ai-agents

One-Minute Overview#

This is a repository of introductory examples for building LLM-based AI agents, accompanying the published book "大模型应用开发 动手做AI Agent". The project provides 10 different types of AI Agent implementations, ranging from basic Coze agents to advanced systems like ReAct agents, Plan-n-Execute agents, and multi-agent systems.

Core Value: Provides a progressive learning path for beginners in AI Agent development, helping them understand core concepts and implementation methods through practical code examples.

Quick Start#

Installation Difficulty: Low - Dependencies are well-organized in requirements files, making setup straightforward

# Install OpenAI and LangChain dependencies
pip install -r requirements_openai_langchain.txt

# Install LlamaIndex dependencies
pip install -r requirements_llama_index.txt

Is this suitable for my needs?

• ✅ AI Agent beginners: The project offers a learning path from simple to complex, perfect for getting started
• ✅ Developers wanting to understand different Agent architectures: Includes implementations for Coze, ReAct, Plan-n-Execute and other architectures
• ❌ Developers seeking production-grade complex agent systems: These are basic examples intended for learning, not direct use in production environments

Core Capabilities#

1. Diverse Agent Architecture Implementations#

The project showcases various AI Agent architectures, from simple demo agents to complex ReAct and Plan-n-Execute agents, helping developers understand the applicable scenarios for different architectures. Actual Value: Developers can choose the most suitable Agent architecture for their project requirements, avoiding the need to figure it out from scratch.

2. Practical Tool Integration#

Includes practical tools like GitHub agents and RAG agents, demonstrating how to integrate AI Agents with real-world development scenarios. Actual Value: Helps developers understand how to integrate Agents with existing tools and services to improve development efficiency.

3. Layered Learning Path#

The 10 chapters are arranged in increasing complexity, from basic Coze agents to complex multi-agent systems, providing a clear learning path. Actual Value: Beginners can master AI Agent development progressively without being overwhelmed by complex concepts from the start.

Tech Stack & Integration#

Development Language: Python Main Dependencies: OpenAI API, LangChain, LlamaIndex Integration Method: Library - As a learning and reference codebase

Maintenance Status#

• Development Activity: As a book companion project, updates are infrequent but the core architecture remains stable
• Recent Updates: The project has some history, but the basic examples remain relevant
• Community Response: With 453 stars and 111 forks, it shows a reasonable level of community attention

Documentation & Learning Resources#

• Documentation Quality: Basic - Includes README and book as references
• Official Documentation: Book link - item.jd.com/14600442.html
• Sample Code: Abundant - Contains 10 different types of Agent implementation examples