Building Custom Agents with LangGraph: An Overview

A brand new architecture for your AI agent development

Github: https://github.com/langchain-ai/langgraph

Source of Truth Data Labs Discord: https://discord.gg/ezW3m8e3

Today, we’re diving into LangGraph, a powerful library within the LangChain ecosystem that allows developers to construct highly customizable and flexible AI agents. In this tutorial, we’ll look at the general concept of Langra and how it builds agents using graph structures.

Understanding LangGraph

In contrast to LangChain, which views agents as objects equipped with tools and prompts, LangGraph conceptualizes agents as graphs. Starting with an initial function, which could be an agent, chain, or any runnable function, LangGraph allows the creation of complex and flexible agent behavior by branching into various pathways defined by nodes and edges.

Nodes and Edges

In LangGraph, the structure of an agent is broken down into nodes and edges:

• Nodes: Represent points where decisions or actions are taken, including Search tools and final answer LLMs.
• Edges: Connect nodes and depict the flow of information or actions.

Nodes can carry out functions or connect to other nodes, creating a highly flexible and extensible agent architecture.

Constructing a Simple Agent

Let’s explore a basic example to illustrate the power and simplicity of Langra:

1. Define the initial agent node that can suggest Search or Final Answer tools based on the input.
2. Connect nodes with edges specifying the pathways.
3. Implement conditional edges using a router to handle various pathways depending on certain conditions.

Example Code

from langchain import LLM
from langra import Graph, Router, Node, Edge

# Define the LLM and tools
search_tool = SearchTool()
final_answer_tool = FinalAnswerTool()

# Define the agent and assign tools
agent = Agent(tools=[search_tool, final_answer_tool])

# Initialize the graph
graph = Graph(agent=agent)

# Define nods and edges
graph.add_node('query_agent')
graph.add_node('search_node')
graph.add_node('final_answer_node')

graph.add_edge(Edge(node='query_agent', next_node='router'))
graph.add_edge(Edge(node='router', conditional=True, conditions=[
    ('search', 'search_node'),
    ('final_answer', 'final_answer_node')
]))

# Compile the graph
graph.compile()

Visualization and Execution

LangGraph allows for graph visualization, making it easy to understand and debug the structures you create. Once constructed, the graph can process various queries, returning responses based on the data fetched and the logic embedded in the nodes and edges.

The final structure of your agent could look something like this:

{
    'query_agent': {
        'next': 'router'
    },
    'router': {
        'condition': {
            'search': 'search_node',
            'final_answer': 'final_answer_node'
        }
    }
}

Advanced Usage and Conclusion

LangGraph provides a robust framework for creating simple and complex agents. While this example scratches the surface, you can expand by adding more nodes and conditions to create advanced agents capable of more sophisticated tasks.

From my experience with both LangChain and LangGraph, I find LangGraph more powerful and intuitive, offering more control over the agent’s behavior and easing the development process. With LangGraph, you can build both basic and highly intricate agents using a consistent and flexible methodology.

Stay tuned for more tutorials where we’ll delve into more complex scenarios and further explore the capabilities of LangGraph.

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