Seven Layers to Keep Agents Alive in Production

What it does

This repository is a reference build for a production-grade agentic system, walking through the infrastructure most demos ignore. It covers modular project structure, data persistence with SQLModel, security safeguards like rate limiting and input sanitization, resilient service patterns including circuit breakers, multi-agent orchestration via LangGraph, and observability with Prometheus and Grafana. The goal is a blueprint teams can adapt rather than a drop-in framework.

The interesting bit

The project treats the agent as just one component in a larger reliability stack. It explicitly pairs LangGraph orchestration with connection pooling, LLM unavailability handling, and an evaluation layer that uses LLM-as-a-Judge to grade outputs—acknowledging that production agents fail on plumbing long before they fail on reasoning.

Key highlights

• Uses FastAPI, LangGraph, and SQLModel for a typed, async-ready Python stack
• Includes rate limiting, input sanitization, and JWT auth flows in the security layer
• Implements circuit breakers and connection pooling to handle LLM flakiness
• Bundles Prometheus metrics and Grafana dashboards for operational visibility
• Provides an evaluation framework with LLM-as-a-Judge and automated grading criteria

Caveats

• The README is structured as a step-by-step tutorial rather than a documented API, so extracting reusable modules requires some digging
• It is explicitly scoped for small-to-mid-scale loads (the README cites “≤10K users actively using our agent”), so teams facing massive traffic will need to extend the patterns

Verdict

Worth bookmarking if you are a Python team trying to operationalize LangGraph agents and need a sane starting point for structure, observability, and safety. Skip it if you are looking for a finished, batteries-included platform or a pure research multi-agent paper.