head to head · open source
headroom vs ruby_llm
headroom has 78,773 GitHub stars, 6,155 forks, 643 open issues and last shipped yesterday. ruby_llm has 4,493 stars, 530 forks, 7 open issues and last shipped 2 days ago. headroom leads on adoption by 1,653% (78,773 vs 4,493 stars). headroom is written in Python under Apache-2.0; ruby_llm is written in Ruby under MIT. headroom has attracted 8% as many forks as stars, ruby_llm 12%. headroom was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (ai, anthropic, 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.
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Side by side
| headroom | ruby_llm | |
|---|---|---|
| GitHub stars | ★ 79K | ★ 4.5K |
| License | Apache-2.0 | MIT |
| Written in | Python | Ruby |
| Last push | 2026-11-04 | 2026-11-03 |
| Forks | ⑂ 6.2K | ⑂ 530 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick headroom if
- You weight community size — 79K 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
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
About headroom
Quickstart · Install · Proof · Agents · Docs · Discord · llms.txt AI agents / LLMs: read /llms.txt here, or fetch the live index · full docs blob . Headroom compresses everything your AI agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM. Same answers, fraction of the tokens. Compression runs on your machine; no prompt or file content is sent anywhere to be compressed. 10,144 → 1,260 tokens. The same FATAL found. What it does Library — compress(messages) in Python or TypeScript, inline in any app. Proxy — headroom proxy port 8787 , zero code changes, any…
read the full headroom overview →
About ruby_llm
Build AI features the Ruby way RubyLLM is the Ruby native AI framework. Work with models, tools, and agents through one consistent API, in plain Ruby or Rails. Website · Getting Started · What's New in 2.0 Battle tested at Fully private work AI [!NOTE] Using RubyLLM? Share your story! Takes 5 minutes. 17 providers. One Ruby API. Build with the models you want. Move between hosted and local providers without rewriting your application, or connect an OpenAI compatible endpoint. Browse models and pricing · Connect a provider Start with one line. Add files, tools, and agents These examples use 2.0.0 . Follow Getting…
read the full ruby_llm overview →
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Frequently asked questions
Is headroom or ruby_llm more popular?
headroom has 78,773 GitHub stars and ruby_llm has 4,493. headroom has the larger community by that measure.
Are headroom and ruby_llm free?
Both are open source. headroom is licensed under Apache-2.0 and ruby_llm under MIT. Neither carries a licence fee.
What is the difference between headroom and ruby_llm?
headroom 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, headroom or ruby_llm?
Choose headroom if you want the larger community (78,773 stars) or its Apache-2.0 licence terms. Choose ruby_llm if its feature set, stack or MIT licence fits better. Both are self-hostable.