MemoryLake
Portable encrypted AI memory that works across every LLM.
MemoryLake solves a real pain: AI amnesia across models. The encryption architecture is genuinely strong, and the LoCoMo benchmark leadership proves recall accuracy. Free tier is generous enough to trial, but heavy users should plan for the $19/mo Pro plan. Best for multi-AI power users who value privacy and control.
- AI power users who interact with multiple LLMs and want unified memory
- Developers building multi-agent systems requiring persistent context
- Enterprises needing compliant, auditable AI memory infrastructure
- Researchers leveraging large-scale open data for AI projects
- Users seeking a completely free, unlimited memory solution (free tier is capped at 300K tokens)
- Teams needing real-time collaboration features (memory is individual passport-based)
- Users preferring a simple, no-configuration memory tool (requires some setup and API usage)
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In short
MemoryLake — Portable encrypted AI memory that works across every LLM. Best for AI power users who interact with multiple LLMs and want unified memory, Developers building multi-agent systems requiring persistent context, Enterprises needing compliant, auditable AI memory infrastructure. Free to start; paid plans from $19/mo.
Viability Score
How likely is MemoryLake to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Persistent memory across ChatGPT, Claude, Qwen, Gemini, OpenClaw, AutoGPT, Manus, Perplexity
- Five memory types: Background, Fact, Event, Dialogue, Reflection, Skill
- Triple-party encrypted memory (AES-256, E2E) with zero-knowledge architecture
- Memory passport: portable, exportable, deletable
- Granular permission controls: each AI sees only what you allow
- Conflict detection: logic, implicit knowledge, and hallucination conflicts
- Memory provenance: full history traceability (Memory Time Travel)
- Built-in open data: 40M+ academic papers, SEC filings, financial data, clinical trials
- Skills Center: compile data/rules/knowledge into reusable skills
- Multi-agent orchestration via OpenClaw (Super Plantree, Team management)
- AgentRL: PPO and Actor-Critic reinforcement learning for agents
- Web search and document processing (PDF, Word, PPT, Excel, CSV, text)
- ISO 27001, SOC 2 Type II, GDPR, CCPA compliance
- Token-based billing with credit balance and auto-recharge
- On-premise deployment with Kubernetes operator (enterprise)
About MemoryLake
MemoryLake is a persistent, encrypted memory layer that stores structured context (facts, events, dialogues, reflections, skills) in a portable passport you own. It works across ChatGPT, Claude, Qwen, Gemini, OpenClaw, AutoGPT, Manus, and any OpenAI-compatible API, enabling cross-session and cross-model continuity. The platform uses triple-party encryption (AES-256) so no single party—including MemoryLake—can read your data. Key capabilities include five memory types, conflict detection with full audit trails, memory provenance (Git-like versioning), and granular permission controls per AI. Built-in open data access gives you 40M+ academic papers, 3M+ SEC filings, and live financial data without setup. The Skills Center lets you compile rules into reusable skills, and multi-agent orchestration via OpenClaw coordinates agent teams. MemoryLake recently hit #1 on the LoCoMo memory benchmark with 94%+ accuracy and achieves sub-30ms recall latency at scale using the D1 v2 engine. It's designed for power users, developers, and enterprises that need secure, portable memory across multiple AIs. Compared to built-in AI memory (ChatGPT Memory, Claude Memory) or open-source alternatives like Mem0 and LangMem, MemoryLake offers stronger encryption, richer memory types, and cross-model portability—but requires token-based budgeting and individual passports rather than real-time team collaboration.
Behind the Verdict
MemoryLake is for anyone frustrated by starting over every time they switch AI models—ChatGPT doesn't know what you told Claude, and vice versa. We'd reach for this when you're actively using two or more LLMs and want continuity without copying/pasting context. The triple-party encryption is architecturally solid; MemoryLake genuinely cannot read your data, which is rare. Where it bites: the token-based pricing. Free tier (300K tokens/month) is enough to test, but heavy document processing or frequent memory inputs will push you to $19/mo Pro or $199/mo Premium. Teams expecting real-time shared memory will be disappointed—memory is individual passport-based, not collaborative. The closest alternative is Mem0, which offers open-source memory with similar portability but weaker encryption and no built-in open data or conflict detection. MemoryLake is more polished for enterprise compliance (ISO 27001, SOC 2, GDPR) and has real benchmark proof. In practice, setup requires some API configuration and understanding of memory types. It's not a plug-and-play consumer product. But for developers building multi-agent systems or power users juggling several AIs, it's the most complete memory layer we've seen.
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Use Cases
- Maintain consistent personal context across ChatGPT, Claude, and Gemini sessions.
- Build a multi-agent research assistant that remembers findings across conversations.
- Deploy compliant enterprise AI memory that satisfies ISO 27001 and SOC 2 audits.
- Create reusable Skills from frequently used document analyses or data transformations.
- Power an AI customer support agent with persistent knowledge of past interactions.
- Enable cross-team memory sharing with isolation policies for sensitive data.
Models Under the Hood
Limitations
- Free tier is limited to 300K tokens/month (~40 pages or 164K characters of memory input).
- Token consumption per operation is fixed: 7,500 tokens per page/image, 45,000 per worksheet/file, 1.83M per 1M characters of memory input.
- Retrieval calls are cheap (250 tokens each) but web search costs 4,800 tokens per search.
- On-premise deployment and white-label options are enterprise-only with no public pricing.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Integrations
Resources & Guides
- Quickstartmemorylake.ai
Getting Started · MemoryLake
Get up and running fast from memorylake.ai
- Documentationmemorylake.ai
Memory Types · MemoryLake
Full product docs from memorylake.ai
- Documentationmemorylake.ai
Api Reference · MemoryLake
Full product docs from memorylake.ai
- Documentationmemorylake.ai
Openclaw Integration · MemoryLake
Full product docs from memorylake.ai
- Documentationmemorylake.ai
Skills Center · MemoryLake
Full product docs from memorylake.ai
- Resourcememorylake.ai
Token Saving Calculator · MemoryLake
Helpful link from memorylake.ai
- Documentationmemorylake.ai
Security · MemoryLake
Full product docs from memorylake.ai
- Documentationmemorylake.ai
On Premise Deployment · MemoryLake
Full product docs from memorylake.ai
- Documentationmemorylake.ai
Comparison · MemoryLake
Full product docs from memorylake.ai
- Documentationmemorylake.ai
Faq · MemoryLake
Full product docs from memorylake.ai
Official links
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