model-compose
Active·★ 72·MIT·Updated 2026-05-28
★ Hidden Gem★ Essential
Docker Compose for AI workflows. Define AI agents, RAG pipelines, MCP servers, and multi-LLM apps in a single YAML file — a lightweight alternative to LangChain.
Model-compose is a declarative framework for defining and deploying AI systems using a single YAML file. It enables composition of models, agents, workflows, and tools, allowing deployment locally, in containers, or across distributed environments.
#AI Workflow Orchestration#Declarative AI#Multi-model Integration#Hybrid AI Deployment#Agent Framework#YAML Configuration#Real-time Streaming#LLM Ops
01
Features
01Declarative YAML Configuration for entire AI systems (workflows, agents, models, tools)
02Hybrid Model Execution and Universal Service Integration (local models and cloud APIs)
03AI Agent Components for building autonomous agents with tool use and planning
04Flexible Deployment and Protocol Adapters (native, Docker, HTTP, WebSocket, MCP)
05Real-time SSE Streaming for live AI responses from any source
02
Compatibility
Python
Runtime
Verified via docs
Docker
Deployment
Verified via docs
Cloud AI APIs (OpenAI, Anthropic, Google)
Integration
Verified via docs
Local Models (HuggingFace, vLLM, llama.cpp)
Model Runtime
Verified via docs
Vector/Graph Stores (Chroma, FAISS, Milvus, Qdrant, Neo4j, ArangoDB)
Data Storage
Verified via docs
Redis
Queue/KV Store
Verified via docs
03
Quick start
1
$ pip install model-compose
04
Use cases
↳Building and orchestrating complex AI workflows and pipelines.
↳Developing autonomous AI agents with custom tools and multi-step reasoning.
↳Creating RAG (Retrieval Augmented Generation) systems with integrated vector/graph stores.
↳Deploying AI services flexibly in hybrid cloud/on-premise environments.
↳Rapid prototyping and deployment of AI applications with instant Web UI and API exposure.
05
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