patchy631/ai-engineering-hub
AI Engineering Hub offers in-depth tutorials and practical resources on Large Language Models, Retrieval-Augmented Generation, and real-world AI agent applications for all skill levels.
Awesome MCP › MCP Resources & Educational Materials
NicholasGoh/fastapi-mcp-langgraph-template
The project "fastapi-mcp-langgraph-template" is a modern, scalable template designed for agentic orchestration that integrates the Model Context Protocol (MCP) and LangGraph. It aims to enable rapid development and deployment of context-aware large language model (LLM) applications. The template leverages community-supported tools and standards to provide a flexible and extensible foundation for building sophisticated LLM-based workflows. Core features include seamless integration with MCP, an open protocol that standardizes how applications provide context to LLMs, allowing interoperability with various MCP servers and avoiding vendor lock-in with LLM providers. LangGraph is used for customizable agentic orchestration, offering native streaming for user experience in complex workflows and persistent chat history and state management. The technology stack includes FastAPI for the Python backend API, SQLModel (a wrapper around SQLAlchemy) for database interactions with validation, LangFuse for LLM observability and metrics, Pydantic for data validation and settings management, Supabase for database role-based access control (RBAC), PostgreSQL as the relational database, PGVector for vector storage, Nginx as a reverse proxy, and Docker Compose for development and production deployment. The architecture features an Inspector component that communicates with MCP servers via Server-Sent Events (SSE) protocol, a template setup where the API server interacts with the database and MCP servers, and a reverse proxy setup using Nginx. Planned features include monitoring and observability with Prometheus and Grafana, authentication and authorization with Auth0 using OAuth 2.0, and CI/CD pipelines deploying to AWS Fargate with infrastructure as code using Terraform. The project provides quick start instructions for building Docker images and setting up the environment for development and production. It is designed to support rapid iteration and scalable deployment of context-aware LLM applications, making it a valuable resource for developers working with MCP and LangGraph in the LLM ecosystem.
https://github.com/NicholasGoh/fastapi-mcp-langgraph-template
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