Multi-Agent LLM How I Use Camel And Langroid Libraries
AI Summary
Video Summary: Multi-Agent LLM Systems with Camel and Langro
- Introduction to Multi-Agent LLM Systems
- Definition of an agent as a wrapper around LLM (language model), akin to an agent in reinforcement learning.
- Explanation of a multi-agent system as an ensemble of multiple agents, illustrated by Meta’s GPT.
- Overview of Camel
- Camel is a multi-agent framework (both a research paper and Python library).
- It introduces a role-playing framework where users define roles for AI assistant and user.
- The AI user gives instructions while the AI assistant implements these instructions.
- Example usage involves defining an idea, roles, and task specifications.
- Various examples provided in the repository, particularly focusing on AI Society.
- Requires Python 3.9 or higher.
- Implementing Camel
- Basic structure with imports for colored text output and role-playing simulation.
- Include your OpenAI API key for functionality.
- A sample run illustrates the conversation flow between agents and task completion.
- Introduction to Langro
- Langro is a lightweight library for building multi-agent LLM applications.
- Supports vector stores (Cudr, Chroma) and function calling.
- Simple setup for defining and executing multi-agent tasks with minimal code required.
- Compared to Camel, Langro offers more flexibility and is designed for ease of use.
- Example of three communicating agents illustrating its functionality.
- Conclusion
- Comparison of Camel and Langro as effective tools for multi-agent systems.
- Encouragement for viewers to explore each library further with links provided in the description for additional resources and example prompts.