Full-Stack AI Agent Template

Production-ready FastAPI + Next.js project generator with AI agents, RAG, and 20+ enterprise integrations.

Quick Start • Features • Demo • Documentation • Configurator • PyPI

🤖 5 AI Agent Frameworks (PydanticAI, LangChain, LangGraph, CrewAI, DeepAgents)
📄 RAG Pipeline (Milvus, Qdrant, pgvector, ChromaDB)
⚡ FastAPI + Next.js 15 (WebSocket streaming, real-time chat UI)
🔒 Enterprise-Ready (JWT, OAuth, admin panel, Celery, Docker, K8s)

Table of Contents

Quick Start
Demo
Screenshots
Why This Template
Features
Architecture
AI Agent
RAG
Observability
Django-style CLI
Generated Project Structure
Configuration Options
Comparison
FAQ
Documentation
Contributing

Related Projects

Tip

Building advanced AI agents? Check out pydantic-deepagents — a deepagent framework built on pydantic-ai for building Claude Code-style AI agents with filesystem tools, subagent delegation, persistent memory, context management, cost tracking, and an interactive CLI.

🚀 Quick Start

Tip

Prefer a visual configurator? Use the Web Configurator to configure your project in the browser and download a ZIP — no CLI installation needed.

Installation

# pip
pip install fastapi-fullstack

# uv (recommended)
uv tool install fastapi-fullstack

# pipx
pipx install fastapi-fullstack

Create Your Project

# Interactive wizard (recommended — runs by default)
fastapi-fullstack

# Quick mode with options
fastapi-fullstack create my_ai_app \
  --database postgresql \
  --frontend nextjs

# Use presets for common setups
fastapi-fullstack create my_ai_app --preset production   # Full production setup
fastapi-fullstack create my_ai_app --preset ai-agent     # AI agent with streaming

# Minimal project (no extras)
fastapi-fullstack create my_ai_app --minimal

Start Development

After generating your project, follow these steps:

1. Install dependencies

cd my_ai_app
make install

Note

Windows Users: The make command requires GNU Make which is not available by default on Windows. Install via Chocolatey (choco install make), use WSL, or run raw commands manually. Each generated project includes a "Manual Commands Reference" section in its README with all commands.

2. Start the database

# PostgreSQL (with Docker)
make docker-db

3. Create and apply database migrations

Warning

Both commands are required! db-migrate creates the migration file, db-upgrade applies it to the database.

# Create initial migration (REQUIRED first time)
make db-migrate
# Enter message: "Initial migration"

# Apply migrations to create tables
make db-upgrade

4. Create admin user

make create-admin
# Enter email and password when prompted

5. Start the backend

make run

6. Start the frontend (new terminal)

cd frontend
bun install
bun dev

Access:

API: http://localhost:8000
Docs: http://localhost:8000/docs
Admin Panel: http://localhost:8000/admin (login with admin user)
Frontend: http://localhost:3000

Quick Start with Docker

Run everything in Docker:

make docker-up       # Start backend + database
make docker-frontend # Start frontend

Using the Project CLI

Each generated project has a CLI named after your project_slug. For example, if you created my_ai_app:

cd backend

# The CLI command is: uv run <project_slug> <command>
uv run my_ai_app server run --reload     # Start dev server
uv run my_ai_app db migrate -m "message" # Create migration
uv run my_ai_app db upgrade              # Apply migrations
uv run my_ai_app user create-admin       # Create admin user

Use make help to see all available Makefile shortcuts.

🎬 Demo

📸 Screenshots

Landing Page & Login

Landing Page	Login

Dashboard, Chat & RAG

Dashboard	Chat with RAG

Documents	Search

Observability

Logfire (PydanticAI)	LangSmith (LangChain)

Admin, Monitoring & API

Celery Flower	SQLAdmin Panel

API Documentation

🎯 Why This Template

Building AI/LLM applications requires more than just an API wrapper. You need:

Type-safe AI agents with tool/function calling
Real-time streaming responses via WebSocket
Conversation persistence and history management
Production infrastructure - auth, rate limiting, observability
Enterprise integrations - background tasks, webhooks, admin panels

This template gives you all of that out of the box, with 20+ configurable integrations so you can focus on building your AI product, not boilerplate.

Perfect For

🤖 AI Chatbots & Assistants - PydanticAI or LangChain agents with streaming responses
📊 ML Applications - Background task processing with Celery/Taskiq
🏢 Enterprise SaaS - Full auth, admin panel, webhooks, and more
🚀 Startups - Ship fast with production-ready infrastructure

AI-Agent Friendly

Generated projects include CLAUDE.md and AGENTS.md files optimized for AI coding assistants (Claude Code, Codex, Copilot, Cursor, Zed). Following progressive disclosure best practices - concise project overview with pointers to detailed docs when needed.

✨ Features

🤖 AI/LLM First

5 AI Frameworks - PydanticAI, LangChain, LangGraph, CrewAI, DeepAgents
4 LLM Providers - OpenAI, Anthropic, Google Gemini, OpenRouter
RAG - Document ingestion, vector search, reranking (Milvus, Qdrant, ChromaDB, pgvector)
WebSocket Streaming - Real-time responses with full event access
Conversation Persistence - Save chat history to database
Image Description - Extract images from documents, describe via LLM vision
Multimodal Embeddings - Google Gemini embedding model (text + images)
Document Sources - Local files, API upload, Google Drive, S3/MinIO
Sync Sources - Configurable connectors (Google Drive, S3) with scheduled sync
Observability - Logfire for PydanticAI, LangSmith for LangChain/LangGraph/DeepAgents

⚡ Backend (FastAPI)

FastAPI + Pydantic v2 - High-performance async API
Multiple Databases - PostgreSQL (async), MongoDB (async), SQLite
Authentication - JWT + Refresh tokens, API Keys, OAuth2 (Google)
Background Tasks - Celery, Taskiq, or ARQ
Django-style CLI - Custom management commands with auto-discovery

🎨 Frontend (Next.js 15)

React 19 + TypeScript + Tailwind CSS v4
AI Chat Interface - WebSocket streaming, tool call visualization
Authentication - HTTP-only cookies, auto-refresh
Dark Mode + i18n

🔌 20+ Enterprise Integrations

Category	Integrations
AI Frameworks	PydanticAI, LangChain, LangGraph, CrewAI, DeepAgents
LLM Providers	OpenAI, Anthropic, Google Gemini, OpenRouter
RAG / Vector Stores	Milvus, Qdrant, ChromaDB, pgvector
RAG Sources	Local files, API upload, Google Drive, S3/MinIO, Sync Sources (configurable, scheduled)
Embeddings	OpenAI, Voyage, Gemini (multimodal), SentenceTransformers
Caching & State	Redis, fastapi-cache2
Security	Rate limiting, CORS, CSRF protection
Observability	Logfire, LangSmith, Sentry, Prometheus
Admin	SQLAdmin panel with auth
Events	Webhooks, WebSockets
DevOps	Docker, GitHub Actions, GitLab CI, Kubernetes

🗺️ Architecture Overview

┌──────────────────────────────────────────────────────────────────────────┐
│                         FRONTEND  (Next.js 15)                           │
│  Chat UI · Knowledge Base · Dashboard · Settings · Dark Mode · i18n      │
└──────────────┬───────────────────────────────────────────┬───────────────┘
               │  REST / WebSocket                         │  Vercel
               ▼                                           ▼
┌──────────────────────────────────────────────────────────────────────────┐
│                         BACKEND  (FastAPI)                               │
│                                                                          │
│  ┌─────────────────────────────────────────────────────────────────┐     │
│  │                     AI AGENTS                                   │     │
│  │  PydanticAI · LangChain · LangGraph · CrewAI · DeepAgents       │     │
│  │  ────────────────────────────────────────────────────────────   │     │
│  │  Tools: datetime · web_search (Tavily) · search_knowledge_base  │     │
│  │  Providers: OpenAI · Anthropic · Gemini · OpenRouter            │     │
│  └─────────────────────────────────────────────────────────────────┘     │
│                                                                          │
│  ┌─────────────────────────────────────────────────────────────────┐     │
│  │                     RAG PIPELINE                                │     │
│  │                                                                 │     │
│  │  Sources        Parse           Chunk          Embed            │     │
│  │  ─────────      ──────────      ──────────     ──────────────   │     │
│  │  Local files    PyMuPDF         recursive      OpenAI           │     │
│  │  API upload     LiteParse       markdown       Voyage           │     │
│  │  Google Drive   LlamaParse      fixed          Gemini (multi)   │     │
│  │  S3/MinIO       python-docx                    SentenceTransf.  │     │
│  │  Sync Sources                                                   │     │
│  │                                                                 │     │
│  │  Store              Search              Rank                    │     │
│  │  ──────────────     ──────────────      ──────────────          │     │
│  │  Milvus             Vector similarity   Cohere reranker         │     │
│  │  Qdrant             BM25 + vector RRF   CrossEncoder            │     │
│  │  ChromaDB           Multi-collection                            │     │
│  │  pgvector                                                       │     │
│  └─────────────────────────────────────────────────────────────────┘     │
│                                                                          │
│  Auth (JWT/API Key/OAuth) · Rate Limiting · Webhooks · Admin Panel       │
│  Background Tasks (Celery/Taskiq/ARQ) · Django-style CLI                 │
│  Observability (Logfire/LangSmith/Sentry/Prometheus)                     │
└───────┬──────────────┬──────────────┬──────────────┬─────────────────────┘
        │              │              │              │
        ▼              ▼              ▼              ▼
   PostgreSQL       Redis         Vector DB      LLM APIs
   MongoDB                        (Milvus/       (OpenAI/
   SQLite                         Qdrant/        Anthropic/
                                  ChromaDB/      Gemini)
                                  pgvector)

🏗️ Architecture

graph TB
    subgraph Frontend["Frontend (Next.js 15)"]
        UI[React Components]
        WS[WebSocket Client]
        Store[Zustand Stores]
    end

    subgraph Backend["Backend (FastAPI)"]
        API[API Routes]
        Services[Services Layer]
        Repos[Repositories]
        Agent[AI Agent]
    end

    subgraph Infrastructure
        DB[(PostgreSQL/MongoDB)]
        Redis[(Redis)]
        Queue[Celery/Taskiq]
    end

    subgraph External
        LLM[OpenAI/Anthropic]
        Webhook[Webhook Endpoints]
    end

    UI --> API
    WS <--> Agent
    API --> Services
    Services --> Repos
    Services --> Agent
    Repos --> DB
    Agent --> LLM
    Services --> Redis
    Services --> Queue
    Services --> Webhook

Layered Architecture

The backend follows a clean Repository + Service pattern:

graph LR
    A[API Routes] --> B[Services]
    B --> C[Repositories]
    C --> D[(Database)]

    B --> E[External APIs]
    B --> F[AI Agents]

Layer	Responsibility
Routes	HTTP handling, validation, auth
Services	Business logic, orchestration
Repositories	Data access, queries

See Architecture Documentation for details.

🤖 AI Agent

Choose from 5 AI frameworks and 4 LLM providers when generating your project:

# PydanticAI with OpenAI (default)
fastapi-fullstack create my_app --ai-framework pydantic_ai

# LangGraph with Anthropic
fastapi-fullstack create my_app --ai-framework langgraph --llm-provider anthropic

# CrewAI with Google Gemini
fastapi-fullstack create my_app --ai-framework crewai --llm-provider google

# DeepAgents with OpenAI
fastapi-fullstack create my_app --ai-framework deepagents

# With RAG enabled
fastapi-fullstack create my_app --rag --database postgresql --task-queue celery

Supported Combinations

Framework	OpenAI	Anthropic	Gemini	OpenRouter
PydanticAI	✓	✓	✓	✓
LangChain	✓	✓	✓	-
LangGraph	✓	✓	✓	-
CrewAI	✓	✓	✓	-
DeepAgents	✓	✓	✓	-

PydanticAI Integration

Type-safe agents with full dependency injection:

# app/agents/assistant.py
from pydantic_ai import Agent, RunContext

@dataclass
class Deps:
    user_id: str | None = None
    db: AsyncSession | None = None

agent = Agent[Deps, str](
    model="openai:gpt-4o-mini",
    system_prompt="You are a helpful assistant.",
)

@agent.tool
async def search_database(ctx: RunContext[Deps], query: str) -> list[dict]:
    """Search the database for relevant information."""
    # Access user context and database via ctx.deps
    ...

LangChain Integration

Flexible agents with LangGraph:

# app/agents/langchain_assistant.py
from langchain.tools import tool
from langgraph.prebuilt import create_react_agent

@tool
def search_database(query: str) -> list[dict]:
    """Search the database for relevant information."""
    ...

agent = create_react_agent(
    model=ChatOpenAI(model="gpt-4o-mini"),
    tools=[search_database],
    prompt="You are a helpful assistant.",
)

WebSocket Streaming

Both frameworks use the same WebSocket endpoint with real-time streaming:

@router.websocket("/ws")
async def agent_ws(websocket: WebSocket):
    await websocket.accept()

    # Works with both PydanticAI and LangChain
    async for event in agent.stream(user_input):
        await websocket.send_json({
            "type": "text_delta",
            "content": event.content
        })

Observability

Each framework has its own observability solution:

Framework	Observability	Dashboard
PydanticAI	Logfire	Agent runs, tool calls, token usage
LangChain	LangSmith	Traces, feedback, datasets

See AI Agent Documentation for more.

📄 RAG (Retrieval-Augmented Generation)

Enable RAG to give your AI agents access to a knowledge base built from your documents.

Vector Store Backends

Backend	Type	Docker Required	Best For
Milvus	Dedicated vector DB	Yes (3 services)	Production, large scale
Qdrant	Dedicated vector DB	Yes (1 service)	Production, simple setup
ChromaDB	Embedded / HTTP	No	Development, prototyping
pgvector	PostgreSQL extension	No (uses existing PG)	Already have PostgreSQL

Document Ingestion (CLI)

# Local files
uv run my_app rag-ingest /path/to/document.pdf --collection docs
uv run my_app rag-ingest /path/to/folder/ --recursive

# Google Drive (service account)
uv run my_app rag-sync-gdrive --collection docs --folder-id <drive_folder_id>

# S3/MinIO
uv run my_app rag-sync-s3 --collection docs --prefix reports/ --bucket my-bucket

Embedding Providers

Provider	Model	Dimensions	Multimodal
OpenAI	text-embedding-3-small	1536	-
Voyage	voyage-3	1024	-
Gemini	gemini-embedding-exp-03-07	3072	Text + Images
SentenceTransformers	all-MiniLM-L6-v2	384	-

Features

Document parsing - PDF (PyMuPDF with tables, headers/footers, OCR), DOCX, TXT, MD + 130+ formats via LlamaParse
Image description - Extract images from documents, describe via LLM vision API (opt-in)
Chunking - RecursiveCharacterTextSplitter with configurable size/overlap
Reranking - Cohere API or local CrossEncoder for improved search quality
Agent integration - All 5 AI frameworks get a search_knowledge_base tool automatically

📊 Observability

Logfire (for PydanticAI)

Logfire provides complete observability for your application - from AI agents to database queries. Built by the Pydantic team, it offers first-class support for the entire Python ecosystem.

graph LR
    subgraph Your App
        API[FastAPI]
        Agent[PydanticAI]
        DB[(Database)]
        Cache[(Redis)]
        Queue[Celery/Taskiq]
        HTTP[HTTPX]
    end

    subgraph Logfire
        Traces[Traces]
        Metrics[Metrics]
        Logs[Logs]
    end

    API --> Traces
    Agent --> Traces
    DB --> Traces
    Cache --> Traces
    Queue --> Traces
    HTTP --> Traces

Component	What You See
PydanticAI	Agent runs, tool calls, LLM requests, token usage, streaming events
FastAPI	Request/response traces, latency, status codes, route performance
PostgreSQL/MongoDB	Query execution time, slow queries, connection pool stats
Redis	Cache hits/misses, command latency, key patterns
Celery/Taskiq	Task execution, queue depth, worker performance
HTTPX	External API calls, response times, error rates

LangSmith (for LangChain)

LangSmith provides observability specifically designed for LangChain applications:

Feature	Description
Traces	Full execution traces for agent runs and chains
Feedback	Collect user feedback on agent responses
Datasets	Build evaluation datasets from production data
Monitoring	Track latency, errors, and token usage

LangSmith is automatically configured when you choose LangChain:

# .env
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=your-api-key
LANGCHAIN_PROJECT=my_project

Configuration

Enable Logfire and select which components to instrument:

fastapi-fullstack new
# ✓ Enable Logfire observability
#   ✓ Instrument FastAPI
#   ✓ Instrument Database
#   ✓ Instrument Redis
#   ✓ Instrument Celery
#   ✓ Instrument HTTPX

Usage

# Automatic instrumentation in app/main.py
import logfire

logfire.configure()
logfire.instrument_fastapi(app)
logfire.instrument_asyncpg()
logfire.instrument_redis()
logfire.instrument_httpx()

# Manual spans for custom logic
with logfire.span("process_order", order_id=order.id):
    await validate_order(order)
    await charge_payment(order)
    await send_confirmation(order)

For more details, see Logfire Documentation.

🛠️ Django-style CLI

Each generated project includes a powerful CLI inspired by Django's management commands:

Built-in Commands

# Server
my_app server run --reload
my_app server routes

# Database (Alembic wrapper)
my_app db init
my_app db migrate -m "Add users"
my_app db upgrade

# Users
my_app user create --email admin@example.com --superuser
my_app user list

Custom Commands

Create your own commands with auto-discovery:

# app/commands/seed.py
from app.commands import command, success, error
import click

@command("seed", help="Seed database with test data")
@click.option("--count", "-c", default=10, type=int)
@click.option("--dry-run", is_flag=True)
def seed_database(count: int, dry_run: bool):
    """Seed the database with sample data."""
    if dry_run:
        info(f"[DRY RUN] Would create {count} records")
        return

    # Your logic here
    success(f"Created {count} records!")

Commands are automatically discovered from app/commands/ - just create a file and use the @command decorator.

my_app cmd seed --count 100
my_app cmd seed --dry-run

📁 Generated Project Structure

my_project/
├── backend/
│   ├── app/
│   │   ├── main.py              # FastAPI app with lifespan
│   │   ├── api/
│   │   │   ├── routes/v1/       # Versioned API endpoints
│   │   │   ├── deps.py          # Dependency injection
│   │   │   └── router.py        # Route aggregation
│   │   ├── core/                # Config, security, middleware
│   │   ├── db/models/           # SQLAlchemy/MongoDB models
│   │   ├── schemas/             # Pydantic schemas
│   │   ├── repositories/        # Data access layer
│   │   ├── services/            # Business logic
│   │   ├── agents/              # AI agents with centralized prompts
│   │   ├── rag/                 # RAG module (vector store, embeddings, ingestion)
│   │   ├── commands/            # Django-style CLI commands
│   │   └── worker/              # Background tasks
│   ├── cli/                     # Project CLI
│   ├── tests/                   # pytest test suite
│   └── alembic/                 # Database migrations
├── frontend/
│   ├── src/
│   │   ├── app/                 # Next.js App Router
│   │   ├── components/          # React components
│   │   ├── hooks/               # useChat, useWebSocket, etc.
│   │   └── stores/              # Zustand state management
│   └── e2e/                     # Playwright tests
├── docker-compose.yml
├── Makefile
└── README.md

Generated projects include version metadata in pyproject.toml for tracking:

[tool.fastapi-fullstack]
generator_version = "0.1.5"
generated_at = "2024-12-21T10:30:00+00:00"

⚙️ Configuration Options

Core Options

Option	Values	Description
Database	`postgresql`, `mongodb`, `sqlite`, `none`	Async by default
ORM	`sqlalchemy`, `sqlmodel`	SQLModel for simplified syntax
Auth	`jwt`, `api_key`, `both`, `none`	JWT includes user management
OAuth	`none`, `google`	Social login
AI Framework	`pydantic_ai`, `langchain`, `langgraph`, `crewai`, `deepagents`	Choose your AI agent framework
LLM Provider	`openai`, `anthropic`, `google`, `openrouter`	OpenRouter only with PydanticAI
RAG	`--rag`	Enable RAG with vector database
Vector Store	`milvus`, `qdrant`, `chromadb`, `pgvector`	pgvector uses existing PostgreSQL
Background Tasks	`none`, `celery`, `taskiq`, `arq`	Distributed queues
Frontend	`none`, `nextjs`	Next.js 15 + React 19

Presets

Preset	Description
`--preset production`	Full production setup with Redis, Sentry, Kubernetes, Prometheus
`--preset ai-agent`	AI agent with WebSocket streaming and conversation persistence
`--minimal`	Minimal project with no extras

Integrations

Select what you need:

fastapi-fullstack new
# ✓ Redis (caching/sessions)
# ✓ Rate limiting (slowapi)
# ✓ Pagination (fastapi-pagination)
# ✓ Admin Panel (SQLAdmin)
# ✓ AI Agent (PydanticAI or LangChain)
# ✓ Webhooks
# ✓ Sentry
# ✓ Logfire / LangSmith
# ✓ Prometheus
# ... and more

🔄 Comparison

vs. Manual Setup

Setting up a production AI agent stack manually means wiring together 10+ tools yourself:

# Without this template, you'd need to manually:
# 1. Set up FastAPI project structure
# 2. Configure SQLAlchemy + Alembic migrations
# 3. Implement JWT auth with refresh tokens
# 4. Build WebSocket streaming for AI responses
# 5. Integrate PydanticAI/LangChain with tool calling
# 6. Set up RAG pipeline (parsing, chunking, embedding, vector store)
# 7. Configure Celery + Redis for background tasks
# 8. Build Next.js frontend with auth and chat UI
# 9. Write Docker Compose for all services
# 10. Add observability, rate limiting, admin panel...

# With this template:
pip install fastapi-fullstack
fastapi-fullstack
# Done. All of the above, configured and working.

vs. Alternatives

Feature	This Template	full-stack-fastapi-template	create-t3-app
AI Agents (5 frameworks)	✅	❌	❌
RAG Pipeline (4 vector stores)	✅	❌	❌
WebSocket Streaming	✅	❌	❌
Conversation Persistence	✅	❌	❌
LLM Observability (Logfire/LangSmith)	✅	❌	❌
FastAPI Backend	✅	✅	❌
Next.js Frontend	✅ (v15)	❌	✅
JWT + OAuth Authentication	✅	✅	✅ (NextAuth)
Background Tasks (Celery/Taskiq/ARQ)	✅	✅ (Celery)	❌
Admin Panel	✅ (SQLAdmin)	❌	❌
Multiple Databases (PG/Mongo/SQLite)	✅	PostgreSQL only	Prisma
Docker + K8s	✅	✅	❌
Interactive CLI Wizard	✅	❌	✅
Django-style Commands	✅	❌	❌
Document Sources (GDrive, S3, API)	✅	❌	❌
AI-Agent Friendly (CLAUDE.md)	✅	❌	❌

❓ FAQ

How is this different from full-stack-fastapi-template?

full-stack-fastapi-template by @tiangolo is a great starting point for FastAPI projects, but it focuses on traditional web apps. This template is purpose-built for AI/LLM applications — it adds AI agents (5 frameworks), RAG with 4 vector stores, WebSocket streaming, conversation persistence, LLM observability, and a Next.js chat UI out of the box.

Can I use this without AI/LLM features?

Yes. The AI agent and RAG modules are optional. You can use this as a pure FastAPI + Next.js template with auth, admin panel, background tasks, and all other infrastructure — just skip the AI framework selection during setup.

What Python and Node.js versions are required?

Python 3.11+ and Node.js 18+ (for the Next.js frontend). We recommend using uv for Python and bun for the frontend.

Can I add integrations after project generation?

The generated project is plain code — no lock-in or runtime dependency on the generator. You can add, remove, or modify any integration manually. The template just gives you a well-structured starting point.

Can I use a different LLM provider than the one I selected?

Yes. The LLM provider is configured via environment variables (AI_MODEL, OPENAI_API_KEY, etc.). You can switch providers by changing the .env file and the model name — no code changes needed for PydanticAI (which supports all providers natively).

📚 Documentation

Document	Description
Architecture	Repository + Service pattern, layered design
Frontend	Next.js setup, auth, state management
AI Agent	PydanticAI, tools, WebSocket streaming
Observability	Logfire integration, tracing, metrics
Deployment	Docker, Kubernetes, production setup
Development	Local setup, testing, debugging
Changelog	Version history and release notes

Star History

🙏 Inspiration

This project is inspired by:

full-stack-fastapi-template by @tiangolo
fastapi-template by @s3rius
FastAPI Best Practices by @zhanymkanov
Django's management commands system

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details.

📄 License

MIT License - see LICENSE for details.

Need help implementing this in your company?

We're Vstorm — an Applied Agentic AI Engineering Consultancy
with 30+ production AI agent implementations.

Made with ❤️ by Vstorm

Name		Name	Last commit message	Last commit date
Latest commit History 216 Commits
.github		.github
assets		assets
docs		docs
fastapi_gen		fastapi_gen
logos		logos
template		template
tests		tests
.actrc		.actrc
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
mkdocs.yml		mkdocs.yml
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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Full-Stack AI Agent Template

Related Projects

🚀 Quick Start

Installation

Create Your Project

Start Development

1. Install dependencies

2. Start the database

3. Create and apply database migrations

4. Create admin user

5. Start the backend

6. Start the frontend (new terminal)

Quick Start with Docker

Using the Project CLI

🎬 Demo

📸 Screenshots

Landing Page & Login

Dashboard, Chat & RAG

Observability

Admin, Monitoring & API

🎯 Why This Template

Perfect For

AI-Agent Friendly

✨ Features

🤖 AI/LLM First

⚡ Backend (FastAPI)

🎨 Frontend (Next.js 15)

🔌 20+ Enterprise Integrations

🗺️ Architecture Overview

🏗️ Architecture

Layered Architecture

🤖 AI Agent

Supported Combinations

PydanticAI Integration

LangChain Integration

WebSocket Streaming

Observability

📄 RAG (Retrieval-Augmented Generation)

Vector Store Backends

Document Ingestion (CLI)

Embedding Providers

Features

📊 Observability

Logfire (for PydanticAI)

LangSmith (for LangChain)

Configuration

Usage

🛠️ Django-style CLI

Built-in Commands

Custom Commands

📁 Generated Project Structure

⚙️ Configuration Options

Core Options

Presets

Integrations

🔄 Comparison

vs. Manual Setup

vs. Alternatives

❓ FAQ

📚 Documentation

Star History

🙏 Inspiration

🤝 Contributing

📄 License

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