Starting the development server, running linting and type checks, evaluating agent behavior, applying database migrations, and launching the service stack with Docker.

They show how to install, run, check, and evaluate the project consistently. This reduces setup mistakes and makes environment-specific operations clearer.

How wassim249/fastapi-langgraph-agent-production-ready-template config

Q: What is fastapi-langgraph-agent-production-ready-template AGENTS.md?

Development instructions for a Python web service that exposes an AI agent through FastAPI and uses LangGraph to organize its steps and tools. They cover common commands for development, checks, evaluations, database changes, and Docker environments.

About the project

FastAPI LangGraph Agent Template is a starter codebase for building AI-agent backends with FastAPI and LangGraph, including conversation state, memory, tool calls, monitoring, rate limits, and authentication. AI engineers use it as a foundation for deploying agent services, and the catalogue instructions and rule support development and operation of those services.

wassim249/fastapi-langgraph-agent-production-ready-template · 2,639 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to wassim249/fastapi-langgraph-agent-production-ready-template. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/wassim249/fastapi-langgraph-agent-production-ready-template/master/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/wassim249/fastapi-langgraph-agent-production-ready-template

Made for: Codex, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/wassim249/fastapi-langgraph-agent-production-ready-template/agents-md.svg)](https://agentmods.dev/instructions/wassim249/fastapi-langgraph-agent-production-ready-template/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/wassim249/fastapi-langgraph-agent-production-ready-template/agents-md"><img src="https://agentmods.dev/badge/instructions/wassim249/fastapi-langgraph-agent-production-ready-template/agents-md.svg" alt="Measured on agentmods" height="20"></a>

Per session 1,955 This file is loaded in full into every session.

When invoked 1,955 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.01955	$0.01955
Opus 5	$0.00978	$0.00978
Sonnet 5	$0.00391	$0.00391
Haiku 4.5	$0.00196	$0.00196

Measured 8d ago against content hash b9a2ef3302ae, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

fastapi-langgraph-agent-production-ready-template AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 224 lines

How it starts

The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI Agent Development Guide

This document provides essential guidelines for AI agents working on this LangGraph FastAPI Agent project.

Quick Commands

make install              # Install deps (uv sync) + pre-commit hooks
make dev                  # Dev server with hot reload (port 8000)
make lint                 # ruff check .
make format               # ruff format .
make typecheck            # uv run pyright (static type check)
make check                # lint + typecheck
make eval                 # Run LLM evals (interactive)
make eval-quick           # Run LLM evals (default settings)
make migrate              # Run DB migrations to latest (Alembic)
make docker-up            # Docker: API + DB (ENV=development by default)
make stack-up ENV=development  # Full stack: API + DB + Prometheus + Grafana

All server/DB/Docker targets accept ENV=development|staging|production|test. Run make help for the full list of targets.

Project Structure

app/
  api/v1/          # Route handlers (auth.py, chatbot.py, api.py)
  core/
    config.py      # Pydantic Settings config
    database.py    # Async DB setup
    langgraph/     # LangGraph agent graph + tools
    logging.py     # structlog setup
    llm.py         # LLM service with retry logic
    limiter.py     # Rate limiting (slowapi)
    metrics.py     # Prometheus metrics
    middleware.py  # ASGI middleware
    prompts/       # System prompts
  models/          # SQLModel ORM models
  schemas/         # Pydantic request/response schemas + graph state
  services/        # Business logic services
  utils/           # Shared utilities
evals/             # LLM evaluation framework (Langfuse-based)
scripts/           # Environment setup, Docker build scripts

Project Overview

This is a production-ready AI agent application built with:

LangGraph for stateful, multi-step AI agent workflows
FastAPI for high-performance async REST API endpoints
Langfuse for LLM observability and tracing
PostgreSQL + pgvector for long-term memory storage (mem0ai)
JWT authentication with session management
Prometheus + Grafana for monitoring

Read the full file on GitHub · 224 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

8d ago First seen · 224 lines · 1,955 tokens per session scan A b9a2ef3302ae

Subscribe to this mod's changes

fastapi-langgraph-agent-production-ready-template AGENTS.md is an instructions file published in the GitHub repository wassim249/fastapi-langgraph-agent-production-ready-template (2,639 stars, last pushed 22d ago), licensed MIT. It adds 1,955 tokens to every session, about $0.0098 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Backend and APIs Building agents LangChain agent agentic-ai docker fastapi fastapi-template

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,182 tokens

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

fastapi-langgraph-agent-production-ready-template: Instructions file for Codex