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How to Build a Conversational Research AI Agent with LangGraph: Step Replay and Time-Travel Checkpoints

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Understanding the Target Audience

The primary audience for the tutorial «How to Build a Conversational Research AI Agent with LangGraph: Step Replay and Time-Travel Checkpoints» includes developers, data scientists, and business managers interested in implementing AI-driven solutions. They are likely to have varying levels of technical expertise but share a keen interest in enhancing business operations through AI technologies.

Pain Points:

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

Goals:

  • To build functional AI agents that can assist in research and customer service.
  • To understand 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:

  • Preference for clear, concise instructions accompanied by code snippets.
  • Accessibility of resources through links to GitHub or official documentation.
  • Value peer-reviewed statistics and case studies that demonstrate real-world applications.

Tutorial: Building a Conversational Research AI Agent with LangGraph

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

Prerequisites

To get 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:

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

If you haven’t done so, 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:

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

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

Creating a Stateful Chatbot

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

We simulate user interactions with the chatbot as follows:

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 allows flexibility in managing conversation flows, enhancing the user’s experience.

Conclusion

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

Resources

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

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