How to Build AI Agents with LangChain - A Complete Tutorial

Read this article to learn how to build an AI Agent with Langchain!

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How to Build AI Agents with LangChain - A Complete Tutorial

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LangChain provides a powerful framework for building AI agents that can interact with various tools and make decisions based on natural language inputs. In this comprehensive guide, we'll explore how to create sophisticated AI agents using LangChain, complete with detailed steps and sample code.

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Understanding LangChain Agents

LangChain agents are AI-powered entities that can use language models to determine a sequence of actions to take in order to accomplish a given task. These agents can interact with various tools, make decisions, and execute actions based on the input they receive.

Key Components of LangChain Agents

  1. Language Model: The core decision-making engine (e.g., OpenAI's GPT models)
  2. Tools: Functions or APIs that the agent can use to interact with external systems
  3. Memory: Allows the agent to retain information across interactions
  4. Prompt Templates: Guide the agent's behavior and decision-making process

Setting Up Your LangChain Agents Environment

Before we start building agents, let's set up our development environment.

Installing LangChain and Dependencies

First, install LangChain and its dependencies:

pip install langchain langchain-openai python-dotenv

Configuring Environment Variables

Create a .env file in your project directory and add your OpenAI API key:

OPENAI_API_KEY=your_api_key_here

Creating Your First LangChain Agents

Let's create a simple agent that can perform web searches and answer questions.

Importing Required Modules

from langchain_openai import ChatOpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import PythonREPLTool
from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()

Initializing the Language Model

llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)

Setting Up Tools for LangChain Agents

tools = load_tools(["wikipedia", "llm-math"], llm=llm)

Creating the LangChain Agents

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

Running the LangChain Agents

response = agent.run("What is the population of France divided by the square root of 2?")
print(response)

This agent will use Wikipedia to find France's population and the LLM-math tool to perform the calculation.

Advanced LangChain Agents Techniques

Let's explore some more advanced techniques for building sophisticated agents.

Creating a Python REPL Agent with LangChain

This agent can execute Python code to solve problems.

python_agent = create_python_agent(
    llm,
    tool=PythonREPLTool(),
    verbose=True
)

python_agent.run("Calculate the first 10 Fibonacci numbers")

Implementing Memory in LangChain Agents

To give our agent the ability to remember previous interactions:

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory(memory_key="chat_history")

agent_with_memory = initialize_agent(
    tools, 
    llm, 
    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
    verbose=True,
    memory=memory
)

agent_with_memory.run("My name is Alice. What's yours?")
agent_with_memory.run("What's my name?")

Building a Custom Tool for LangChain Agents

Let's create a custom tool that our agent can use.

Defining a Custom Tool

from langchain.tools import BaseTool
from typing import Optional, Type
from pydantic import BaseModel, Field

class WeatherInput(BaseModel):
    city: str = Field(description="The city to get the weather for")

class WeatherTool(BaseTool):
    name = "weather"
    description = "Get the current weather in a given city"
    args_schema: Type[BaseModel] = WeatherInput

    def _run(self, city: str):
        # In a real scenario, you'd call a weather API here
        return f"The weather in {city} is sunny and 25°C"

    def _arun(self, city: str):
        # For async implementation
        raise NotImplementedError("WeatherTool does not support async")

weather_tool = WeatherTool()

Incorporating the Custom Tool into LangChain Agents

tools.append(weather_tool)

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

agent_with_custom_tool.run("What's the weather like in Paris?")

Here's a continuation of the article on creating a multi-step task agent with LangChain:

Creating a Multi-Step Task Agent with LangChain

Let's build an agent that can handle more complex, multi-step tasks.

Defining the Multi-Step Agent

To create a multi-step task agent, we'll use the create_openai_functions_agent function from LangChain. This agent type is well-suited for handling complex tasks that may require multiple steps or tools to complete.

First, let's import the necessary components:

from langchain_openai import ChatOpenAI
from langchain.agents import create_openai_functions_agent
from langchain.agents import AgentExecutor
from langchain.tools import Tool
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.tools import DuckDuckGoSearchRun

Now, let's define our tools:

# Wikipedia tool
wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
wikipedia_tool = Tool(
    name="Wikipedia",
    func=wikipedia.run,
    description="Useful for querying Wikipedia to get detailed information on a topic."
)

# Web search tool
search = DuckDuckGoSearchRun()
search_tool = Tool(
    name="Web Search",
    func=search.run,
    description="Useful for searching the internet for current information or topics not found in Wikipedia."
)

tools = [wikipedia_tool, search_tool]

Next, we'll create our language model and the agent:

llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")

agent = create_openai_functions_agent(llm, tools, prompt)

Finally, we'll create an agent executor:

agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

Using the Multi-Step Agent

Now that we have our agent set up, let's test it with a multi-step task:

task = """
1. Find out who won the Nobel Prize in Physics in 2022.
2. Research their main contribution to the field.
3. Explain how their work impacts everyday technology.
"""

response = agent_executor.invoke({"input": task})
print(response["output"])

This task requires the agent to:

  1. Search for current information (2022 Nobel Prize winner)
  2. Gather detailed information about their work
  3. Analyze and explain the practical implications of their research

The agent will use both the web search and Wikipedia tools to gather information, and then synthesize this information to provide a comprehensive response.

Enhancing the Multi-Step Agent

To make our agent even more powerful, we can add additional tools or customize its behavior:

Add more specialized tools: Depending on the types of tasks you expect the agent to handle, you might add tools for mathematical calculations, data analysis, or even API calls to specific services.

Implement memory: For tasks that require maintaining context over multiple interactions, you can add a memory component to the agent.

Custom prompts: Tailor the agent's behavior by customizing its prompt. This can help guide the agent to approach tasks in a specific way or to focus on certain aspects of the problem.

Error handling: Implement robust error handling to manage situations where tools fail or return unexpected results.

By creating a multi-step task agent, we've significantly expanded the capabilities of our AI assistant. This agent can now handle complex queries that require multiple sources of information and several steps of reasoning, making it a powerful tool for a wide range of applications.

Conclusion

LangChain agents and multi-agent systems represent a powerful paradigm for creating more flexible and capable AI applications. By combining language models with tools and decision-making capabilities, agents can tackle complex, multi-step tasks that would be challenging for traditional chatbots or question-answering systems.

Frameworks like LangGraph enable developers to create sophisticated agent workflows with multiple specialized agents collaborating to achieve goals. While there are still challenges around reliability and control, agent-based architectures show great promise for expanding the capabilities of language model applications.

As the field evolves, we can expect to see increasingly advanced multi-agent systems that can reason, plan, and act in more human-like ways to solve real-world problems.

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