head to head · open source

headroom vs langgraph

headroom has 72,811 GitHub stars, 5,598 forks, 643 open issues and last shipped 2 days ago. langgraph has 41,863 stars, 7,069 forks, 786 open issues and last shipped yesterday. headroom leads on adoption by 74% (72,811 vs 41,863 stars). headroom is written in Python under Apache-2.0; langgraph is written in Python under MIT. headroom has attracted 8% as many forks as stars, langgraph 17%. langgraph was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (ai, langchain, 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 langgraph ★ 42K category AI & Machine Learning

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Side by side

headroom langgraph
GitHub stars ★ 73K ★ 42K
License Apache-2.0 MIT
Written in Python Python
Last push 2026-09-17 2026-09-18
Forks ⑂ 5.6K ⑂ 7.1K
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 langgraph if

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

full langgraph 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 langgraph

LangGraph is a low level Python orchestration framework for building stateful, long running agents, aimed at developers and platform teams who need durable execution, human in the loop control, and persistent memory instead of a turnkey agent application.

read the full langgraph overview →

More in AI & Machine Learning

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

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openclaw vs langgraph openclaw vs dify openclaw vs multica 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 langgraph more popular?

headroom has 72,811 GitHub stars and langgraph has 41,863. headroom has the larger community by that measure.

Are headroom and langgraph free?

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

What is the difference between headroom and langgraph?

headroom is written in Python and langgraph 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 langgraph?

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