- You want to reduce LLM context growth from tool outputs
- You are tuning agents.defaults.contextPruning
---
# Session Pruning
Session pruning trims **old tool results** from the in-memory context right before each LLM call. It does **not** rewrite the on-disk session history (`*.jsonl`).
## When it runs
- When `mode: "cache-ttl"` is enabled and the last Anthropic call for the session is older than `ttl`.
- Only affects the messages sent to the model for that request.
- Only active for Anthropic API calls (and OpenRouter Anthropic models).
- For best results, match `ttl` to your model `cacheControlTtl`.
- After a prune, the TTL window resets so subsequent requests keep cache until `ttl` expires again.
- Only active for Anthropic API calls (and OpenRouter Anthropic models).
- For best results, match `ttl` to your model `cacheControlTtl`.
- After a prune, the TTL window resets so subsequent requests keep cache until `ttl` expires again.
## Smart defaults (Anthropic)
- **OAuth or setup-token** profiles: enable `cache-ttl` pruning and set heartbeat to `1h`.
- **API key** profiles: enable `cache-ttl` pruning, set heartbeat to `30m`, and default `cacheControlTtl` to `1h` on Anthropic models.
- If you set any of these values explicitly, OpenClaw does **not** override them.
## What this improves (cost + cache behavior)
- **Why prune:** Anthropic prompt caching only applies within the TTL. If a session goes idle past the TTL, the next request re-caches the full prompt unless you trim it first.
- **What gets cheaper:** pruning reduces the **cacheWrite** size for that first request after the TTL expires.
- **Why the TTL reset matters:** once pruning runs, the cache window resets, so follow‑up requests can reuse the freshly cached prompt instead of re-caching the full history again.
- **What it does not do:** pruning doesn’t add tokens or “double” costs; it only changes what gets cached on that first post‑TTL request.
## What can be pruned
- Only `toolResult` messages.
- User + assistant messages are **never** modified.
- The last `keepLastAssistants` assistant messages are protected; tool results after that cutoff are not pruned.
@@ -34,35 +39,43 @@ Session pruning trims **old tool results** from the in-memory context right befo
- Tool results containing **image blocks** are skipped (never trimmed/cleared).
## Context window estimation
Pruning uses an estimated context window (chars ≈ tokens × 4). The base window is resolved in this order:
2. Model definition `contextWindow` (from the model registry).
3. Default `200000` tokens.
If `agents.defaults.contextTokens` is set, it is treated as a cap (min) on the resolved window.
## Mode
### cache-ttl
- Pruning only runs if the last Anthropic call is older than `ttl` (default `5m`).
- When it runs: same soft-trim + hard-clear behavior as before.
## Soft vs hard pruning
- **Soft-trim**: only for oversized tool results.
- Keeps head + tail, inserts `...`, and appends a note with the original size.
- Skips results with image blocks.
- **Hard-clear**: replaces the entire tool result with `hardClear.placeholder`.
## Tool selection
-`tools.allow` / `tools.deny` support `*` wildcards.
- Deny wins.
- Matching is case-insensitive.
- Empty allow list => all tools allowed.
## Interaction with other limits
- Built-in tools already truncate their own output; session pruning is an extra layer that prevents long-running chats from accumulating too much tool output in the model context.
- Compaction is separate: compaction summarizes and persists, pruning is transient per request. See [/concepts/compaction](/concepts/compaction).
## Defaults (when enabled)
-`ttl`: `"5m"`
-`keepLastAssistants`: `3`
-`softTrimRatio`: `0.3`
@@ -72,33 +85,37 @@ If `agents.defaults.contextTokens` is set, it is treated as a cap (min) on the r
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Ayaan Zaidi