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

1. OpenAI Agent (Neural Memory Module)

   • Demonstrates memory mechanisms
   • Real-time decay simulation
   • Interactive memory retrieval

2. Anthropic Agent (Memory as Context)

   • Historical context integration
   • Language modeling demonstrations

3. Mistral Agent (Memory as Gate)

   • Short-term/long-term memory gating
   • Efficiency demonstrations

4. Groq Agent (Memory as Layer)

   • Neural network layer integration
   • Architecture comparisons

5. Gemini Agent (Experimental Validation)

   • Scalability demonstrations
   • Information retrieval tasks

6. Cohere Agent (Innovations)

   • Memory design principles
   • Real-world applications

7. Emergence Agent (Analysis)

   • Architecture analysis
   • Scalability discussions

1. Install dependencies:

   pip install -r requirements.txt

2. 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

3. Run the platform:

   python main.py

1. Access the web interface at http://localhost:8000
2. View demonstrations via /demonstrate endpoint
3. Interact with agents through WebSocket connections
4. 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.