LangGraph Series

Welcome to the LangGraph Playlist repository!

This playlist is designed to help you understand LangGraph from the ground up — starting with basic graph concepts like nodes and edges, all the way to advanced topics like tool nodes, multi-agent systems, persistence, and human-in-the-loop workflows.
Each video walks you through real, practical examples so you can build production‑ready AI applications using stateful, graph-based pipelines.

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🐍 Install Python Using Miniconda / Miniforge

To keep your AI projects clean and organized, it is recommended to use conda environments. Follow the steps below to install Miniforge and set up your environment.

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🔗 Download Miniforge for macOS (ARM64)

Download from the official repository:
https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh

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💻 Install Miniforge

Run the following commands:

chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate

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🧱 Create a project-specific conda environment

conda create --prefix ./env python=3.13
conda activate ./env

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📦 Install packages from requirements.txt

pip install -r requirements.txt

Your LangGraph environment is ready to build powerful AI apps 🚀

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📺 Playlist Breakdown

1. Basic Graph

• Introduction to LangGraph fundamentals.
• Understanding nodes, edges, and state in a graph.

2. Sequential Graph

• Building graphs where nodes execute one after another.
• Understanding how data flows through a linear pipeline.

3. Conditional Graph

• Adding conditional edges to route execution based on state.
• Implementing dynamic decision-making within a graph.

4. Looping Graph

• Creating graphs with cycles and loops for iterative processing.
• Understanding when and how to break out of loops.

5. Parallel Graph

• Running multiple nodes in parallel to improve efficiency.
• Fan-out and fan-in patterns in LangGraph.

6. Reducers in Graph

• Understanding state reducers to manage and merge state updates.
• Using custom reducers for complex state management.

7. LLM in Graph

• Integrating a Language Model as a node inside the graph.
• Passing state context to and from the LLM.

8. LLM with Conditional in Graph

• Combining LLM calls with conditional routing for smarter pipelines.
• Building graphs that adapt based on LLM output.

9. Multi-Turn Chatbot

• Building a conversational chatbot using LangGraph state management.
• Maintaining context across multiple turns of a conversation.

10. Stream Response in Graph

• Implementing streaming responses from LLMs inside a graph.
• Delivering real-time output to the user as tokens are generated.

11. Tool Node in Graph

• Adding tool nodes that can call external functions and APIs.
• Connecting LangGraph with real-world capabilities.

12. Tool Node with LLM in Graph

• Combining LLM reasoning with tool execution in a unified graph.
• Building an agent-like pipeline that decides when to use tools.

13. Persistence & Checkpoint Memory in Graph

• Adding checkpointing to save and restore graph state.
• Enabling long-running and resumable workflows.

14. Human-in-the-Loop in Graph

• Pausing graph execution to incorporate human feedback.
• Building workflows where humans can review, approve, or correct AI actions.

15. Subgraph in LangGraph

• Composing complex systems using nested subgraphs.
• Encapsulating reusable graph logic into modular components.

16. Send API in Graph

• Using LangGraph's Send API to dynamically dispatch messages to nodes.
• Enabling fine-grained control over node execution and state passing.

17. Multi-Agent in Graph

• Orchestrating multiple AI agents within a single LangGraph system.
• Building collaborative agent networks for complex, multi-step tasks.

18. Agentic RAG in Graph

• Implementing Retrieval-Augmented Generation (RAG) inside a LangGraph agent.
• Combining vector search, LLM reasoning, and graph state for intelligent document Q&A.

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📄 requirements.txt

langgraph
langchain
langchain-openai
langchain-core
python-dotenv
notebook

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🤝 Contributing

Got suggestions or improvements?
Feel free to open an issue or submit a pull request.

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📜 License

This project is licensed under the MIT License.
See the LICENSE file for details.

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📬 Stay Connected

• YouTube Channel
• LinkedIn

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Thank you for checking out the LangGraph Playlist!
Happy building with AI 🚀