Multi-Agent Workflow with LangChain and LangGraph

This project demonstrates a collaborative multi-agent system using LangChain and LangGraph. It is designed to process user queries by leveraging two specialized AI agents: a Research Agent and a Writer Agent. Each agent performs a distinct role and collaborates to generate high-quality answers.

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Key Features

1. Dynamic Multi-Agent Collaboration:

   • The Research Agent fetches relevant information based on the user's query.
   • The Writer Agent synthesizes the research results into a concise and well-structured summary.

2. Role-Specific Prompts:

   • Tailored prompts ensure that each agent performs its role effectively using ChatPromptTemplate.

3. Workflow Orchestration:

   • Built using langgraph.graph.StateGraph to manage the execution order and interaction between agents.

4. Modular Design:

   • Each agent's functionality is encapsulated, making the system easy to extend or modify.

5. Simple Execution:

   • A single function, run_multi_agent_system, handles the user query and executes the workflow seamlessly.

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How It Works

Workflow

1. Input:

   • The user provides a query, such as a topic or question they need information about.

2. Agent Tasks:

   • Research Agent: Gathers detailed information related to the query.
   • Writer Agent: Summarizes the research results into a concise and polished format.

3. Output:

   • The final output is a user-friendly summary of the research results.

Example Query

For example, the query:

"What are the key challenges in AI-based electricity theft detection?"

Results in:

1. The Research Agent fetching detailed insights.
2. The Writer Agent summarizing those insights into a clear answer.

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Technology Stack

• LangChain:
  • Provides prompt management and integration with OpenAI models.
• LangGraph:
  • Manages the multi-agent workflow using a state graph.
• OpenAI API:
  • Powers the agents using GPT-3.5-turbo.

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Installation

Prerequisites

• Python 3.8 or later
• OpenAI API key

Steps

1. Clone the repository:

   git clone <repository-url>
   cd <repository-folder>

2. Create a virtual environment:

   python -m venv env
   source env/bin/activate  # On Windows: .\env\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set your OpenAI API key:

   export OPENAI_API_KEY="your-openai-api-key"  # On Windows: set OPENAI_API_KEY="your-openai-api-key"

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Usage

1. Run the script:

   python main.py

2. Enter your query when prompted:

   Enter your query: What are the key benefits of AI in healthcare?
   

3. View the final summarized answer:

   Final answer: AI in healthcare offers benefits like personalized treatments, early disease detection, and enhanced diagnostic accuracy.
   

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Code Overview

Core Components

1. Agents:

   • Research Agent: Gathers information based on the query.
   • Writer Agent: Summarizes the gathered information into a user-friendly answer.

2. Workflow Management:

   • Built using StateGraph to define and orchestrate the multi-agent flow.

Key Functions

• research_agent_func: Fetches detailed information using the research prompt.

• writer_agent_func: Summarizes the research results into a concise format.

• run_multi_agent_system(query): Executes the workflow for a given query and returns the final answer.

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Future Enhancements

• Add more specialized agents, such as:
  • Validator Agent to verify the accuracy of research.
  • Editor Agent to improve grammar and style.
• Expand the system to handle multi-query workflows.
• Integrate with external knowledge bases for enhanced research capabilities.

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Contact

For any questions or collaboration opportunities, feel free to reach out:

• Email: aman_srivastava14@outlook.com
• LinkedIn: InfiniteLoopster-coder
• GitHub: InfiniteLoopster-coder