https://github.com/zeesshhh0/learning_langgraph
This repository contains my code and notes as I learn about Agentic AI using LangGraph
https://github.com/zeesshhh0/learning_langgraph
agents langgraph langgraph-python notes
Last synced: 16 days ago
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This repository contains my code and notes as I learn about Agentic AI using LangGraph
- Host: GitHub
- URL: https://github.com/zeesshhh0/learning_langgraph
- Owner: zeesshhh0
- Created: 2025-10-02T13:00:35.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-10-03T07:13:34.000Z (7 months ago)
- Last Synced: 2025-10-03T09:13:52.846Z (7 months ago)
- Topics: agents, langgraph, langgraph-python, notes
- Language: Jupyter Notebook
- Homepage:
- Size: 80.1 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# LangGraph - My Learning Journey
This repository contains my code and notes as I learn about Agentic AI using LangGraph from the [Agentic AI using LangGraph](https://youtube.com/playlist?list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL) playlist by CampusX.
## What I've Learned
I will be checking off the topics as I complete them. You can find the code for each topic in the respective folders.
- [x] Agentic AI Fundamentals
- [x] LangChain vs. LangGraph
- [x] Sequential Workflows
- [x] Parallel Workflows
- [x] Branching & Nested Workflows
- [x] Iterative Workflows
- [x] Memory (Short-term & Long-term)
- [x] Chatbot Application
---
## Workflows and Projects
This repository showcases a variety of workflows built with LangGraph, demonstrating different patterns for building AI agents and applications.
### Sequential Workflows
These workflows execute tasks in a specific order, with the output of one step feeding into the next.
* **Simple LLM Workflow** (`sequential_workflow/simple_llm_workflow.ipynb`): A basic example of a sequential workflow that takes a question from the user, passes it to a large language model (LLM), and returns the answer.
* **Blog Generator Workflow** (`sequential_workflow/blog_generator_workflow.ipynb`): This workflow automates the process of writing a blog post. It takes a title as input, generates a 3-point outline, and then uses both the title and the outline to create the full blog content.
* **BMI Calculator Workflow** (`sequential_workflow/bmi_calculator_workflow.ipynb`): A non-LLM example of a sequential workflow. This simple application takes a user's height and weight, calculates their BMI, and then categorizes the result (e.g., underweight, normal weight, overweight).
### Parallel Workflow
This workflow executes multiple tasks simultaneously to improve efficiency.
* **UPSC Essay Review Workflow** (`parallel_workflow/upse_essay_review_workflow.ipynb`): This workflow is designed to provide comprehensive feedback on an essay. It evaluates the essay on three different criteria—**language**, **clarity**, and **depth**—in parallel. After all evaluations are complete, it generates a final summary of the feedback and calculates an average score.
### Conditional Workflow
This workflow uses conditional logic to decide which tasks to execute based on the input.
* **Review Response Workflow** (`conditional_workflow/review_response_workflow.ipynb`): This workflow automates customer review responses. It first analyzes the sentiment of a customer's review.
* If the review is **negative**, the workflow first runs a "diagnosis" to understand the problem and then generates an appropriate, empathetic reply.
* If the review is **positive**, it skips the diagnosis step and immediately generates a positive response.
### Chatbot Application
This is a simple chatbot built with a Streamlit frontend and a LangGraph backend.
* **`chatbot/chatbot_app.py`**: The user-facing application built with Streamlit that provides a simple chat interface.
* **`chatbot/chatbot_app_backend.py`**: The backend of the chatbot, powered by LangGraph. It manages the conversation state, allowing the chatbot to remember previous messages in the conversation.
* **`chatbot/chatbot_v1.py` and `chatbot/chatbot_v1_2.py`**: Earlier, simpler versions of the chatbot that demonstrate the basic principles of building conversational AI with LangGraph.
---
## How to Run These Projects
1. **Clone the repository:**
```bash
git clone https://github.com/zeesshhh0/learning_langgraph.git
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Explore the Jupyter Notebooks:** The workflows are contained in Jupyter Notebooks (`.ipynb`) in their respective folders. You can run them cell by cell to see how they work.
4. **Run the Chatbot:** To run the chatbot application, navigate to the `chatbot` directory and run the following command:
```bash
streamlit run chatbot_app.py
```