head to head · open source

Agent-Reach vs langgraph

Agent-Reach has 82,870 GitHub stars, 7,248 forks, 137 open issues and last shipped 4 days ago. langgraph has 41,863 stars, 7,069 forks, 786 open issues and last shipped yesterday. Agent-Reach leads on adoption by 98% (82,870 vs 41,863 stars). Agent-Reach is written in Python under MIT; langgraph is written in Python under MIT. Agent-Reach has attracted 9% 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.

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

Agent-Reach ★ 83K langgraph ★ 42K category AI & Machine Learning

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Side by side

Agent-Reach langgraph
GitHub stars ★ 83K ★ 42K
License MIT MIT
Written in Python Python
Last push 2026-09-15 2026-09-18
Forks ⑂ 7.2K ⑂ 7.1K
Self-hosting Yes Yes
Data ownership Your server Your server

pick Agent-Reach if

  • You weight community size — 83K stars and counting
  • You want the MIT license terms
  • Your stack matches Python
  • You value the larger contributor base for long-term maintenance

full Agent-Reach 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 Agent-Reach

Agent Reach is an open source Python command line tool that gives AI agents the ability to read and search the wider internet — Twitter/X, Reddit, YouTube, GitHub, Bilibili, and XiaoHongShu among others — for developers and agent builders who want that reach without paying for platform APIs.

read the full Agent-Reach 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.

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

Frequently asked questions

Is Agent-Reach or langgraph more popular?

Agent-Reach has 82,870 GitHub stars and langgraph has 41,863. Agent-Reach has the larger community by that measure.

Are Agent-Reach and langgraph free?

Both are open source. Agent-Reach is licensed under MIT and langgraph under MIT. Neither carries a licence fee.

What is the difference between Agent-Reach and langgraph?

Agent-Reach 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, Agent-Reach or langgraph?

Choose Agent-Reach if you want the larger community (82,870 stars) or its MIT licence terms. Choose langgraph if its feature set, stack or MIT licence fits better. Both are self-hostable.