🧠

Model

Full Stack Ai Agent Template

by Vstorm Co gh-model--vstorm-co--full-stack-ai-agent-template

Nexus Index

0.0 Top 18%

S: Semantic 50

A: Authority 0

P: Popularity 0

R: Recency 0

Q: Quality 0

Tech Context

Vital Performance

0 DL / 30D

0.0%

Full-Stack AI Agent Template Production-Ready AI/LLM Applications — In Minutes, Not Weeks Quick Start • Features • Demo • Website • Configurator • PyPI • Docs ...

Source →

Tiny - Params

- Context

0 Downloads

Model Information Summary
Entity Passport
Registry ID	gh-model--vstorm-co--full-stack-ai-agent-template
Provider	github

📜

Cite this model

Academic & Research Attribution

BibTeX

@misc{gh_model__vstorm_co__full_stack_ai_agent_template,
  author = {Vstorm Co},
  title = {Full Stack Ai Agent Template Model},
  year = {2026},
  howpublished = {\url{https://github.com/vstorm-co/full-stack-ai-agent-template}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

Vstorm Co. (2026). Full Stack Ai Agent Template [Model]. Free2AITools. https://github.com/vstorm-co/full-stack-ai-agent-template

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🐙 Git Clone

git clone https://github.com/vstorm-co/full-stack-ai-agent-template

⚖️ Nexus Index V2.0

Methodology Index Protocol

0.0

TOP 18% SYSTEM IMPACT

Semantic (S) 50

Authority (A) 0

Popularity (P) 0

Recency (R) 0

Quality (Q) 0

💬 Index Insight

FNI V2.0 for Full Stack Ai Agent Template: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:0), Quality (Q:0).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit

Unbiased Data Node Refresh: VFS Live

---

🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Technical Deep Dive

Full-Stack AI Agent Template

Production-Ready AI/LLM Applications — In Minutes, Not Weeks

Quick Start • Features • Demo • Website • Configurator • PyPI • Docs

🤖 PydanticAI • 🦜 LangChain, LangGraph & DeepAgents • 👥 CrewAI • 🎯 Fully Type-Safe

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.

🎯 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

PydanticAI or LangChain - Choose your preferred AI framework
WebSocket Streaming - Real-time responses with full event access
Conversation Persistence - Save chat history to database
Custom Tools - Easily extend agent capabilities
Multi-provider Support - OpenAI, Anthropic, OpenRouter
Observability - Logfire for PydanticAI, LangSmith for LangChain

⚡ 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 (optional)

🔌 20+ Enterprise Integrations

Category	Integrations
AI Frameworks	PydanticAI, LangChain
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

🎬 Demo

FastAPI Fullstack Generator Demo

📸 Screenshots

Chat Interface

Light Mode	Dark Mode

Authentication

Register	Login

Observability

Logfire (PydanticAI)	LangSmith (LangChain)

Admin, Monitoring & API

Celery Flower	SQLAdmin Panel

API Documentation

🚀 Quick Start

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)
fastapi-fullstack new

# Quick mode with options
fastapi-fullstack create my_ai_app \
  --database postgresql \
  --auth jwt \
  --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

bash

cd my_ai_app
make install

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

bash

# PostgreSQL (with Docker)
make docker-db

3. Create and apply database migrations

⚠️ Important: 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: 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:

bash

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:

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.

🏗️ 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 for details.

🤖 AI Agent

Choose between PydanticAI or LangChain when generating your project, with support for multiple LLM providers:

bash

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

# PydanticAI with Anthropic
fastapi-fullstack create my_app --ai-agent --ai-framework pydantic_ai --llm-provider anthropic

# PydanticAI with OpenRouter
fastapi-fullstack create my_app --ai-agent --ai-framework pydantic_ai --llm-provider openrouter

# LangChain with OpenAI
fastapi-fullstack create my_app --ai-agent --ai-framework langchain

# LangChain with Anthropic
fastapi-fullstack create my_app --ai-agent --ai-framework langchain --llm-provider anthropic

Supported LLM Providers

Framework	OpenAI	Anthropic	OpenRouter
PydanticAI	✓	✓	✓
LangChain	✓	✓	-

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	Agent runs, tool calls, token usage
LangChain	LangSmith	Traces, feedback, datasets

See AI Agent Documentation for more.

📊 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.

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 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.

🛠️ 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 [email protected] --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

text

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
│   │   ├── 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`	Choose your AI agent framework
LLM Provider	`openai`, `anthropic`, `openrouter`	OpenRouter only with PydanticAI
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

📚 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

🚀 Quick Start

bash

# pip
pip install fastapi-fullstack

# uv (recommended)
uv tool install fastapi-fullstack

# pipx
pipx install fastapi-fullstack

📝 Limitations & Considerations

• Benchmark scores may vary based on evaluation methodology and hardware configuration.
• VRAM requirements are estimates; actual usage depends on quantization and batch size.
• FNI scores are relative rankings and may change as new models are added.
• Source: Unknown

🐙 Data Source: GitHub ↗

🔄 Daily sync (03:00 UTC)

AI Summary: Based on GitHub metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id: gh-model--vstorm-co--full-stack-ai-agent-template
source: github
author: Vstorm Co
tags: pythonai-agentsdockerfastapillmlogfiremongodbnextjspostgresqlpydantic-aireacttemplatetypescriptceleryfull-stacklangchainlanggraphredissaas-boilerplateclawdbot

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag: text-generation

📊 Engagement & Metrics

likes: 0
downloads: 0

Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)

Welcome to Free2AI Tools!

Smart Search

FNI Score

You're All Set!

Cite this model

🔬Technical Deep Dive

Quick Commands

⚖️ Nexus Index V2.0

💬 Index Insight

Verification Authority

🚀 What's Next?

Find Training Datasets

Compare Benchmarks

Deployment Guide

Technical Deep Dive

Full-Stack AI Agent Template

Related Projects

🎯 Why This Template

Perfect For

AI-Agent Friendly

✨ Features

🤖 AI/LLM First

⚡ Backend (FastAPI)

🎨 Frontend (Next.js 15)

🔌 20+ Enterprise Integrations

🎬 Demo

📸 Screenshots

Chat Interface

Authentication

Observability

Admin, Monitoring & API

🚀 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

🏗️ Architecture

Layered Architecture

🤖 AI Agent

Supported LLM Providers

PydanticAI Integration

LangChain Integration

WebSocket Streaming

Observability

📊 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

📚 Documentation

Star History

🙏 Inspiration

🤝 Contributing

📄 License

Need help implementing this in your company?

🚀 Quick Start

📝 Limitations & Considerations

🛡️ Model Transparency Report

🆔 Identity & Source

⚙️ Technical Specs

📊 Engagement & Metrics