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
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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
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Groq Agent (Memory as Layer)
- Neural network layer integration
- Architecture comparisons
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Gemini Agent (Experimental Validation)
- Scalability demonstrations
- Information retrieval tasks
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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
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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
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Run the platform:
python main.py
- Access the web interface at
http://localhost:8000
- View demonstrations via
/demonstrate
endpoint - 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.