[GitHub Trending] ranxianglei/billion-context

7.9 relevance

Context compression for billion-token sessions is novel and directly relevant to AI/ML agent orchestration and LLM workflows.

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Why It Matters

For platform engineers building long-lived coding agents, this addresses the fundamental context window bottleneck by enabling month-long single sessions with billions of tokens, maintaining 95–97% prefix-cache hit rates and ≤2% compression overhead, directly impacting agent reliability and cost at scale.

Key Takeaways

Evaluate billion-context as a proxy layer for your agent workflows to eliminate context window violations and reduce token costs by up to 71% in long-running sessions.

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 with incremental, reversible, and prefix-cache-friendly compression. A 4.5-month production study across three hosts with 174,327 model calls and 18.76B cumulative input tokens achieved zero window violations on 204,800-token models, with marathon sessions of 8,584–12,049 calls. 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, outperforming RAG arms (bm25, embedding, embedding-rerank) with DeepSeek V4.1 Flash and Qwen3 models.