coderClaw

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, CoderClaw 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 base window is resolved in this order:

1. models.providers.*.models[].contextWindow override.
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.

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

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