LLM Agent Pipeline with LangGraph & Groq LLaMA3

A lightweight, modular LLM agent framework built using LangGraph, powered by Groq's ultra-fast LLaMA3-8B model. Demonstrates goal-oriented agents that break down user prompts into sub-tasks, execute them using LLM reasoning, and provide structured summaries through graph-based flow.

Features

• Modular graph architecture using LangGraph
• Ultra-fast LLM responses with Groq's llama3-8b-8192
• Dynamic planning, execution, and summarization workflow
• Clean output at each step for inspection and reuse
• Extensible framework for tools, memory, and custom nodes

Architecture Flow

graph TD
    A[User Input] --> B[Planner Node]
    B --> C[Executor Node]
    C --> D[Summarizer Node]
    D --> E[Final Output]

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LangGraph Flow Pattern

The project implements a StateGraph pattern with three main nodes:

• Planner Node: Analyzes user input and breaks down complex tasks into actionable sub-tasks
• Executor Node: Processes each sub-task using LLM reasoning and generates intermediate results
• Summarizer Node: Consolidates all outputs into a coherent final response

Each node maintains state context and can pass information to subsequent nodes, enabling complex multi-step reasoning workflows.

Tech Stack

Component	Purpose
LangGraph	State-based flow control for LLM agents
LangChain	LLM interface abstraction
Groq API	High-speed inference with LLaMA3
Python	Core logic and execution
dotenv	Secure API key management

Quick Start

Setup

# Install dependencies
pip install -r requirements.txt

# Configure environment
echo "GROQ_API_KEY=your_groq_api_key_here" > .env

# Run application
python main.py

Example Usage

Input: "Plan and write a blog post on benefits of AI in education"

Planner Output:

• Research recent uses of AI in education
• Draft key benefits with examples
• Conclude with future outlook

Final Output: Comprehensive blog-ready content connecting all planned points

Why LangGraph?

LangGraph enables building stateful, multi-step agents that mirror human reasoning processes. This project provides a practical implementation of LLM-powered pipelines with clear separation of planning, execution, and summarization phases.

Extension Ideas

• Integrate web search capabilities (SerpAPI)
• Add persistent memory for long-term task tracking
• Convert to CLI application or Streamlit dashboard
• Implement custom tool integrations

Requirements

• Python 3.8+
• Groq API key
• LangGraph and LangChain dependencies

License

MIT License