MemoriPy

MemoriPy — AI Memory Layer for Smarter Agents is an open-source tool that equips AI agents with short-term and long-term memory, transforming repetitive systems into context-aware, intelligent assistants. Designed for developers, Memoripy helps create smarter conversations, improves accuracy, and reduces token usage by retrieving only the most relevant data. It integrates effortlessly with platforms like OpenAI, Ollama, and others, enabling a wide range of AI-driven applications from support bots to personal assistants and learning agents.

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Key Value Propositions

• Enhances interactions with context-rich memory, enabling more natural and productive conversations.
• Improves accuracy by prioritizing relevant information through concept clustering and memory decay.
• Reduces costs by optimizing LLM calls and minimizing token usage.
• Seamless integration with popular AI runtimes and platforms for quick adoption.

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How It Works

1. Memory Layer: Stores and retrieves information across short-term and long-term contexts.
2. Context Retrieval: Uses relevance ranking to fetch pertinent memories for current interactions.
3. Memory Decay & Clustering: Applies decay over time and clusters concepts to maintain useful, concise context.
4. Integration: Works with your existing AI stack (e.g., OpenAI, Ollama) with minimal changes.
5. Consumption: The retrieved memories are fed back into the LLM to produce context-aware responses.

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Use Cases

• Support Bots: Remember past conversations to resolve queries faster and more accurately.
• Personal Assistants: Adapt to user preferences and evolve over time.
• Learning Agents: Build autonomous systems that improve with each engagement.
• Smart Retail: Improve recommendations by recalling customer purchases and preferences.

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How to Get Started

• Install via pip:

pip install memoripy

• Explore documentation and examples on the official repository to integrate memory capabilities into your AI agents.

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Safety and Ethical Considerations

• Manage memory responsibly with privacy controls, especially regarding sensitive user data.
• Clearly communicate memory usage to users and provide options to opt out or delete memories as needed.

Core Features

• Short-term and long-term memory layers for AI agents
• Context-aware responses with memory retrieval
• Concept clustering and memory decay for relevance and freshness
• Token-efficient operation to reduce LLM call costs
• Seamless integration with OpenAI, Ollama, and other platforms
• Developer-focused, open-source tooling with extensible architecture
• Support for diverse AI-driven use cases (support bots, personal assistants, learning agents, smart retail)