Multi-Agent AI Research System
Overview
This project implements a sophisticated multi-agent AI system designed to conduct comprehensive research across various websites based on user queries. Utilizing CrewAI, Langchain, and local open source model (dolphin-llama3), this system automates the process of gathering, analyzing, and synthesizing information from websites, forums, reddit and providing users with well-structured and insightful reports.
Features
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Dynamic Query Processing: Accepts user-defined queries for targeted website research.
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Multi-source Analysis: Searches across multiple relevant websites and subreddits to gather diverse perspectives.
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Engagement Metrics: Collects and analyzes post engagement data (upvotes, comments) to gauge topic popularity.
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Comprehensive Reporting: Generates detailed reports synthesizing findings into coherent, insightful content.
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Scalable Agent Architecture: Utilizes specialized AI agents for research and content creation tasks.
Technologies
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CrewAI: For orchestrating multiple AI agents.
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Langchain: For building applications with large language models.
-
Ollama:
Installation
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Clone the repository:
git clone https://github.com/strcoder4007/Multi-Agent-AI-System
cd Multi-Agent-AI-System
-
Install dependencies:
pip install -r requirements.txt
-
Set up API keys:
Usage
Run the main script:
python agent.py
Follow the prompts to enter your research query. The system will then:
- Conduct research across relevant subreddits.
- Analyze the gathered information.
- Generate a comprehensive report based on the findings.
System Architecture
-
Reddit Research Specialist: An AI agent specialized in navigating and extracting relevant information from various subreddit communities.
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Content Synthesizer and Analyst: An AI agent focused on analyzing research findings and creating coherent, insightful reports.
Output
The system produces two main outputs:
- A detailed bullet-point analysis of the research findings.
- A comprehensive blog post synthesizing the gathered information.
Future Enhancements
- Implement RAG (Retrieval-Augmented Generation) support for improved context and accuracy.
- Develop a user-friendly web interface for easier interaction with the system.
- Expand support for multiple, interconnected tasks to handle more complex research scenarios.
- Integrate with Reddit API for direct data access and more comprehensive analysis.
Contributing
Contributions to improve the system are welcome. Please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch
).
- Make your changes and commit (
git commit -am 'Add some feature'
).
- Push to the branch (
git push origin feature-branch
).
- Create a new Pull Request.