headroom vs tooltrim
Context compression for AI agents compared
headroom and tooltrim reduce the context sent to LLM agents by compressing tool outputs and other content. headroom offers broader context compression across inputs such as files, logs, and retrieval chunks, while tooltrim focuses on tool results and includes query-aware selection and reference-based expansion.
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.
tooltrim: Compress LLM Agent Tool Outputs
tooltrim trims large tool results before they enter an LLM agent’s context, using content-aware, query-aware extraction and optional retrieval of the original output. It suits teams looking to reduce context use without changing their provider or agent framework.
| headroom | tooltrim | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | MIT |
| Stars | 74.3k | 3 |
| Forks | 5.7k | 0 |
| Last analyzed | Oct 3, 2026 | Oct 9, 2026 |
Key differences
- headroom handles tool outputs as well as files, logs, source code, and retrieval context; tooltrim focuses on agent tool outputs such as web pages, JSON, logs, and documents.
- headroom offers Python and TypeScript libraries, a proxy, and MCP integrations; tooltrim provides a Python library, proxy, and integrations including decorators, LangChain, LlamaIndex, and OpenAI Agents.
- tooltrim offers optional query-aware selection using BM25 or embeddings and a reference-based mechanism to expand compressed results; headroom describes reversible compression and local retrieval of originals.
- headroom is licensed under Apache-2.0 and tooltrim under MIT.
- The supplied project facts list 74.3k stars and 5.7k forks for headroom, compared with 2 stars and 0 forks for tooltrim.
Choose headroom if you…
- need to compress varied inputs such as files, logs, source code, and retrieval context.
- want Python or TypeScript SDKs, or shared context and memory across supported agents.
- need a local proxy or MCP tools as part of a broader context-compression setup.
Choose tooltrim if you…
- want to focus compression on tool results returned to an agent.
- need optional query-aware selection and a reference-based path to retrieve original outputs or slices.
- want integrations such as LangChain, LlamaIndex, OpenAI Agents, or an OpenAI-compatible proxy with fail-open behavior.
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.