Find related memories by combining knowledge graph traversal with semantic similarity, enabling discovery of associations between memory entries.
MIT
134,919 tools. Last updated 2026-05-25 21:11
"Hybrid Memory Models Combining Relational, Graph, and RAG Approaches" matching MCP tools:
- Perform hybrid web searches combining broad coverage with technical documentation focus. Choose between general web search or documentation-optimized mode to get relevant results with titles, URLs, and snippets.
- Creates or updates a node in a persistent graph memory for long-term RAG retrieval. Requires initialized NFT matrix; if missing, purchase license key first.Business Source 1.1
- Analyze monetization strategies including free, subscription, credits, and hybrid models to determine optimal approaches for your project.MIT
- Search memory files using hybrid, lexical, or vector methods to retrieve relevant information from stored data.MIT
- List running models to display which models are currently loaded in memory.AGPL 3.0
Matching MCP Servers
- Alicense-qualityCmaintenanceA modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.Last updatedMIT
- Alicense-qualityCmaintenanceEnables Claude to perform hybrid search across local documents by combining semantic vector retrieval and BM25 keyword matching for optimal context recovery. It supports multiple file formats including PDF, CSV, and Markdown, leveraging local Ollama models for private and efficient document querying.Last updated2MIT
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- Search your personal knowledge graph using hybrid search to find relevant thoughts, filter sources, and explore connections.MIT
- Update entities by adding observations to their data in the Elasticsearch Knowledge Graph, enhancing the memory-like storage and retrieval for AI models.MIT
- Export skills to Weaviate format for hybrid vector and BM25 search, enabling production-ready RAG applications.MIT
- Search the R2R knowledge base using semantic, hybrid, graph, or web search methods. Configure parameters manually or use presets for development, research, debugging, and production scenarios to find relevant documents and information.
- Create new report definitions in Acquia CDP by specifying name, type (CUBE or RELATIONAL), and configuration as JSON. Supports both cube-based and relational data reporting structures.
- Analyze complex data using hybrid local+cloud processing with reasoning, technical, or creative approaches to extract insights.
- Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.MIT
- Permanently remove incorrect or unwanted information from AI agent memory by ID, including deletion from vector index and knowledge graph.MIT
- Execute a full RAG pipeline to answer user questions by retrieving relevant document chunks and quoting them directly.MIT
- Retrieve memory graph nodes and edges for given entity names. Returns connected subgraph with edge weights and source provenance.MIT
- Free GPU VRAM by unloading cached ComfyUI models. Use between runs when switching model families or low on VRAM. Optionally unload only models or only memory.MIT
- Loads episodic context for a new task by combining RAG store, vault history, and sealed handoff data. Returns JSON with rag_matches, vault_history, handoff, and continuity type.MIT
- Reconstruct memory state and link graph at a specific timestamp to view past memories and connections exactly as they existed.MIT