open-agent-spec 1.5.0

YAML-first agent specs: run with `oa run` or generate a full Python project with `oa init`.

Open Agent Spec (OA)

Define AI agents as contracts, not scattered prompts.

Open Agent Spec lets you define an agent once in YAML, validate inputs and outputs against a schema, and either run it directly with oa run or generate a Python scaffold with oa init.

Why This Exists

Most agent systems are hard to reason about:

• outputs are not strictly typed
• behaviour is buried in prompts
• logic is split across Python, Markdown, and framework abstractions
• swapping models often breaks things in subtle ways

The Idea

Open Agent Spec treats an agent like infrastructure.

Think OpenAPI or Terraform, but for AI agents.

You define:

• input schema
• output schema
• prompts
• model configuration

Then OA enforces the boundary:

input -> LLM -> validated output

If the output does not match schema, the task fails fast with a validation error.

For example, this shape mismatch can silently break downstream systems:

{"msg":"hello"}

instead of:

{"response":"hello"}

Super Quick Start

Install (Python 3.10+):

pipx install open-agent-spec

oa init aac
oa validate aac
export OPENAI_API_KEY=your_key_here
oa run --spec .agents/example.yaml --task greet --input '{"name":"Alice"}' --quiet

With OA you can:

• define tasks, prompts, model config, and expected I/O in YAML
• run a spec directly without generating code first
• keep .agents/*.yaml in your repo and call them from CI
• generate a Python project scaffold when you want to customize implementation

First Run

Shortest path from install to a working agent:

1. Create the agents-as-code layout (aac = repo-native .agents/ directory):

oa init aac

This creates:

.agents/
├── example.yaml   # minimal hello-world spec
├── review.yaml    # code-review agent that accepts a diff file
├── change.diff    # sample diff for immediate review-agent testing
└── README.md      # quick usage notes

2. Validate the generated specs:

oa validate aac

3. Set an API key for the engine in your spec (OpenAI by default):

export OPENAI_API_KEY=your_key_here

4. Run the example agent:

oa run --spec .agents/example.yaml --task greet --input '{"name":"Alice"}' --quiet

--quiet prints the task output JSON only, good for piping to jq or scripting:

{
  "response": "Hello Alice!"
}

Omit --quiet for the full execution envelope with Rich formatting.

5. Run the review agent with the bundled sample diff:

oa run --spec .agents/review.yaml --task review --input .agents/change.diff --quiet

Or review your own change:

git diff > change.diff
oa run --spec .agents/review.yaml --task review --input change.diff --quiet

Write Your Own Spec

Start from this shape:

open_agent_spec: "1.5.0"

agent:
  name: hello-world-agent
  role: chat

intelligence:
  type: llm
  engine: openai
  model: gpt-4o

tasks:
  greet:
    description: Say hello to someone
    input:
      type: object
      properties:
        name:
          type: string
      required: [name]
    output:
      type: object
      properties:
        response:
          type: string
      required: [response]

prompts:
  system: >
    You greet people by name.
  user: "{{ name }}"

Validate first, then run:

oa validate --spec agent.yaml
oa run --spec agent.yaml --task greet --input '{"name":"Alice"}' --quiet

Generate a Python Scaffold

If you want editable generated code instead of running the YAML directly:

oa init --spec agent.yaml --output ./agent

Generated structure:

agent/
├── agent.py
├── models.py
├── prompts/
├── requirements.txt
├── .env.example
└── README.md

Core Idea

Most agent projects end up hand-rolling the same pieces:

• prompt templates
• model configuration
• task definitions
• routing glue
• runtime wrappers

OA moves those concerns into a declarative spec so they can be reviewed, versioned, and reused.

The intended model is:

• spec defines the agent contract
• oa run executes the spec directly
• oa init generates a starting implementation when you need code
• external systems can orchestrate multiple specs however they want

OA deliberately does not prescribe:

• orchestration
• evaluation
• governance
• long-running runtime architecture

Common Commands

Command	Purpose
oa init aac	Create .agents/ with starter specs
oa validate aac	Validate all specs in .agents/
oa validate --spec agent.yaml	Validate one spec
oa test agent.test.yaml	Run YAML eval cases (model + assertions on task output); --quiet for CI JSON
oa run --spec agent.yaml --task greet --input '{"name":"Alice"}' --quiet	Run one task directly from YAML
oa init --spec agent.yaml --output ./agent	Generate a Python scaffold
oa update --spec agent.yaml --output ./agent	Regenerate an existing scaffold

Specification

The formal specification defines what a conforming OAS runtime must do, independent of any specific implementation.

Resource	Contents
spec/open-agent-spec-1.4.md	Formal specification — normative MUST/SHOULD/MAY requirements for OAS 1.5.0
spec/schema/oas-schema-1.4.json	Canonical JSON Schema for validating spec documents
spec/conformance/README.md	Conformance test structure and contribution guide

An independent implementor can build a conforming runtime from spec/open-agent-spec-1.4.md alone.

More Detail

Resource	Contents
openagentspec.dev	Project website
docs/REFERENCE.md	Spec structure, engines, templates, .agents/ usage
examples/multi-agent	Multi-agent orchestration example — manager, workers, task board, dashboard
Repository	Source, issues, workflows

Notes

• The CLI command is oa (not oas).
• Python 3.10+ is required.
• oa run requires the relevant provider API key for the engine in your spec.

About

• OA Open Agent Spec was dreamed up by Andrew Whitehouse in late 2024, with a desire to give structure and standardiasation to early agent systems
• In early 2025 Prime Vector was formed taking over the public facing project

License

MIT | see LICENSE.

Open Agent Stack