A fully-automated framework for creating and deploying LLM agents without writing code, featuring natural language customization capabilities and a multi-agent collaborative system that supports various large language models.
One-Minute Overview#
AutoAgent is a zero-code framework for creating and deploying LLM agents using natural language instead of programming. It offers three modes: Deep Research Mode (multi-agent research assistant), Agent Editor, and Workflow Editor. Whether you're a researcher, developer, or regular user, you can easily build complex AI systems without coding expertise.
Core Value: Create powerful AI agent systems without coding skills, saving significant development time and costs.
Quick Start#
Installation Difficulty: Medium - Requires Docker environment and API key configuration
# Clone and install
git clone https://github.com/HKUDS/AutoAgent.git
cd AutoAgent
pip install -e .
# Configure API keys
cp .env.template .env
# Edit .env file to add required API keys
Is this suitable for my scenario?
- ✅ Researchers: Need a powerful AI research assistant for information gathering and analysis
- ✅ Developers: Want to quickly prototype AI applications without writing complex code
- ✅ Enterprise Users: Need customized AI solutions without professional development teams
- ❌ Highly Customized Requirements: Current version has limited features, not suitable for complex enterprise applications
Core Capabilities#
1. Zero-Code Agent Creation#
Create agents and workflows through natural language descriptions, no programming knowledge required. Actual Value: Significantly lowers the barrier to AI application development, enabling non-technical professionals to build complex AI systems
2. Multi-Agent Collaboration System#
Built-in Deep Research Mode featuring multiple collaborative agents for information retrieval, analysis, and report generation. Actual Value: Provides a one-stop research solution comparable to commercial tools like Deep Research but at a lower cost
3. Multi-Model Support#
Compatible with most major LLMs including OpenAI, Anthropic, Deepseek, Gemini, and more. Actual Value: Flexibly choose the best model for your task and budget,不受限于单一供应商
4. File Processing Capabilities#
Supports file uploads and interaction, enhancing the agent's ability to handle complex information. Actual Value: Directly process documents, data, and other real-world information, improving agent practicality
5. Cost-Effective Alternative#
Open-source alternative to commercial tools (like the $200/month Deep Research). Actual Value: Significantly reduces usage costs, making advanced AI capabilities more accessible to individuals and small teams
Technology Stack and Integration#
Development Language: Python Key Dependencies: Docker (containerized environment), LiteLLM (multi-model support) Integration Method: CLI interface, Docker container, API key authentication