memobase and EverMemOS
These appear to be competitors, with both projects offering long-term memory solutions for AI agents and chatbots, specifically targeting different agent frameworks and chatbot applications.
About memobase
memodb-io/memobase
User Profile-Based Long-Term Memory for AI Chatbot Applications.
Structures user data into dynamically-evolving profiles and timestamped event timelines, enabling sub-100ms memory retrieval through SQL queries rather than vector search. Supports Python, Node.js, and Go SDKs with batch processing buffers to reduce LLM token costs by 40-50%, and includes a Model Context Protocol (MCP) server for seamless integration with AI frameworks. Achieves state-of-the-art performance on the LOCOMO benchmark while maintaining configurable memory schemas, allowing developers to define precisely which user attributes their applications capture.
About EverMemOS
EverMind-AI/EverMemOS
Long-term memory for your 24/7 OpenClaw agents across LLMs and platforms.
Provides structured memory extraction from conversations using LLM-based encoding, organizes data into episodes and user profiles stored across MongoDB/Milvus/Elasticsearch, and exposes a REST API for retrieval with BM25, semantic embedding, and agentic search capabilities. Integrates directly with OpenClaw agents and supports TEN Framework for real-time applications, Claude Code plugins, and computer-use scenarios requiring persistent context across sessions.
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