openclaw/docs/concepts/compaction.md

summary, read_when, title

summary	read_when	title
How OpenClaw summarizes long conversations to stay within model limits	You want to understand auto-compaction and /compact You are debugging long sessions hitting context limits	Compaction

Every model has a context window: the maximum number of tokens it can process. When a conversation approaches that limit, OpenClaw compacts older messages into a summary so the chat can continue.

How it works

1. Older conversation turns are summarized into a compact entry.
2. The summary is saved in the session transcript.
3. Recent messages are kept intact.

OpenClaw keeps assistant tool calls paired with their matching toolResult entries when it picks a compaction split point. If the point lands inside a tool block, OpenClaw moves the boundary so the pair stays together and the current unsummarized tail is preserved.

The full conversation history stays on disk. Compaction only changes what the model sees on the next turn.

New configs default `agents.defaults.compaction.mode` to `"safeguard"` (stricter guardrails, summary quality audits). Set `mode: "default"` explicitly to opt out.

Auto-compaction

Auto-compaction is on by default. It runs when the session nears the context limit, or when the model returns a context-overflow error (in which case OpenClaw compacts and retries).

You will see:

• embedded run auto-compaction start / complete in normal Gateway logs.
• 🧹 Auto-compaction complete in verbose mode.
• /status showing 🧹 Compactions: <count>.

Before compacting, OpenClaw automatically reminds the agent to save important notes to [memory](/concepts/memory) files. This prevents context loss. OpenClaw matches dozens of provider-specific overflow error strings (Anthropic, OpenAI, Bedrock, Gemini, Ollama, OpenRouter, and more). Common examples:

- `request_too_large`
- `context length exceeded`
- `input exceeds the maximum number of tokens`
- `input token count exceeds the maximum number of input tokens` (Bedrock)
- `input is too long for the model`
- `ollama error: context length exceeded`

Manual compaction

Type /compact in any chat to force a compaction. Add instructions to guide the summary:

/compact Focus on the API design decisions

When agents.defaults.compaction.keepRecentTokens is set (default: 20,000), manual compaction honors that cut-point and keeps the recent tail in rebuilt context. Without an explicit keep budget, manual compaction behaves as a hard checkpoint and continues from the new summary alone.

Configuration

Configure compaction under agents.defaults.compaction in your openclaw.json. The most common knobs are listed below; for the full reference, see Session management deep dive.

Using a different model

By default, compaction uses the agent's primary model. Set agents.defaults.compaction.model to delegate summarization to a more capable or specialized model. The override accepts a provider/model-id string or a bare alias configured under agents.defaults.models:

{
  "agents": {
    "defaults": {
      "compaction": {
        "model": "openrouter/anthropic/claude-sonnet-4-6"
      }
    }
  }
}

Bare configured aliases resolve to their canonical provider and model before compaction starts. If a bare value matches both an alias and a configured literal model ID, the literal model ID wins. An unmatched bare value remains a model ID on the active provider.

This works with local models too, for example a second Ollama model dedicated to summarization:

{
  "agents": {
    "defaults": {
      "compaction": {
        "model": "ollama/llama3.1:8b"
      }
    }
  }
}

When unset, compaction starts with the active session model. If summarization fails with a model-fallback-eligible provider error, OpenClaw retries that compaction attempt through the session's existing model fallback chain. The fallback choice is temporary and is not written back to session state. An explicit agents.defaults.compaction.model override remains exact and does not inherit the session fallback chain.

Identifier preservation

Compaction summarization preserves opaque identifiers by default (identifierPolicy: "strict"). Override with identifierPolicy: "off" to disable, or identifierPolicy: "custom" plus identifierInstructions for custom guidance.

Active transcript byte guard

When agents.defaults.compaction.maxActiveTranscriptBytes is set, OpenClaw triggers normal local compaction before a run if transcript history reaches that size. This is useful for long-running sessions where provider-side context management may keep model context healthy while persisted transcript history keeps growing. It does not split raw bytes; it asks the normal compaction pipeline to create a semantic summary.

The byte guard applies to the active SQLite transcript history. Legacy JSONL checkpoint artifacts are not the active compaction target.

Successor transcripts

When agents.defaults.compaction.truncateAfterCompaction is enabled, OpenClaw does not rewrite the existing transcript in place. It creates a new active successor transcript from the compaction summary, preserved state, and unsummarized tail, then records checkpoint metadata that points branch/restore flows at that compacted successor. Successor transcripts also drop exact duplicate long user turns that arrive inside a short retry window, so channel retry storms are not carried into the next active transcript after compaction.

OpenClaw no longer writes separate .checkpoint.*.jsonl copies for new compactions. Existing legacy checkpoint files can still be used while referenced and are pruned by normal session cleanup.

Compaction notices

By default, compaction runs silently. Set notifyUser to show brief status messages when compaction starts and completes, and to surface a degraded notice when a pre-compaction memory flush is exhausted but the reply still continues:

{
  agents: {
    defaults: {
      compaction: {
        notifyUser: true,
      },
    },
  },
}

Memory flush

Before compaction, OpenClaw can run a silent memory flush turn to store durable notes to disk. Set agents.defaults.compaction.memoryFlush.model when this housekeeping turn should use a local model instead of the active conversation model:

{
  "agents": {
    "defaults": {
      "compaction": {
        "memoryFlush": {
          "model": "ollama/qwen3:8b"
        }
      }
    }
  }
}

The memory-flush model override is exact and does not inherit the active session fallback chain. See Memory for details and config.

Pluggable compaction providers

Plugins can register a custom compaction provider via registerCompactionProvider() on the plugin API. When a provider is registered and configured, OpenClaw delegates summarization to it instead of the built-in LLM pipeline.

To use a registered provider, set its id in your config:

{
  "agents": {
    "defaults": {
      "compaction": {
        "provider": "my-provider"
      }
    }
  }
}

Setting a provider automatically forces mode: "safeguard". Providers receive the same compaction instructions and identifier-preservation policy as the built-in path, and OpenClaw still preserves recent-turn and split-turn suffix context after provider output.

If the provider fails or returns an empty result, OpenClaw falls back to built-in LLM summarization.

Compaction vs pruning

	Compaction	Pruning
What it does	Summarizes older conversation	Trims old tool results
Saved?	Yes (in session transcript)	No (in-memory only, per request)
Scope	Entire conversation	Tool results only

Session pruning is a lighter-weight complement that trims tool output without summarizing.

Troubleshooting

Compacting too often? The model's context window may be small, or tool outputs may be large. Try enabling session pruning.

Context feels stale after compaction? Use /compact Focus on <topic> to guide the summary, or enable the memory flush so notes survive.

Need a clean slate? /new starts a fresh session without compacting.

For advanced configuration (reserve tokens, identifier preservation, custom context engines, OpenAI server-side compaction), see the Session management deep dive.

Related

• Session: session management and lifecycle.
• Session pruning: trimming tool results.
• Context: how context is built for agent turns.
• Hooks: compaction lifecycle hooks (before_compaction, after_compaction).