"ManicTime - Time Tracking Software" matching MCP servers:

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

Semantic search over Nordic economic data — market announcements, quarterly reports (162 companies), macro data (NO/SE/DK/FI), commodity prices, and press releases. 180,000+ vectors.

An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.

An MCP server that implements memory with decay mechanics, allowing AI agents to store and retrieve memories that fade over time unless accessed, with a permanent journal for verification.

Gives AI agents private, persistent, semantically searchable long-term memory with RAG, governed multi-agent scopes, and real-time memory-change subscriptions.

Provides long-term memory storage for AI assistants with semantic search, enabling persistent storage of preferences, decisions, and context with relationship tracking between memories.

Enables semantic search over local markdown note collections using vector embeddings, with real-time file watching and zero-config setup.

Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.

Enables persistent memory for AI systems by providing tools for episodic, semantic, and procedural data storage through a vector-and-graph-enhanced database. It allows models to maintain long-term continuity using similarity search, thematic clustering, and identity tracking.

Enables AI-powered analysis of Ethereum blockchain data through semantic search, natural language queries, and structured filtering. Provides comprehensive access to addresses, transactions, blocks, tokens, and smart contracts with real-time blockchain intelligence.

A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

Provides semantic search capabilities using Qdrant vector database with multiple embedding providers, including hybrid search, code indexing, and git history search. Adds optional time-based recency scoring to search results.

An MCP server that provides persistent semantic memory backed by PostgreSQL and pgvector for storing and searching thoughts via vector embeddings. It enables dimensional organization, conflict detection, and historical tracking of facts, decisions, and observations.

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.

Enables indexing and semantic search of software project files via MCP, with automatic file watching for updates.

Provides long-term memory for AI agents through time-decay validity scoring, so stored facts, notes, and entities are automatically forgotten or flagged as stale instead of being recalled forever. Enables logging high-signal memories, searching them with freshness guidance, extracting daily timelines, and maintaining long-lived entity references across sessions.

A local-first MCP server for persistent memory with vector search, metadata filtering, fact tracking, and graceful degradation when dependencies fail.

Enables AI agents to maintain persistent, multi-layer cognitive memory with vector search, deterministic verification, experiment tracking, and cost-aware recall, all through 87 MCP tools and a local Flask UI.

Enables semantic code search across multiple repositories using AST-aware chunking and relationship tracking. Supports local LLM embeddings, real-time indexing, and cross-codebase dependency analysis through vector and graph databases.