agentbelt gives every AI coding app (Claude Code, Codex, Cursor, Copilot, DeepSeek, LM Studio) the same safe, audited dev tools over MCP. Version 0.2.0 added context compaction for agents running on local models with small context windows (gpt-oss 20B, 16k context, on a MacBook Air). 0.3.0 adds a Goose adapter and a demo GIF.
I ran the same tasks with compaction off and on, 3 runs each. Token counts are what Ollama itself reported.
| Task | Compaction | Correct | Avg input tokens | Largest prompt |
|---|---|---|---|---|
| read 5 files | off | 3/3 | 46,916 | 13,317 |
| read 5 files | on | 3/3 | 50,410 | 10,269 |
| failing test log, twice | off | 1/3 | 42,267 | 6,182 |
| failing test log, twice | on | 1/3 | 18,448 | 3,338 |
Takeaway: for local agents, the biggest win is not repeating the same logs. Shrinking file contents can backfire.
Full method and raw results: docs/token-optimization.md
npx agentbelt setup
Then ask your AI app: "Use agentbelt's agent_run with compact: true to …"
⭐ If this is useful, a star on GitHub helps other developers find it. And if you run agents on local models, I'd love to hear what you've measured.