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model-compose

One YAML file. Any model. Any protocol. Any runtime.

Build chat APIs, RAG pipelines, autonomous agents, and MCP servers without writing orchestration code — then run the same definition anywhere, like docker-compose.

AI systems shouldn't be locked into a single provider, runtime, or cloud. model-compose is built on four principles:

• Composable — Models, agents, workflows, tools, memory, and protocols are interchangeable building blocks.
• Portable — Define your AI system once and run it anywhere without re-engineering.
• Model-Agnostic — Mix local models and cloud APIs in the same system.
• Stream-Native — Tokens, audio, video frames, and events flow through workflows as first-class values.

Quick Start · What You Can Build · Documentation · Examples

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Quick Start

Install with uv:

uv pip install model-compose

Or with pip:

pip install model-compose

Create model-compose.yml:

controller:
  adapter:
    type: http-server
    port: 8080
  webui:
    port: 8081

workflow:
  job:
    component: chatgpt
    input:
      prompt: ${input.prompt}
    output: ${output as stream/text}

component:
  id: chatgpt
  type: http-client
  base_url: https://api.openai.com/v1
  action:
    path: /chat/completions
    method: POST
    headers:
      Authorization: Bearer ${env.OPENAI_API_KEY}
      Content-Type: application/json
    body:
      model: gpt-4o
      messages:
        - role: user
          content: ${input.prompt}
      stream: true
    stream_format: json
    output: ${response[].choices[0].delta.content}

Run it:

export OPENAI_API_KEY=your-key
model-compose up

That's it. You're serving GPT-4o at http://localhost:8080 with a web UI at http://localhost:8081. No application code. No framework boilerplate. Same file runs locally, in Docker, or in production.

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Automatic Web UI

Every workflow gets a browser UI for free — inputs, outputs, streaming, and file uploads are inferred from the schema. Two lines of YAML, no frontend code.

controller:
  webui:
    driver: gradio
    port: 8081

Perfect for iteration, demos, and sharing a workflow with non-engineers before wiring it into production.

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What You Can Build

Here's what a single YAML file can serve today — just a few examples.

🤖 Autonomous Agents

Build a ReAct agent that plans, uses tools, and completes multi-step tasks — declaratively.

component:
  id: research-agent
  type: agent
  tools: [search-web, fetch-page]
  max_iteration_count: 10
  action:
    model:
      component: chatgpt
    system_prompt: You are a web research assistant.
    user_prompt: ${input.question}

See simple agents like a code reviewer, a RAG assistant, and a web researcher in agents/.

🧠 Local Models

Run open-weight models on your own hardware with PyTorch, vLLM, or llama.cpp — same YAML surface as cloud APIs, no SDK code.

component:
  type: model
  task: chat-completion
  model: HuggingFaceTB/SmolLM3-3B
  action:
    messages:
      - role: system
        content: ${input.system_prompt}
      - role: user
        content: ${input.user_prompt}