Build a Multi-Agent AI App from Scratch – no frameworks needed 🔥

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AI Summary

Video Summary

Building a Multi-Agent AI System from Scratch

• Introduction
  • The video is a tutorial on building a multi-agent AI system without using orchestration frameworks.
  • The focus is on pure Python development for those interested in learning the process.
  • Large language models (LLMs) are leveraged for task performance.
  • The tutorial covers both open-source and closed-source LLMs, with this video focusing on OpenAI and a future video on using oLama.

AI Agents

• AI agents are like AI employees that perform tasks, collaborate, and make decisions.
• They can be autonomous, making decisions without human intervention.

Development Process

• The tutorial is lengthy, aimed at beginners to experts.
• It includes diagrams, workflows, logging, and explanations of each part of the code.
• The goal is to build a Streamlit application that can handle various tasks such as summarizing medical text, writing and refining research articles, and sanitizing medical data.

Streamlit Application

• The application has a user interface built with Streamlit.
• It features tasks like summarizing medical text, writing/refining research articles, and sanitizing medical data.
• The app demonstrates how multiple AI agents collaborate to generate outputs.

Code Walkthrough

• The tutorial goes through the code line by line, explaining each part.
• It covers creating a virtual environment, installing dependencies, and setting up the project structure.
• The code includes creating base classes for agents, defining tools for summarization, article writing, data sanitization, and validation.
• The tutorial also covers logging and monitoring setups.

Running the Application

• The app is run using Streamlit, and the tutorial demonstrates its functionality.
• The video shows how to input tasks and receive outputs from the AI agents.

Conclusion

• The tutorial concludes with an overview of the system built and encourages viewers to clone the GitHub repository, contribute, and experiment with adding more tools and functionalities.

GitHub Repository

• The complete code is available on GitHub for viewers to access, clone, and contribute to.

Next Steps

• A future video will cover building the same system using the oLama model.

Detailed Instructions and URLs

No specific CLI commands, website URLs, or detailed instructions were provided in the summary.