head to head · open source

ragflow vs headroom

ragflow has 90,889 GitHub stars, 10,759 forks, 1,525 open issues and last shipped yesterday. headroom has 72,745 stars, 5,589 forks, 643 open issues and last shipped yesterday. ragflow leads on adoption by 25% (90,889 vs 72,745 stars). ragflow is written in Go under Apache-2.0; headroom is written in Python under Apache-2.0. ragflow has attracted 12% as many forks as stars, headroom 8%. headroom was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 2 topic tags (ai, context-engineering), 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.

ragflow ★ 91K headroom ★ 73K category AI & Machine Learning

← all 8884 open source comparisons

Side by side

ragflow headroom
GitHub stars ★ 91K ★ 73K
License Apache-2.0 Apache-2.0
Written in Go Python
Last push 2026-09-17 2026-09-17
Forks ⑂ 11K ⑂ 5.6K
Self-hosting Yes Yes
Data ownership Your server Your server

pick ragflow if

  • You weight community size — 91K stars and counting
  • You want the Apache-2.0 license terms
  • Your stack matches Go
  • You value the larger contributor base for long-term maintenance

full ragflow profile →

pick headroom if

  • You want the headroom feature set and don't need the biggest community
  • You prefer the Apache-2.0 license terms
  • Your stack matches Python
  • You evaluated both and headroom fits your workflow better

full headroom profile →

About ragflow

RAGFlow is an open source Retrieval Augmented Generation engine that fuses RAG with agent capabilities into a context layer for large language models, built for developers and teams that need to turn complex, unstructured documents into production AI systems.

read the full ragflow overview →

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 →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 246K Ollama ★ 181K Dify ★ 156K Open WebUI ★ 152K langchain ★ 147K

Related comparisons

dify vs ragflow dify vs langchain dify vs ponytail langchain vs ponytail dify vs graphify langchain vs graphify ponytail vs graphify dify vs claude-mem openclaw vs hermes-agent openclaw vs open-webui openclaw vs lobechat openclaw vs anythingllm openclaw vs cherry-studio openclaw vs nanobot openclaw vs jan openclaw vs librechat ollama vs llama-cpp ollama vs vllm ollama vs gpt4all ollama vs llama-index ollama vs localai llama-cpp vs vllm ollama vs pageindex ollama vs langfuse

More AI Development Platforms projects

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

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

Frequently asked questions

Is ragflow or headroom more popular?

ragflow has 90,889 GitHub stars and headroom has 72,745. ragflow has the larger community by that measure.

Are ragflow and headroom free?

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

What is the difference between ragflow and headroom?

ragflow is written in Go and headroom 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, ragflow or headroom?

Choose ragflow if you want the larger community (90,889 stars) or its Apache-2.0 licence terms. Choose headroom if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.