4 Main Agent Types

LangChain provides multiple agent types, each optimized for different use cases. Choosing the right type is crucial for performance, reliability, and cost.

Interactive: Agent Type Comparison

⚡

Zero-Shot React

General-purpose agent that reasons about which tool to use on the fly

When to use: Multiple tools, unknown task type, flexible reasoning needed

✅ Pros

•Most flexible

•Handles any tool

•No training needed

⚠️ Cons

•Can be verbose

•More LLM calls

•May loop unnecessarily

Code Example:

agent = initialize_agent(
  tools=tools,
  llm=llm,
  agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
  verbose=True
)

🎯 Quick Selection Guide

🚀

Prototyping

Start with Zero-Shot ReAct

Fastest to iterate, works with any tools

💼

Production

Use OpenAI Functions

Most reliable, structured outputs

💬

Chatbots

Choose Conversational

Built-in memory, natural dialogue flow

🧠

Complex Tasks

Go with ReAct

Explicit reasoning, easier debugging

💡 Advanced: Custom Agents

Beyond the 4 main types, LangChain lets you build custom agents with your own prompts, parsers, and logic. Use when:

•

Standard agents don't fit your domain-specific needs

•

You need custom output parsing or validation

•

You want fine-grained control over the agent loop

LangChain Agents

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