README.md

metadata

title: Langgraph
emoji: 📈
colorFrom: yellow
colorTo: red
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: Langgraph

🚀 LangGraph: Building Stateful AI Agents as Graphs

Official Links

• GitHub Repository
• Documentation
• LangChain Academy Course (Free Intro)
• Prebuilt Agents & Templates

---

📌 Overview

LangGraph is a Python library for creating stateful, multi-step AI workflows using Large Language Models (LLMs). It enables dynamic decision-making through graph-based architectures, allowing developers to design agents that loop, branch, and interact with tools/APIs. Inspired by Pregel and Apache Beam, it’s widely used by companies like LinkedIn, Uber, and Elastic.

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🔑 Key Features

1. Graph-Based Workflows

   • Define workflows as nodes (tasks) and edges (connections).
   • Supports conditional edges (e.g., "If tool call needed, route to tools node").

2. State Management

   • Persist conversation history, tool outputs, or custom data across interactions using checkpoints.

3. Multi-Actor Support

   • Build collaborative agents (e.g., one agent retrieves data, another generates responses).

4. Prebuilt Agents

   • Use templates like create_react_agent for ReAct-style agents or customize workflows.

5. LangGraph Platform (Commercial)

   • Deploy agents at scale with streaming, background runs, and monitoring.

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🛠️ Getting Started

Installation

pip install -U langgraph  # Core library 
pip install langchain_groq streamlit  # For Groq models and Streamlit UI

Basic Example: ReAct Agent

from langgraph.prebuilt import create_react_agent
from langchain_groq import ChatGroq  # Changed import
import os

# Define a search tool (remains the same)
@tool
def search(query: str):
    if "san francisco" in query.lower():
        return "60°F, foggy"
    return "90°F, sunny"

# Initialize agent with Groq
model = ChatGroq(
    temperature=0,
    model_name="mixtral-8x7b-32768"  # Groq model name
)
checkpointer = MemorySaver()  # Persists state
app = create_react_agent(model, [search], checkpointer=checkpointer)

# Invoke with thread_id for state retention (same)
response = app.invoke(
    {"messages": [{"role": "user", "content": "Weather in SF?"}]},
    config={"configurable": {"thread_id": 42}}
)
print(response["messages"][-1].content)  # Outputs weather details

Key:

1. Uses langchain_groq.ChatGroq
2. Specifies Groq model name (mixtral-8x7b-32768 or llama2-70b-4096)
3. Requires GROQ_API_KEY environment variable

Set your API key:

os.environ["GROQ_API_KEY"] = "your-key-here"

---

🚀 Running the Streamlit App

This project includes a Streamlit-based web interface for interacting with the Groq ReAct Agent. Follow the steps below to set up and run the app.

Prerequisites

1. Python 3.8+: Ensure Python is installed on your system.
2. Groq API Key: Obtain your API key from the Groq Cloud Console.

Steps to Run

1. Clone the repository:

   git clone https://github.com/aitiwari/langgraph.git
   cd langgraph
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Set your Groq API key as an environment variable:

   export GROQ_API_KEY="your-api-key-here"
   

4. Start the Streamlit app:

   streamlit run app.py
   

5. Open your browser and navigate to the URL provided in the terminal (usually http://localhost:8501).

Using the App

• Input Field: Type your question or query in the chat input box at the bottom of the screen.
• Chat History: The conversation history will be displayed above the input field.
• API Key: If you didn't set the GROQ_API_KEY environment variable, you'll be prompted to enter it in the app.

Example Queries

• "What's the weather in San Francisco?"
• "Tell me about the weather in New York."

Stopping the App

To stop the Streamlit app, press Ctrl+C in the terminal where the app is running.

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🌟 Use Cases

1. Basic Chatbot

Prompt: What is generative AI?
Reference:

2. Chatbot with Tool

Prompt: Latest news from the India vs Australia match?
Reference:

3. Appointment Receptionist

Prompt 1: Book an appointment for priti.
Prompt 2: Yes or No.
Reference:

4. Customer Support

Reference:

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📚 Templates & Community

Explore prebuilt templates for chatbots, retrieval agents, and more:

langgraph new  # CLI to create apps from templates 

Template	Description	Link
ReAct Agent	Tool-calling agent with memory	GitHub
Retrieval Agent	RAG with dynamic context fetching	Docs

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📖 References

1. Official Documentation
2. GitHub Examples
3. Agentic RAG Guide
4. Production Case Studies

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For advanced use cases (multi-agent systems, human-in-the-loop), refer to the LangGraph Platform or explore the LangChain Academy.

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This version integrates the Streamlit app instructions seamlessly into the existing structure while maintaining the original tone and style.