[GitHub Trending] ranxianglei/billion-context

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Context compression for long sessions, novel and relevant to AI agent memory.

Open Source github.com

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Why It Matters

For engineers building long-lived coding agents, this solves the fundamental context window problem without hard truncation, using incremental, reversible compression that preserves prefix-cache efficiency—critical for maintaining session continuity and controlling API costs in production AI workflows.

Key Takeaways

Evaluate billion-context as a drop-in proxy for your agentic coding workflows to compress context by 5× while maintaining high retrieval quality and avoiding session degradation.

A context-compression plugin for month-long single sessions (billions of tokens) ,small context windows (a 100K context is enough) and token savings (5x fewer tokens) .上下文压缩插件，月级长会话会话，兼顾小窗口(100k上下...

Summary

billion-context is an open-source context-compression proxy that sits between any agent and its model API, rewriting Anthropic/OpenAI streams to enable month-long coding sessions with billions of tokens. In a 4.5-month production study across three hosts with 174,327 model calls and 18.76B cumulative input tokens, it achieved zero window violations on 204,800-token models and maintained a 95–97% prefix-cache hit rate. Independent benchmarks show it ranked #1 on ContextBench (0.3097 Utilized F1, -58.3% cost vs full context) and achieved 100% survival on 1.48M-token MemoryAgentBench TTL where full context failed 199/199 tests.