The home of AI-native open source
Code should be ready for AI contributors.
Open source was organized for people to discover, understand, modify, and maintain. AI agents are becoming active software contributors. Code for AI defines how software should be published for both.
Definition
AI-native open source
AI-native open source is software published not only for human use and collaboration, but for AI agents to discover, understand, operate, modify, verify, attribute, and govern under explicit, machine-readable constraints.
Why now
The primary software consumer is changing.
A traditional repository assumes that a person will read its documentation, infer its architecture, locate its extension points, interpret its license, and validate every change. Coding agents increasingly perform part of that work.
A README alone cannot reliably communicate every architectural invariant, operational command, modification boundary, verification requirement, attribution rule, and governance constraint an autonomous contributor needs.
Code for AI treats those elements as first-class software assets: explicit, versioned, inspectable, and usable by machines.
Core principles
What makes a repository AI-native?
Discoverable
Purpose, capabilities, interfaces, ownership, and canonical sources are explicit.
Understandable
Architecture, concepts, dependencies, invariants, and design decisions are represented clearly.
Operable
Setup, execution, tool use, permissions, and recovery paths are stable and machine-actionable.
Modifiable
Extension points, generated files, compatibility boundaries, and prohibited changes are declared.
Verifiable
Builds, tests, benchmarks, security checks, and acceptance criteria can validate agent changes.
Attributable
Licensing, authorship, dependencies, provenance, and derivative work remain traceable.
Governable
Authority, review requirements, policy boundaries, and human approval points are enforceable.
Human-accountable
AI participation never removes the need for human oversight, auditability, and responsibility.
Maturity model
From human-oriented to AI-native.
Human-oriented
The repository is documented primarily for people. Agents must infer structure, rules, and validation paths.
Agent-ready
The repository provides dedicated agent instructions, reliable commands, a navigable architecture, and explicit constraints.
AI-native
Agent comprehension, operation, modification, verification, attribution, and governance are designed into the software lifecycle.
Scope
More than an instruction file.
Files such as AGENTS.md are an important step toward agent-ready repositories.
AI-native open source is the broader publishing model around them.
It includes repository semantics, stable interfaces, execution contracts, verification, provenance, licensing, authority, and lifecycle governance. The objective is not merely to help an agent generate code, but to help it change software correctly and responsibly.
About the initiative
Code for AI is an open initiative founded by Silan Hu.
Silan Hu is an AI systems researcher and Computer Science PhD student at the National University of Singapore. His work focuses on AI-native data systems, agent infrastructure, verifiable workflows, and the publication of digital assets for AI discovery and use.
Learn more at silan.tech.