ChatGPT_Chatbot

📘 Description

This project implements a functional memory-enabled chatbot using LangChain, OpenAI’s GPT-4o-mini, and LangGraph’s agent orchestration. It demonstrates how agents can reason and act in multiple steps, maintain session-based memory, and integrate with external tools for live information retrieval.

The chatbot runs entirely in a Jupyter Notebook and supports both single-turn and multi-turn conversations with tool use, showcasing how LangGraph can structure conversational logic around external API access and memory state.

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🔧 Features

• LangGraph Agent Executor: Manages control flow and decision-making using a ReAct-style loop.
• Tavily Search Tool: Allows the agent to fetch live web results during conversation.
• Threaded Memory: Uses MemorySaver to persist user interactions across chat turns.
• Tool Use History: Supports step-by-step breakdowns of tool calls and thought processes.
• Live Streaming UX: Streams LLM output in real time for responsiveness.
• Session Control: Each chat is scoped to a thread_id to preserve or isolate context.

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💡 Key Insight

The true power of conversational AI comes from embedding reasoning, retrieval, and memory into structured workflows. This chatbot is more than a UI — it’s a stateful autonomous agent, built with tools and traceability from the ground up.

🔗 View the source code on GitHub