How I Trained AI Agents to Automate my Work? (CrewAI)

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AI Summary

Summary of Video Transcript: Training Crew AI

• Introduction to Crew AI:
  • Crew AI involves multiple AI agents working together to automate complex tasks.
  • Automating tasks with Crew AI can save time and make automatic decisions.
  • Training Crew AI can lead to better results and less hallucination.
• Example Use Case:
  • A researcher agent searches the internet and sends results to a report analyst.
  • The report analyst then prepares a blog article.
  • This process automates what would be a time-consuming task if done manually.
• Setting Up Crew AI:
  • Install Crew AI using pip install crew Ai.
  • Export the Serer API key for internet searches.
  • Create crew research agents with crew AI create crew research agents.
  • Modify agents and tasks as needed.
  • Add tools to agents, such as the Serer Dev tool, for internet searching.
  • Add the OpenAI API key to use the GPT-4 model.
• Running Crew AI:
  • Navigate to the research agents folder and install packages with pip install.
  • Start running the crew with crew AI run.
  • The researcher agent searches for AI LMS information and sends summarized data to the report analyst.
• Training Crew AI:
  • Train the crew with crew AI train -n 2, which trains each agent twice.
  • Provide feedback to the agents to improve their output.
  • After training, the crew’s knowledge is stored in trained_agents_data.pickle and training_data.pickle.
  • The final report is saved in report.md.
• Testing Crew AI:
  • Run the trained crew with crew AI run.
  • Test the crew with crew AI test to see the final output and test scores.
  • The article produced by the crew is published on the website.
• Conclusion:
  • The video demonstrates how to set up, run, train, and test Crew AI.
  • The process is aimed at automating internet research and report generation tasks.

(Note: The summary does not include any CLI commands, website URLs, or detailed instructions as none were provided in the text.)