Context Compactor

Automatic context compaction for OpenClaw when using local models that don't properly report token limits or context overflow errors.

The Problem

Cloud APIs (Anthropic, OpenAI) report context overflow errors, allowing OpenClaw's built-in compaction to trigger. Local models (MLX, llama.cpp, Ollama) often:

• Silently truncate context
• Return garbage when context is exceeded
• Don't report accurate token counts

This leaves you with broken conversations when context gets too long.

The Solution

Context Compactor estimates tokens client-side and proactively summarizes older messages before hitting the model's limit.

How It Works

┌─────────────────────────────────────────────────────────────┐
│  1. Message arrives                                         │
│  2. before_agent_start hook fires                           │
│  3. Plugin estimates total context tokens                   │
│  4. If over maxTokens:                                      │
│     a. Split into "old" and "recent" messages              │
│     b. Summarize old messages (LLM or fallback)            │
│     c. Inject summary as compacted context                 │
│  5. Agent sees: summary + recent + new message             │
└─────────────────────────────────────────────────────────────┘

Installation

# One command setup (recommended)
npx jasper-context-compactor setup

# Restart gateway
openclaw gateway restart

The setup command automatically:

• Copies plugin files to ~/.openclaw/extensions/context-compactor/
• Adds plugin config to openclaw.json with sensible defaults

Configuration

Add to openclaw.json:

{
  "plugins": {
    "entries": {
      "context-compactor": {
        "enabled": true,
        "config": {
          "maxTokens": 8000,
          "keepRecentTokens": 2000,
          "summaryMaxTokens": 1000,
          "charsPerToken": 4
        }
      }
    }
  }
}

Options

Tuning for Your Model

MLX (8K context models):

{
  "maxTokens": 6000,
  "keepRecentTokens": 1500,
  "charsPerToken": 4
}

Larger context (32K models):

{
  "maxTokens": 28000,
  "keepRecentTokens": 4000,
  "charsPerToken": 4
}

Small context (4K models):

{
  "maxTokens": 3000,
  "keepRecentTokens": 800,
  "charsPerToken": 4
}

Commands

/compact-now

Force clear the summary cache and trigger fresh compaction on next message.

/compact-now

/context-stats

Show current context token usage and whether compaction would trigger.

/context-stats

Output:

📊 Context Stats

Messages: 47 total
- User: 23
- Assistant: 24
- System: 0

Estimated Tokens: ~6,234
Limit: 8,000
Usage: 77.9%

✅ Within limits

How Summarization Works

When compaction triggers:

1. Split messages into "old" (to summarize) and "recent" (to keep)
2. Generate summary using the session model (or configured summaryModel)
3. Cache the summary to avoid regenerating for the same content
4. Inject context with the summary prepended

If the LLM runtime isn't available (e.g., during startup), a fallback truncation-based summary is used.

Differences from Built-in Compaction

Context Compactor is complementary — it catches cases before they hit the model's hard limit.

Troubleshooting

Summary quality is poor:

• Try a better summaryModel
• Increase summaryMaxTokens
• The fallback truncation is used if LLM runtime isn't available

Compaction triggers too often:

• Increase maxTokens
• Decrease keepRecentTokens (keeps less, summarizes earlier)

Not compacting when expected:

• Check /context-stats to see current usage
• Verify enabled: true in config
• Check logs for [context-compactor] messages

Characters per token wrong:

• Default of 4 works for English
• Try 3 for CJK languages
• Try 5 for highly technical content

Logs

Enable debug logging:

{
  "plugins": {
    "entries": {
      "context-compactor": {
        "config": {
          "logLevel": "debug"
        }
      }
    }
  }
}

Look for:

• [context-compactor] Current context: ~XXXX tokens
• [context-compactor] Compacted X messages → summary

Links

• GitHub: https://github.com/E-x-O-Entertainment-Studios-Inc/openclaw-context-compactor
• OpenClaw Docs: https://docs.openclaw.ai/concepts/compaction