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BytesAgain β€Ί Skills β€Ί Session Context Compressor
πŸ¦€ ClawHub

Session Context Compressor

by @beboxos

Compress OpenClaw session context to reduce token usage and extend session lifetime. Uses NLP summarization (Sumy) to intelligently compact conversation history while preserving essential context. Triggers on mentions of session compression, token reduction, context cleanup, or when session size exceeds safe thresholds (~300KB). Use when (1) OpenClaw approaches 50% context limit, (2) Sessions are slowing down due to large context, (3) Reducing API costs from excessive token consumption, (4) Exte

Versionv1.0.0
Downloads1,486
#clawhub
TERMINAL
clawhub install clawpressor

πŸ“– About This Skill

name: clawpressor description: Compress OpenClaw session context to reduce token usage and extend session lifetime. Uses NLP summarization (Sumy) to intelligently compact conversation history while preserving essential context. Triggers on mentions of session compression, token reduction, context cleanup, or when session size exceeds safe thresholds (~300KB). Use when (1) OpenClaw approaches 50% context limit, (2) Sessions are slowing down due to large context, (3) Reducing API costs from excessive token consumption, (4) Extending session lifetime without forced reboots.

ClawPressor - Session Context Compressor

Intelligently compress OpenClaw session files to reduce token usage by 85-96%.

Author: JARVIS (AI Coder) | Managed by: BeBoX License: MIT | Version: 1.0.0

Quick Start

# Preview compression without changes
python3 scripts/compress.py --dry-run
Apply compression

python3 scripts/compress.py --apply
Restore from backup

python3 scripts/compress.py --restore

When to Use

| Situation | Action | |-----------|--------| | Context at 30-40% | Plan compression soon | | Context at 50% | URGENT β€” OpenClaw will force compact | | Session > 300KB | Compress to restore performance | | Slow responses | Large context likely the cause | | High API costs | Compress regularly to save tokens |

How It Works

1. Preserves recent context β€” Keeps last 5 messages intact for immediate context 2. Summarizes old messages β€” Uses LexRank algorithm to extract key information 3. Replaces with compact block β€” Single system message containing summary 4. Creates backup β€” Original preserved as .backup file

Prerequisites

pip install sumy
python -c "import nltk; nltk.download('punkt_tab'); nltk.download('stopwords')"

Command Reference

# Find and compress latest session (dry-run)
python3 scripts/compress.py
Compress specific session

python3 scripts/compress.py --session /path/to/session.jsonl --apply
Keep more recent messages (default: 5)

python3 scripts/compress.py --keep 10 --apply
Restore if something went wrong

python3 scripts/compress.py --restore
View compression statistics

python3 scripts/compress.py --stats

Typical Results

| Metric | Before | After | Gain | |--------|--------|-------|------| | Messages | 168 | 6 | -96% | | Size | 347 KB | 12 KB | -96% | | Context tokens | ~50k | ~8k | -84% | | Session duration | ~30 min | ~2-3h | +400% |

Integration with Workflows

In HEARTBEAT.md: 

## Context Maintenance (1x/jour)

  • Check session size: ls -lh ~/.openclaw/agents/main/sessions/*.jsonl
    • 

  • If > 200KB: python3 skills/clawpressor/scripts/compress.py --apply
    •

    Manual check: 

    # See current session stats
ls -lh ~/.openclaw/agents/main/sessions/*.jsonl | head -1

    Safety

  • Always creates .backup before compressing
  • --restore recovers original session
  • Recent messages always preserved intact
  • Summary stored as system message (visible to model)

    • Troubleshooting

    | Issue | Solution | |-------|----------| | "Sumy not installed" | Run pip install sumy and NLTK downloads | | No session found | Check ~/.openclaw/agents/main/sessions/ exists | | Backup not found | File may have been overwritten; no recovery | | Poor summaries | Increase --keep to preserve more context |

    Credits

  • Coding: JARVIS (AI Assistant)
  • Project Management: BeBoX
  • Technique: NLP summarization via Sumy (LexRank algorithm)

    • Related

  • See memory/openclaw-context-optimization.md for full strategy
  • Combine with SOUL_MIN/USER_MIN files for maximum efficiency

    • ⚑ When to Use

    TriggerAction
    |-----------|--------|
    | Context at 30-40% | Plan compression soon |
    | Context at 50% | **URGENT** β€” OpenClaw will force compact |
    | Session > 300KB | Compress to restore performance |
    | Slow responses | Large context likely the cause |
    | High API costs | Compress regularly to save tokens |

    πŸ’‘ Examples

    # Preview compression without changes
python3 scripts/compress.py --dry-run
    Apply compression
    
python3 scripts/compress.py --apply
    Restore from backup
    
python3 scripts/compress.py --restore

    βš™οΈ Configuration

    pip install sumy
python -c "import nltk; nltk.download('punkt_tab'); nltk.download('stopwords')"

    πŸ“‹ Tips & Best Practices

    | Issue | Solution | |-------|----------| | "Sumy not installed" | Run pip install sumy and NLTK downloads | | No session found | Check ~/.openclaw/agents/main/sessions/ exists | | Backup not found | File may have been overwritten; no recovery | | Poor summaries | Increase --keep to preserve more context |