"Temporal" matching MCP servers:

Gives AI agents persistent memory with bi-temporal tracking, automatically extracting entities from natural language and enabling time-travel queries to understand facts as they existed at any point in history.

A local, offline memory server for AI agents that stores and retrieves memories without LLM calls, enabling persistent cross-session memory with bi-temporal querying and auditable records.

An MCP server that gives AI agents durable, temporal memory over local markdown vaults, with tools for searching, asserting facts, querying point-in-time state, and reinforcing useful knowledge.

Durable hybrid memory for AI agents. Combines vector search, BM25, temporal retrieval, and optional Memgraph knowledge graph via reciprocal rank fusion. 6 MCP tools: health, search, answer, feedback, graph_query, graph_add_triple. Self-hosted with Qdrant backend.

Local-first memory for Claude Code and any MCP client: hybrid vector + keyword search and a bi-temporal knowledge graph in one SQLite file. Local embeddings, no API key, $0/token.

Enables AI agents to store, retrieve, and connect information in a Neo4j graph database as persistent memory, with semantic relationships, natural language search, and temporal tracking across conversations.

Enables AI agents to record, recall, correct, and forget evidence-backed factual claims with temporal history, while explaining whether remembered information is current, historical, or contested.

Provides AI agents with a temporal knowledge graph where knowledge relevance decays over time, enabling memory recall, reinforcement, and auto-archiving through MCP tools.

Enables AI agents and MCP clients to store, retrieve, and manage bi-temporal, graph-aware facts in an embedded SQLite memory engine with token-budgeted recall and contradiction tracking.

Local-first temporal knowledge graph with six read-only MCP retrieval tools. Supports Ollama and embedded LadybugDB; additions use a separate CLI proposal queue with human approval and explicit application.

Cross-client AI memory hub for AI agents. Lets Claude Code, Cursor, Windsurf, and 20+ other clients share one physical SQLite memory database. Features bi-temporal time axes, source attribution, Q-Value usage-based ranking, and FTS5 full-text search with SQLite triggers.

Long-term memory for Claude Code, Cursor, and MCP clients with zero-config embedded PostgreSQL. Stores and retrieves facts, preferences, and decisions using 4-channel semantic, BM25, entity, and temporal search with cross-encoder reranking.

Long-term persistent memory for AI agents with hybrid search (DiskANN + FTS), bi-temporal knowledge graph, and 81.1% LongMemEval accuracy.

Persistent AI memory server with hybrid search and embedded sync. Enables AI agents to store, retrieve, and manage information across sessions with temporal knowledge graph support.

Enables AI coding tools and developers to store, search, and supersede durable engineering discoveries with temporal validity, and to coordinate active work across sessions via MCP.

Persistent memory system for AI agents that records episodic memories with care-weighting and emotional valence, and provides full-text search with temporal chaining and automatic consolidation.

Portable, provider-agnostic memory for AI agents using Azure Data Explorer (Kusto) with temporal and semantic scoring.

Open-source AI memory layer for LLM agents. Importance scoring, temporal decay, hierarchical memory (facts, summaries, themes), YMYL prioritization, and active retrieval with contradiction detection. Supports OpenAI, Anthropic, Ollama. Local-first with SQLite + FAISS.

Provides AI agents an auditable memory layer with provable deletion via signed certificates, a tamper-evident hash-chained audit trail, and bi-temporal time-travel recall over SQLite or Postgres.

A local-first persistent memory server that uses Voronoi partitioning and temporal intelligence to provide LLMs with long-term context across different sessions and clients. It features automatic drift detection to prevent hallucinations by identifying and penalizing outdated information as project requirements evolve.