headroom vs ctx-gate
Context compression for AI agents compared
headroom and ctx-gate both compress context before it reaches an LLM, aiming to reduce token use while retaining useful information. headroom offers several integration paths, while ctx-gate focuses on a Python proxy with evaluation tools for measuring retention and savings.
headroom: Compress Context for AI Agents
Headroom compresses tool outputs, logs, files, and retrieval context before they reach an LLM, helping reduce token use while retaining important details. It offers local library, proxy, and MCP integrations for agent workflows.
ctx-gate: Reduce LLM Context Tokens Through a Proxy
ctx-gate is a Python proxy that compresses LLM conversation context while aiming to preserve facts relevant to the current prompt. It supports Anthropic and OpenAI-compatible APIs, with evaluation tools to check retention and token savings.
| headroom | ctx-gate | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | MIT |
| Stars | 74.3k | 0 |
| Forks | 5.7k | 0 |
| Last analyzed | Oct 3, 2026 | Oct 4, 2026 |
Key differences
- headroom provides Python and TypeScript libraries, a local proxy, and MCP tools; ctx-gate centers on a Python proxy for Anthropic and OpenAI-compatible APIs.
- headroom routes JSON, source code, and prose to different compression methods and can store originals for retrieval; ctx-gate describes summaries, relevance-based retention, file diffs, and tool-output trimming.
- headroom includes shared context and memory across supported agents; ctx-gate offers session checkpoints, per-client state isolation, and persistent usage statistics.
- headroom is licensed under Apache-2.0 and reports 74.3k stars; ctx-gate is licensed under MIT, reports 0 stars, and describes itself as early-stage.
- headroom supports agent workflows through libraries, proxy, and MCP integrations; ctx-gate emphasizes proxying existing client traffic and evaluating fact retention. Both projects are written in Python.
Choose headroom if you…
- need Python or TypeScript libraries, a proxy, or MCP integrations for context compression.
- want to route different content types to different compression methods or retrieve locally stored originals.
- use multiple supported agents and want shared context and memory.
Choose ctx-gate if you…
- want a Python proxy for Anthropic Messages or OpenAI-compatible chat completions, including streaming.
- need evaluation tools to compare token savings with fact retention.
- want options such as relevance-based retention, file diffs, tool-output trimming, or task-shift detection.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.