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MemOS
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harne
open-sourcememory-knowledge
11.7k
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+972
Stars/month
264
Commits (90d)
10
Releases (6m)
Star Growth
+2.9k (33.3%)estimated from velocity
Overview
MemOS provides a unified API for storing, retrieving, editing, and deleting memory structured as a graph. It supports multi-modal memory including text, images, and tool traces, and offers multi-cube knowledge base management with asynchronous ingestion via MemScheduler.
Deep Analysis
Key Differentiator
Provides a unified memory operating system with graph-structured memory that's inspectable and editable, not just a black-box embedding store.
⚡ Capabilities
- • persistent memory
- • hybrid retrieval
- • multi-modal memory support
- • knowledge base management
- • asynchronous ingestion
- • memory feedback and correction
🔗 Integrations
DeepSeek HarnessHermes AgentOpenClaw
✓ Best For
- ✓ AI agents needing long-term memory
- ✓ multi-agent collaboration systems
- ✓ developers building context-aware agents
✗ Not Ideal For
- ✗ end-user AI applications
- ✗ simple chatbot implementations
- ✗ non-AI systems
⚠ Known Limitations
- ⚠ requires integration with existing agent frameworks
- ⚠ no standalone end-user interface
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memvid
Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.
Works with MemOS
Tools that integrate with MemOS, often used together in the same stack.
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