Inflleenet AdVenteers LLM Agent Network Proposal
Table of Contents
Project Overview
The Inflleenet AdVenteers project aims to create a distributed system simulation where AI agents navigate a network, interact with content, and generate user data while a player attempts to prevent full data synchronization across the network.
LangChain-based Agent Network
Core Agents
Network Simulator Agent
- Purpose: Manage the overall simulation environment
- Responsibilities:
- Initialize and maintain the network structure
- Handle node connections, status, and latency
- Coordinate agent movements and interactions
- LangChain Tools:
- Custom tools for network manipulation
- SQLite tool for storing network state
Player Interface Agent
- Purpose: Facilitate interaction between the human player and the simulation
- Responsibilities:
- Present game state and options to the player
- Interpret and execute player actions
- Provide feedback on the consequences of player actions
- LangChain Tools:
- Human input/output tools
- Custom tools for action execution
Philosopher Agents (Wittgenstein, Alonzo Church, Bertrand Russell)
- Purpose: Simulate the behavior of the main AI agents in the network
- Responsibilities:
- Navigate the network based on interests and available connections
- Generate search queries and interaction data
- Adapt behavior based on network conditions
- LangChain Tools:
- Custom tools for movement and data generation
- VectorStore for maintaining agent knowledge and interests
Data Synchronization Agent
- Purpose: Manage the flow and synchronization of data across the network
- Responsibilities:
- Track data generation and movement
- Implement synchronization algorithms
- Detect full network synchronization
- LangChain Tools:
- Custom tools for data management
- VectorStore for efficient data comparisons
Ad Targeting Agent
- Purpose: Generate targeted ads based on synchronized user data
- Responsibilities:
- Analyze user search history and interests
- Create relevant ad content
- Deliver ads to appropriate nodes
- LangChain Tools:
- OpenAI functions for content generation
- VectorStore for ad relevance matching
Support Agents
Logging and Analytics Agent
- Purpose: Track and analyze game statistics and player performance
- LangChain Tools:
- SQLite tool for data storage
- Pandas DataFrame agent for data analysis
Difficulty Scaling Agent
- Purpose: Adjust game difficulty based on player performance
- LangChain Tools:
- Custom tools for difficulty parameter adjustment
Alternative Agent Frameworks
AutoGPT
- Advantages:
- Long-term memory and goal-oriented behavior
- Built-in internet access for up-to-date information
- Potential Use: Enhance Philosopher Agents with more autonomous behavior
BabyAGI
- Advantages:
- Task creation and prioritization
- Recursive task breakdown
- Potential Use: Improve Player Interface Agent for more dynamic gameplay options
CAMEL (Communicative Agents for "Mind" Exploration of Large Scale Language Model Society)
- Advantages:
- Multi-agent role-playing capabilities
- Enhanced inter-agent communication
- Potential Use: Implement more complex interactions between Philosopher Agents
Microsoft's TaskMatrix
- Advantages:
- Integration with various AI models and tools
- Visual and code generation capabilities
- Potential Use: Enhance the simulation's visual representation and code generation aspects
Implementation Strategy
Phase 1: Core Simulation
- Develop Network Simulator Agent
- Implement basic Philosopher Agents
- Create Player Interface Agent
Phase 2: Data Management
- Develop Data Synchronization Agent
- Implement UUID and search history tracking
- Create Ad Targeting Agent
Phase 3: Enhancement and Scaling
- Implement Support Agents
- Integrate alternative agent frameworks for specific tasks
- Optimize performance and scalability
Phase 4: Testing and Refinement
- Conduct thorough testing of the agent network
- Gather user feedback
- Refine agent behaviors and game mechanics
Conclusion
This LLM agent network proposal provides a comprehensive framework for developing the Inflleenet AdVenteers project. By leveraging LangChain and other agent frameworks, we can create a dynamic and engaging simulation that captures the complexities of data synchronization in distributed networks.