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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.

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Quick Start

Install with pip:

pip install model-compose

Or with uv:

uv 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.

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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/.

🔍 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/.

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From Development to Production

The same YAML that runs on your laptop scales without a rewrite.

1. Develop locally

model-compose up

Runs on your machine with a Gradio web UI at :8081 — perfect for iteration.

2. Deploy as a container

Add a runtime: block. Same file, same behavior:

controller:
  runtime:
    type: docker
    image: my-ai-service:latest
    ports: [ "8080:8080" ]

3. Scale horizontally

Add a queue. Dispatchers accept jobs, subscribers process them across N machines:

controller:
  adapter: { type: http-server, port: 8080 }
  queue:
    driver: redis
    host: redis.internal
    name: my-queue

No shared filesystem. No code changes. Just add more subscribers to scale.

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Why model-compose?

	model-compose	Managed APIs (OpenAI, etc.)	Code Frameworks (LangChain, etc.)
Time to first API	Minutes (one YAML)	Hours (SDK + server code)	Days (framework + integration)
Provider Coupling	Multi-provider via config	Single provider per SDK	Multi-provider via abstractions
Code Coupling	Declarative YAML — no application code	Application code required	Framework-specific code required
Infrastructure Control	Full Sovereignty	Provider-controlled	Heavy Abstraction
Runtime Flexibility	Hybrid-First (Local + Cloud)	Cloud Only	Complex to customize
Protocol Support	HTTP / WebSocket / MCP	Provider-specific	Limited
Data Streaming	First-class across all stages	Response-only (SSE tokens)	Framework-wrapped generators
Deployment	Docker / Native / Virtualenv / Process	Provider-managed	Manual integration

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Highlights

• Any model, anywhere — HuggingFace, vLLM, llama.cpp locally, or OpenAI/Anthropic/Google/xAI via HTTP
• Agents in YAML — ReAct loops, tool use, multi-step reasoning — no code
• Human-in-the-loop — pause workflows for approval, resume from CLI/UI/API
• 90+ components — models, agents, HTTP/WebSocket clients, vector/graph stores, shell, browsers, and more
• Any protocol — HTTP REST, WebSocket, or MCP with one line
• Any runtime — Docker, native, virtualenv, process, embedded — switch in one line
• Distributed — Redis queue dispatch for horizontal scaling
• Instant Web UI — Gradio-powered UI in 2 lines of YAML
• Streaming everywhere — SSE, WebSocket, and inter-job streams as first-class values

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Examples

Browse examples by category:

Category	What's inside
agents/	Code reviewer, RAG assistant, Web researcher, Web page analyzer, ...
showcase/	End-to-end pipelines: disk analysis, face-based scene search, real-time TTS
model-providers/	OpenAI, Anthropic, xAI, Google, ElevenLabs, vLLM
model-tasks/	Local chat, embedding, TTS, VLM, face embedding, ...
media-processing/	Audio/video/image processing: extraction, conversion, scene detection
media-broadcast/	Live broadcasting pipelines (e.g. YouTube Live over RTMP)
web-automation/	Web scraping, headless browser automation, page capture
text-processing/	Text chunking and preprocessing
mcp-servers/	Build MCP servers exposed to Claude, Cursor, ChatGPT
workflow-queue/	Redis-backed distributed dispatch (streaming + non-streaming)
data-streaming/	Video-to-frames, YouTube live chat, streaming inputs
job-flow/	Workflow control: conditional routing, hooks, interrupts
integrations/	Vector/graph/KV stores, search engines, channels, tunnels
runtime/	Component execution runtimes: Docker, Apple Container, virtualenv, ...

Browse the full catalog in examples/README.md.

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Architecture

Protocol adapters → Composition engine → Runtime executors

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Contributing

We welcome all contributions — bug fixes, docs improvements, new examples.

git clone https://github.com/hanyeol/model-compose.git
cd model-compose
pip install -e .

See CONTRIBUTING if available, or open a PR directly.

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License

MIT License © 2025-2026 Hanyeol Cho.

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Contact

Have questions, ideas, or feedback? Open an issue or start a discussion on GitHub Discussions.