Session Context Compressor

通过本地 Python 脚本压缩 OpenClaw 会话 .jsonl 文件：保留最近若干条消息，用 Sumy 的 LexRank 对旧消息做摘要并替换为紧凑系统消息，压缩前生成 .backup 备份，支持 --dry-run、--apply、--restore、--stats 等命令，声称可减少约 85-96% 的会话体积与 token 占用。

beboxos

热度 79.90

最近更新 2026-05-17 11:56:01

分类：效率依赖：代码执行 / 读写文件应用场景：自动化脚本 / 工作流 / 摘要总结

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add clawpressor

Metadata

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 — 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

Preserves recent context — Keeps last 5 messages intact for immediate context
Summarizes old messages — Uses LexRank algorithm to extract key information
Replaces with compact block — Single system message containing summary
Creates backup — 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)

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