🚀 LangGraph: Modular LLM Workflows Playground

Unlock the power of Language Models through interactive, modular workflows—right in your browser.

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✨ What is LangGraph?

LangGraph is an open-source Python library built on top of LangChain that enables the creation of complex, stateful, and cyclic workflows for AI agents—especially those powered by Large Language Models (LLMs). It adopts a graph-based architecture to model agent behavior, offering more flexible, dynamic, and reusable execution flows than traditional linear pipelines.

Explore patterns like:

• 🤖 Agentic Reasoning
• 🔁 Iterative Prompting
• 🔗 Prompt Chaining
• ⚡ Parallel Inference
• 🔀 Conditional Routing

All within one interactive playground.

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🧩 Workflow Patterns

LangGraph supports the following modular workflow patterns:

🔗 Sequential

Tasks run in order — perfect for multi-step reasoning.

⚡ Parallel

Execute multiple prompts or analyses concurrently.

🔀 Conditional

Branch your logic based on model output or custom conditions.

🔁 Iterative

Loop until the LLM gives you what you want.

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🗂️ Notebook Catalog

Notebook	Description
bmi.ipynb	Calculate BMI using code logic and LLM reasoning
customer_sentiment.ipynb	Analyze customer feedback sentiment using LLMs
llm_workflow.ipynb	Build modular agent workflows and evaluate their output
parallel.ipynb	Run concurrent prompt chains for faster results
prompt_chaining.ipynb	Train LLMs for multi-step reasoning and chaining
quadratic.ipynb	Solve quadratic equations using LLMs as solvers
uspc.ipynb	Explore a user-defined structured process chain
x_post.ipynb	Advanced post-processing and formatting with agents

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🌟 Why Use LangGraph?

✅ Modular Design – Swap, link, or loop your agent blocks with ease
✅ Jupyter First – No special UI, just your familiar playground
✅ Educational – Great for tinkerers, students, and LLM engineers
✅ Practical – Real use cases for agent-based LLM development

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🚀 Getting Started

git clone https://github.com/sifanmomin1/langgraph.git
cd langgraph
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
jupyter notebook