Agentic Semantic Memory System
Empowers Claude agents with a sophisticated semantic memory system for learning, recall, and knowledge management.
About
This system provides Claude agents with an advanced agentic semantic memory, allowing them to effectively store, retrieve, and manage long-term knowledge. It leverages vector embeddings for efficient recall, combining semantic similarity with keyword matching for powerful hybrid search capabilities. Agents can learn from past experiences across sessions, consolidate similar memories, and manage the memory lifecycle through importance decay and expiration, ultimately enabling them to build robust knowledge graphs and continuously improve their performance.
Key Features
- Enables memory consolidation through deduplication and merging of similar memories.
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- Stores and retrieves memories with vector embeddings using OpenAI embeddings.
- Fully integrated with Claude's Model Context Protocol (MCP) for agent interaction.
- Performs hybrid search, combining semantic similarity with keyword matching.
- Manages memory lifecycle including importance scoring, decay, consolidation, and archival.
Use Cases
- Automating knowledge capture for software development tasks, such as bug fixes and new feature implementations.
- Structuring and retrieving complex information to enhance agent decision-making and planning processes.
- Enabling Claude agents to learn and adapt across multiple sessions for continuous improvement.
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