Session pruning

Session pruning trims old tool results from the context before each LLM call. It reduces context bloat from accumulated tool outputs (exec results, file reads, search results) without rewriting normal conversation text.

Why it matters

Long sessions accumulate tool output that inflates the context window. This increases cost and can force compaction sooner than necessary.

Pruning is especially valuable for Anthropic prompt caching. After the cache TTL expires, the next request re-caches the full prompt. Pruning reduces the cache-write size, directly lowering cost.

How it works

Pruning runs in cache-ttl mode, gated on both a time check and a context-size check:

1. Wait for the cache TTL to expire (default 5 minutes when set manually; see Smart defaults for the Anthropic auto-default). Before the TTL elapses, pruning is skipped entirely to preserve prompt-cache reuse for nearby turns.
2. Once the TTL has elapsed, estimate total context size against the model's context window. Below roughly 30% usage, pruning is skipped and the TTL clock keeps running.
3. Soft-trim oversized tool results: results over 4,000 characters keep their first and last 1,500 characters with ... in between.
4. If context usage is still at or above roughly 50% and at least 50,000 characters of prunable tool content remain, hard-clear those results: replace their content with a placeholder (default [Old tool result content cleared], configurable via agents.defaults.contextPruning.hardClear.placeholder; set hardClear.enabled: false to skip this step).
5. Reset the TTL clock only when pruning actually changed the context, so follow-up requests reuse the fresh cache.

Two safety rules apply regardless of thresholds: the last three assistant turns are never pruned, and nothing before the session's first user message is ever pruned (protects bootstrap reads like SOUL.md/USER.md). The size thresholds and trim windows above are built-in behavior, not config keys; the configurable surface is agents.defaults.contextPruning (mode, ttl, tools, hardClear).

Only toolResult messages are eligible; normal conversation text is left alone. Use agents.defaults.contextPruning.tools.{allow,deny} to scope which tool names are prunable.

Legacy image cleanup

OpenClaw also builds a separate idempotent replay view for sessions that persist raw image blocks or prompt-hydration media markers in history.

• It preserves the 3 most recent completed turns byte-for-byte so prompt cache prefixes for recent follow-ups stay stable. This count includes all completed turns, not just image-bearing ones, so text-only turns consume the window too.
• In the replay view, older already-processed image blocks from user or toolResult history are replaced with [image data removed - already processed by model].
• Older textual media references such as [media attached: ...], [Image: source: ...], and media://inbound/... are replaced with [media reference removed - already processed by model]. Current-turn attachment markers stay intact so vision models can still hydrate fresh images.
• The raw session transcript is not rewritten, so history viewers can still render the original message entries and their images.
• This is separate from normal cache-TTL pruning above. It exists to stop repeated image payloads or stale media refs from busting prompt caches on later turns.

Smart defaults

The bundled Anthropic plugin auto-configures pruning and heartbeat cadence the first time it resolves an Anthropic (or Claude CLI) auth profile, but only for fields you have not already set explicitly:

If you set agents.defaults.contextPruning.mode or agents.defaults.heartbeat.every yourself, OpenClaw does not override them. This auto-default only fires for Anthropic-family auth; other providers get pruning off unless you configure it.

Enable or disable

Pruning is off by default for non-Anthropic providers. To enable:

{  agents: {    defaults: {      contextPruning: { mode: "cache-ttl", ttl: "5m" },    },  },}

To disable: set mode: "off".

Pruning vs compaction

They complement each other -- pruning keeps tool output lean between compaction cycles.

Further reading

• Compaction: summarization-based context reduction
• Gateway Configuration: all pruning config knobs (contextPruning.*)

Related

• Session management
• Session tools
• Context engine