billion-context

Product Introduction

1. Definition: billion-context is an advanced context-compression plugin and local proxy server designed specifically to support month-long single sessions involving billions of tokens within small context windows (such as 100K token limits). Operating as a model-agnostic middleware interceptor, it bridges the gap between AI developer agents (like Claude Code, Codex, Cursor, and Aider) and upstream model APIs (Anthropic, OpenAI, and compatible endpoints) through intelligent, incremental summarization.
2. Core Value Proposition: It addresses the fundamental economic and technical bottlenecks of extended LLM interactions by reducing token consumption by up to 5x while maintaining high compression quality. By shifting the burden of context management from hard truncation limits to model-driven, incremental hierarchical compression, it enables developers and heavy AI users to run continuous, long-term coding sessions spanning months without hitting token limits, suffering context degradation, or inflating API operational costs.

Main Features

1. Model-Driven Incremental Hierarchical Compression (acp-kernel): The proxy injects four active context-management tools (compress, decompress, search_context, and acp_status) directly into the agent conversation stream. As context grows, the model dynamically evaluates and calls these tools to fold message ranges into high-fidelity, detailed summaries. Unlike host built-in summarizers, this compression is strictly incremental, reversible, and prefix-cache friendly, keeping prefix-cache hit rates at 95%–97% while core compression costs remain at or below 2%.
2. Multi-Client Native Plugin & Launcher Integration Architecture: Supporting a wide array of command-line tools and code editors—including Pi, OpenCode, Open Interpreter (omp), Dsh, Kimi Code, Hermes, Zcode, Claude Code, and Codex—billion-context provides zero-config launchers and native plugin installation routines. It manages self-contained port allocation zones starting at 18787 with automatic drift management (+1 port hopping on collision) and lane-aware attachment, ensuring seamless middleware routing without breaking client-side execution environments.
3. Multi-Stage Context-Management Tool Suite: The system exposes runtime tools that allow agents to autonomously manage their working memory. The compress tool folds historical conversation ranges into compressed blocks, the decompress tool restores exact details on-demand when historical references are required, search_context executes keyword queries across both visible messages and compressed summaries, and acp_status provides real-time context-usage telemetry and compressible range tracking.

Problems Solved

1. Pain Point: Context window exhaustion, context degradation over long execution spans, exponential cost scaling from redundant input token processing, and catastrophic amnesia in long-lived coding or autonomous agent workflows.
2. Target Audience: Software engineers utilizing AI coding agents, DevOps professionals running multi-day automation pipelines, enterprise architecture teams managing heavy programmatic workflows, and power users executing extended single-session AI research or content generation.
3. Use Cases: Maintaining month-long single-session context for autonomous coding agents (such as Claude Code or Codex) across hundreds of thousands of lines of codebase changes; executing long-running longitudinal research loops that exceed standard 200K token limits without resetting agent memory; and optimizing API spend for heavy enterprise deployments by achieving up to 5x token reduction and preserving a 95%+ prefix-cache hit rate.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):
   • Pros: Delivers up to 5x token reduction; maintains high cache-hit efficiency (95%–97%); supports month-long single sessions; integrates seamlessly across numerous popular CLI tools and IDEs via native plugins or launchers; offers reversible context folding via on-demand decompression and keyword search over historical data.
   • Cons: Requires local proxy execution or native hook configuration; certain closed-source Go binaries (such as Gemini CLI or Copilot CLI) lack native plugin seams and require launcher-based URL rewrites or cert-MITM configurations; mutually exclusive with standalone in-process extensions to prevent double-compression.
2. Key Alternatives & Differentiation: Compared to traditional RAG (Retrieval-Augmented Generation) systems (such as BM25 or vector embedding rerankers) and standard hard-truncation context management, billion-context provides end-to-end conversation continuity without losing structural narrative context. Benchmarks indicate superior performance, such as outperforming standard RAG arms on LongMemEval-S (79.20% vs 62.60%) and achieving 100% survival rates on million-token memory stress tests where traditional full-context models fail entirely.

Frequently Asked Questions (FAQ)

1. How does billion-context achieve token savings without losing important conversation history? It utilizes model-driven incremental hierarchical compression via an injected tool suite. Instead of hard-truncating old messages, the model itself determines when and what to fold into structured, high-fidelity summaries. These compressed ranges remain in history and can be dynamically decompressed or searched using decompress and search_context whenever precise historical details are required.
2. Is billion-context compatible with my specific AI coding assistant or CLI tool? Yes. It offers native plugin support or launcher integration for a vast array of clients, including Pi, OpenCode, Open Interpreter (omp), Dsh, Kimi Code, Hermes, Zcode, Claude Code, Codex, and various terminal-based coding environments. Installation is handled via simple commands like bili plugin install <client> or dedicated launcher wrappers.
3. What happens to my API prefix-cache hit rates when using this proxy? The proxy is specifically optimized to preserve prefix-caching. Because summaries are written in small, incremental ranges and historical message structures remain stable, healthy sessions maintain a 95% to 97% prefix-cache hit rate, with compression overhead accounting for no more than 2% of total operational costs.