ContextVault
Shared memory vault that every AI client on a team reads from and writes to, over MCP and API
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contextvault.devAbout ContextVault
ContextVault is a shared memory layer for teams using AI, built on the observation that individual AI memory does not compound across an organisation. Each person's assistant learns their working context and then that knowledge dies in their session history, so the next colleague asking the same question starts from zero and the team pays the onboarding cost repeatedly. ContextVault stores memories once in a single vault that every AI client reads from and writes to, scoped per user, per agent and per tenant, and durable across sessions — replacing the usual sprawl of scattered Markdown files and out-of-date local project folders. Beyond avoiding duplicated explanation, the pitch includes a concrete efficiency argument: pulling the right memory reduces the overall context load of each AI request rather than stuffing every prompt with the same boilerplate. Access is over MCP and a REST API, so it plugs into whichever clients a team already uses. Pricing is organised around seats, groups and memory count rather than tokens: a 7-day free trial gives 1 seat and 50 memories, Solo at $9.99/month gives 1 seat and 500 memories with full MCP and API access, and Team at $49.99/month covers 10 seats, 15 groups, 2,500 memories and 15,000 monthly queries with member management and shared collections. Enterprise removes the caps and adds data export and security-review support.
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Key Features
ContextVault Pros & Cons
✅ Pros
- +Team-scoped memory is a genuine gap in per-user AI assistants
- +Seat and memory pricing is easier to forecast than token metering
- +Data export is available rather than trapping team knowledge
⚠️ Cons
- −Only a 7-day trial — no permanent free tier
- −2,500 memories across 10 seats fills quickly for an active team
- −Shared memory across a team is a real access-control surface to get right
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