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piyushagni5 / repository
A comprehensive collection of LangGraph implementations, tutorials, and advanced AI workflows covering Agentic RAG systems, MCP (Model Context Protocol) development, and practical AI application patterns.
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A comprehensive collection of LangGraph implementations, tutorials, and advanced AI workflows covering Agentic RAG systems, MCP (Model Context Protocol) development, and practical AI application patterns.
This repository serves as a implementation guide for building sophisticated AI applications using LangGraph. It contains practical examples, tutorials, and production-ready implementations across multiple domains:
langgraph-ai/
├── rag/
│ ├── rag-from-scratch/
│ │ └── 1_rag_overview.ipynb
│ ├── rag-agents/
│ │ ├── Building an Advanced RAG Agent.ipynb
│ │ └── rag-as-tool-in-langgraph-agents.ipynb
│ ├── agentic-rag/
│ │ ├── agentic-rag-systems/
│ │ │ └── building-adaptive-rag/
│ │ └── agentic-workflow-pattern/
│ │ ├── 1-prompting_chaining.ipynb
│ │ ├── 2-routing.ipynb
│ │ ├── 3-parallelization.ipynb
│ │ ├── 4-orchestrator-worker.ipynb
│ │ └── 5-Evaluator-optimizer.ipynb
├── mcp/
│ ├── 01-build-your-own-server-client/
│ ├── 02-build-mcp-client-with-multiple-server-support/
│ ├── 03-build-mcp-server-client-using-sse/
│ └── 04-build-streammable-http-mcp-client/
├── langgraph-cookbook/
│ ├── human-in-the-loop/
│ │ ├── 01-human-in-the-loop.ipynb
│ │ ├── 02-human-in-the-loop.ipynb
│ │ └── 03-human-in-the-loop.ipynb
│ └── tool-calling -vs-react.ipynb
├── .gitignore
├── .gitmodules
├── README.md
└── requirements.txt
Before setting up this repository, ensure you have the following installed:
git clone https://github.com/piyushagni5/langgraph-ai.git
cd langgraph-ai
If you haven't installed UV yet, install it using:
curl -LsSf https://astral.sh/uv/install.sh | sh
For Windows (PowerShell):
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
Navigate to the specific project directory you want to work with. For example, to work with the Adaptive RAG system:
cd langgraph-cookbook/agentic-patterns
Create a virtual environment using UV:
uv venv --python 3.10
On macOS/Linux:
source .venv/bin/activate
On Windows:
.venv\Scripts\activate
Using UV (Recommended):
uv pip install -r requirements.txt
Using pip (Alternative):
pip install -r requirements.txt
To use your UV virtual environment with Jupyter notebooks, you need to install ipykernel and register the environment as a kernel: Install ipykernel in the virtual environment:
uv pip install ipykernel
Register the virtual environment as a Jupyter kernel:
python -m ipykernel install --user --name=langgraph-ai --display-name="LangGraph AI"
When you open a notebook, you can select the "LangGraph AI" kernel from the kernel menu.
Create a .env file in your project directory with the necessary API keys:
ANTHROPIC_API_KEY="your-anthropic-api-key"
# LANGCHAIN_API_KEY="your-langchain-api-key" # optional
# LANGCHAIN_TRACING_V2=True # optional
# LANGCHAIN_PROJECT="multi-agent-swarm" # optional
Note: The LANGCHAIN_API_KEY is required if you enable tracing with LANGCHAIN_TRACING_V2=true.
cd agentic-rag/agentic-rag-systems/building-adaptive-rag
uv run main.py
uv run pytest . -s -v
Contributions are welcome! Please feel free to submit pull requests or open issues for:
This project is open source and available under the MIT License.
Note: This repository contains multiple independent projects. Each project has its own requirements and setup instructions. Please refer to individual project README files for specific details.