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Session Pruning

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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).
• Asboblarni ruxsat berish/taqiqlash ro‘yxatlari — bu asboblar, ko‘nikmalar emas.
• After a prune, the TTL window resets so subsequent requests keep cache until ttl expires again.

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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.

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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.

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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).

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Context window estimation

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

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Mode

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cache-ttl

• Pruning only runs if the last Anthropic call is older than ttl (default 5m).
• Ishga tushganda: avvalgidek bir xil soft-trim + hard-clear xatti-harakati.

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Soft va hard pruning

• Soft-trim: faqat haddan tashqari katta tool natijalari uchun.
  • Bosh va oxirini saqlaydi, ... qo‘shadi va asl hajmi bilan eslatma ilova qiladi.
  • Rasm bloklari bo‘lgan natijalarni o‘tkazib yuboradi.
• Hard-clear: butun tool natijasini hardClear.placeholder bilan almashtiradi.

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Tool tanlash

• tools.allow / tools.deny * wildcardlarni qo‘llab-quvvatlaydi.
• Deny ustun keladi.
• Moslash katta-kichik harfga sezgir emas.
• Bo‘sh allow ro‘yxati => barcha tool’lar ruxsat etilgan.

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Boshqa limitlar bilan o‘zaro ta’sir

• O‘rnatilgan tool’lar allaqachon o‘z chiqishini qisqartiradi; session pruning esa model kontekstida uzoq davom etadigan chatlarda juda ko‘p tool chiqishi to‘planib ketmasligi uchun qo‘shimcha qatlamdir.
• Compaction alohida: compaction xulosa qiladi va saqlab qoladi, pruning esa har bir so‘rov uchun vaqtinchalik. Eng yaxshi natijalar uchun ttl ni modelingizdagi cacheControlTtl bilan moslang.

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Standartlar (yoqilganda)

• 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]" }

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Misollar

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

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

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