head to head · open source

Agent-Reach vs ruby_llm

Agent-Reach has 83,525 GitHub stars, 7,317 forks, 137 open issues and last shipped 5 days ago. ruby_llm has 4,390 stars, 504 forks, 7 open issues and last shipped 2 days ago. Agent-Reach leads on adoption by 1,803% (83,525 vs 4,390 stars). Agent-Reach is written in Python under MIT; ruby_llm is written in Ruby under MIT. Agent-Reach has attracted 9% as many forks as stars, ruby_llm 11%. ruby_llm 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 ★ 84K ruby_llm ★ 4.4K category AI & Machine Learning

← all 20902 open source comparisons

Side by side

Agent-Reach ruby_llm
GitHub stars ★ 84K ★ 4.4K
License MIT MIT
Written in Python Ruby
Last push 2026-09-15 2026-09-18
Forks ⑂ 7.3K ⑂ 504
Self-hosting Yes Yes
Data ownership Your server Your server

pick Agent-Reach if

  • You weight community size — 84K 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 ruby_llm if

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

full ruby_llm 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 ruby_llm

RubyLLM is the Ruby native AI framework that gives Ruby and Rails applications chats, agents, tools, images, audio, and video through one consistent API spanning 17 providers.

read the full ruby_llm overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 247K Ollama ★ 181K Dify ★ 157K Open WebUI ★ 153K 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 comfyui openclaw vs odysseus openclaw vs lobechat openclaw vs anythingllm openclaw vs ollama ollama vs llama-cpp ollama vs vllm ollama vs odysseus ollama vs gpt4all ollama vs llama-index ollama vs localai open-webui vs gpt4all

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 ruby_llm more popular?

Agent-Reach has 83,525 GitHub stars and ruby_llm has 4,390. Agent-Reach has the larger community by that measure.

Are Agent-Reach and ruby_llm free?

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

What is the difference between Agent-Reach and ruby_llm?

Agent-Reach is written in Python and ruby_llm in Ruby. 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 ruby_llm?

Choose Agent-Reach if you want the larger community (83,525 stars) or its MIT licence terms. Choose ruby_llm if its feature set, stack or MIT licence fits better. Both are self-hostable.