MemoryLake：Persistent multimodal memory for AI agents, copilots, and enterprise workflows

#rag #showdev #agents #ai

I've been building AI agents for the past few years, and kept hitting the same wall: they forget everything between sessions. You spend weeks training an agent on your workflow, then it wakes up the next day like it's never met you.

That's why we built MemoryLake (https://memorylake.ai) – a persistent, multimodal memory layer for AI agents that survives across sessions, platforms, and even model switches.

The Problem

Most AI "memory" solutions today are just key-value stores that remember user preferences ("I live in Beijing"). That's useful, but it's not real memory. Real memory means:

Cross-session continuity – Your agent remembers the project you discussed 3 months ago
Conflict resolution – When different sources contradict each other, the system detects and resolves it
Multimodal understanding – It can parse your Excel sheets, PDFs, meeting recordings
Provenance tracking – Every fact is traceable to its source (Git-like version control)
Zero trust architecture – We can't read your memories. Literally. Three-party encryption means no single entity holds all keys.

What Makes It Different

vs. RAG/Vector DBs: Those are retrieval layers. MemoryLake is a cognitive layer – it understands, organizes, and reasons over memories.

vs. Long context: Longer context ≠ memory. MemoryLake compresses and structures information, cutting token costs by up to 91% while maintaining 99.8% recall accuracy.

vs. ChatGPT Memory / Claude Projects: Those are siloed. MemoryLake is your "memory passport" – one memory layer that works across Hermes,OpenClaw, ChatGPT, Claude, Kimi, any LLM.

Tech Highlights

MemoryLake-D1 VLM – domain model for multimodal memory extraction (99.8% accuracy on complex docs)
Temporal knowledge graph – Tracks how facts evolve over time
Multi-hop reasoning – Sub-second queries across millions of memory nodes
Built-in open data – 40M+ papers, 3M+ SEC filings, 500K+ clinical trials, real-time financial data

Real-World Use

We're serving 2M+ users globally. Enterprise customers include major document platforms and mobile office apps processing 100+ trillion records. In head-to-head tests with cloud giants, we've achieved 10x better cost/performance.

We recently launched Hermes/OpenClaw integration – if you're running agents, you can plug in MemoryLake in 60 seconds.

Open Questions

How do you handle memory decay? (We're experimenting with confidence-weighted forgetting)
Should memory be mutable or append-only? (Currently hybrid – facts are versioned, events are immutable)
What's the right granularity for memory isolation? (We support global/agent/session levels)

Would love your feedback, especially from folks running production agents or working on long-context systems.

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