ContextVault

ContextVault is a shared memory layer for AI systems. The idea is that a team stores a useful memory once and every AI client anyone on that team uses can recall it, reuse it, and build on it, instead of each person re-explaining the same context to a fresh chat every morning. The site's own framing of the goal is short, stop your team from starting from scratch with every AI session.

The problem it names is familiar to anyone who's leaned on AI tools at work for more than a few weeks. Context ends up scattered across Markdown files, half-stale project folders, and individual chat histories nobody else can see. One engineer solves a gnarly deployment problem on Tuesday and the answer dies in their session, so a teammate reruns the same investigation on Thursday. Most AI tools help individuals work faster, and ContextVault's pitch is that it's aimed one level up, at helping organizations retain and reuse what they learn. A useful side effect it claims is lower context load on every request, since clients pull what's relevant from the vault rather than having everything stuffed into a prompt up front.

Mechanically it's a single vault your clients read from and write to over MCP. Every entry is scoped to an organization and gated further by role and group access controls, so nothing crosses tenant boundaries and memories stay visible to the right team, department, or project group without leaking context everywhere. Retrieval is hybrid, combining vector and full-text ranking in one query, which the site says is tuned for code and ops recall rather than general prose. Memories stay durable across sessions, agents, and model vendors, so decisions, fixes, preferences, and lessons carry forward even when a chat resets, a project gets archived, or a tool gets swapped out entirely.

Because it rides on MCP, the client list is broad and setup is short. Claude Code, Claude Desktop, OpenAI Codex, ChatGPT, Microsoft CoPilot and its desktop app, and Google Gemini all connect, as do VSCode, Cursor, Windsurf, Visual Studio, and JetBrains IDEs. Google Gemini Desktop is marked planned. Anything else that speaks MCP plugs in the same way, and the FAQ pegs integration at seconds since you're only adding an MCP server to a client you already run. You can use it from as many machines as you like so long as the account is valid and authenticated, sign-in goes through single sign on with major providers, and the vault is model-agnostic by design, so nobody gets locked into one vendor's client.

The comparison table on the site is where the positioning gets clearest. ContextVault sets itself against local AI memory and prompt docs on six axes, namely whether knowledge is shared across teams, whether it works across different AI tools, whether storage is centralized for the organization, whether past solutions are searchable, whether it's MCP compatible, and whether it was built for long-term organizational learning. Local memory and a folder of prompt docs each tick some of those boxes partially. The argument is that neither does all of them, which is a fair way to frame it.

On the security side, ContextVault runs a shared Postgres with org_id enforcement in the application layer, encryption at rest, and per-tenant audit trails. Enterprise customers can get dedicated single-tenant deploys. That's a reasonable posture for a young product, though it's worth reading closely if your memories would hold client-confidential material, because app-layer enforcement is a different guarantee from physical separation on the lower tiers. Who it's for is teams rather than solo tinkerers, even though a Solo plan exists. If you're one person with one editor, a folder of Markdown probably still does the job. If you're ten people spread across Claude Code, Cursor, and ChatGPT who keep rediscovering the same things, that's the gap it aims at.

Access is currently a private beta and the pricing ladder is public. A seven-day free trial gives one seat, a single default group, and 50 memories to test the workflow. Solo runs $9.99 a month for one seat and 500 memories with full MCP and API access, pitched at a single operator, consultant, or founder. Team is $49.99 a month for 10 seats, 15 groups, 2,500 memories, and 15,000 monthly queries, and adds member management, workspace controls, and shared memory collections. Enterprise is custom and brings 20 seats, unlimited groups, memories, and queries, plus data export and procurement support. Importing from an existing vector store isn't available yet and is planned as a soft beta feature once the core store and search workflow settle.