Provides LLM agents with a persistent, hippocampal-style memory layer, enabling novelty-gated memorization, hybrid semantic and keyword recall, reflection, and consolidation across global and project scopes via MCP tools.
Enables AI agents to maintain persistent memory across sessions by capturing conversations, extracting durable knowledge, and injecting relevant context, supporting various MCP-compatible platforms.
A production-grade MCP server that provides persistent long-term memory for AI agents using MongoDB, enabling them to store, search, update, delete, retrieve, and summarize structured project memories across developer workflows.
Enables MCP-compatible agents to automatically distill conversations into durable local memory and recall relevant context on demand through seven MCP tools, using user-configurable OpenAI-compatible endpoints for extraction and embeddings.
Persistent memory for AI agents. Store, recall, and share knowledge across sessions with five MCP tools: remember, recall, context, forget, and share. Includes semantic search and agent/user/org scoping.
Provides persistent, searchable memory for AI agents across any MCP-compatible client, storing project context, user preferences, and session learnings locally in SQLite with tools to save, retrieve, search, and manage them.
