--- summary: "Session pruning: tool-result trimming to reduce context bloat" read_when: - 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, Moltbot 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](/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): ```json5 { agent: { contextPruning: { mode: "off" } } } ``` Enable TTL-aware pruning: ```json5 { agent: { contextPruning: { mode: "cache-ttl", ttl: "5m" } } } ``` Restrict pruning to specific tools: ```json5 { agent: { contextPruning: { mode: "cache-ttl", tools: { allow: ["exec", "read"], deny: ["*image*"] } } } } ``` See config reference: [Gateway Configuration](/gateway/configuration)