves to `openrouter.ai`). Repointing the model at an arbitrary OpenAI-compatible proxy URL stops this injection.

`contextPruning.mode: "cache-ttl"` is allowed for `openrouter/anthropic/*`, `openrouter/deepseek/*`, `openrouter/moonshot/*`, `openrouter/moonshotai/*`, and `openrouter/zai/*` model refs, because these routes handle provider-side prompt caching without needing OpenClaw's injected markers.

Source: `extensions/openrouter/index.ts` (`OPENROUTER_CACHE_TTL_MODEL_PREFIXES`).

DeepSeek cache construction on OpenRouter is best-effort and can take a few seconds; an immediate follow-up request may still show `cached_tokens: 0`. Verify with a repeated same-prefix request after a short delay, using `usage.prompt_tokens_details.cached_tokens` as the cache-hit signal.

### Google Gemini (direct API)

- Direct Gemini transport (`api: "google-generative-ai"`) reports cache hits through upstream `cachedContentTokenCount`, mapped to `cacheRead`.
- Eligible model families: `gemini-2.5*` and `gemini-3*` (excludes Live/preview variants outside that prefix match, for example `gemini-live-2.5-flash-preview`).
- When `cacheRetention` is set on an eligible model, OpenClaw automatically creates, reuses, and refreshes a `cachedContents` resource for the system prompt - no manual cached-content handle needed. TTL is `300s` for `cacheRetention: "short"` and `3600s` for `"long"`.
- You can still pass a pre-existing Gemini cached-content handle through as `params.cachedContent` (or legacy `params.cached_content`); an explicit handle skips the automatic cache-management path entirely.
- This is separate from Anthropic/OpenAI prompt-prefix caching: OpenClaw manages a provider-native `cachedContents` resource for Gemini instead of injecting inline cache markers.

Source: `src/agents/embedded-agent-runner/google-prompt-cache.ts`.

### CLI-harness providers (Claude Code, Gemini CLI)

CLI backends that emit JSONL usage events (`jsonlDialect: "claude-stream-json"` or `"gemini-stream-json"`) go through a shared usage parser that recognizes several field-name variants, including a plain `cached` counter mapped to `cacheRead`. When the CLI's JSON payload omits a direct input-token field, OpenClaw derives it as `input_tokens - cached`. This is usage normalization only - it does not create Anthropic/OpenAI-style prompt-cache markers for these CLI-driven models.

Claude Code has no OpenClaw-controlled `cache_control` breakpoint on `--append-system-prompt-file`, so OpenClaw keeps its complete system prompt in that transport. When the bounded version probe on first CLI execution finds Claude Code 2.1.98 or newer, bundled `claude-cli` also passes `--exclude-dynamic-system-prompt-sections`. Concurrent executions share that probe, and API catalog discovery does not start it. That Claude Code flag moves only Claude's own per-machine cwd, environment, memory-path, and Git-status sections out of its native system prompt; an older, unknown, or failed probe keeps the established argv. `cacheRetention` still has no effect on this path.

Source: `src/agents/cli-output.ts` (`toCliUsage`).

### Other providers

If a provider does not support any of the above cache modes, `cacheRetention` has no effect.

## System-prompt cache boundary

OpenClaw splits the system prompt into a **stable prefix** and a **volatile suffix** at an internal cache-prefix boundary. Content above the boundary (tool definitions, skills metadata, workspace files) is ordered to stay byte-identical across turns. Content below the boundary (for example runtime timestamps and other per-turn metadata) can change without invalidating the cached prefix.

Key design choices:

- Stable workspace project-context files are ordered before volatile per-turn metadata so routine churn does not bust the stable prefix.
- The boundary applies across Anthropic-family, OpenAI-family, Google, and CLI transport shaping, so all supported providers benefit from the same prefix stability.
- Codex Responses and Anthropic Vertex requests are routed through boundary-aware cache shaping so cache reuse stays aligned with what providers actually receive.
- System-prompt fingerprints are normalized (whitespace, line endings, hook-added context, runtime capability ordering) so semantically unchanged prompts share cache across turns.

If you see unexpected `cacheWrite` spikes after a config or workspace change, check whether the change lands above or below the cache boundary. Moving volatile content below the boundary (or stabilizing it) usually resolves the issue.

## OpenClaw cache-stability guards

- Bundled MCP tool catalogs are sorted deterministically (by server name, then tool name) before tool registration, so `listTools()` order changes do not churn the tools block and bust prompt-cache prefixes.
- Legacy sessions with persisted image blocks keep the **3 most recent completed turns** intact (counting all completed turns, not just image-bearing ones). Older already-processed image blocks are replaced with a text marker so image-heavy follow-ups do not keep re-sending large stale payloads.

## Tuning patterns

### Mixed traffic (recommended default)

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

```yaml
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.

## Live regression tests

OpenClaw runs one combined live cache regression gate covering repeated prefixes, tool turns, image turns, MCP-style tool transcripts, and an Anthropic no-cache control.

- `src/agents/live-cache-regression.live.test.ts`
- `src/agents/test-helpers/live-cache-regression-runner.ts`
- `src/agents/live-cache-regression-baseline.ts`

Run it with:

```sh
OPENCLAW_LIVE_TEST=1 OPENCLAW_LIVE_CACHE_TEST=1 pnpm test:live:cache
```

The baseline file stores the most recently observed live numbers plus the provider-specific regression floors the test checks against. Each run uses fresh per-run session IDs and prompt namespaces so previous cache state does not pollute the current sample. Anthropic and OpenAI use different enforcement: an Anthropic floor miss is a hard regression (test fails), while an OpenAI floor miss is watch-only (recorded as a warning, does not fail the run). They do not share a single cross-provider threshold.

Claude CLI prompt reuse has a separate Docker lane because it exercises Claude Code's native session transport rather than the direct Anthropic API. After a fresh turn and tool-bearing warmup resume, it allows a no-tool settlement resume to run hot or cold, dirties the workspace, and requires at least 90% reuse on the following resume without rotating the settled live-session generation. It also verifies that a thinking-level change rotates the generation and that the next steady resume restores at least 90% reuse:

```sh
pnpm test:docker:live-cli-backend:claude:cache
```

### Anthropic live expectations

- Expect explicit warmup writes via `cacheWrite`.
- Expect near-full history reuse on repeated turns, because Anthropic's cache control advances the cache breakpoint through the conversation.
- Baseline floors for stable, tool, image, and MCP-style lanes are hard regression gates.

### OpenAI live expectations

- Expect `cacheRead` only; `cacheWrite` stays `0` on Chat Completions.
- Treat repeated-turn cache reuse as a provider-specific plateau, not Anthropic-style moving full-history reuse.
- Floors are watch-only (a miss is logged as a warning, not a test failure), derived from observed live behavior on `gpt-5.4-mini`:

| Scenario             | `cacheRead` floor | Hit-rate floor |
| -------------------- | ----------------: | -------------: |
| Stable prefix        |             4,608 |           0.90 |
| Tool transcript      |             4,096 |           0.85 |
| Image transcript     |             3,840 |           0.82 |
| MCP-style transcript |             4,096 |           0.85 |

The most recently observed baseline numbers (from `live-cache-regression-baseline.ts`) landed at: stable prefix `cacheRead=4864`, hit rate `0.966`; tool transcript `cacheRead=4608`, hit rate `0.896`; image transcript `cacheRead=4864`, hit rate `0.954`; MCP-style transcript `cacheRead=4608`, hit rate `0.891`.

Why the assertions differ: Anthropic exposes explicit cache breakpoints and moving conversation-history reuse, while OpenAI's effective reusable prefix in live traffic can plateau earlier than the full prompt. Comparing the two providers against a single cross-provider percentage threshold produces false regressions.

## `diagnostics.cacheTrace` config

```yaml
diagnostics:
  cacheTrace:
    enabled: true
```

`enabled` defaults to `false`. Cache traces otherwise write to `$OPENCLAW_STATE_DIR/logs/cache-trace.jsonl` and include messages, prompt text, and the system prompt by default. Output-path and payload-inclusion overrides are environment-only controls for one-off debugging.

### Env toggles (one-off debugging)

| Variable                             | Effect                               |
| ------------------------------------ | ------------------------------------ |
| `OPENCLAW_CACHE_TRACE=1`             | Enables cache tracing                |
| `OPENCLAW_CACHE_TRACE_FILE=path`     | 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 with staged snapshots like `session:loaded`, `prompt:before`, `stream:context`, and `session:after`.
- Per-turn cache token impact is visible in normal usage surfaces: `cacheRead` and `cacheWrite` show up in `/usage tokens`, `/status`, session usage summaries, and custom `messages.usageTemplate` layouts.
- For Anthropic, expect both `cacheRead` and `cacheWrite` when caching is active.
- For OpenAI, expect `cacheRead` on cache hits; `cacheWrite` is populated only on Responses API payloads that include it (see [OpenAI](#openai-direct-api) above).
- OpenAI also returns tracing and rate-limit headers such as `x-request-id`, `openai-processing-ms`, and `x-ratelimit-*`; use those for request tracing, but cache-hit accounting should still come from the usage payload, not from headers.

## Quick troubleshooting

- **High `cacheWrite` on most turns**: check for volatile system-prompt inputs; verify the model/provider supports your cache settings.
- **High `cacheWrite` on Anthropic**: often means the cache breakpoint is landing on content that changes every request.
- **Low OpenAI `cacheRead`**: verify the stable prefix is at the front, the repeated prefix is at least 1024 tokens, and the same `prompt_cache_key` is reused for turns that should share a cache.
- **No effect from `cacheRetention`**: confirm the model key matches `agents.defaults.models["provider/model"]`.
- **Bedrock Nova requests with cache settings**: expected - these resolve to no cache retention at runtime.

Related docs:

- [Anthropic](/providers/anthropic)
- [Token use and costs](/reference/token-use)
- [Session pruning](/concepts/session-pruning)
- [Gateway configuration reference](/gateway/configuration-reference)

## Related

- [Token use and costs](/reference/token-use)
- [API usage and costs](/reference/api-usage-costs)
