LangChain Tutorial

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Table of Contents

1. Introduction to LangChain
2. Why Build AI Agents with LangChain?
3. Setting Up Your Environment
4. Building Your First AI Agent: Step-by-Step
   • Step 1: Install LangChain
   • Step 2: Get API Keys for LLM Providers
   • Step 3: Create Prompt Templates
   • Step 4: Build and Run Your Agent
5. Understanding LangChain Components
6. Best Practices and Next Steps
7. Conclusion

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Introduction to LangChain

LangChain is an open-source Python framework designed to simplify the creation of AI-driven applications by orchestrating Large Language Models (LLMs) with external tools, data, and memory. It enables developers to build chatbots, question-answering systems, and intelligent agents by combining LLMs with modular workflows.

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Why Build AI Agents with LangChain?

LangChain empowers developers to create AI agents capable of reasoning, interacting with external APIs, remembering past conversations, and performing complex tasks dynamically. Unlike simple chains with fixed steps, LangChain agents decide on-the-fly what actions to take based on input, which makes your applications highly adaptable and powerful.

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Setting Up Your Environment

Before building your AI agent, prepare your environment:

• Ensure you have Python 3.7+ installed.
• Set up a virtual environment.
• Obtain API keys from LLM providers such as OpenAI or Cohere.
• Install LangChain via pip:

bashpip install langchain

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Building Your First AI Agent: Step-by-Step

Step 1: Install LangChain

Run the installation command above to get the LangChain package and its dependencies.

Step 2: Get API Keys for LLM Providers

Register and get API keys from services such as OpenAI. Set your keys as environment variables or pass them in your code for authentication.

Step 3: Create Prompt Templates

Use LangChain’s PromptTemplate class to design reusable prompts with placeholders:

pythonfrom langchain.prompts import PromptTemplate

template = PromptTemplate(template="Hello, {name}! How can I assist you today?", input_variables=["name"])

Step 4: Build and Run Your Agent

Create an LLM instance and define the agent’s behavior:

pythonfrom langchain.llms import OpenAI
from langchain.agents import Tool, AgentExecutor

# Initialize the language model
llm = OpenAI(api_key="your_openai_api_key")

# Define a simple tool for searching documents
def search_documents(query: str) -> str:
    # Dummy implementation
    return f"Searching for: {query}"

search_tool = Tool(name="DocumentSearch", func=search_documents)

# Create an agent that uses the tool
agent = AgentExecutor(llm=llm, tools=[search_tool])

# Run the agent with a query
response = agent.run("Find information about LangChain.")
print(response)

This example shows your agent dynamically choosing to use the “DocumentSearch” tool based on the query.

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Understanding LangChain Components

• Prompt Templates: Craft reusable prompts with dynamic inputs.
• LLM Wrappers: Standardized interfaces for various language models.
• Chains: Sequences of LLM calls, executed in order.
• Memory: Save context from past interactions for conversational AI.
• Agents & Tools: Let LLMs decide actions and interact with APIs or functions.

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Best Practices and Next Steps

• Start with simple chains before exploring agents.
• Leverage LangChain’s extensive documentation and community examples.
• Gradually integrate memory and external tools to enhance capabilities.
• Experiment with LangChain Expression Language (LCEL) for complex workflows.
• Stay updated with LangChain releases for new features.

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Conclusion

LangChain makes it easy for developers to build sophisticated AI agents that can interact, reason, and operate dynamically with external systems. This 30-minute tutorial guides you through setting up a basic agent, and with continued exploration, you can unlock advanced AI-powered applications tailored to your needs.

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Would you like a hands-on coding workshop or deeper examples on LangChain’s memory management and tool integration?

1. https://python.langchain.com/docs/tutorials/
2. https://www.youtube.com/watch?v=TAdpwQs6FUs
3. https://dev.to/fonyuygita/the-complete-beginners-guide-to-langchain-why-every-developer-needs-this-framework-in-2025part-1-2d55
4. https://latenode.com/blog/langchain-python-tutorial-complete-beginners-guide-to-getting-started
5. https://www.linkedin.com/pulse/explain-langchain-quick-tutorial-beginners-priyanka-yadav-nh85c
6. https://sdlccorp.com/post/can-you-provide-a-complete-guide-and-tutorial-for-langchain/
7. https://www.digitalocean.com/community/tutorials/langchain-language-model
8. https://www.youtube.com/watch?v=Gi7nqB37WEY
9. https://python.langchain.com/docs/how_to/