humanlayer/12-factor-agents
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #302 of 2,503 repositories GStars tracks this week (top 12%).
The project lists 15 contributors on GitHub.
Peaked at #24 in the weekly ranking the week of Jun 28, its best in the past year.
Use Cases & Benefits
- Defines principles for building reliable, production-ready LLM-powered software using a 12-factor methodology.
- Enables developers to create scalable, maintainable AI agents by applying modular, proven engineering patterns.
- Use for integrating robust LLM agents into existing SaaS products to enhance AI capabilities without full rewrites.
- Use for designing AI workflows that unify natural language prompts, tool calls, and context management effectively.
- Use for building small, focused, and stateless AI agents that can be triggered flexibly and recover from errors.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Other
GitHub could not identify this license; read the LICENSE file before depending on it.
Community files
- README: present
Watch-outs
- License not identified by GitHub; read the LICENSE file before depending on it.
- Warning: No commits in over a year (last push Sep 2025).
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
In the spirit of 12 Factor Apps. The source for this project is public at https://github.com/humanlayer/12-factor-agents, and I welcome your feedback and contributions. Let's figure this out together!
Hi, I'm Dex. I've been hacking on AI agents for a while.
I've tried every agent framework out there, from the plug-and-play crew/langchains to the "minimalist" smolagents of the world to the "production grade" langraph, griptape, etc.
I've talked to a lot of really strong founders, in and out of YC, who are all building really impressive things with AI. Most of them are rolling the stack themselves. I don't see a lot of frameworks in production customer-facing agents.
I've been surprised to find that most of the products out there billing themselves as "AI Agents" are not all that agentic. A lot of them are mostly deterministic code, with LLM steps sprinkled in at just the right points to make the experience truly magical.
Agents, at least the good ones, don't follow the "here's your prompt, here's a bag of tools, loop until you hit the goal" pattern. Rather, they are comprised of mostly just software.
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+2,120 stars over 86 days across 15 snapshot days; best day +88 on Jul 28.
Forks
2.0K+130
Open issues
27+1
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 11, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| storytold/artcraft | 29.3K | +22.3K | 75 | 19 days ago |
| ifixai-ai/iFixAi | 28.4K | +6.8K | 75 | today |
| heygen-com/hyperframes | 64.6K | +5.3K | 45 | today |
| vxcontrol/pentagi | 30.5K | +5.1K | 75 | 10 days ago |
| calesthio/OpenMontage | 68.6K | +3.3K | 75 | — |
| humanlayer/12-factor-agents | 26.7K | +0 | 75 | — |
| Category median | 30.5K | +5.3K | 75 | — |
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