AI Agents with LangGraph
Iftekhar Ahmed
AI Agents with LangGraph
Welcome to the AI Agents with LangGraph project, developed as part of the DeepLearning.AI course. This project explores how to build stateful, multi-step AI agents using LangGraph, an innovative framework for building agentic workflows on top of LangChain.
📖 Project Overview
LangGraph extends LangChain by enabling cyclic, stateful computation using a graph-based model. This project demonstrates how to:
Create AI agents that reason through complex tasks.
Manage memory and state transitions between agent steps.
Use LangGraph to define conditional logic and loops.
Build interactive and adaptive workflows with LLMs.
🚀 Features
Agent creation with memory and tool use.
Directed graph logic for stateful execution.
Integration with LangChain tools and agents.
Demonstration of multi-turn task completion.
Fine-grained control over flow and branching.
🛠️ Tech Stack
LangGraph – Agent orchestration.
LangChain – LLM interface and tools.
Python – Core logic and flow control.
OpenAI / Hugging Face – LLM providers (optional).
📅 Use Cases Demonstrated
Multi-step task execution (e.g., writing, planning, summarizing).
Tool-augmented agents (e.g., using search, calculators).
Stateful agent loops (e.g., retrying tasks, reflecting).
📄 Getting Started
Clone the repo:
Install dependencies:
Run the example:
🌐 Resources
Feel free to fork this repo and experiment with your own agentic workflows!
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Posted Aug 5, 2025
Built autonomous multi-agent workflows using LangGraph for planning, task execution, and LLM-based reasoning.
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Jul 8, 2025 - Jul 22, 2025
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