Snodo¶
AI-native Software Development Lifecycle protocol engine. AI agents as first-class team members, governed by declared policy with structural enforcement — bounded non-determinism, no trust required.
Core idea: policy vs mechanism¶
Declare what a valid development process looks like (protocol.yml — modes, validators, constraints, disagreement policies). The engine enforces it structurally: tokens issue only when validators agree, mutations require valid tokens, and a single blocker halts execution unconditionally regardless of policy. You write the policy; the engine provides the mechanism.
2+N model¶
Two human-in-control roles — producer (code generation) and reviewer (integration) — with structurally separated tool sets. Plus N specialised AI agents that operate within those roles. The model is validated at load time: overlapping tools cause a WF1 violation and the protocol won't load.
Get started¶
pip install snodo
snodo init --template team
snodo run "your first task" --mock
The language¶
Every enforcement rule is declared in protocol.yml — modes, validators, severity caps, constraints, and disagreement policies. The full DSL reference is one document.
Design rationale¶
Why PyJWT over custom HMAC? Why does warn withhold approval? Why ESCALATE → resolve → resume rather than auto-retry? Every design decision is documented as an ADR, extracted from the development audit log.