HN 표시: 공개 에이전트 사양. 프롬프트 체인이 아닌 입력된 함수처럼 AI 에이전트를 처리합니다.

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 . 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 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"} 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 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 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 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 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 directlyoa 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 | 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 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 | | Resource | Contents | |---|---| | openagentspec.dev | Project website | | docs/REFERENCE.md | Spec structure, engines, templates, .agents/ usage | | Repository | Source, issues, workflows | - The CLI command is oa (notoas ). - Python 3.10+ is required. oa run requires the relevant provider API key for the engine in your spec. - 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 MIT | see LICENSE.