Use FastGPT

Master FastGPT core concepts, hands-on tutorials, deep guides, and practical cases to build enterprise-grade AI applications.

FastGPT is an AI application orchestration platform providing out-of-the-box model orchestration, accurate knowledge base retrieval, autonomous Agent planning, and visual workflow development.

Quick Start and Tutorials

Follow structured tutorials to build operational AI systems in 5 to 10 minutes:

Core Concepts and Architecture (Explanation)

Understand system design, retrieval mechanics, and selection criteria (aligned with the "Core Concepts" sidebar section):

Core Concepts and Topology

Understand applications, collections, chunks, tools, Skills, version snapshots, and permissions.

Application Types and Selection

Compare Chat Agent, Chat Agent V2, and Workflow across execution models and decision trade-offs.

Workflow Orchestration Overview

Understand node scheduling, edge state transitions, loops, parallel execution, and resumption.

RAG Principles and Retrieval Architecture

Master vector recall, full-text search, RRF hybrid fusion, and Rerank scoring.

Practical Guides and Case Studies (How-to Guides)

Follow standardized recipes and enterprise blueprints for concrete business goals:

Accurate Knowledge-Base Q&A

Master text chunking, vector retrieval with reranking, prompt constraints, and citations.

Lesson 1: Corporate Email Assistant

Build a structured business email generator with Chat Agent instructions and tool extensions.

Lesson 2: Civil Code Q&A Assistant

Combine document chunking, vector retrieval, and grounded prompts for legal clause lookup.

Lesson 3: Content Review Workflow

Orchestrate question classification, AI rewriting, and human confirmation branches on the canvas.

Lesson 4: Data Analysis Agent V2

Run Python aggregation and chart generation inside the virtual machine sandbox.

Role-Specific Entry Points

Developer Portal: Integrate through the API or explore plugin development for tools, model presets, debugging, and publishing.
Deployment and Operations: Deploy via Docker or Sealos, configure vector stores and models, monitor workloads, and upgrade versions.
Knowledge Base Lifecycle: Study text chunking, indexing maintenance, incremental sync, and tag management.
Extensions and Tool Ecosystem: Connect HTTP tools, system plugins, code execution sandboxes, and MCP services.

Community and Technical Support

Documentation follows the current user interface. In self-hosted environments, capability availability depends on version licensing, infrastructure services, team resource permissions, and backend model support.

Report technical issues through GitHub Issues. When reporting bugs, provide the software version, step-by-step reproduction instructions, expected outcomes, and sanitized log output.