| layout | default |
|---|---|
| title | AutoAgent Tutorial |
| nav_order | 140 |
| has_children | true |
| format_version | v2 |
Learn how to use
HKUDS/AutoAgentto create and orchestrate LLM agents through natural-language workflows, with support for CLI operations, tool creation, and benchmark-oriented evaluation.
AutoAgent targets zero-code agent building via natural language and automated orchestration, making it useful for teams exploring dynamic agent creation without deep framework coding.
This track focuses on:
- launching AutoAgent quickly in CLI mode
- understanding user/agent-editor/workflow-editor modes
- configuring tools and model providers safely
- evaluating planning workflows and governance controls
- repository:
HKUDS/AutoAgent - stars: about 8.7k
flowchart LR
A[User natural-language intent] --> B[AutoAgent mode selector]
B --> C[Agent or workflow generation]
C --> D[Tool and model orchestration]
D --> E[Task execution and refinement]
E --> F[Reusable agent workflows]
| Chapter | Key Question | Outcome |
|---|---|---|
| 01 - Getting Started | How do I install and run AutoAgent quickly? | Working baseline |
| 02 - Architecture and Interaction Modes | How do user/agent/workflow modes differ? | Strong usage model |
| 03 - Installation, Environment, and API Setup | How do I configure runtime and model access safely? | Stable setup baseline |
| 04 - Agent and Workflow Creation Patterns | How do I create agents and workflows with NL prompts? | Better creation discipline |
| 05 - Tooling, Python API, and Custom Extensions | How do I extend AutoAgent behavior programmatically? | Extensibility baseline |
| 06 - CLI Operations and Provider Strategy | How do I run reliable daily operations across model providers? | Operational reliability |
| 07 - Benchmarking, Evaluation, and Quality Gates | How do I evaluate AutoAgent output quality? | Evaluation discipline |
| 08 - Contribution Workflow and Production Governance | How do teams adopt and govern AutoAgent safely? | Governance runbook |
- how to operate AutoAgent across its core interaction modes
- how to configure providers and runtime settings for stable execution
- how to extend workflows with custom tools and Python interfaces
- how to evaluate and govern AutoAgent usage in team settings
Start with Chapter 1: Getting Started.
- Start Here: Chapter 1: Getting Started
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- Chapter 1: Getting Started
- Chapter 2: Architecture and Interaction Modes
- Chapter 3: Installation, Environment, and API Setup
- Chapter 4: Agent and Workflow Creation Patterns
- Chapter 5: Tooling, Python API, and Custom Extensions
- Chapter 6: CLI Operations and Provider Strategy
- Chapter 7: Benchmarking, Evaluation, and Quality Gates
- Chapter 8: Contribution Workflow and Production Governance
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