Build a Conversational Research AI Agent with LangGraph: A Step-by-Step Guide for Developers and Data Scientists

Understanding the Target Audience

The main audience for this tutorial includes developers, data scientists, and business managers who are eager to leverage AI-driven solutions. They come from diverse backgrounds, with varying levels of technical expertise, but they all share a common goal: improving business operations through innovative AI technologies.

Pain Points

• Lack of knowledge about integrating conversational AI into existing systems.
• Challenges in managing conversation flows effectively.
• Concerns regarding the reproducibility and transparency of AI models.

Goals

• To build functional AI agents that can assist in research and customer service.
• To grasp the technical frameworks necessary for implementing AI solutions.
• To optimize workflow through efficient management of conversational interactions.

Interests

• Practical applications of AI in business settings.
• Latest trends and advancements in AI technologies.
• Hands-on tutorials and code implementations that can be directly applied.

Communication Preferences

The audience prefers clear, concise instructions that include code snippets. They value accessibility of resources, such as links to GitHub or official documentation, and appreciate peer-reviewed statistics and case studies that highlight real-world applications.

Tutorial: Building a Conversational Research AI Agent with LangGraph

This tutorial will guide you through how LangGraph can help manage conversation flows while introducing time travel through checkpoints. By creating a chatbot that utilizes the free Gemini model and a Wikipedia tool, you’ll learn how to add multiple steps to a dialogue, record each checkpoint, replay conversation history, and resume from previous states. This interactive approach demonstrates how LangGraph’s capabilities facilitate clear and controlled conversation progression.

Prerequisites

Before getting started, ensure you have the following libraries installed:

pip install -U langgraph langchain langchain-google-genai google-generativeai typing_extensions
pip install requests==2.32.4

Setting Up Your Environment

Import the necessary modules and initialize the Gemini model as shown below:

import os
import json
import getpass
import requests
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.prebuilt import ToolNode

Next, enter your Google API Key:

os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter your Google API Key (Gemini): ")
llm = init_chat_model("google_genai:gemini-2.0-flash")

Implementing the Wikipedia Search Tool

We will set up a tool to search Wikipedia with the following function:

def _wiki_search_raw(query: str, limit: int = 3):
    # Function definition here...

This function utilizes the MediaWiki API to return search results in a structured format.

Creating a Stateful Chatbot

Next, define the graph state and the chatbot node:

class State(TypedDict):
    messages: List[Dict[str, Any]]

graph_builder = StateGraph(State)
llm_with_tools = llm.bind_tools([wiki_search])

Checkpointing and Time-Travel Functionality

We’ll implement checkpointing to allow users to revert or replay conversation states:

memory = InMemorySaver()
graph = graph_builder.compile(checkpointer=memory)

Simulating User Interactions

Here’s how to simulate user interactions with the chatbot:

first_turn = {"messages": [{"role": "system", "content": SYSTEM_INSTRUCTIONS}, {"role": "user", "content": "I'm learning LangGraph."}]}
second_turn = {"messages": [{"role": "user", "content": "Maybe I'll build an agent with it!"}]}

Replaying Conversation History

Users can review the history of interactions and choose to resume from a specific checkpoint:

history = list(graph.get_state_history(config))
to_replay = pick_checkpoint_by_next(history, node_name="tools")

This functionality enhances flexibility in managing conversation flows, ultimately improving the user experience.

Conclusion

In this tutorial, we have explored how LangGraph’s checkpointing and time-travel capabilities provide control and clarity in managing conversations. By following these steps, users can build reliable research assistants and effectively integrate AI solutions into their business workflows. Further exploration of the LangGraph framework can lead to more complex applications where reproducibility and transparency are essential.

Resources

For the complete codes and additional tutorials, visit our GitHub Page. Follow us on Twitter for updates, and subscribe to our newsletter for the latest information.

FAQ

• What is LangGraph? LangGraph is a framework designed to facilitate the creation and management of conversational AI agents.
• How can I integrate LangGraph into my existing systems? You can integrate it by following the setup instructions and utilizing the provided tools to connect with your existing frameworks.
• What are the benefits of checkpointing in conversational AI? Checkpointing allows users to save conversation states, enabling them to revert to earlier points in the dialogue, enhancing user experience.
• Is prior coding experience required to use LangGraph? While some coding knowledge is beneficial, the tutorial provides step-by-step instructions to help users of varying skill levels.
• Where can I find more resources on AI and LangGraph? Additional resources can be found on the LangGraph GitHub page and through various online AI communities.