GitHub - prime-vector/open-agent-spec: Open Agent Spec is a declarative YAML standard and CLI for defining and generating AI agents. One spec, any LLM engine (OpenAI, Anthropic, Grok, Cortex, local, custom).

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:

instead of:

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):

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:

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: "1.3.0"

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

More Detail

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