🚀 LangGraph Grok Agent

A production-ready Agentic AI project built with LangGraph, Groq, and Python that demonstrates graph-based AI workflows, multi-step reasoning, tool calling, state persistence, and reactive agents.

This repository contains both a basic LangGraph agent and an advanced reactive agent, making it a great starting point for learning modern AI agent development.

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✨ Features

• 🤖 LangGraph-powered AI Agents
• ⚡ Groq LLM Integration
• 🔄 Graph-based Workflow Execution
• 🛠️ Tool Calling
• 🧠 Multi-step Reasoning
• 💬 Stateful Conversations
• 💾 Checkpoint Support
• 📜 Execution Logging
• 🔧 Environment Variable Configuration
• 🚀 FastAPI Deployment Example
• 📦 Modular Project Structure

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📂 Project Structure

langgraph-grok-agent/
├── langgraph_grok_agent.py          # Basic LangGraph agent implementation
├── langgraph_reactive_agent.py      # Advanced reactive AI agent
├── setup_and_config.py              # Configuration & utility functions
├── requirements.txt                 # Project dependencies
├── .env                             # API keys & environment variables
├── README.md                        # Project documentation
├── checkpoints/                     # Saved LangGraph checkpoints
├── logs/                            # Runtime logs
└── examples/
    ├── example_basic.py             # Basic usage example
    ├── example_advanced.py          # Advanced workflows
    └── example_api_server.py        # FastAPI deployment example

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⚙️ Tech Stack

• Python 3.11+
• LangGraph
• LangChain
• Groq API
• Pydantic
• python-dotenv
• FastAPI
• Uvicorn

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🚀 Installation

Clone Repository

git clone https://github.com/Ronit049/LangGraph_Grok_Agent.git

cd langgraph-grok-agent

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Create Virtual Environment

Windows

python -m venv .venv

.venv\Scripts\activate

Linux / macOS

python3 -m venv .venv

source .venv/bin/activate

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Install Dependencies

pip install -r requirements.txt

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🔑 Environment Variables

Create a .env file in the project root.

GROQ_API_KEY=your_groq_api_key

GEMINI_API_KEY=your_gemini_api_key

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▶️ Running the Project

Basic LangGraph Agent

python langgraph_grok_agent.py

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Reactive Agent

python langgraph_reactive_agent.py

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FastAPI Server

python examples/example_api_server.py

or

uvicorn examples.example_api_server:app --reload

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📖 Examples

Basic Example

python examples/example_basic.py

Demonstrates:

• Building a LangGraph workflow
• Executing an AI agent
• Managing graph state

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Advanced Example

python examples/example_advanced.py

Demonstrates:

• Multi-step reasoning
• Tool execution
• Dynamic routing
• Stateful conversations

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API Deployment

python examples/example_api_server.py

Demonstrates:

• FastAPI integration
• REST API endpoint
• JSON responses
• Production deployment

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🧠 Agent Workflow

User Input
     │
     ▼
Task Analysis
     │
     ▼
Reasoning
     │
     ▼
Tool Selection
     │
     ▼
Tool Execution
     │
     ▼
State Update
     │
     ▼
Response Generation
     │
     ▼
Final Answer

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📦 Main Components

langgraph_grok_agent.py

• Basic LangGraph workflow
• Tool execution
• Graph state management
• AI response generation

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langgraph_reactive_agent.py

• Advanced reactive agent
• Conditional routing
• Dynamic tool selection
• Multi-step reasoning
• Context-aware responses

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setup_and_config.py

• Environment configuration
• API initialization
• Logging setup
• Utility functions

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📁 Checkpoints

The checkpoints/ directory stores saved LangGraph execution states, allowing interrupted workflows to resume later.

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📝 Logs

Execution logs are written to the logs/ directory for debugging, monitoring, and tracing agent behavior.

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🎯 Learning Objectives

This project demonstrates:

• LangGraph Fundamentals
• Agentic AI
• Graph-based AI Workflows
• Tool Calling
• State Persistence
• Multi-step Reasoning
• Prompt Engineering
• API Integration
• FastAPI Deployment

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🚀 Future Improvements

• Memory Integration
• RAG (Retrieval-Augmented Generation)
• Multi-Agent Collaboration
• Streaming Responses
• Human-in-the-Loop Workflows
• Database Persistence
• Docker Support
• CI/CD Pipeline
• Cloud Deployment (Railway, Render, AWS)

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a feature branch

git checkout -b feature-name

3. Commit your changes

git commit -m "Add new feature"

4. Push your branch

git push origin feature-name

5. Open a Pull Request

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📄 License

This project is licensed under the MIT License.

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👨‍💻 Author

Ronit Raj

Computer Science Engineering Student | Python Developer | Agentic AI Enthusiast

Skills

• Python
• LangGraph
• LangChain
• Groq
• Gemini
• FastAPI
• AI Agents
• Machine Learning
• Generative AI

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⭐ If you found this project useful, please Star the repository and consider contributing!