<p align="center">
<a href="https://github.com/vstorm-co/agenticos">
<img src="assets/agenticos-banner-light.png" width="100%" alt="AgenticOS by Vstorm, the Sovereign Agentic AI Layer: AI agents your whole team can use and improve. Open source (Apache-2.0), self-hosted, built on Pydantic AI, with budgets, approvals, guardrails and activity built in. Links to the AgenticOS repository on GitHub.">
</a>
</p>
<p align="center"><sub>From the team behind this repo: <a href="https://github.com/vstorm-co/agenticos"><b>AgenticOS</b></a>, the Sovereign Agentic AI Layer. AI agents your whole team can use and improve: open source (Apache-2.0), self-hosted, built on Pydantic AI.</sub></p>
<h1 align="center">Full-Stack AI Agent Template</h1>
<p align="center">
<i>Production-ready FastAPI + Next.js project generator with AI agents, RAG, and 20+ enterprise integrations.</i>
</p>
<p align="center">
<a href="#-quick-start">Quick Start</a> •
<a href="#-features">Features</a> •
<a href="#-demo">Demo</a> •
<a href="https://vstorm-co.github.io/full-stack-ai-agent-template/">Documentation</a> •
<a href="https://pypi.org/project/fastapi-fullstack/">PyPI</a>
</p>
<p align="center">
<a href="https://pypi.org/project/fastapi-fullstack/"><img src="https://img.shields.io/pypi/v/fastapi-fullstack?color=green&logo=pypi&logoColor=white" alt="PyPI"></a>
<a href="https://pepy.tech/projects/fastapi-fullstack"><img src="https://static.pepy.tech/badge/fastapi-fullstack/month" alt="PyPI Downloads"></a>
<a href="https://github.com/vstorm-co/full-stack-ai-agent-template/stargazers"><img src="https://img.shields.io/github/stars/vstorm-co/full-stack-ai-agent-template?style=flat&logo=github&color=yellow" alt="GitHub Stars"></a>
<a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.11+-blue?logo=python&logoColor=white" alt="Python 3.11+"></a>
<a href="https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/LICENSE"><img src="https://img.shields.io/github/license/vstorm-co/full-stack-ai-agent-template?color=blue" alt="License"></a>
<img src="https://img.shields.io/badge/coverage-100%25-brightgreen" alt="Coverage">
<a href="https://github.com/vstorm-co/full-stack-ai-agent-template/actions/workflows/ci.yml"><img src="https://github.com/vstorm-co/full-stack-ai-agent-template/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
<a href="https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/SECURITY.md"><img src="https://img.shields.io/badge/security-policy-blueviolet?logo=shieldsdotio&logoColor=white" alt="Security Policy"></a>
<a href="https://www.bestpractices.dev/projects/12539"><img src="https://www.bestpractices.dev/projects/12539/badge" alt="OpenSSF Best Practices"></a>
<a href="https://github.com/pydantic/pydantic-ai"><img src="https://img.shields.io/badge/Powered%20by-Pydantic%20AI-E92063?logo=pydantic&logoColor=white" alt="Pydantic AI"></a>
<a href="https://x.com/Kacper95682155"><img src="https://img.shields.io/badge/X-000000?logo=x&logoColor=white" alt="X"></a>
</p>
<p align="center">
<img src="https://raw.githubusercontent.com/vstorm-co/full-stack-ai-agent-template/main/assets/new3/chat_demo_with_tasks.gif" alt="AI chat with live plan & task checklist" width="100%">
</p>
<p align="center">
<b>🤖 5 AI Agent Frameworks</b> <i>(Pydantic AI, Pydantic Deep Agents, LangChain, LangGraph, DeepAgents)</i>
<br>
<b>📄 RAG Pipeline</b> <i>(Milvus, Qdrant, pgvector, ChromaDB)</i>
<br>
<b>⚡ FastAPI + Next.js 15</b> <i>(WebSocket streaming, real-time chat UI)</i>
<br>
<b>🔗 Conversation Sharing</b> <i>(direct sharing, public links, admin browser)</i>
<br>
<b>🔒 Enterprise-Ready</b> <i>(JWT, OAuth, admin panel, Celery, Docker, K8s)</i>
</p>
<details>
<summary><b>Table of Contents</b></summary>
- [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)
</details>
---
## Vstorm OSS Ecosystem
This template is part of a broader open-source ecosystem for production AI agents:
| Project | Description | |
|---------|-------------|---|
| **[AgenticOS](https://github.com/vstorm-co/agenticos)** | Open-source (Apache-2.0), self-hosted platform to build, share and govern AI agents across a company. | [](https://github.com/vstorm-co/agenticos) |
| **[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) |
| **[subagents-pydantic-ai](https://github.com/vstorm-co/subagents-pydantic-ai)** | Declarative multi-agent orchestration with token tracking. | [](https://github.com/vstorm-co/subagents-pydantic-ai) |
| **[summarization-pydantic-ai](https://github.com/vstorm-co/summarization-pydantic-ai)** | 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 `pydantic_deep` framework option — install it standalone with `curl -fsSL .../install.sh | bash`.
Browse all projects at [github.com/vstorm-co](https://github.com/vstorm-co)
---
## 🚀 Quick Start
> [!TIP]
> **Want to review every option first?** The [installation guide](https://vstorm-co.github.io/full-stack-ai-agent-template/installation/) covers the interactive wizard, non-interactive flags and presets; `fastapi-fullstack templates` lists every option.
### Installation
```bash
# 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:
```bash
# 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 `admin@example.com` / `admin123`. It's idempotent — re-run it anytime.
**Then access:**
| | URL | |
|---|---|---|
| Backend API | <http://localhost:8000> | |
| Docs | <http://localhost:8000/docs> | OpenAPI / Swagger |
| Admin | <http://localhost:8000/admin> | `admin@example.com` / `admin123` (after `make seed`) |
| Frontend | <http://localhost:3000> | `make dev-frontend` (Docker) or `cd frontend && bun install && bun dev` (local) |
### Day-to-day commands
```bash
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.
<details>
<summary><b>Other ways to generate (flags, presets, minimal)</b></summary>
Skip the wizard and pass options directly:
```bash
# Non-interactive with explicit options
fastapi-fullstack create my_ai_app --database postgresql --frontend nextjs
# Presets for common scenarios (run `fastapi-fullstack templates` for the full list)
fastapi-fullstack create my_ai_app --preset ai-agent # AI agent with streaming
fastapi-fullstack create my_ai_app --preset production # Full production setup
fastapi-fullstack create my_ai_app --preset production-saas # SaaS: billing, teams, admin
# Bare-bones project (PostgreSQL, no Docker/Redis/CI)
fastapi-fullstack create my_ai_app --minimal
```
</details>
### 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:
```bash
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:
```bash
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`:
```bash
make upgrade ARGS=--to=0.3.0
uvx fastapi-fullstack@latest upgrade --with-new-features
```
See the [version-upgrade guide](docs/guides/version-upgrade.md) for the full walkthrough
(including projects generated before upgrade support existed).
### Environments
| `make` target | Compose file | When to use |
|---|---|---|
| `make dev` | `docker-compose.dev.yml` | Local development with hot-reload + bind-mounted source. |
| `make stage` | `docker-compose.yml` | Production-like build (no bind mounts) running on localhost. Sanity-check before deploy. |
| `make prod` | `docker-compose.prod.yml` | Production. Requires `backend/.env` (copy from `backend/.env.example`, fill real secrets) + external Nginx using `nginx/nginx.conf`. |
Each env has matching `-down`, `-logs`, `-rebuild` siblings.
> [!NOTE]
> **Windows users:** `make` requires GNU Make. Install via [Chocolatey](https://chocolatey.org/) (`choco install make`) or use **WSL2 / Git Bash**. The Docker workflow is identical across macOS, Linux, and WSL2.
<details>
<summary><b>Local backend (no Docker, for IDE breakpoints)</b></summary>
If you want to run the backend on the host while the database stays in Docker:
```bash
cd my_ai_app
make install # uv sync + pre-commit hooks
# Start only infrastructure containers
docker compose -f docker-compose.dev.yml up -d db redis # add 'milvus etcd minio' if RAG
make db-upgrade # apply migrations
make create-admin # interactive
make run # uvicorn --reload
```
</details>
<details>
<summary><b>Production deploy</b></summary>
```bash
# On your server
git clone <your-repo>
cd my_ai_app
cp backend/.env.example backend/.env # fill in real secrets
# Configure your nginx host using nginx/nginx.conf as reference
make prod # builds + starts + migrates
make prod-logs # tail logs
```
For frontend deployment to **Vercel**:
```bash
cd frontend && npx vercel --prod
```
In the Vercel dashboard set `BACKEND_URL` (server-side), plus `NEXT_PUBLIC_API_URL`,
`NEXT_PUBLIC_WS_URL` (`wss://…`) and `NEXT_PUBLIC_SITE_URL` — the `NEXT_PUBLIC_*` ones are
inlined at build time, so they need a redeploy to take effect.
</details>
### 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 <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
**CLI generator** — configure and scaffold a full-stack AI project in under 60 seconds:
<table>
<tr>
<td width="50%">
**AI chat** — streaming responses, tool calls, reasoning, and ask-user pauses:
</td>
<td width="50%">
**RAG ingestion** — drop a document, watch it get chunked, embedded, and answered against:
</td>
</tr>
</table>
**Generated marketing site** — public landing page with hero, pricing, blog, and legal pages (`enable_marketing_site`):
---
## 📸 Screenshots
### AI Chat
The chat UI streams responses over WebSocket and renders each tool call as a purpose-built card.
<table>
<tr>
<td width="50%">
**Plan & tasks** — sticky checklist updating live as the agent works through steps.
</td>
<td width="50%">
**Subagents** — live feed and side panel showing each subagent's status and messages.
</td>
</tr>
<tr>
<td width="50%">
**Charts** — interactive bar/area/line/pie/scatter charts rendered inline.
</td>
<td width="50%">
**Code execution** — `run_python` shows code + stdout/result in a collapsible card.
</td>
</tr>
<tr>
<td width="50%">
**Ask user** — agent pauses to ask clarifying questions; card keeps the full transcript.
</td>
<td width="50%">
**Reasoning** — clean thinking view + answered-question history for long agent turns.
</td>
</tr>
</table>
### Auth & Dashboard
<table>
<tr>
<td width="50%">
**Login** — split-screen with Google OAuth + email/password, HTTP-only cookie session.
</td>
<td width="50%">
**Register** — same split-screen layout with confirm-password and terms acceptance.
</td>
</tr>
<tr>
<td width="50%">
**Dashboard (light)** — stat cards, usage timeline, recent activity, onboarding banner.
</td>
<td width="50%">
**Dashboard (dark)** — same view in dark theme; saved per-device.
</td>
</tr>
</table>
### Teams & Knowledge Bases
<table>
<tr>
<td width="50%">
**Workspaces** — list of all orgs with plan tier and role; switch or create new workspace.
</td>
<td width="50%">
**Team management** — workspace profile, member list with roles, invite button.
</td>
</tr>
<tr>
<td width="50%">
**Knowledge bases** — list of RAG collections; toggle active bases, upload documents.
</td>
<td width="50%">
**Documents & sync sources** — preview files, manage Google Drive/S3 connectors, view run logs.
</td>
</tr>
</table>
### Billing & Usage
<table>
<tr>
<td width="50%">
**Billing overview** — current plan, seats, storage usage, Customer Portal link.
</td>
<td width="50%">
**Usage charts** — daily credits-spent + call-count charts, by-model token breakdown.
</td>
</tr>
<tr>
<td width="50%">
**Credits** — balance, immutable transaction ledger, usage sparkline.
</td>
<td width="50%">
**Subscription & invoices** — plan management, invoice list, payment methods — all via Stripe.
</td>
</tr>
</table>
### Profile & Settings
<table>
<tr>
<td width="50%">
**Profile** — avatar upload, display name, email, active sessions with per-device revoke.
</td>
<td width="50%">
**Account & security** — password change, "sign out everywhere", account deletion zone.
</td>
</tr>
<tr>
<td width="50%">
**Slash commands** — toggle built-ins, create custom prompt shortcuts for the chat palette.
</td>
<td width="50%">
**Appearance** — light/dark/system theme + brand color picker (5 presets, saved per-device).
</td>
</tr>
</table>
### Admin Panel
<table>
<tr>
<td width="50%">
**Overview** — workspace-wide metrics (users, sessions, conversations, MRR) + activity feed.
</td>
<td width="50%">
**User management** — search by email/name, roles, status, join date, inspect/suspend actions.
</td>
</tr>
<tr>
<td width="50%">
**Conversation browser** — filter by status/owner, open any conversation read-only.
</td>
<td width="50%">
**Message ratings** — approval rate, daily chart, filterable rating table with comments.
</td>
</tr>
<tr>
<td width="50%">
**Stripe events log** — webhook event browser with manual replay for debugging.
</td>
<td width="50%">
**System health** — live readiness checks: API, DB, Redis, vector store, LLM, worker, Stripe.
</td>
</tr>
</table>
### Marketing Site
<table>
<tr>
<td width="50%">
**Pricing** — three-tier page with monthly/annual toggle; pulls live Stripe plan data.
</td>
<td width="50%">
**Blog** — engineering blog from MDX files; tags, featured posts, author bylines. No CMS needed.
</td>
</tr>
</table>
### Background Tasks, Observability & Channels
<table>
<tr>
<td width="50%">
**Prefect** — self-hosted server with RAG, billing, and email flows on cron schedules.
</td>
<td width="50%">
**Prefect flow runs** — per-run history, task timeline, and retry visibility.
</td>
</tr>
<tr>
<td width="50%">
**Logfire** — distributed tracing: FastAPI, Pydantic AI, DB, Redis, Celery, HTTPX in one timeline.
</td>
<td width="50%">
**LangSmith** — trace viewer for LangChain/LangGraph: chains, token usage, feedback.
</td>
</tr>
<tr>
<td width="50%">
**Telegram bot** — multi-bot, polling + webhook, per-thread sessions, group concurrency control.
</td>
<td width="50%">
**API docs** — auto-generated OpenAPI / Swagger UI with schemas, auth, and example payloads.
</td>
</tr>
</table>
---
## 🎯 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** - Pydantic AI 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.
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.
---
## ✨ Features
<p align="center">
<a href="https://ai.pydantic.dev"><img src="https://img.shields.io/badge/Pydantic%20AI-E92063?logo=pydantic&logoColor=white" alt="Pydantic AI"></a>
<a href="https://python.langchain.com"><img src="https://img.shields.io/badge/LangChain-1C3C3C?logo=langchain&logoColor=white" alt="LangChain"></a>
<a href="https://langchain-ai.github.io/langgraph/"><img src="https://img.shields.io/badge/LangGraph-005A9C?logo=langchain&logoColor=white" alt="LangGraph"></a>
<a href="https://milvus.io"><img src="https://img.shields.io/badge/Milvus-FF6B35?logoColor=white" alt="Milvus"></a>
<a href="https://openai.com"><img src="https://img.shields.io/badge/OpenAI-412991?logo=openai&logoColor=white" alt="OpenAI"></a>
<a href="https://anthropic.com"><img src="https://img.shields.io/badge/Anthropic-D4A373?logo=anthropic&logoColor=white" alt="Anthropic"></a>
<a href="https://ai.google.dev"><img src="https://img.shields.io/badge/Gemini-4285F4?logo=google&logoColor=white" alt="Google Gemini"></a>
<a href="https://openrouter.ai"><img src="https://img.shields.io/badge/OpenRouter-6366F1?logoColor=white" alt="OpenRouter"></a>
</p>
<p align="center">
<a href="https://fastapi.tiangolo.com"><img src="https://img.shields.io/badge/FastAPI-009688?logo=fastapi&logoColor=white" alt="FastAPI"></a>
<a href="https://nextjs.org"><img src="https://img.shields.io/badge/Next.js_15-000000?logo=next.js&logoColor=white" alt="Next.js 15"></a>
<a href="https://react.dev"><img src="https://img.shields.io/badge/React_19-61DAFB?logo=react&logoColor=black" alt="React 19"></a>
<a href="https://www.typescriptlang.org"><img src="https://img.shields.io/badge/TypeScript-3178C6?logo=typescript&logoColor=white" alt="TypeScript"></a>
<a href="https://tailwindcss.com"><img src="https://img.shields.io/badge/Tailwind_v4-06B6D4?logo=tailwindcss&logoColor=white" alt="Tailwind CSS"></a>
<a href="https://www.sqlalchemy.org"><img src="https://img.shields.io/badge/SQLAlchemy-D71F00?logo=sqlalchemy&logoColor=white" alt="SQLAlchemy"></a>
</p>
<p align="center">
<a href="https://www.postgresql.org"><img src="https://img.shields.io/badge/PostgreSQL-4169E1?logo=postgresql&logoColor=white" alt="PostgreSQL"></a>
<a href="https://redis.io"><img src="https://img.shields.io/badge/Redis-DC382D?logo=redis&logoColor=white" alt="Redis"></a>
<a href="https://milvus.io"><img src="https://img.shields.io/badge/Milvus-00A1EA?logoColor=white" alt="Milvus"></a>
<a href="https://qdrant.tech"><img src="https://img.shields.io/badge/Qdrant-FF6B6B?logoColor=white" alt="Qdrant"></a>
<a href="https://www.trychroma.com"><img src="https://img.shields.io/badge/ChromaDB-FF6F61?logoColor=white" alt="ChromaDB"></a>
<a href="https://docs.celeryq.dev"><img src="https://img.shields.io/badge/Celery-37814A?logo=celery&logoColor=white" alt="Celery"></a>
<a href="https://www.prefect.io"><img src="https://img.shields.io/badge/Prefect-070E10?logo=prefect&logoColor=white" alt="Prefect"></a>
<a href="https://logfire.pydantic.dev"><img src="https://img.shields.io/badge/Logfire-E92063?logo=pydantic&logoColor=white" alt="Logfire"></a>
<a href="https://sentry.io"><img src="https://img.shields.io/badge/Sentry-362D59?logo=sentry&logoColor=white" alt="Sentry"></a>
<a href="https://prometheus.io"><img src="https://img.shields.io/badge/Prometheus-E6522C?logo=prometheus&logoColor=white" alt="Prometheus"></a>
</p>
<p align="center">
<a href="https://www.docker.com"><img src="https://img.shields.io/badge/Docker-2496ED?logo=docker&logoColor=white" alt="Docker"></a>
<a href="https://kubernetes.io"><img src="https://img.shields.io/badge/Kubernetes-326CE5?logo=kubernetes&logoColor=white" alt="Kubernetes"></a>
<a href="https://github.com/features/actions"><img src="https://img.shields.io/badge/GitHub_Actions-2088FF?logo=githubactions&logoColor=white" alt="GitHub Actions"></a>
<a href="https://aws.amazon.com/s3/"><img src="https://img.shields.io/badge/S3-569A31?logo=amazons3&logoColor=white" alt="S3"></a>
</p>
### 🤖 AI/LLM First
- **5 AI Frameworks** - [Pydantic AI](https://ai.pydantic.dev), [Pydantic Deep Agents](https://github.com/vstorm-co/pydantic-deepagents), [LangChain](https://python.langchain.com), [LangGraph](https://langchain-ai.github.io/langgraph/), [DeepAgents](https://github.com/langchain-ai/deepagents)
- **5 LLM Providers** - OpenAI, Anthropic, Google Gemini, OpenRouter, and any OpenAI-compatible gateway or server
- **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 Pydantic AI, LangSmith for LangChain/LangGraph/DeepAgents
### ⚡ Backend (FastAPI)
- **[FastAPI](https://fastapi.tiangolo.com)** + **[Pydantic v2](https://docs.pydantic.dev)** - 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
| Category | Integrations |
|----------|-------------|
| **AI Frameworks** | Pydantic AI, Pydantic Deep Agents, LangChain, LangGraph, DeepAgents |
| **LLM Providers** | OpenAI, Anthropic, Google Gemini, OpenRouter, OpenAI-compatible endpoints |
| **RAG / Vector Stores** | Milvus, Qdrant, ChromaDB, pgvector |
| **RAG Sources** | Local files, API upload, Google Drive, S3/MinIO, Sync Sources (per-org UI, scheduled) |
| **Embeddings** | OpenAI, Voyage, Gemini (multimodal), SentenceTransformers |
| **Background Tasks** | Celery, Taskiq, ARQ, Prefect |
| **Billing** | Stripe subscriptions (seat-based), credits + usage metering, invoices, Customer Portal |
| **Caching & State** | Redis, fastapi-cache2 |
| **Security** | Per-plan sliding-window rate limiting (per user / org / IP), CORS, SSRF-guarded webhook delivery, httpOnly + SameSite session cookies |
| **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 │ │
│ │ Pydantic AI · 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
```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)]
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:
```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 **5 AI frameworks** and **4 LLM providers** when generating your project:
```bash
# Pydantic AI 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
| Framework | OpenAI | Anthropic | Gemini | OpenRouter | OpenAI-compatible |
|-----------|:------:|:---------:|:------:|:----------:|:-----------------:|
| **Pydantic AI** | ✓ | ✓ | ✓ | ✓ | ✓ |
| **Pydantic Deep Agents** | ✓ | ✓ | ✓ | ✓ | ✓ |
| **LangChain** | ✓ | ✓ | ✓ | - | - |
| **LangGraph** | ✓ | ✓ | ✓ | - | - |
| **DeepAgents** | ✓ | ✓ | ✓ | - | - |
**OpenAI-compatible** (`--llm-provider openai_compatible`) reaches any gateway or server that speaks the OpenAI API by its base URL: model routers and LLM gateways (LiteLLM, Requesty, OrcaRouter, Portkey) and self-hosted servers (vLLM, llama.cpp, LM Studio, Ollama). Set `OPENAI_COMPATIBLE_BASE_URL`, `AI_MODEL` as the endpoint names it, and `OPENAI_COMPATIBLE_API_KEY` if it checks one.
### Pydantic AI 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 |
|-----------|--------------|-----------|
| **Pydantic AI** | [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 <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 Pydantic AI)
[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[Pydantic AI]
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 |
|-----------|-------------|
| **Pydantic AI** | Agent runs, tool calls, LLM requests, token usage, streaming events |
| **FastAPI** | Request/response traces, latency, status codes, route performance |
| **PostgreSQL** | 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 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:
```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`, `none` | Async PostgreSQL (SQLAlchemy 2.0 + Alembic) |
| **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`, `pydantic_deep`, `langchain`, `langgraph`, `deepagents` | Choose your AI agent framework |
| **LLM Provider** | `openai`, `anthropic`, `google`, `openrouter`, `openai_compatible`, `all` | OpenRouter and OpenAI-compatible only with Pydantic AI / Pydantic Deep Agents |
| **RAG** | `--rag` | Enable RAG with vector database |
| **Vector Store** | `milvus`, `qdrant`, `chromadb`, `pgvector` | pgvector uses existing PostgreSQL |
| **Background Tasks** | `none`, `celery`, `taskiq`, `arq`, `prefect` | Distributed queues / orchestration |
| **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 (per user/org/IP, Redis or in-memory)
# ✓ Pagination (fastapi-pagination)
# ✓ Admin Panel (SQLAdmin)
# ✓ AI Agent (Pydantic AI 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 Pydantic AI/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/Prefect) | ✅ | ✅ (Celery) | ❌ |
| **Billing & Credits** (Stripe + usage metering) | ✅ | ❌ | ❌ |
| **Admin Panel** | ✅ (SQLAdmin) | ❌ | ❌ |
| **Async PostgreSQL** (SQLAlchemy 2.0 + pgvector) | ✅ | ✅ | Prisma |
| **Docker + K8s** | ✅ | ✅ | ❌ |
| **Interactive CLI Wizard** | ✅ | ❌ | ✅ |
| **Django-style Commands** | ✅ | ❌ | ❌ |
| **Document Sources** (GDrive, S3, API) | ✅ | ❌ | ❌ |
| **AI-Agent Friendly** (CLAUDE.md) | ✅ | ❌ | ❌ |
---
## ❓ FAQ
<details>
<summary><b>How is this different from full-stack-fastapi-template?</b></summary>
[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.
</details>
<details>
<summary><b>Can I use this without AI/LLM features?</b></summary>
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.
</details>
<details>
<summary><b>What Python and Node.js versions are required?</b></summary>
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.
</details>
<details>
<summary><b>Can I add integrations after project generation?</b></summary>
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.
</details>
<details>
<summary><b>Can I use a different LLM provider than the one I selected?</b></summary>
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 Pydantic AI (which supports all providers natively).
</details>
---
## 📚 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) | Pydantic AI, 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-ai-agent-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.
<a href="https://github.com/vstorm-co/full-stack-ai-agent-template/graphs/contributors">
<img src="https://contrib.rocks/image?repo=vstorm-co/full-stack-ai-agent-template" alt="Contributors" />
</a>
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## 📄 License
MIT License - see [LICENSE](https://github.com/vstorm-co/full-stack-ai-agent-template/blob/main/LICENSE) for details.
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### Need help implementing this in your company?
<p>We're <a href="https://vstorm.co"><b>Vstorm</b></a> — an Applied Agentic AI Engineering Consultancy<br>with 30+ production AI agent implementations.</p>
<a href="https://vstorm.co/contact-us/">
<img src="https://img.shields.io/badge/Talk%20to%20us%20%E2%86%92-0066FF?style=for-the-badge&logoColor=white" alt="Talk to us">
</a>
<br><br>
Made with ❤️ by <a href="https://vstorm.co"><b>Vstorm</b></a>
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