Building Sophisticated Agents and Tools
Having established the core execution model and customization possibilities within LangChain, we now turn our attention to constructing more autonomous systems: agents. Unlike predefined chains, agents employ a Large Language Model (LLM) as a reasoning engine to determine sequences of actions, often interacting with external environments through tools.
This chapter provides the foundation for building and managing these sophisticated agents. You will learn to:
- Compare and implement different agent architectures like ReAct, Self-Ask, and Plan-and-Execute.
- Develop custom tools that grant agents specific capabilities, such as interacting with APIs or databases.
- Implement strategies for handling errors that may occur when an agent attempts to use a tool.
- Design systems involving multiple collaborating agents.
- Utilize structured tool calling (function calling) features available in newer LLMs for more reliable agent-tool interactions.
- Trace and analyze the decision-making process of an agent to understand its behavior and debug issues.
By the end of this chapter, you will be equipped to design agents capable of tackling complex, multi-step problems that require dynamic planning and interaction with the outside world.
Sections
2.1 Agent Architectures: ReAct, Self-Ask, Plan-and-Execute
2.2 Developing Custom Tools for Agents
2.3 Handling Tool Errors and Agent Recovery
2.4 Multi-Agent Systems and Collaboration Patterns
2.5 Structured Tool Calling and Function Integration
2.6 Agent Execution Tracing and Analysis
2.7 Practice: Creating an Agent with Custom API Tools
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