This results in AI agents that respond with contextually rich, non‑repetitive answers while conserving token usage for higher cost-efficiency. It includes integrations with OpenAI and Ollama for embeddings and modular storage options like in-memory, JSON files, or custom databases. MemoriPy is ideal for building smarter chatbots, personal assistants, tutoring agents, or any LLM-based system that benefits from remembering past interactions
- AI Developers: Enhance LLM apps with memory without reinventing the wheel.
- Chatbot Builders: Ensure bots retain past dialogues for richer user engagement.
- EdTech Engineers: Track learner progress and customize training sessions.
- Research Teams: Investigate memory decay, clustering, and human-like recall patterns.
- Support Bot Creators: Enable agents to remember user preferences and history.
How to Use MemoriPy?
- Install the library: – pip install memoripy.
- Import and configure: Use MemoryManager with your choice of embedding/chat models like OpenAI or Ollama.
- Input new interactions: Feed prompts and responses into memory.
- Retrieve relevant context: MemoriPy uses embeddings, graph-based activation, and decay logic.
- Store memories : Choose JSON or in-memory storage; integrate with others as needed.
- Generate smarter replies: LLM prompts include retrieved memories to improve coherence and depth.
- Dual Memory System: Separates recent session memory from long-term recall.
- Semantic Clustering & Graph Models: Organizes memories by concept and relationship context.
- Adaptive Decay & Reinforcement: Forgetting irrelevant info, reinforcing essential details dynamically.
- Token Cost Reduction: Only relevant context sent to LLM reduces usage costs.
- Flexible Model & Storage Support: Works with OpenAI, Ollama, and JSON/in-memory options for maximum customization.
- Enables human‑like memory recall in AI agents.
- Reduces token usage and API costs.
- Highly modular—ideal for experimentation and research.
- Community-driven with active GitHub contributions.
- Requires programming knowledge to implement.
- Advanced use may demand tuning decay rates and memory thresholds.
- No production-level UI—requires CLI or code integration.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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