This platform implements seven AI agents demonstrating key concepts from the paper "Titans: Learning to Memorize at Test Time". Each agent specializes in a different aspect of the architecture and works collaboratively to provide a comprehensive understanding.
The development of the Github Repository was inspired by the "Titans: Learning to Memorize at Test Time" paper. To read the full paper, visit https://arxiv.org/pdf/2501.00663v1
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OpenAI Agent (Neural Memory Module)
- Demonstrates memory mechanisms
 - Real-time decay simulation
 - Interactive memory retrieval
 
 - 
Anthropic Agent (Memory as Context)
- Historical context integration
 - Language modeling demonstrations
 
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Mistral Agent (Memory as Gate)
- Short-term/long-term memory gating
 - Efficiency demonstrations
 
 - 
Groq Agent (Memory as Layer)
- Neural network layer integration
 - Architecture comparisons
 
 - 
Gemini Agent (Experimental Validation)
- Scalability demonstrations
 - Information retrieval tasks
 
 - 
Cohere Agent (Innovations)
- Memory design principles
 - Real-world applications
 
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Emergence Agent (Analysis)
- Architecture analysis
 - Scalability discussions
 
 
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Install dependencies:
pip install -r requirements.txt
 - 
Set up environment variables in
.env:- OPENAI_API_KEY
 - ANTHROPIC_API_KEY
 - MISTRAL_API_KEY
 - GROQ_API_KEY
 - GOOGLE_API_KEY
 - COHERE_API_KEY
 - EMERGENCE_API_KEY
 
 - 
Run the platform:
python main.py
 
- Access the web interface at 
http://localhost:8000 - View demonstrations via 
/demonstrateendpoint - Interact with agents through WebSocket connections
 - View real-time visualizations and metrics
 
- Real-time demonstrations
 - Interactive agent communication
 - Dynamic visualizations
 - Performance metrics
 - Collaborative analysis
 
The platform uses a modular architecture where each agent implements the base TitansAgent interface. The main orchestrator manages agent interactions and provides a unified API for demonstrations and user interactions.