What are LangChain Agents?
Think of LangChain Agents as decision-making brains that sit on top of a language model like GPT-4. Instead of just answering a question, they decide how to answer it—and what tools to use.
🔍 Real-World Analogy
Imagine you asked a personal assistant:
“Can you book a table for 2 at an Italian restaurant near me for tonight?”
A simple chatbot might say,
“Sure! Italian restaurants are great.”
But a LangChain Agent would:
1. Search for nearby Italian restaurants using a web tool.
2. Check available times via a booking API.
3. Book the table.
4. Confirm the reservation with you.
🔧 What Can Agents Do?
Agents can:Agents can:
• Use tools like web search, calculators, APIs, databases, etc.
• Break complex tasks into steps and solve them.
• Dynamically choose which tool to use based on the user’s query.
How agents make AI more dynamic
ithout agents, an LLM like GPT-4 only responds to prompts with plain text.
With agents:
• The LLM thinks through what actions to take.
• It can use external tools to look up information or perform calculations.
• It works in multiple steps, following a reasoning loop.
📌 Example:
User Input:
“What’s 25% of the sum of 456 and 789?”
With an Agent:
1. Recognize the math task.
2. Use the calculator tool to compute 456 + 789 = 1245.
3. Calculate 25% of 1245 = 311.25.
4. Return:
“The result is 311.25.”
💡 Without an agent, GPT might try to do the math internally (and might get it wrong!). But with a calculator tool, it’s accurate and reliable.
Using built-in LangChain agents
LangChain offers ready-made agents you can use right away.
🧰 Popular Built-in Agents:
• initialize_agent() — General-purpose agent setup function.
• ConversationalAgent — Great for chatbot-like applications.
• ZeroShotAgent — Uses prompts directly with tools, no training required.
⚙️ Example Code:
This agent:
• Uses the Calculator for square root
• Uses Search to get current population data
