"Codebeamer application lifecycle management platform" matching MCP servers:

Enables natural language management of Hammerspace storage clusters with automated file ingestion, tagging, tier management, and vector embedding generation. Supports real-time file monitoring, multi-format document processing, and Kubernetes-based ingestion workflows with Milvus integration.

Dual-mode tool providing long-term memory management for Claude Code and MCP clients, with semantic search and user/session isolation via Qdrant.

Self-hosted Mem0 MCP server integrating Qdrant, Neo4j, and Ollama for semantic memory search, graph entity relationships, and memory management via OpenMemory API.

Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.

An MCP server that integrates with LangChain and ChromaDB to provide documentation search for AI libraries and vector database management.

A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.

Enables users to build and manage a complete retrieval-augmented generation pipeline through conversation, including file ingestion, collection management, hybrid search, reranking, citations, and a guided setup wizard.

Enables persistent memory management with semantic vector recall, BM25 keyword search, and 3-way fused retrieval combining lexical, semantic, and tag-based signals.

A fully-local, three-layer memory plugin for Claude Code with Chinese support, offering hybrid FTS5/trigram and HNSW vector search, self-iterating memory management, consolidation, and a web console.

Persistent memory with knowledge graph visualization, semantic/hybrid search, importance scoring, and cloud sync (S3/R2) for cross-session context management.

Self-hosted mem0 MCP server for Claude Code that adds durable async ingestion, document support, and reranking, enabling persistent memory management with Qdrant, Neo4j, and Ollama.

A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.

Provides persistent memory management for AI agents with 14 tools, including semantic search, context synthesis, auto-context middleware, and write-back actions to external services like Slack.

Scalable, high-performance knowledge graph memory system with semantic search, temporal awareness, and advanced relation management.

A high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.

Enables a single MCP server for interacting with multiple vector databases through pluggable backends, starting with Pinecone. It supports schema-aware index management, upserts, hybrid search, embeddings, TTL conventions, and namespace operations.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

An MCP server for querying and managing LlamaIndex documents stored in Qdrant vector databases, with automatic embedding model detection and extensive tools for search, retrieval, and collection management.