aigent-os
Prevents context window degradation and token waste during long-running development sessions by automating state persistence through verified git-based capsu…
Claude Code harness and alternative to context compaction: at 80% context it writes a verified capsule, runs /clear and resumes with persistent memory in git. Long-running and multi-agent Claude Code sessions, auto clear instead of auto-compact. MIT, local, Node 18+. Codex adapter.
- Automate context management by writing memory capsules when token limits approach 80%
- Maintain persistent project memory across long-running multi-agent coding sessions
- Clear chat history automatically to prevent context overflow during complex development
Prevents context window degradation and token waste during long-running development sessions by automating state persistence through verified git-based capsules. This maintains high reasoning accuracy without manual context management.
Engineering teams managing complex, multi-agent Claude Code sessions that require persistent memory across extended development lifecycles.
https://github.com/wrg32786/aigent-os
By wrg32786
How to Get It
claude plugins install wrg32786/aigent-os
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me automate context management by writing memory capsules when token limits approach 80%
Trust Signals Auto-scanned
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Discussed on Hacker News
- Show HN: Aigent-OS, a Claude Code harness that clears and resumes — Hacker News · 2 pts
1 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 25 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 25 GitHub stars; contributors unknown; last commit 0d ago; license MIT.