head to head · open source

headroom vs graphrag

headroom has 72,811 GitHub stars, 5,598 forks, 643 open issues and last shipped 2 days ago. graphrag has 36,022 stars, 3,795 forks, 47 open issues and last shipped 3 days ago. headroom leads on adoption by 102% (72,811 vs 36,022 stars). headroom is written in Python under Apache-2.0; graphrag is written in Python under MIT. headroom has attracted 8% as many forks as stars, graphrag 11%. headroom was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (llm), so they are genuine substitutes rather than adjacent tools.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

headroom ★ 73K graphrag ★ 36K category AI & Machine Learning

← all 13541 open source comparisons

Side by side

headroom graphrag
GitHub stars ★ 73K ★ 36K
License Apache-2.0 MIT
Written in Python Python
Last push 2026-09-17 2026-09-16
Forks ⑂ 5.6K ⑂ 3.8K
Self-hosting Yes Yes
Data ownership Your server Your server

pick headroom if

  • You weight community size — 73K stars and counting
  • You want the Apache-2.0 license terms
  • Your stack matches Python
  • You value the larger contributor base for long-term maintenance

full headroom profile →

pick graphrag if

  • You want the graphrag feature set and don't need the biggest community
  • You prefer the MIT license terms
  • Your stack matches Python
  • You evaluated both and graphrag fits your workflow better

full graphrag profile →

About headroom

Headroom is an Apache 2.0 Python library, local proxy, agent wrapper and MCP server that compresses tool outputs, logs, files, RAG chunks and conversation history before they reach an LLM, and it is built for developers running AI coding agents or LLM applications who want to cut token usage without changing the answers they get.

read the full headroom overview →

About graphrag

GraphRAG is a modular, Python based, MIT licensed data pipeline and transformation suite from Microsoft Research that uses large language models to extract structured data from unstructured text, then exploits the resulting knowledge graph to form targeted context for question answering over private data.

read the full graphrag overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 247K Ollama ★ 181K Dify ★ 156K Open WebUI ★ 152K langchain ★ 147K

Related comparisons

openclaw vs dify openclaw vs multica openclaw vs langgraph n8n vs dify dify vs langflow dify vs open-webui dify vs langchain dify vs ponytail openclaw vs hermes-agent openclaw vs opencode openclaw vs n8n openclaw vs open-webui openclaw vs lobechat openclaw vs anythingllm hermes-agent vs opencode hermes-agent vs n8n openclaw vs ollama ollama vs llama-cpp ollama vs vllm ollama vs gpt4all ollama vs llama-index ollama vs localai open-webui vs gpt4all llama-cpp vs vllm

More AI Development Platforms projects

Compare either of these against the rest of the AI Development Platforms field.

headroom vs Dify headroom vs langchain headroom vs ponytail headroom vs generative-ai-for-beginners headroom vs graphify headroom vs claude-mem headroom vs ragflow headroom vs PaddleOCR headroom vs Agent-Reach headroom vs Mem0 headroom vs daily_stock_analysis headroom vs LiteLLM

Frequently asked questions

Is headroom or graphrag more popular?

headroom has 72,811 GitHub stars and graphrag has 36,022. headroom has the larger community by that measure.

Are headroom and graphrag free?

Both are open source. headroom is licensed under Apache-2.0 and graphrag under MIT. Neither carries a licence fee.

What is the difference between headroom and graphrag?

headroom is written in Python and graphrag in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, headroom or graphrag?

Choose headroom if you want the larger community (72,811 stars) or its Apache-2.0 licence terms. Choose graphrag if its feature set, stack or MIT licence fits better. Both are self-hostable.