ai-project-template: A Complete Monorepo Starter Template for Building AI SaaS Applications

This article introduces mlexpertio/ai-project-template — an AI SaaS monorepo starter template based on Python FastAPI and Next.js. It integrates core features like RAG document management, streaming chat, and LangGraph workflows, helping developers launch a complete application with retrieval-augmented generation (RAG) and agentic workflow capabilities within hours, saving time on infrastructure setup.

With the increasing popularity of generative AI application development, developers face the need to quickly build SaaS projects with complete functionality and clear architecture. This template was created to address this pain point, providing a monorepo solution that integrates modern AI components.

Backend Tech Stack Selection

The backend is based on the FastAPI framework, using uv to manage Python dependencies. Core dependencies include LangChain (LLM integration, RAG), LangGraph (workflow orchestration), Pydantic (model validation), and Pytest (asynchronous testing). It adopts a layered design (routers, service layer).

Frontend Tech Stack Selection

The frontend is based on Next.js App Router, using TypeScript to ensure type safety, and supports automatic generation of typed clients from the backend's OpenAPI specification.

Document Management and RAG Infrastructure

Supports uploading text, Markdown, and PDF files (5MB limit per file), automatically extracts content and builds indexes, providing private knowledge base support for RAG applications.

Threaded Conversation Management

Implements the concept of threads, supports maintaining multi-turn conversation context, and allows creating, viewing, and deleting conversation threads.

Streaming Chat and AI SDK Integration

Uses the UI Message Stream format from Vercel AI SDK v5, supports SSE streaming responses, and coordinates RAG retrieval and LLM generation via LangGraph workflow orchestration.

Multi-Provider LLM Support

Configurable via environment variables, supports LLM providers like Ollama (local), OpenAI, and Anthropic, allowing flexible model switching.

Code Quality Toolchain

Configures pre-commit hooks to automatically run YAML/JSON validation, Ruff code checks, ESLint frontend checks, Pytest tests, and TypeScript type checks.

Development Commands

• Start backend: uv run --project backend uvicorn app.main:app --reload --app-dir backend
• Start frontend: npm --prefix frontend run dev

Client Tools and API Exploration

Provides a command-line client tool (client.py) supporting document operations; the backend exposes an OpenAPI specification (/openapi.json) for generating typed clients.

Applicable Scenarios

• AI customer service system: Q&A bot based on enterprise knowledge base
• Content generation tool: Personalized writing assistant integrated with RAG
• Internal knowledge management: Intelligent retrieval and Q&A for enterprise documents
• AI SaaS MVP: Rapidly validate product concepts

Usage Recommendations

For production-level applications, it is recommended to add: user authentication and authorization, comprehensive error handling and monitoring, vector database persistence, and prompt optimization for specific scenarios.

ai-project-template integrates best practices for generative AI applications—from RAG infrastructure to streaming chat, from type safety to code quality—saving developers a lot of time on infrastructure setup. It is an excellent starter solution for quickly entering AI application development.