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Billion Context
Billion Context is a context-compression plugin that reduces token usage for AI coding agents and small context windows.
Billion Context is a context-compression plugin that sits between any coding agent and its model API to manage long sessions. It rewrites Anthropic and OpenAI streams using kernel compression, letting the AI model decide when and what to compress into layered summaries rather than relying on hard truncation limits. The plugin injects four context-management tools into the conversation: compress, decompress, searchcontext, and acpstatus. Compression is incremental, reversible, and prefix-cache friendly so summaries can be decompressed on demand. It supports numerous clients and CLIs including Pi, OpenCode, Kimi, Claude, Codex, and Zed. The codebase includes an open-source paper detailing its multi-generational context management.
Billion Context at a glance
Key features
- Fold message ranges into detailed summaries with compress
- Restore compressed ranges on demand with decompress
- Keyword search over compressed summaries and visible messages
- View context-usage overview with acpstatus
- Incremental, reversible, and prefix-cache friendly compression
- Rewrite Anthropic and OpenAI model API streams
Use it for
- Running month-long single coding sessions with billions of tokens
- Managing long coding sessions without blowing up context windows
- Saving tokens during extensive interactions with AI coding models
Alternatives to Billion Context
- CodeSight — Reduces AI token usage significantly and maps projects instantly.
- Context Mode — Saves up to 98 percent of context per session by keeping raw data out of the LLM.
- ContextMCP — Maintains fresh, accurate context for AI agents using AST-aware chunking.
- Context7 — Provides up-to-date documentation for AI agents and code editors.
- JFrog Boost — Lowers token usage for AI coding agents through input filtering.
- ContextStream — It maintains project context and history to reduce agent errors.
- Headroom — Reduces token usage by 60 to 95 percent for JSON and lowers costs while maintaining the same answers.
- Rubi — It cuts AI token waste by keeping sessions short and mapping project knowledge.
- Ctxfw — Reduces AI context token mass while preserving code structure and dependencies.
- Paritok — It reduces token bills and increases session length by compressing prompt data.
- AI Memory — Shared memory for coding agents that works across machines and team members.
- Context Engineering Kit — Improves agent reliability and code generation quality with minimal token footprint.
All Billion Context alternatives