context/token compression for LLM agents
Headroom is a context compression layer for AI agents: it compresses tool outputs, logs, files, and RAG chunks before they reach the LLM, via a Python/TypeScript library, proxy, or MCP server, reducing token usage.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose it when you want a zero-dependency single Rust binary proxy instead of Python/TypeScript libraries.
CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose it when you need a TypeScript agent-layer with sandboxed tool output and session memory rather than standalone compression.
Context window optimization for AI coding agents. Sandboxes tool output (98% reduction), persists session memory, and enforces routing across 17 platforms via MCP + hooks.
Choose it when you want a single Rust binary that filters and remembers context instead of a Python/TS compression library.
LeanCTX — Context Intelligence for AI systems.
Choose it when you want codebase-aware context retrieval for coding agents rather than generic content compression.
Turbocharge Claude Code, Cursor, Codex, Gemini & every coding agent: faster, cheaper, with contextual understanding specific to your codebase.
Choose it when you want a code knowledge graph to cut tool calls/tokens instead of generic compression.
Pre-indexed code knowledge graph, auto syncs on code changes, for Claude Code, Codex, Gemini, Cursor, OpenCode, AntiGravity, Kiro, CoPilot, and Hermes Agent — fewer tokens, fewer tool calls, 100% local
Choose it when you want a high-performance MCP knowledge-graph code index for near-zero token lookups instead of generic compression.
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
Choose it when you need enterprise code intelligence/impact analysis via MCP or plugins instead of generic compression.
Enterprise-grade (40m+ LOC) codebase intelligence, zero-setup, local & private Plugin/Skill/Extension or MCP: hybrid semantic search, polyglot dependency graphs, symbol-level impact analysis & call-flow, interactive HTML viewer, cross-project & branch-aware search, DB/API/infra knowledge. 61% less tokens, 84% fewer calls, 37x faster. Cloud in beta.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose it when your need is code search with minimal tokens rather than general content compression.
Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read
Choose it when your token issue is database schemas/data access via MCP, not general content compression.
Token conscious database MCP server for Postgres, MySQL, SQL Server, MariaDB, SQLite.
Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.
Choose it when you want a Claude Code skill that changes language style to cut tokens, no library/proxy.
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
These projects were analysed and named headroom among their alternatives. The relationship is not symmetric — how headroom rates them is a separate judgement, made when headroom is analysed in its own right.
calls headroom “when you need a Python-based compressor proxy that also handles logs, files, and RAG chunks.”
CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies
calls headroom “Python library/proxy/MCP that compresses outputs across the stack; choose for a Python-native same-approach alternative.”
Context window optimization for AI coding agents. Sandboxes tool output (98% reduction), persists session memory, and enforces routing across 17 platforms via MCP + hooks.
calls headroom “Use when you need token reduction via input compression instead of behavioral code-minimalism — it's a proxy/MCP library, not a skill.”
Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.
calls headroom “Choose it when you want algorithmic compression of tool outputs, logs, and RAG chunks rather than a prompt-style change; works with any LLM integration, not just Claude Code skills.”
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
calls headroom “choose for generic output compression (logs/RAG) before the LLM rather than code graph indexing.”
Pre-indexed code knowledge graph, auto syncs on code changes, for Claude Code, Codex, Gemini, Cursor, OpenCode, AntiGravity, Kiro, CoPilot, and Hermes Agent — fewer tokens, fewer tool calls, 100% local