MemoryLake

A closer look at MemoryLake

MemoryLake is a neutral, multimodal memory layer for individuals and enterprises: it stores conversations, documents, spreadsheets, audio and video as typed, versioned memory, works with ChatGPT, Claude, Qwen, Gemini, OpenClaw, Codex and any API, and is presented as free forever, private, encrypted and portable.

Editorial guidance based on published documentation. The workflow below is a suggested starting point, not a measured product result.

When to choose MemoryLake

• People and teams whose memory must work across many AIs and agents instead of being locked inside one chat product.
• Claude Code and Codex users who want to install memory once and keep the same memory in the terminal.
• Teams that want to meter memory use by token and pay on demand beyond a plan, rather than buying seats.

When to choose something else

• Users who expect unlimited token volume on the free tier, since the Free plan includes 300,000 tokens a month and larger volumes belong to Pro, Premium or credits.
• Users who need team-scale workloads on Pro, since Premium is the plan described for heavy usage and team-scale workloads.
• Users who want memory only inside a single chatbot, since MemoryLake is built as a cross-tool memory infrastructure layer.

A practical starting workflow

1. Install MemoryLake for the tool you use, such as Claude Code or Codex, and connect your key where asked.
2. Let it store multimodal memory from conversations, documents, spreadsheets, audio and video.
3. Check token use against the plan, and use credits for additional usage beyond the plan when needed.

Check before committing

• MemoryLake meters all operations by token, so a monthly cost depends on the mix of writes, page processing and image processing, not on a flat seat count.
• The pricing page states on-demand pricing at about $3.125 per million tokens; the word about means the per-token figure is approximate, not a fixed quote.

Common questions

Compare approaches for this task

• Mem0 — Developers building agents or apps that must remember user preferences and past facts across sessions instead of starting each conversation blank.; Teams that want memory as infrastructure, with add and search calls from an SDK rather than building their own store and retrieval layer.; Builders testing memory features first, since the pricing page describes a Free plan for developers building and testing with Mem0.
• MemMachine — Agent builders who want persistent memory across sessions, agents and models, with profile memory that improves personalisation over time.; Developers building locally who want a self-hosted open-source memory layer before paying for managed cloud hosting.; Teams building production agents who need higher rate limits, premium integrations and managed hosting on the Pro plan.