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model-compose

Deploy production-ready AI services in minutes.

One YAML file. Any model. Any protocol. Any runtime. Build chat APIs, RAG pipelines, autonomous agents, and MCP servers without writing application code — then deploy the same file anywhere, like docker-compose.

AI systems should not 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, deploy anywhere without re-engineering.
• Hybrid-First — Bridge cloud APIs and local models on your own terms.
• Stream-Native — Data flows through workflows as it arrives — tokens, audio, frames, and events as first-class values.

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}

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}
    body:
      model: gpt-4o
      messages:
        - role: user
          content: ${input.prompt}

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.

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/.

🔍 RAG Pipelines

Compose embedding, vector search, and generation into a single workflow — no glue code.

workflow:
  jobs:
    - id: embed
      component: embedder
      input: { text: ${input.query} }

    - id: retrieve
      component: knowledge
      action: search
      input: { vector: ${jobs.embed.output} }

    - id: answer
      component: chatgpt
      input:
        context: ${jobs.retrieve.output}
        question: ${input.query}

Native drivers ship for Chroma, Milvus, Qdrant, FAISS, Neo4j, ArangoDB, and Redis.

🌐 MCP Servers

Turn any workflow into an MCP server that Claude, ChatGPT, or Cursor can use — one line change.

controller:
  adapter:
    type: mcp-server   # ← was: http-server
    port: 8080

Full examples live in mcp-servers/, including a Slack bot MCP.

⚡ Streaming Multi-Modal Workflows

Stream tokens, audio chunks, and video frames end-to-end — first-class across every stage.

workflow:
  job:
    component: chatgpt
    output: ${output as sse-text}

component:
  id: chatgpt
  type: http-client
  action:
    body: { stream: true, ... }
    stream_format: json
    output: ${response[].choices[0].delta.content}

Real-time TTS, video-to-frames, and live chat examples live under data-streaming/ and showcase/.

From Development to Production