AI Agent with LangChain

Description

Build an intelligent AI agent using LangChain with memory, tools, and multi-step reasoning capabilities.

Objective

Create a conversational agent that can reason, remember, and interact with external tools for complex tasks.

Solution

Environment Setup

  1. 1Install Python 3.10+ and create virtual environment.
  2. 2Install LangChain and dependencies.
  3. 3
    bashpip install langchain langchain-community langchain-core openai faiss

Define LLM

  1. 1Choose a model (OpenAI, Ollama, HuggingFace).
  2. 2
    pythonfrom langchain_ollama import ChatOllama
llm = ChatOllama(model="llama3", temperature=0.2)

Add Memory

  1. 1Use ConversationBufferMemory or ConversationSummaryMemory.
  2. 2
    pythonfrom langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(return_messages=True)

Add Tools

  1. 1Load built-in tools like search or calculator.
  2. 2
    pythonfrom langchain.agents import load_tools
tools = load_tools(["serpapi", "llm-math"], llm=llm)

Build Agent

  1. 1Initialize agent with tools, LLM, and memory.
  2. 2
    pythonfrom langchain.agents import initialize_agent, AgentType
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    memory=memory,
    verbose=True
)

Multi-Step Reasoning

  1. 1Use ReAct framework for reasoning + acting.
  2. 2
    pythonresponse = agent.run("Find the latest AI conference in India and summarize its agenda.")
print(response)

Extend with Custom Tools

  1. 1Define custom tools for domain-specific tasks.
  2. 2
    pythonfrom langchain.tools import tool

@tool
def get_weather(city: str) -> str:
    return f"Weather in {city} is sunny."

Validation

  1. 1Test multi-step queries.
  2. 2Confirm memory retains context.
  3. 3Implement rollback/error handling.

Tool Stack Used

  • 🤖LangChain
  • 🤖LLM (OpenAI/Ollama/HuggingFace)
  • 🔧Memory
  • 🔧Tools (Search, Calculator, Custom APIs)