What are LangChain Agents?
LangChain agents are AI systems that use LLMs to make decisions, use tools, and execute multi-step plans. They represent the next evolution of AI applications.
Setting Up LangChain
pip install langchain langchain-openai chromadb
Building a Simple Agent
from langchain.agents import create_react_agent
from langchain.tools import Tool
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o")
tools = [
Tool(name="Search", func=search_function, description="Search the web"),
Tool(name="Calculator", func=calculator, description="Do math"),
]
agent = create_react_agent(llm, tools)
Adding Memory
Give your agent conversation memory so it remembers context across interactions. LangChain supports multiple memory types: buffer, summary, and vector store memory.
Multi-Agent Systems
Build teams of agents with specialized roles: a researcher agent, a writer agent, and a reviewer agent that collaborate on complex tasks.
Real-World Applications
- Customer support agents with knowledge base access
- Research agents that search, analyze, and summarize
- Coding agents that write, test, and debug code
- Data analysis agents that query databases and create reports