CrewAI Multi-Agent-Orchestration - NEW AI-Framework build on top of LangChain

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AI Summary

Crew AI Project Overview

• Introduction to Crew AI
  • Combines multiple autonomous agents into a team.
  • Agents are LLM-based systems that autonomously process user requests.
• Agent Capabilities
  • Utilize tools for tasks (e.g., database access, internet searches).
  • Recursive self-prompting to find solutions.
• Framework Features
  • Built on Leng Chain with understandable syntax.
  • Compatible with OpenAI and open-source LLMs like Llama.
• Setup Process
  • Install Crew AI with pip install crei.
  • Install tools like Duck Duck Go search.
  • Import necessary classes: Agent, Task, Crew, Process, and chat models.
• Configuration
  • Set environment variables for API keys.
  • Instantiate tools and LLMs (e.g., CH OPI or Llama LLM).
• Creating Agents
  • Define agent roles, goals, and backstories.
  • Assign tools and set delegation permissions.
  • Enable verbose output for insight into agent processing.
• Example Agents
  • AI Market Researcher: Searches for AI trends and market demands.
  • Brand Manager: Creates brand names for new AI products.
  • Marketing Strategist: Develops marketing strategies and personas.
• Task Creation
  • Use Task class to define tasks with descriptions and assign responsible agents.
• Combining Agents and Tasks
  • Use Crew class to group agents and tasks.
  • Set task processing order (sequential, concurrent, hierarchical in future).
• Execution
  • Call kickoff method to start task processing.
  • Agents use their tools to execute tasks sequentially.
• Results
  • Agents complete tasks, such as identifying target groups and creating personas.
• Conclusion
  • Crew AI demonstrates ease of use and clear philosophy.
  • Anticipation for the project’s evolution.
• Closing
  • Acknowledgment of the project’s promise and potential.