MemoryLake
MemoryLake is portable AI memory for ChatGPT, Claude, Codex and Gemini — encrypted, exportable, and shared across every agent you run.
Portability plus audit trail is the reason to pay here, not raw recall. If you bounce between Claude Code, Codex and OpenClaw daily, typed memories, conflict resolution and provenance chains are tangible within a month. Watch the write economics: those 826 Pro pages are the entire month, so document-heavy pipelines should model credits before committing.
Verified 3d ago · liveness 78/100 · cite: rightaichoice.com/tools/memorylake
- People running three or more assistants who want one memory that follows them between Claude Code, Codex, OpenClaw and ChatGPT
- Developers building multi-agent systems that need shared, versioned, conflict-resolved context
- Regulated enterprises that need exportable provenance chains, immutable audit logs and ISO 27001 / SOC 2 Type 2 coverage
- Research, legal and finance teams querying papers, SEC filings, patents and live market data from inside the same memory
- Anyone using a single AI assistant, where built-in memory is simpler and free
- Document-heavy intake pipelines that will exhaust Pro's roughly 826 PDF pages per month without budgeting credits
- Teams expecting real-time collaborative memory editing — memory is individual Passport-based
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Skip MemoryLake if you only ever use one assistant, since native memory is already simpler and you'd be paying token-per-page for context that app already keeps for you.
Every operation is token-metered, and file, image and text processing count as memory writes rather than queries — so a document-heavy month eats your quota faster than retrieval-heavy use.
For a solo power user, $19/mo Pro (about $2.58 per 1M tokens if you pay yearly) sits in the same range as a single ChatGPT or Claude subscription and buys cross-model memory plus 6.2M tokens. Premium at $199/mo fits heavy team-scale ingestion at 66M tokens. Against developer-first memory layers like Mem0 or Zep, MemoryLake costs more per seat but bundles audit-grade provenance, versioning and built-in corpora you'd otherwise assemble yourself.
In short
MemoryLake — MemoryLake is portable AI memory for ChatGPT, Claude, Codex and Gemini — encrypted, exportable, and shared across every agent you run. Best for People running three or more assistants who want one memory that follows them between Claude Code, Codex, OpenClaw and ChatGPT, Developers building multi-agent systems that need shared, versioned, conflict-resolved context, Regulated enterprises that need exportable provenance chains, immutable audit logs and ISO 27001 / SOC 2 Type 2 coverage. Free to start; paid plans from $3.125.
What's new in MemoryLake
Checked 2 days agoAcross the latest 5 updates: 5 feature updates.
v1.6.0 — OpenClaw Domain Knowledge & Platform Scale
Domain knowledge injection for OpenClaw across 10+ verticals with 10PB+ structured knowledge, plus on-premise Kubernetes deployment, QueryAgent v2, white-label option and WPS365/Notion/Lark/DingTalk integrations. LoCoMo accuracy improved to 95.1%.
v1.5.0 — OpenClaw Integration: One-Click Memory for Agents
One-click OpenClaw installation, AgentRL with PPO and Actor-Critic reinforcement learning, hallucination-aware conflict detection, Memory Time Travel provenance, and recall latency reduced to under 30ms P99.
v1.4.0 — Built-in Open Data & Memory Code Access
Added access to 40M+ academic papers, 3M+ SEC filings, live financial data, 500K+ clinical trials and 2M+ drug compounds, plus Memory Code for programmable memory operations and 10x faster ingestion.
v1.3.0 — Skills Center & Multi-Agent Runtime
Launched Skills Center for compiling data, rules and knowledge into reusable skills, a Multi-Agent Runtime with Super Plantree orchestration, memory isolation policies, and AutoGPT and Manus integrations.
v1.2.0 — WorkBrain Enterprise Knowledge Engine
Introduced WorkBrain enterprise knowledge engine with team memory sharing, Office 365 and Google Workspace integrations, meeting memory extraction, and an organizational knowledge graph with department boundaries.
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: October 2026
How we score →Key Features
- Persistent cross-model memory shared across ChatGPT, Claude, Codex, Gemini, Qwen, OpenClaw, AutoGPT, Manus and
- Memory Passport: one-click export and one-click permanent deletion, no vendor-retained copies
- Six typed memory stores: Background, Fact, Event, Dialogue, Reflection and Skill
- Multimodal ingestion of conversations, PDFs, Word/PPT, Excel, CSV, text, images, audio and video
- MemoryLake-D1 v2 reasoning engine with visual and logical validation on mixed documents
- Triple-party AES-256 encryption so no single party holds all keys
- Per-AI visibility controls limiting what each assistant or agent can read
- Conflict detection at ingest with recency, source-priority, confidence-weighted or manual resolution
- Git-like versioning with branch, merge, rollback and diff on memory timelines
- Memory provenance chains with source attribution and immutable audit logs exportable for compliance
- QueryAgent v2 retrieval with sub-30ms P99 recall latency
- Memory Code for programmable memory operations via code runners
- Skills Center for compiling data, rules and knowledge into reusable skills
- AgentRL with PPO and Actor-Critic reinforcement learning for agents
- One-click OpenClaw installation with domain knowledge injection across 10+ verticals (v1.6.0)
About MemoryLake
MemoryLake is a persistent memory layer for people who run more than one AI. Instead of each assistant starting cold, MemoryLake stores what you feed ChatGPT, Claude, Codex, Gemini, Qwen, OpenClaw, AutoGPT, Manus and Perplexity inside a portable, encrypted Memory Passport, then injects the relevant context into whichever model or agent you're in. Everything stays in your Passport — exportable in one click, deletable in one click, with no copies retained. Memory is typed, not one flat blob. Background, Fact, Event, Dialogue, Reflection and Skill stores are kept separately so retrieval hits the right kind of memory, and each entry carries a provenance chain showing its source, creation time, transformations and access log. When two sources disagree — ChatGPT says dark mode, Claude says light mode — the Conflict Engine catches it at ingest and resolves by recency, source priority, confidence weighting or a manual rule, keeping both versions in a git-like timeline with branch, merge, rollback and diff. Ingestion is multimodal: PDFs, Word and PPT pages, Excel worksheets, CSV, text, images, audio and video, with the MemoryLake-D1 v2 engine doing visual and logical validation on mixed documents. Pricing is metered by token, and the distinction that matters is write vs. query — PDF, image, spreadsheet and text processing are memory writes, not retrievals. The vendor's own table puts Pro at roughly 826 PDF pages and 24,800 retrieval calls per month. Security is architectural rather than promised: triple-party AES-256 means no single party, MemoryLake included, holds all keys, and per-AI visibility controls decide which assistant reads what. ISO/IEC 27001, SOC 2 Type 2, ISO 20000-1, GDPR, PCI DSS and ISO 9001/14001/45001 are all listed. Positioning-wise, this is the auditable, cross-vendor option next to developer-first libraries like Mem0 and Zep.
Behind the Verdict
The pitch that survives scrutiny here is ownership, not benchmarks. MemoryLake's own CopyPassport framing is unusually blunt — export everything in one click, delete everything in one click, no backups kept — and that is what a security reviewer will actually ask about, not a LoCoMo chart. Where it earns its keep is multi-terminal work. Install once for Claude Code, keep the same memory in Codex, connect an OpenClaw key and the context follows you. The v1.6.0 release extended that to domain knowledge injection across 10+ verticals on top of the 10PB+ structured knowledge layer, so agents inherit vertical context without you shipping a vector store first. QueryAgent v2 and Memory Code cover the programmable side. We'd pass if you live inside one assistant. Native memory is free, already wired in, and nothing about a Passport beats it for a single-model workflow. Teams expecting shared real-time editing will also find memory is individual Passport-based, with sharing arriving through WorkBrain layers and department boundaries rather than a Google-Docs-style canvas. The real cost trap is comprehension vs. retrieval. Reading a 200-page PDF is a memory write; asking a question about it is a retrieval call. Pro's 826 pages and 24,800 calls look generous until half your month is document intake. Budget credits at $3.125 per 1M tokens before, not after, you hit the wall. Against Mem0 and Zep, the tradeoff is clear: they are lighter, cheaper for pure developer plumbing, and closer to the metal. MemoryLake is the heavier, opinionated stack — typed memory, conflict resolution, git-like versioning, immutable audit logs, on-prem Kubernetes with a white-label option. If your compliance team needs an exportable audit chain, that weight is the product.
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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.
Connect each assistant to your Memory Passport, let MemoryLake ingest an existing project folder of PDFs and notes, then ask a question in Claude that references a decision you made in ChatGPT last month.
Outcome: One consistent context across all three assistants, with the conflicting-preference case caught at ingest and both versions preserved in the audit trail instead of silently overwritten.
Install the OpenClaw one-click memory integration, wire the MCP Server into Claude Code, and let agents write findings to Instance-, Agent- and Session-scoped memory with sharing policies between them.
Outcome: Agents stop re-deriving the same conclusions across terminals, retrieval returns in under 30ms P99, and you can diff or roll back a memory timeline when an agent learns something wrong.
Query the built-in 40M+ academic papers, 3M+ SEC filings and live financial feeds straight from memory alongside your own ingested decks and spreadsheets, with per-AI visibility set so each assistant sees only its slice.
Outcome: Grounded answers without standing up an ingestion pipeline or managing API keys for each corpus, and token spend is predictable because per-operation rates are published.
Use Cases
- Keep consistent personal context across ChatGPT, Claude and Gemini sessions instead of re-explaining yourself.
- Build a multi-agent research assistant that remembers findings across conversations and terminals.
- Give Claude Code or Codex memory installed once and reused across terminals and agents.
- Deploy compliant enterprise AI memory with provenance chains and audit logs for ISO 27001 and SOC 2 review.
- Compile frequently used document analyses or data transformations into reusable Skills.
- Power a customer support agent with persistent knowledge of past interactions.
- Share memory across teams with isolation policies at Instance, Agent, Session and Team levels.
- Ground AI outputs in built-in corpora — 40M+ papers, 3M+ SEC filings, live market data — without building pipelines.
Models Under the Hood
as of 2026-10-18
Limitations
- MemoryLake meters every operation by token, and file, image and text processing count as memory writes rather than queries — so heavy multimodal ingestion consumes quota faster than the retrieval numbers suggest.
- On Free that means roughly 40 PDF/Word/PPT pages, 40 images or 6 Excel sheets a month; Pro's 6.2M tokens covers roughly 826 pages or 826 images, and anything beyond your plan draws on a prepaid credit balance billed at $3.125 per 1M tokens (per operation: $0.0225 per PDF page or image, $0.135 per Excel sheet or CSV, $0.00075 per retrieval call, $0.015 per web search).
- WorkBrain is the team sharing layer; the core memory is individual Passport-based, so it is not a real-time collaborative document.
- The product is neutral memory infrastructure — it does nothing useful until you connect it to external AI assistants or agent platforms, and most capabilities depend on connectivity.
as of 2026-10-08
Verification history
We have re-verified MemoryLake 10 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-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
Showing the 6 most recent of 10 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
Someone testing cross-model memory with one project — a handful of PDFs, images or spreadsheets over a month.
What this tier adds
Free entry point: 300,000 tokens per month, roughly 40 PDF/Word/PPT pages, 40 images, 6 Excel sheets or 6 CSV files, about 1,200 retrieval calls and 62 web searches.
Pro
$19/mo
Ideal for
Regular individual or small team running several assistants with ongoing document ingestion.
What this tier adds
Adds 6.2M tokens per month (~826 PDF pages or images, ~24,800 retrieval calls); effective ~$3.06 per 1M monthly or ~$2.58 yearly with about 17% savings on annual billing.
Premium
$199/mo
Ideal for
Heavy users and team-scale workloads running large document backfills and high retrieval volume.
What this tier adds
Adds 66M tokens per month (~8,800 PDF pages or images, ~264,000 retrieval calls, ~13,750 web searches); effective ~$3.02 per 1M monthly or ~$2.52 yearly with about 19% savings.
Credit Balance
$3.125 per 1M tokens
Ideal for
Anyone who occasionally exceeds their plan quota and wants usage to continue without changing tiers.
What this tier adds
Prepaid token pool billed from $3.125 per 1M tokens, consumed only after plan quota is exhausted, no expiration, with auto-recharge available.
Where the pricing makes sense
The company stage and team size where MemoryLake's pricing actually pencils out — and where peers do it cheaper.
For a solo power user, $19/mo Pro (about $2.58 per 1M tokens if you pay yearly) sits in the same range as a single ChatGPT or Claude subscription and buys cross-model memory plus 6.2M tokens. Premium at $199/mo fits heavy team-scale ingestion at 66M tokens. Against developer-first memory layers like Mem0 or Zep, MemoryLake costs more per seat but bundles audit-grade provenance, versioning and built-in corpora you'd otherwise assemble yourself.
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.
Solo power user: connect one assistant and ingest a starter folder in well under an hour, first useful recall same session. Developer wiring an agent platform: an afternoon to install the OpenClaw or MCP integration and define sharing policies. Enterprise on-premise with Kubernetes operator plus WorkBrain: days, mostly compliance and residency approvals rather than 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 ChatGPT Memory: export what you can, then ingest your conversations and notes so typed memories replace the flat chat log.
- →From Claude Memory: use the Claude integration to keep the same context available in Claude and every other assistant you connect.
- →From Mem0 or Zep: re-ingest your existing vector store or export files, then rebuild retrieval as typed memories with conflict rules attached.
- →From a personal notes or Obsidian vault: ingest Markdown and text files directly — text processing runs at $0.45 per 1M characters.
- ↗To Mem0 or Zep: export your Memory Passport and re-ingest into the target's store, then rewrite retrieval calls against its API.
- ↗To ChatGPT Memory or Claude Memory: export your memories and keep them in the native assistant, accepting that the other assistants lose that context.
- ↗To a self-hosted vector database: export the Passport and reconstruct your own retrieval and conflict layers manually.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “MemoryLake”, and we withheld 6: 6 could not be judged, because “MemoryLake” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about MemoryLake.
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
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Cyera
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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 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.
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.
Alternatives to MemoryLake
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Cyberhaven
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