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humanlayer
12-factor-agents
TypeScript2025–2026
humanlayer / 12-factor-agents

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?

AI & Machine LearningScience & EngineeringDevOps & Cloud
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27.1KGitHub stars
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What it is

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 is used

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What people are saying

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Also in these discussions

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This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 3:30:57 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month11 dated mentions

Observed mentions: 2025-04-11 09:24:35 UTC to 2026-09-18 13:02:24 UTC.

01232025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 commentsApr 20252025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsDec 20252026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-08-01 00:00:00 UTC to 2026-09-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-09-01 00:00:00 UTC to 2026-10-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsSep 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC321
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC101
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC202
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC110
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC000
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC000
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC000
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC101
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC000
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC110
Aug 20262026-08-01 00:00:00 UTC2026-09-01 00:00:00 UTC110
Sep 20262026-09-01 00:00:00 UTC2026-10-01 00:00:00 UTC110

What Hacker News says

3 sources
hhimanshu·Apr 17, 2025
I am wondering how libraries like DSPY [0] fits in your factor-2 [1] As I was reading, I saw mention of BAML > (the above example uses BAML to generate the prompt ... Personally, in my experience hand-writing prompts for extracting structured information from unstructured data has never been easy. With DSPY, my experience has been quite good so far. As you have used raw prompt from BAML, what do you think of using the raw prompts from DSPY [2]? [0] https://dspy.ai/ [1] https://github.com/humanlayer/12-factor-agents/blob/main/con... [2] https://dspy.ai/tutorials/observability/#using-inspect_histo...
In 12-factor Agents: Patterns of reliable LLM applications
dhorthy·Apr 15, 2025Submission

12-factor Agents: Patterns of reliable LLM applications

I've been building AI agents for a while. After trying every framework out there and talking to many founders building with AI, I've noticed something interesting: most "AI Agents" that make it to production aren't actually that agentic. The best ones are mostly just well-engineered software with LLMs sprinkled in at key points. So I set out to document what I've learned about building production-grade AI systems: https://github.com/humanlayer/12-factor-agents. It's a set of principles for building LLM-powered software that's reliable enough to put in the hands of production customers. In the spirit of Heroku's 12 Factor Apps (https://12factor.net/), these principles focus on the engineering practices that make LLM applications more reliable, scalable, and maintainable. Even as models get exponentially more powerful, these core techniques
Read the rest
will remain valuable. I've seen many SaaS builders try to pivot towards AI by building greenfield new projects on agent frameworks, only to find that they couldn't get things past the 70-80% reliability bar with out-of-the-box tools. The ones that did succeed tended to take small, modular concepts from agent building, and incorporate them into their existing product, rather than starting from scratch. The full guide goes into detail on each principle with examples and patterns to follow. I've seen these practices work well in production systems handling real user traffic. I'm sharing this as a starting point—the field is moving quickly so these principles will evolve. I welcome your feedback and contributions to help figure out what "production grade" means for AI systems!
475 points · Open discussion

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