Context-compression plugin — billion-context is all you need.
small context windows (100K is enough) · 5× fewer tokens · month-long single sessions (billions of tokens) · high compression quality
npm install -g billion-context --prefix=~/.local
Cache health at a glance: a healthy session keeps a 95–97% prefix-cache hit rate — compression itself costs ≤2%. Sustained lower? Check attribution with
/acpor/acp-cache(see FAQ); usual causes, in order: upstream cache TTL expiry · model switch · a bili bug (please report) · other/unknown.
QQ Group: 1056132097 (full) 1108730198 (open)
- Model-Driven Incremental Hierarchical Compression: Training-Free Multi-Generational Context Management for Long-Lived Coding Agents (English, v0.2)
📝 The paper itself is open-sourced under the MIT License as part of the codebase (
paper/). It is a living document — anyone may edit it; improvements are welcome via pull request.
A production-scale longitudinal study: 4.5 months, three hosts, 174,327 model calls, 18.76B cumulative input tokens (~24.7B across all hosts), zero window violations on 204,800-token models, marathon sessions of 8,584–12,049 calls.
billion-context sits between any agent and its model API, rewriting Anthropic/OpenAI streams with acp-kernel compression. The model decides when and what to compress into high-fidelity summaries — not a hard truncation limit.
Long coding sessions blow up context. Each provider charges per token, and once you pass the context window the session degrades or dies. billion-context compresses consumed conversation into layered summaries so you can run a single session for days — billions of tokens through one context window.
Unlike a host's built-in summarizer, compression here is incremental, reversible, and prefix-cache friendly: summaries are written in small ranges, can be decompressed on demand, and the cache prefix stays intact.