fix: cap context window resolution (#6187) (thanks @iamEvanYT)

This commit is contained in:
Ayaan Zaidi
2026-02-01 19:49:35 +05:30
committed by Ayaan Zaidi
parent 5d3c898a94
commit 0992c5a809
7 changed files with 50 additions and 58 deletions

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@@ -129,6 +129,7 @@ Docs: https://docs.openclaw.ai
- Mentions: honor mentionPatterns even when explicit mentions are present. (#3303) Thanks @HirokiKobayashi-R.
- Discord: restore username directory lookup in target resolution. (#3131) Thanks @bonald.
- Agents: align MiniMax base URL test expectation with default provider config. (#3131) Thanks @bonald.
- Agents: respect configured context window cap for compaction safeguard. (#6187) Thanks @iamEvanYT.
- Agents: prevent retries on oversized image errors and surface size limits. (#2871) Thanks @Suksham-sharma.
- Agents: inherit provider baseUrl/api for inline models. (#2740) Thanks @lploc94.
- Memory Search: keep auto provider model defaults and only include remote when configured. (#2576) Thanks @papago2355.

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@@ -3,36 +3,30 @@ 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
title: "Session Pruning"
---
# 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 `cacheRetention`.
- 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 `cacheRetention: "short"` on Anthropic models.
- **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 followup requests can reuse the freshly cached prompt instead of re-caching the full history again.
- **What it does not do:** pruning doesnt add tokens or “double” costs; it only changes what gets cached on that first postTTL 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.
@@ -40,42 +34,35 @@ 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:
1) `models.providers.*.models[].contextWindow` override.
2) Model definition `contextWindow` (from the model registry).
3) Default `200000` tokens.
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.
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`
@@ -85,37 +72,33 @@ Pruning uses an estimated context window (chars ≈ tokens × 4). The window siz
- `hardClear`: `{ enabled: true, placeholder: "[Old tool result content cleared]" }`
## Examples
Default (off):
```json5
{
agent: {
contextPruning: { mode: "off" },
},
contextPruning: { mode: "off" }
}
}
```
Enable TTL-aware pruning:
```json5
{
agent: {
contextPruning: { mode: "cache-ttl", ttl: "5m" },
},
contextPruning: { mode: "cache-ttl", ttl: "5m" }
}
}
```
Restrict pruning to specific tools:
```json5
{
agent: {
contextPruning: {
mode: "cache-ttl",
tools: { allow: ["exec", "read"], deny: ["*image*"] },
},
},
tools: { allow: ["exec", "read"], deny: ["*image*"] }
}
}
}
```

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@@ -77,7 +77,7 @@ describe("context-window-guard", () => {
cfg,
provider: "openrouter",
modelId: "tiny",
modelContextWindow: undefined,
modelContextWindow: 64_000,
defaultTokens: 200_000,
});
const guard = evaluateContextWindowGuard({ info });
@@ -85,7 +85,7 @@ describe("context-window-guard", () => {
expect(guard.shouldBlock).toBe(true);
});
it("falls back to agents.defaults.contextTokens", () => {
it("caps with agents.defaults.contextTokens", () => {
const cfg = {
agents: { defaults: { contextTokens: 20_000 } },
} satisfies OpenClawConfig;
@@ -93,7 +93,7 @@ describe("context-window-guard", () => {
cfg,
provider: "anthropic",
modelId: "whatever",
modelContextWindow: undefined,
modelContextWindow: 200_000,
defaultTokens: 200_000,
});
const guard = evaluateContextWindowGuard({ info });
@@ -102,6 +102,21 @@ describe("context-window-guard", () => {
expect(guard.shouldBlock).toBe(false);
});
it("does not override when cap exceeds base window", () => {
const cfg = {
agents: { defaults: { contextTokens: 128_000 } },
} satisfies OpenClawConfig;
const info = resolveContextWindowInfo({
cfg,
provider: "anthropic",
modelId: "whatever",
modelContextWindow: 64_000,
defaultTokens: 200_000,
});
expect(info.source).toBe("model");
expect(info.tokens).toBe(64_000);
});
it("uses default when nothing else is available", () => {
const info = resolveContextWindowInfo({
cfg: undefined,

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@@ -11,9 +11,7 @@ export type ContextWindowInfo = {
};
function normalizePositiveInt(value: unknown): number | null {
if (typeof value !== "number" || !Number.isFinite(value)) {
return null;
}
if (typeof value !== "number" || !Number.isFinite(value)) return null;
const int = Math.floor(value);
return int > 0 ? int : null;
}
@@ -25,11 +23,6 @@ export function resolveContextWindowInfo(params: {
modelContextWindow?: number;
defaultTokens: number;
}): ContextWindowInfo {
const fromModel = normalizePositiveInt(params.modelContextWindow);
if (fromModel) {
return { tokens: fromModel, source: "model" };
}
const fromModelsConfig = (() => {
const providers = params.cfg?.models?.providers as
| Record<string, { models?: Array<{ id?: string; contextWindow?: number }> }>
@@ -39,16 +32,19 @@ export function resolveContextWindowInfo(params: {
const match = models.find((m) => m?.id === params.modelId);
return normalizePositiveInt(match?.contextWindow);
})();
if (fromModelsConfig) {
return { tokens: fromModelsConfig, source: "modelsConfig" };
const fromModel = normalizePositiveInt(params.modelContextWindow);
const baseInfo = fromModelsConfig
? { tokens: fromModelsConfig, source: "modelsConfig" as const }
: fromModel
? { tokens: fromModel, source: "model" as const }
: { tokens: Math.floor(params.defaultTokens), source: "default" as const };
const capTokens = normalizePositiveInt(params.cfg?.agents?.defaults?.contextTokens);
if (capTokens && capTokens < baseInfo.tokens) {
return { tokens: capTokens, source: "agentContextTokens" };
}
const fromAgentConfig = normalizePositiveInt(params.cfg?.agents?.defaults?.contextTokens);
if (fromAgentConfig) {
return { tokens: fromAgentConfig, source: "agentContextTokens" };
}
return { tokens: Math.floor(params.defaultTokens), source: "default" };
return baseInfo;
}
export type ContextWindowGuardResult = ContextWindowInfo & {

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@@ -197,10 +197,7 @@ export default function compactionSafeguardExtension(api: ExtensionAPI): void {
try {
const runtime = getCompactionSafeguardRuntime(ctx.sessionManager);
const modelContextWindow = resolveContextWindowTokens(model);
const contextWindowTokens = Math.min(
runtime?.contextWindowTokens ?? modelContextWindow,
modelContextWindow,
);
const contextWindowTokens = runtime?.contextWindowTokens ?? modelContextWindow;
const turnPrefixMessages = preparation.turnPrefixMessages ?? [];
let messagesToSummarize = preparation.messagesToSummarize;

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@@ -124,7 +124,7 @@ export function registerStatusHealthSessionsCommands(program: Command) {
["openclaw sessions --json", "Machine-readable output."],
["openclaw sessions --store ./tmp/sessions.json", "Use a specific session store."],
])}\n\n${theme.muted(
"Shows token usage per session when the agent reports it; set agents.defaults.contextTokens to see % of your model window.",
"Shows token usage per session when the agent reports it; set agents.defaults.contextTokens to cap the window and show %.",
)}`,
)
.addHelpText(

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@@ -122,7 +122,7 @@ export type AgentDefaultsConfig = {
* Include elapsed time in message envelopes ("on" | "off", default: "on").
*/
envelopeElapsed?: "on" | "off";
/** Optional display-only context window override (used for % in status UIs). */
/** Optional context window cap (used for runtime estimates + status %). */
contextTokens?: number;
/** Optional CLI backends for text-only fallback (claude-cli, etc.). */
cliBackends?: Record<string, CliBackendConfig>;