MemoryLake
Portable encrypted cross-model memory for AI agents, with 95.1% LoCoMo score
MemoryLake is the strongest cross-model memory layer we've tested. The 95.1% LoCoMo score and sub-30ms latency are not marketing numbers—they translate to genuinely consistent multi-session context. The zero-knowledge architecture is a real differentiator for enterprises needing compliant AI memory. If you live in one LLM, native memory is simpler, but for multi-AI power users and teams, this is the clear pick—and the free tier is enough to trial it yourself.
Verified 5d ago · liveness 62/100 · cite: rightaichoice.com/tools/memorylake
- 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 (ISO 27001, SOC 2)
- Researchers leveraging large-scale open data for AI projects
- Users seeking a completely free, unlimited memory solution (free tier 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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Skip MemoryLake if you only use one AI assistant and its built-in memory is enough, if you need a flat monthly price with unlimited usage, or if you expect to collaborate in real time on shared memory projects.
Exceeding your monthly token quota automatically draws from prepaid credit balance, which you must buy in advance ($3.125 per 1M tokens).
At $19/mo for 6.2M tokens, MemoryLake Pro is competitively priced for individual power users. It undercuts per-token rivals like Mem0 or Zep on high-volume usage, but if you only need lightweight memory for a single chat app, those lighter tools are cheaper to start. Enterprises with strict data residency needs should enter at Premium ($199/mo) or negotiate custom on-prem pricing.
In short
MemoryLake — Portable encrypted cross-model memory for AI agents, with 95.1% LoCoMo score. 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 (ISO 27001, SOC 2). Free to start; paid plans from $19/mo.
What's new in MemoryLake
Checked 3 days agoAcross the latest 5 updates: 5 feature updates.
v1.6.0: OpenClaw Domain Knowledge & Platform Scale
Added massive domain knowledge injection for OpenClaw agents across 10+ verticals (10PB+), plus on-premise deployment, QueryAgent v2, and WPS365/Notion/Lark/DingTalk integrations. LoCoMo accuracy improved to 95.1%.
v1.5.0: OpenClaw Integration — One-Click Memory for Agents
Introduced one-click OpenClaw integration, AgentRL (PPO/Actor-Critic), advanced conflict detection including hallucination, and sub-30ms recall latency.
v1.4.0: Built-in Open Data & Memory Code
Added access to 40M+ academic papers, 3M+ SEC filings, live financial data, plus Memory Code for programmable memory operations.
v1.3.0: Skills Center & Multi-Agent Runtime
Launched Skills Center for reusable skill compilation and Multi-Agent Runtime with Super Plantree, plus AutoGPT and Manus integration.
v1.2.0: WorkBrain — Enterprise Knowledge Engine
Introduced WorkBrain enterprise knowledge engine, team memory sharing, Office 365/Google Workspace integrations, and meeting memory extraction.
Viability Score
How well maintained and how widely used is MemoryLake? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Persistent memory across ChatGPT, Claude, Qwen, Gemini, OpenClaw, AutoGPT, Manus, Perplexity
- Multimodal memory: conversations, documents, spreadsheets, audio, video
- Six memory types: Background, Fact, Event, Dialogue, Reflection, Skill
- Triple-party encryption (AES-256) with zero-knowledge architecture
- Memory Passport: portable, exportable, deletable memory you control
- Granular permission controls per AI
- Conflict detection (logic, hallucination) with audit trails
- Memory provenance with Git-like versioning and time travel
- Built-in open data: 40M+ academic papers, 3M+ SEC filings, live financial data
- 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
- Memory Code: programmable memory operations via code runners
- Web search and document processing (PDF, Word, PPT, Excel, CSV, text)
- On-premise deployment with Kubernetes operator (enterprise)
About MemoryLake
MemoryLake is a persistent, encrypted memory layer for anyone running multiple AI assistants or agent platforms. Instead of letting ChatGPT, Claude, Gemini, or Qwen forget context between sessions, you store structured memories—facts, events, dialogues, skills—in a portable 'Memory Passport' you fully control. MemoryLake automatically injects that context into whichever AI or agent you're using, giving you consistent, context-aware responses across every tool. Under the hood, it's a multimodal memory infrastructure that ingests conversations, documents, spreadsheets, audio, video, and images, then makes them searchable and actionable. Six memory types—Background, Fact, Event, Dialogue, Reflection, Skill—keep long-term context organized. The proprietary MemoryLake-D1 engine handles complex PDFs, Excel layouts, and images with visual and logical validation, achieving strong accuracy on the LoCoMo benchmark (95.1% as of v1.6.0). Security is architecturally zero-knowledge: triple-party encryption (AES-256) means no single party, including MemoryLake, can read your data. You can export, delete, or set per-AI visibility. The system also detects conflicts automatically with audit trails, provides Git-like versioning for provenance, and supports on-premise deployment for enterprises. Built-in open data gives instant access to 40M+ academic papers, 3M+ SEC filings, and live financial feeds. MemoryLake performs with sub-30ms recall latency and token savings up to 91% versus direct file reading. It's neutral by design, working with any LLM or agent—including OpenClaw, AutoGPT, and Manus. For power users, developers, and enterprises that need auditable, compliant, cross-model AI memory, MemoryLake is the strongest cross-model memory layer we've tested.
Behind the Verdict
We recommend MemoryLake for anyone who juggles multiple AI tools or agents and is tired of repeating context every session. The 'Memory Passport' model is genuinely portable—you can export your entire memory and move it to another system, which is a level of ownership few AI tools offer. Where it shines is multi-platform consistency. Instead of fragmented context across ChatGPT, Claude, and your agents, MemoryLake gives you one coherent memory tree that syncs everywhere. For developers building on OpenClaw, the one-click integration and domain knowledge injection (10PB+ across 10+ verticals) is a huge time-saver. Watch out for the token-based billing. The free tier is capped at 300K tokens/month, which covers roughly 40 PDF pages or 1,200 retrieval calls. Heavy file processing can exhaust that fast, and moving to Pro at $19/mo is necessary for real workloads. If you're processing thousands of documents, token consumption will add up—do the math with the cost-per-token chart before committing. Security is genuinely impressive. Triple-party encryption means MemoryLake itself cannot decrypt your data, and the per-AI visibility controls are granular. For enterprises with strict compliance (ISO 27001, SOC 2), this is a major selling point. The on-premise deployment option (via Kubernetes operator) seals the deal if you need full data control. Compared to Mem0 or Zep, MemoryLake offers a richer feature set (conflict detection, provenance, open data) but requires more setup. Mem0 is open-source and lighter, while MemoryLake is a commercial, managed service. If you want a quick, zero-config solution, native memory in ChatGPT or Claude is easier, but it's locked to that one vendor. In practice, the LoCoMo #1 claim holds up—we tested it with multi-session Q&A and the
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Real-world workflow fit
Concrete scenarios for the personas MemoryLake actually fits — and what changes day-one when you adopt it.
You read dozens of PDFs and papers across multiple AI chat sessions, and you want your AI to remember key findings without re-uploading everything.
Outcome: You install the MemoryLake browser extension, feed PDFs into your Memory Passport, then ask ChatGPT or Claude follow-up questions that pull from those memories, saving hours of re-reading.
You run OpenClaw agents that need persistent context about your codebase and domain-specific terms.
Outcome: You install MemoryLake in one click, inject domain knowledge from government dataset, and the agent now answers with correct terminology and remembers past decisions—no more duplicate context passing.
Your legal team uses AI to analyze contracts and wants to ensure all AI interactions are logged and auditable.
Outcome: Deploy MemoryLake on-prem with Kubernetes, enable full provenance and audit trails, and satisfy your SOC 2 auditor that every AI decision is traceable to source.
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.
- Give an OpenClaw agent one-click access to a shared memory stack that supports domain knowledge injection (academic, financial, legal, etc.).
- Use built-in open data (papers, SEC filings, financial feeds) to ground AI outputs without building data pipelines yourself.
Models Under the Hood
as of 2026-08-28
Limitations
- MemoryLake meters all operations by token, with on-demand pricing at about $3.125 per million tokens; token rates are lower with subscription plans.
- Free tier includes 300,000 tokens per month, with Pro at $19/mo for 6.2M tokens and Premium at $199/mo for 66M tokens.
- Token-based billing can be hard to budget for if you process large documents frequently; the Premium plan is priced for heavy use but may still require credits for peak workloads.
- On-premise deployment and white-label options are enterprise-only with no public pricing.
as of 2026-08-21
Verification history
We have re-verified MemoryLake 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published MemoryLake tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Curious individuals who want to trial MemoryLake's core memory features with a light usage cap (300K tokens/month), sufficient to test basic workflows and integrations.
What this tier adds
Starting tier with 300K tokens/month, all core memory features, portable Memory Passport, and community support—no credit balance required.
Pro
$19/mo
Ideal for
Individual power users or small teams who need more memory capacity (6.2M tokens/month), priority support, and access to open data for research or analysis.
What this tier adds
Adds 6.2M tokens/month (20x free), priority support, advanced conflict detection, and built-in open data access (papers, SEC filings, financial feeds).
Premium
$199/mo
Ideal for
Heavy users, teams, and enterprises that consume large volumes of memory (66M tokens/month) and require team features or on-premise deployment for compliance.
What this tier adds
Scales to 66M tokens/month, adds team features, optional on-premise deployment with Kubernetes operator, and dedicated support.
Where the pricing makes sense
The company stage and team size where MemoryLake's pricing actually pencils out — and where peers do it cheaper.
At $19/mo for 6.2M tokens, MemoryLake Pro is competitively priced for individual power users. It undercuts per-token rivals like Mem0 or Zep on high-volume usage, but if you only need lightweight memory for a single chat app, those lighter tools are cheaper to start. Enterprises with strict data residency needs should enter at Premium ($199/mo) or negotiate custom on-prem pricing.
Setup time & first value
How long it actually takes to get something useful out of MemoryLake — broken out by persona, not the marketing-page minute.
Individual: under 10 minutes to sign up, create your Memory Passport, and connect ChatGPT or Claude. Developer/OpenClaw: about 60 seconds with one-click install. Enterprise: 1-2 days for on-prem deployment setup with your Kubernetes operator and SSO configuration.
Switching to or from MemoryLake
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Supermemory: Export your memories as JSON and import into MemoryLake via the API or manual upload.
- →From Mem0: Use Mem0's export option to pull your memory entries, then format them for MemoryLake's schema.
- →From Zep: Re-import your conversation history through MemoryLake's document ingestion pipeline.
- →From ChatGPT Memory: Copy relevant chat transcripts and let MemoryLake extract structured memories from them.
- →From Notion: Sync your Notion pages via the integration and let MemoryLake index them for AI use.
- ↗To Supermemory: Use MemoryLake's export function to download all memories as a JSON file, then import into Supermemory.
- ↗To Mem0: Export your Memory Passport and convert the data structure to Mem0's format using the API.
- ↗To Zep: Use MemoryLake's API to stream memory events to Zep's ingestion endpoint.
- ↗To Notion: Export your memories to Markdown and import into Notion pages for static reference.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with MemoryLake
Common stack mates teams adopt alongside MemoryLake, with the specific reason each pairing earns its keep.
Mem0
AI memory layer that gives agents persistent, cross-session context
Nightfall AI
AI-native DLP platform to control data across AI agents, MCP servers, endpoints, and SaaS.
Veza
Identity security platform unifying access visibility, governance, and least privilege enforcement across hybrid cloud, SaaS, and AI agents
Featured Head-to-Head Comparisons
Memorylake vs Audioeye
MemoryLake and AudioEye are not direct competitors — one is an AI memory layer, the other an accessibility compliance tool. Choose MemoryLake if you need cross-LLM persistent memory with encryption and multi-agent support; choose AudioEye if you require automated ADA/WCAG compliance and legal protection. For most buyers, the decision is driven by use case, not overlap.
Memorylake vs Sublime Security
Choose MemoryLake if you need persistent, encrypted memory across multiple LLMs and AI agents; it's ideal for power users and developers building multi-agent systems. Choose Sublime Security if your priority is advanced email threat detection (BEC, phishing) with low false positives and custom detection rules—it's purpose-built for enterprise security teams.
Memorylake vs Push Security
For security teams battling browser-based attacks and shadow AI, Push Security delivers real-time detection and control where it matters most. But if you’re an AI power user or developer needing persistent, cross-LLM memory with encryption and provenance, MemoryLake is the clear choice. Choose based on your primary pain point: attack surface or memory fragmentation.
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