FastGPT is an open-source AI Agent platform built with Next.js and TypeScript that lets users create custom AI-powered chatbots and knowledge assistants by visually designing workflows and integrating RAG (Retrieval-Augmented Generation) systems. It targets developers, enterprise teams, and AI product builders who need to deploy domain-specific AI assistants without deep LLM engineering expertise. The platform combines data preprocessing, model agnosticism, and visual flow design to reduce the complexity of building production-grade AI applications.
Technically, FastGPT supports Docker-based deployment, integrates with OpenAI-compatible APIs (including Claude, DeepSeek, Qwen), and leverages a modular architecture with AI Proxy and Laf for extensibility. It provides a full-stack solution with a Next.js frontend, MongoDB backend, and support for vector databases via Chroma or other compatible stores. Users can self-host or use the cloud version, with optional commercial licensing for SaaS deployment.
What You Get
- Visual Workflow Orchestration - Drag-and-drop Flow editor to design AI workflows with nodes for LLM calls, knowledge base queries, API integrations, and RPA-style actions.
- Multi-Format Knowledge Base Import - Supports PDF, DOCX, PPTX, TXT, MD, CSV, XLSX, and web URLs with automated text chunking, QA pair extraction, and vector embedding.
- OpenAI-Compatible API - Full /v1/chat/completions endpoint support, enabling seamless integration with existing GPT clients, Discord bots, Slack apps, and third-party tools.
- RAG Debugging Tools - View full call chains, inspect retrieved chunks, modify references in real-time, and analyze response accuracy with detailed logs and evaluation metrics.
- Template Marketplace - Pre-built AI agent templates for customer support, HR assistants, and product documentation, enabling rapid deployment without starting from scratch.
- Multi-Model Compatibility - Plug in any LLM with OpenAI API compatibility including OpenAI, Claude, DeepSeek, Qwen, and others via AI Proxy or direct API configuration.
Common Use Cases
- Building a customer support chatbot - A SaaS company uses FastGPT to ingest product documentation and FAQs, then builds a visual workflow that routes questions to the right knowledge base and calls Claude for nuanced responses.
- Creating an internal HR assistant - An enterprise IT team imports company policies and onboarding materials into FastGPT’s knowledge base, then deploys a Slack-integrated AI agent to answer employee questions automatically.
- Developing a research assistant for academic teams - A university lab uses FastGPT to process PDF papers, extract key findings via QA splitting, and build a workflow that retrieves relevant studies and summarizes them in plain language.
- Integrating AI into legacy systems - A financial firm connects FastGPT to their internal CRM via OpenAPI, creating an AI agent that fetches client data, analyzes it, and generates personalized investment summaries.
Under The Hood
Architecture
- Monorepo structure with clear separation of SDKs, frontend, and backend projects, enabling independent development and deployment
- Dependency injection via modular SDKs abstracts data access layers, promoting loose coupling and interchangeable backends
- Explicit package boundaries enforced through pnpm workspaces and TypeScript module resolution, minimizing circular dependencies
- Next.js frontend leverages server-side rendering and component-based UI with i18next for internationalization
Tech Stack
- Next.js with Rspack bundler and Chakra UI for a performant, themable React frontend
- Express-based backend services augmented with custom SDKs for logging, storage, and observability
- MongoDB as primary database with in-memory test instances and MinIO for object storage
- Comprehensive tooling including Vitest, ESLint, Prettier, and Husky integrated via pnpm workspaces for consistent development
- CI/CD orchestrated through Makefile and Docker with multi-environment and proxy-aware deployment support
Code Quality
- Extensive test coverage across APIs, utilities, and security-critical paths with robust mocking and parallel execution
- Strong TypeScript usage with well-defined interfaces and enums ensuring type safety for core entities and API contracts
- Centralized error handling and input validation prevent common vulnerabilities like SSRF and malformed requests
- Clean layering between services, routes, and utilities enhances maintainability and testability
- Security-focused practices including sandbox restrictions, IP validation, and audit-ready data operations
What Makes It Unique
- Native MDX documentation system with recursive page resolution enables dynamic, hierarchical content without hardcoded routes
- Folder-based resource selection with lazy-loaded stateful trees eliminates need for external state management libraries
- Unified dataset deletion pipeline combines immediate cleanup, background queuing, and audit logging in a single atomic flow
- Context-aware workflow dividers dynamically render icons and text based on environment, not hardcoded logic
- Permission-aware API handlers enforce ownership and team scope at the data layer, not just endpoint level
- Automatic audit logging tied directly to data operations, capturing user actions and dataset context without manual instrumentation