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?
Usage guide
12-factor-agents is an open-source project around 12-factor, agents, context-window with 23,641 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in TypeScript, useful for judging integration effort in a similar stack.
- GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
- GitHub is the main evaluation surface; review the README, issues, and recent commits first.
Best for
- Evaluating 12-factor-agents for TypeScript AI workflows.
- Comparing a GitHub project with 23,641 stars and current repository activity.
Pros
- 12-factor-agents has visible GitHub traction with 23,641 stars. Topics: 12-factor, 12-factor-agents, agents.
- The GitHub repository is the primary evaluation surface.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- No license was detected, so usage risk needs manual review.
Production readiness
12-factor-agents should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
GitHub did not report a license, which usually requires manual legal review before production use.
12-factor-agents architecture preview
12-factor-agents's main path starts at the entry surface, runs through Agent orchestration runtime, combines LLM / model client, Runtime context, GitHub, and returns Grounded answers / search results.
Entry
Repository setup
12-factor-agents starts from the repository setup path and documented examples.
git clone https://github.com/humanlayer/12-factor-agents.git
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
Grounded answers / search results
The final result is an answer or ranked result grounded in retrieved context.
answer output
Install tutorial
Before you install
- Node.js and the package manager used by the project
- A clean working directory for the first test run
Check the runtime environment
12-factor-agents uses a Node.js-style toolchain. Confirm the Node version and package manager before installing.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/humanlayer/12-factor-agents.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
Knowledge-base assistant
Use it for document-grounded AI workflows where retrieval quality matters.
What are the principles we can use to build LLM-powered software that
This is one of the documented reasons to evaluate 12-factor-agents before choosing a stack.
Focus area: 12-factor
This is one of the documented reasons to evaluate 12-factor-agents before choosing a stack.
RAG project comparison
Compare 12-factor-agents with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official 12-factor-agents setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented 12-factor-agents example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is 12-factor-agents?
12-factor-agents is an open-source rag project. 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?
How do I install 12-factor-agents?
Start with the official README. The first detected setup step is: git clone https://github.com/humanlayer/12-factor-agents.git.
Is 12-factor-agents beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can 12-factor-agents be used commercially?
GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
Does 12-factor-agents need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt 12-factor-agents?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.