FastAPI LangGraph Agent Production-Ready Template

One-Minute Overview#

This is a complete production-ready template for building AI agent applications with FastAPI and LangGraph. It's designed for developers who need to deploy stable, scalable AI services with integrated memory management, LLM services, monitoring, and evaluation capabilities. If you want to quickly build AI agent services with long-term memory and real-time interactions, this template helps you skip tedious infrastructure setup.

Core Value: Provides a one-stop solution for AI agent application architecture, security, monitoring, and evaluation, allowing developers to focus on core functionality implementation.

Quick Start#

Installation Difficulty: Medium - Requires Python environment and PostgreSQL database, but provides complete Docker solution

# Clone the repository
git clone https://github.com/wassim249/fastapi-langgraph-agent-production-ready-template
cd fastapi-langgraph-agent-production-ready-template

# Create environment file
cp .env.example .env.development

# Install dependencies and run
make dev

Is this suitable for my scenario?

• ✅ Enterprise-grade AI agent services: Complete authentication, monitoring, and evaluation features
• ✅ AI applications with long-term memory: Memory system based on mem0ai and pgvector
• ✅ Real-time interactive chatbots: Streaming responses and tool calling support
• ❌ Simple chatbots: Too feature-rich, lightweight solutions might be more appropriate
• ❌ Pure research purposes: Production optimization components might not be necessary

Core Capabilities#

1. Production-Ready Architecture#

• High-performance FastAPI async API with uvloop optimization
• LangGraph integration with state persistence
• Langfuse LLM observability and monitoring
• Structured logging with environment-specific formatting
• Configurable rate limiting per endpoint
• PostgreSQL with pgvector for data persistence and vector storage
• Docker and Docker Compose support
• Prometheus metrics and Grafana dashboards for monitoring

Actual Value: Provides all necessary infrastructure components for enterprise deployment without building from scratch.

2. Long-Term Memory System#

• Semantic memory storage with mem0ai and pgvector
• User-specific isolated memory spaces
• Automatic memory extraction, storage, and retrieval
• Efficient vector search
• Configurable models for memory processing and embeddings

Actual Value: AI agents can remember user information across conversations, providing more personalized and consistent interaction experiences.

3. Multi-Model LLM Service#

• Support for multiple models including GPT-4o, GPT-4o-mini, GPT-5
• Automatic retry logic using tenacity
• Environment-specific tuning
• Graceful fallback mechanisms

Actual Value: Flexibly choose the most suitable model based on different scenarios and cost requirements, ensuring service stability and response speed.

4. Model Evaluation Framework#

• Automated evaluation of model outputs based on metrics
• Trace analysis integration with Langfuse
• Detailed JSON reports
• Interactive command-line interface
• Customizable evaluation metrics

Actual Value: Continuously monitor and improve AI agent performance through data-driven optimization of service quality.

Tech Stack & Integration#

Development Language: Python 3.13+ Key Dependencies:

• FastAPI - Web framework
• LangGraph - AI agent workflows
• PostgreSQL + pgvector - Database and vector storage
• mem0ai - Long-term memory system
• Langfuse - LLM observability
• structlog - Structured logging
• tenacity - Automatic retry logic
• Prometheus + Grafana - Monitoring
• Docker - Containerization

Integration Method: Complete FastAPI application, API/SDK integration

Maintenance Status#

• Development Activity: Actively maintained with ongoing commits and updates
• Recent Updates: Recent releases and updates
• Community Response: Clear contribution guidelines and security policy

Documentation & Learning Resources#

• Documentation Quality: Comprehensive
• Official Documentation: README.md
• Example Code: Complete quick start guide and API documentation included