vstorm-co/full-stack-ai-agent-template

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, PydanticDeep, LangChain, LangGraph, 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)

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Vstorm OSS Ecosystem

This template is part of a broader open-source ecosystem for production AI agents:

Want the runtime behind this template’s AI agents? pydantic-deepagents powers the deepagents framework option — install it standalone with curl -fsSL .../install.sh | bash.

Browse all projects at oss.vstorm.co

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🚀 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

From zero to a running app

Three steps. The wizard scaffolds the project, make bootstrap brings up the whole backend, and the frontend runs with a single command:

# 1. Generate your project — just answer the wizard's prompts
fastapi-fullstack

# 2. Backend + PostgreSQL up, migrations applied, default admin seeded
cd my_ai_app
make bootstrap

# 3. Frontend (in a second terminal)
cd frontend && bun install && bun dev

What make bootstrap does (= make dev + make seed): builds the backend Docker image, starts the stack via docker-compose.dev.yml, waits for PostgreSQL (pg_isready), applies Alembic migrations, and seeds [email protected] / admin123. It’s idempotent — re-run it anytime.

Then access:

Day-to-day commands

make dev           # bootstrap or restart (no admin re-seed)
make seed          # one-shot admin creation (no-op if admin exists)
make dev-down      # stop everything
make dev-logs      # tail container logs
make dev-rebuild   # force-rebuild backend image (after pyproject.toml changes)
make dev-frontend  # start the Next.js container

After the first make bootstrap, day-to-day you just run make dev (skips admin re-seed). Run make help inside the project for the full list.

Keeping your project up to date

Your project doesn’t get stranded on the template version you generated from. Pull the latest template improvements into your existing project with a real 3-way merge — your customizations are preserved, conflicts are left for you to resolve in your IDE, and the whole thing lands on a dedicated branch so it’s fully reversible:

make upgrade-dry-run     # preview what would change (no changes made)
make upgrade             # apply on a `template-upgrade/v…` branch
# resolve any conflicts in your IDE's 3-way merge editor, then:
make upgrade-finalize    # bump the manifest to the new version

Files only you changed are kept, files only the template changed are updated, and new features/migrations are pulled in. By default an upgrade keeps your existing feature set — to also adopt optional features added since your version, use:

make upgrade-new-features   # prompts Yes/No for each new optional feature

One-off flags (e.g. pinning a target version) go through ARGS, and you can always call the CLI directly instead of make:

make upgrade ARGS=--to=0.3.0
uvx fastapi-fullstack@latest upgrade --with-new-features

See the version-upgrade guide for the full walkthrough (including projects generated before upgrade support existed).

Environments

Each env has matching -down, -logs, -rebuild siblings.

[!NOTE] Windows users: make requires GNU Make. Install via Chocolatey (choco install make) or use WSL2 / Git Bash. The Docker workflow is identical across macOS, Linux, and WSL2.

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  
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.

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🎬 Demo

CLI generator — configure and scaffold a full-stack AI project in under 60 seconds:

Generated marketing site — public landing page with hero, pricing, blog, and legal pages (enable_marketing_site):

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📸 Screenshots

AI Chat

The chat UI streams responses over WebSocket and renders each tool call as a purpose-built card.

Auth & Dashboard

Teams & Knowledge Bases

Billing & Usage

Profile & Settings

Admin Panel

Marketing Site

Background Tasks, Observability & Channels

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🎯 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.

They also ship a ready-to-use .claude/ toolkit that adapts to the options you selected:

• Agent Skills (.claude/skills/) — model-invoked playbooks that auto-trigger when relevant: alembic-migration, pytest-suite, agent-tool (framework-aware), frontend-feature, rag-knowledge, background-task (queue-aware), billing-stripe, and channel-bot. Feature-gated — only the skills that match your stack are generated.
• Slash commands (.claude/commands/) — /add-endpoint, /fix-issue, /review.
• Convention rules (.claude/rules/) — architecture, code style, schemas, exceptions/security, testing, and frontend conventions, loaded automatically.

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✨ Features

🤖 AI/LLM First

• 5 AI Frameworks - PydanticAI, PydanticDeep, LangChain, LangGraph, 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
• Rich Chat UI - Specialized tool-call cards (web search, knowledge base, Python, charts, skills), live subagent feed, citation sources panel, plan/task checklist, reasoning view, and in-chat file previews
• Agent Tools - Web search, URL fetch, charts, code execution (run_python), skills, ask_user, plus optional Deep Research (TODO planner + parallel subagents)
• 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 - Provider-aware: OpenAI, Voyage (Anthropic), Gemini (multimodal text + images)
• Document Sources - Local files, API upload, Google Drive, S3/MinIO
• Sync Sources - Per-organization connector management UI (Google Drive, S3/MinIO) with scheduled sync, manual triggers, encrypted credentials, and per-run logs
• Observability - Logfire for PydanticAI, LangSmith for LangChain/LangGraph/DeepAgents

⚡ Backend (FastAPI)

• FastAPI + Pydantic v2 - High-performance async API
• PostgreSQL (async) - SQLAlchemy 2.0 + Alembic migrations, pgvector-ready
• Authentication - JWT + Refresh tokens, API Keys, OAuth2 (Google)
• Background Tasks - Celery, Taskiq, ARQ, or Prefect
• 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, password reset, magic link
• Marketing Site - hero, pricing, FAQ, blog, contact form, legal pages (PL + EN)
• Billing Dashboard - subscription, payment methods, invoices, credits balance/ledger, and usage charts (Stripe)
• User Settings - profile, API keys CRUD (sk_* tokens), onboarding tracking
• Admin Panel - workspace stats, message-rating analytics, Stripe events browser
• SEO - per-page metadata, OG image, sitemap, robots, manifest, favicons
• Dark Mode + i18n (PL/EN via next-intl, locale-prefixed routes)

🔌 20+ Enterprise Integrations

🗺️ 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 · 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       │
│  Billing (Stripe + credits) · Background Tasks (Celery/Taskiq/ARQ/       │
│  Prefect) · Django-style CLI · Observability (Logfire/LangSmith/         │
│  Sentry/Prometheus)                                                      │
└───────┬──────────────┬──────────────┬──────────────┬─────────────────────┘
        │              │              │              │
        ▼              ▼              ▼              ▼
   PostgreSQL       Redis         Vector DB      LLM APIs
   (async)                        (Milvus/       (OpenAI/
                                  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)]
        Redis[(Redis)]
        Queue[Celery/Taskiq/ARQ/Prefect]
    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]

See Architecture Documentation for details.

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🤖 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

# 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

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:

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

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 

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

Embedding Providers

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

LangSmith (for LangChain)

LangSmith provides observability specifically designed for LangChain applications:

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 [email protected] --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

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📁 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 2.0 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

Presets

Integrations

Select what you need:

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

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🔄 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

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❓ FAQ

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📚 Documentation

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Star History

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🙏 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

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🤝 Contributing

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

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📄 License

MIT License - see LICENSE for details.

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