LangChain Agents and Tool Concepts
Understand how LangChain Agents can reason and use tools to perform complex tasks beyond simple question answering.
What are LangChain Agents?
Welcome to Agents! So far, you've learned to build chains that perform a fixed sequence of steps. But what if you need more flexibility?
LangChain Agents are systems that allow an LLM to dynamically decide which actions to take, observe the results, and then decide the next action. Think of them as giving the LLM a 'brain' to reason and 'hands' to interact with the world.
The Agent's Reasoning Loop
Agents operate on a continuous Observe-Think-Act loop:
- Observe: The agent receives an input (your query) and the results of its last action.
- Think: The LLM reasons about the current situation and decides what to do next.
- Act: The agent performs an action, often by using a tool.
This loop continues until the agent determines it has enough information to answer your question or complete its task.
All lessons in this course
- LangChain Agents and Tool Concepts
- Building Multi-Agent RAG Workflows
- Integrating External APIs as Tools
- Memory and State in Agentic RAG