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LangChain Agents

Master LangChain, the most popular framework for building production-ready AI agents

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4 Core Components

Every LangChain agent is built from 4 fundamental components. Understanding these building blocks is essential for creating powerful, production-ready agents.

Interactive: Component Explorer

🤖

LLM / Chat Model

The brain of your agent - handles reasoning, planning, and natural language

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
  model="gpt-4",
  temperature=0
)
✓Reasoning engine
✓Multiple providers
✓Streaming support
✓Function calling

🔗 How Components Work Together

1
User Input → LLM
Chat model receives user query and decides next action
2
LLM → Tool Selection
Agent decides which tool(s) to use based on the task
3
Tool Execution → Results
Executor runs tool, gets results, stores in memory
4
Results → LLM → Response
LLM processes results, generates final answer for user

💡 Component Best Practices

•
LLM: Use temperature=0 for deterministic agents, >0 for creativity
•
Tools: Write clear descriptions - the LLM uses them to decide when to call
•
Memory: Use ConversationBufferMemory for short chats, SummaryMemory for long ones
•
Executor: Set max_iterations (default 15) to prevent infinite loops
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