This project provides a blueprint for building production-grade agentic AI systems, addressing core architectural layers like agent orchestration, memory, security, scalability, and fault handling. It targets developers and teams needing to deploy reliable, observable, and safe AI agents in real-world workloads, offering a structured approach to manage agent behavior and system performance.

How It Works

The system employs a modular architecture with a well-defined directory structure for separation of concerns. Dependencies are managed via pyproject.toml, specifying core libraries like FastAPI, LangChain, LangGraph, and PostgreSQL. Environment configuration leverages Pydantic Settings and .env files for distinct development, staging, and production setups. Containerization is handled by docker-compose.yml, orchestrating services including PostgreSQL with pgvector for vector search, a FastAPI application with hot-reloading, and an observability stack (Prometheus, Grafana, cAdvisor). Data persistence uses SQLModel for structured data (User, Session, Thread models), with DTOs (Pydantic schemas) ensuring type safety and security between the API and database layers. Security is enforced through rate limiting (SlowAPI) and input sanitization utilities. LangGraph manages agent state and workflows, integrated with Langfuse for LLM tracing.

Quick Start & Requirements

• Primary install: Clone the repository (git clone https://github.com/FareedKhan-dev/production-grade-agentic-system).
• Prerequisites: Docker, Python (>=3.13 recommended by pyproject.toml).
• Dependencies: FastAPI, LangChain ecosystem, LangGraph, Langfuse, PostgreSQL, Pydantic, bcrypt, etc., managed via pyproject.toml.
• Links: GitHub Repository

Highlighted Details

• Comprehensive "7 layers" architecture for robust agent deployment.
• Integrated pgvector extension for efficient vector similarity search.
• LangGraph for complex, stateful agent orchestration.
• Langfuse for detailed LLM tracing, monitoring, and evaluation.
• Robust security features including rate limiting, input sanitization, and secure password hashing.
• Full observability stack (Prometheus, Grafana, cAdvisor) for system health and performance monitoring.
• Modular design and Docker Compose for streamlined development and deployment.

Maintenance & Community

Information regarding maintenance, notable contributors, sponsorships, or community channels (like Discord/Slack) is not detailed in the provided README content.

Licensing & Compatibility

The specific license type and any compatibility notes for commercial use or closed-source linking are not detailed in the provided README content.

Limitations & Caveats

The provided README focuses on the system's architecture and implementation details. Specific limitations, unsupported platforms, known bugs, or alpha status are not explicitly detailed.