https://github.com/vstorm-co/full-stack-ai-agent-template
Full-stack AI app generator — FastAPI + Next.js with agents, RAG, streaming, auth, and 20+ integrations out of the box.
https://github.com/vstorm-co/full-stack-ai-agent-template
agent-framework ai-agent-template ai-agents crewai docker fastapi full-stack langchain langgraph llm nextjs postgresql pydantic-ai pydantic-deep python rag react typescript vstorm websocket
Last synced: about 6 hours ago
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Full-stack AI app generator — FastAPI + Next.js with agents, RAG, streaming, auth, and 20+ integrations out of the box.
- Host: GitHub
- URL: https://github.com/vstorm-co/full-stack-ai-agent-template
- Owner: vstorm-co
- License: mit
- Created: 2025-12-19T12:23:15.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-04-18T11:17:23.000Z (about 8 hours ago)
- Last Synced: 2026-04-18T11:24:42.024Z (about 8 hours ago)
- Topics: agent-framework, ai-agent-template, ai-agents, crewai, docker, fastapi, full-stack, langchain, langgraph, llm, nextjs, postgresql, pydantic-ai, pydantic-deep, python, rag, react, typescript, vstorm, websocket
- Language: Python
- Homepage: https://vstorm-co.github.io/full-stack-ai-agent-template/
- Size: 35 MB
- Stars: 1,048
- Watchers: 12
- Forks: 199
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
- Governance: GOVERNANCE.md
- Agents: AGENTS.md
- Dco: DCO
Awesome Lists containing this project
README
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
🤖 6 AI Agent Frameworks (PydanticAI, PydanticDeep, LangChain, LangGraph, CrewAI, DeepAgents)
📄 RAG Pipeline (Milvus, Qdrant, pgvector, ChromaDB)
⚡ FastAPI + Next.js 15 (WebSocket streaming, real-time chat UI)
🔗 Conversation Sharing (direct sharing, public links, admin browser)
🔒 Enterprise-Ready (JWT, OAuth, admin panel, Celery, Docker, K8s)
Table of Contents
- [Quick Start](#-quick-start)
- [Demo](#-demo)
- [Screenshots](#-screenshots)
- [Why This Template](#-why-this-template)
- [Features](#-features)
- [Architecture](#-architecture)
- [AI Agent](#-ai-agent)
- [RAG](#-rag-retrieval-augmented-generation)
- [Observability](#-observability)
- [Django-style CLI](#-django-style-cli)
- [Generated Project Structure](#-generated-project-structure)
- [Configuration Options](#-configuration-options)
- [Comparison](#-comparison)
- [FAQ](#-faq)
- [Documentation](#-documentation)
- [Contributing](#-contributing)
---
## Vstorm OSS Ecosystem
This template is part of a broader open-source ecosystem for production AI agents:
| Project | Description | |
|---------|-------------|---|
| **[pydantic-deepagents](https://github.com/vstorm-co/pydantic-deepagents)** | The modular agent runtime for Python. Claude Code-style CLI with Docker sandbox, browser automation, multi-agent teams, and /improve. | [](https://github.com/vstorm-co/pydantic-deepagents) |
| **[pydantic-ai-shields](https://github.com/vstorm-co/pydantic-ai-shields)** | Drop-in guardrails for Pydantic AI agents. 5 infra + 5 content shields. | [](https://github.com/vstorm-co/pydantic-ai-shields) |
| **[pydantic-ai-subagents](https://github.com/vstorm-co/pydantic-ai-subagents)** | Declarative multi-agent orchestration with token tracking. | [](https://github.com/vstorm-co/pydantic-ai-subagents) |
| **[summarization-pydantic-ai](https://github.com/vstorm-co/pydantic-ai-summarization)** | Smart context compression for long-running agents. | [](https://github.com/vstorm-co/summarization-pydantic-ai) |
| **[pydantic-ai-backend](https://github.com/vstorm-co/pydantic-ai-backend)** | Sandboxed execution for AI agents. Docker + Daytona. | [](https://github.com/vstorm-co/pydantic-ai-backend) |
> **Want the runtime behind this template's AI agents?** [pydantic-deepagents](https://github.com/vstorm-co/pydantic-deepagents) powers the `deepagents` framework option — install it standalone with `curl -fsSL .../install.sh | bash`.
Browse all projects at [oss.vstorm.co](https://oss.vstorm.co)
---
## 🚀 Quick Start
> [!TIP]
> **Prefer a visual configurator?** Use the [Web Configurator](https://oss.vstorm.co/projects/full-stack-ai-agent-template/configurator/) to configure your project in the browser and download a ZIP — no CLI installation needed.
### Installation
```bash
# pip
pip install fastapi-fullstack
# uv (recommended)
uv tool install fastapi-fullstack
# pipx
pipx install fastapi-fullstack
```
### Create Your Project
```bash
# 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 (Docker — recommended)
The fastest way to get running — 2 commands:
```bash
cd my_ai_app
make docker-up # Start backend + database + migrations + admin user
make docker-frontend # Start frontend
```
**Access:**
- API:
- Docs:
- Admin Panel:
- Frontend:
> [!TIP]
> That's it. Docker handles database setup, migrations, and admin user creation automatically.
Manual setup (without Docker)
#### 1. Install dependencies
```bash
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](https://chocolatey.org/) (`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
```bash
# 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.
```bash
# 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
```bash
make create-admin
# Enter email and password when prompted
```
#### 5. Start the backend
```bash
make run
```
#### 6. Start the frontend (new terminal)
```bash
cd frontend
bun install
bun dev
```
**Access:**
- API:
- Docs:
- Admin Panel: (login with admin user)
- Frontend:
### Using the Project CLI
Each generated project has a CLI named after your `project_slug`. For example, if you created `my_ai_app`:
```bash
cd backend
# The CLI command is: uv run
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) |
|:---:|:---:|
|  |  |
### Messaging Channels
| Telegram Bot |
|:---:|
|  |
### 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](https://humanlayer.dev/blog/writing-a-good-claude-md) best practices - concise project overview with pointers to detailed docs when needed.
---
## ✨ Features
### 🤖 AI/LLM First
- **6 AI Frameworks** - [PydanticAI](https://ai.pydantic.dev), [PydanticDeep](https://github.com/vstorm-co/pydantic-deep), [LangChain](https://python.langchain.com), [LangGraph](https://langchain-ai.github.io/langgraph/), [CrewAI](https://www.crewai.com), [DeepAgents](https://github.com/vstorm-co/pydantic-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
- **Messaging Channels** - Telegram and Slack multi-bot integration with polling, webhooks, per-thread sessions, group concurrency control
- **Conversation Sharing** - Share conversations with users or via public links, admin conversation browser
- **Conversation Persistence** - Save chat history to database
- **Message Ratings** - Like/dislike responses with feedback, admin analytics
- **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](https://fastapi.tiangolo.com)** + **[Pydantic v2](https://docs.pydantic.dev)** - 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, PydanticDeep, 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 |
| **Collaboration** | Conversation sharing (direct + link), admin conversation browser |
| **Messaging** | Telegram multi-bot (polling + webhook), Slack multi-bot (Events API + Socket Mode) |
| **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
```mermaid
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:
```mermaid
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](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/architecture.md) for details.
---
## 🤖 AI Agent
Choose from **6 AI frameworks** and **4 LLM providers** when generating your project:
```bash
# 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** | ✓ | ✓ | ✓ | ✓ |
| **PydanticDeep** | ✓ | ✓ | ✓ | - |
| **LangChain** | ✓ | ✓ | ✓ | - |
| **LangGraph** | ✓ | ✓ | ✓ | - |
| **CrewAI** | ✓ | ✓ | ✓ | - |
| **DeepAgents** | ✓ | ✓ | ✓ | - |
### PydanticAI Integration
Type-safe agents with full dependency injection:
```python
# 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:
```python
# 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:
```python
@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](https://logfire.pydantic.dev) | Agent runs, tool calls, token usage |
| **LangChain** | [LangSmith](https://smith.langchain.com) | Traces, feedback, datasets |
See [AI Agent Documentation](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/ai-agent.md) 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)
```bash
# 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
# 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 6 AI frameworks get a `search_knowledge_base` tool automatically
---
## 📊 Observability
### Logfire (for PydanticAI)
[Logfire](https://logfire.pydantic.dev) 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.
```mermaid
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](https://smith.langchain.com) 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:
```bash
# .env
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=your-api-key
LANGCHAIN_PROJECT=my_project
```
### Configuration
Enable Logfire and select which components to instrument:
```bash
fastapi-fullstack new
# ✓ Enable Logfire observability
# ✓ Instrument FastAPI
# ✓ Instrument Database
# ✓ Instrument Redis
# ✓ Instrument Celery
# ✓ Instrument HTTPX
```
### Usage
```python
# Automatic instrumentation in app/main.py
import logfire
logfire.configure()
logfire.instrument_fastapi(app)
logfire.instrument_asyncpg()
logfire.instrument_redis()
logfire.instrument_httpx()
```
```python
# 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](https://logfire.pydantic.dev/docs/integrations/).
---
## 🛠️ Django-style CLI
Each generated project includes a powerful CLI inspired by Django's management commands:
### Built-in Commands
```bash
# 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:
```python
# 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.
```bash
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:
```toml
[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:
```bash
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:
```bash
# 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](https://github.com/fastapi/full-stack-fastapi-template) | [create-t3-app](https://github.com/t3-oss/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](https://github.com/fastapi/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](https://docs.astral.sh/uv/) for Python and [bun](https://bun.sh) 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](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/architecture.md) | Repository + Service pattern, layered design |
| [Frontend](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/frontend.md) | Next.js setup, auth, state management |
| [AI Agent](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/ai-agent.md) | PydanticAI, tools, WebSocket streaming |
| [Observability](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/observability.md) | Logfire integration, tracing, metrics |
| [Deployment](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/deployment.md) | Docker, Kubernetes, production setup |
| [Development](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/development.md) | Local setup, testing, debugging |
| [Changelog](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/docs/CHANGELOG.md) | Version history and release notes |
---
## Star History
[](https://www.star-history.com/#vstorm-co/full-stack-fastapi-nextjs-llm-template&type=date&legend=top-left)
---
## 🙏 Inspiration
This project is inspired by:
- [full-stack-fastapi-template](https://github.com/fastapi/full-stack-fastapi-template) by @tiangolo
- [fastapi-template](https://github.com/s3rius/fastapi-template) by @s3rius
- [FastAPI Best Practices](https://github.com/zhanymkanov/fastapi-best-practices) by @zhanymkanov
- Django's management commands system
---
## 🤝 Contributing
Contributions are welcome! Please read our [Contributing Guide](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/CONTRIBUTING.md) for details.
---
## 📄 License
MIT License - see [LICENSE](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/LICENSE) for details.
---
