clawdbot/docs/concepts/session-pruning.md

summary, read_when

summary	read_when
Session pruning: tool-result trimming to reduce context bloat	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.

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, Clawdbot 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.
• If there aren’t enough assistant messages to establish the cutoff, pruning is skipped.
• Tool results containing image blocks are skipped (never trimmed/cleared).

Context window estimation

Pruning uses an estimated context window (chars ≈ tokens × 4). The window size is resolved in this order:

1. Model definition contextWindow (from the model registry).
2. models.providers.*.models[].contextWindow override.
3. agents.defaults.contextTokens.
4. Default 200000 tokens.

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.

Defaults (when enabled)

• ttl: "5m"
• keepLastAssistants: 3
• softTrimRatio: 0.3
• hardClearRatio: 0.5
• minPrunableToolChars: 50000
• softTrim: { maxChars: 4000, headChars: 1500, tailChars: 1500 }
• hardClear: { enabled: true, placeholder: "[Old tool result content cleared]" }

Examples

Default (off):

{
  agent: {
    contextPruning: { mode: "off" }
  }
}

Enable TTL-aware pruning:

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

Restrict pruning to specific tools:

{
  agent: {
    contextPruning: {
      mode: "cache-ttl",
      tools: { allow: ["exec", "read"], deny: ["*image*"] }
    }
  }
}

See config reference: Gateway Configuration