ContextVault
Summary
Every AI session starts blank — the fix your team debugged last Tuesday, the architectural decision from last month, the prompt that finally worked: gone, unless someone remembered to paste it back in. ContextVault is a shared memory layer that persists what your AI clients learn across sessions, agents, and model vendors.
The core mechanic is an MCP-compatible vault that Claude, ChatGPT, Codex, Copilot, and any other compatible client reads from and writes to — so the fix one developer's session surfaces becomes findable by the next. Retrieval combines vector and full-text ranking tuned for code and ops recall, which means a keyword search and a semantic search run together rather than forcing you to choose. Memory is scoped at the user, group, and org level with audit trails, so the right context reaches the right team without bleeding across projects. The ceiling arrives when you need the vault to act — ContextVault stores and retrieves, it does not plan or execute. Teams that want autonomous task chains will build that layer themselves and use ContextVault as the knowledge store underneath.
Bottom line: The right call for a dev team juggling three AI clients who are tired of re-explaining their stack every morning — a weaker fit if your session memory problem is actually an automation problem, where passive retrieval won't replace the task-running loop you need.
Pricing Plans
SubscriptionFree trial
7 days, 1 seat, 50 memories
- MCP and API access
Solo
1 seat, 500 memories
- Full MCP and API access
Team
10 seats, 15 groups, 2,500 memories, 15,000 monthly queries
- Member management
- Shared memory collections
Enterprise
20+ seats, unlimited everything
- Data export
- Procurement and security review support
View full pricing on contextvault.dev →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- MCP-compatible connections to Claude, Codex, ChatGPT, Copilot, and major code editors, so your team's memory layer doesn't fragment when different developers prefer different AI clients.
- Hybrid vector and full-text retrieval in a single query, which means you don't lose relevant results because the phrasing in the vault doesn't exactly match what you typed today.
- Group-scoped access with audit trails and database-level isolation, so a team sharing a workspace doesn't accidentally surface another group's sensitive context in their queries.
- Durable memory across session resets, model changes, and tool switches, which means a hard-won debugging fix isn't lost the moment a chat window closes or a developer switches from Claude to Codex.
- Organization-level knowledge retention rather than per-user silos, so when a consultant leaves or a team rotates, the institutional knowledge they built with AI stays queryable.
Cons
Sign in to edit- ContextVault retrieves; it does not act. Teams that need their memory layer to trigger follow-up tasks, run tool calls, or chain steps will find a passive store insufficient — at that point they are building an agent layer on top and maintaining ContextVault as one component of a larger system they did not plan for.
- No self-hosted option exists, per the vendor page. Teams in regulated industries where data-residency policy requires on-premises or private-cloud deployment will fail a security review before completing a proof of concept, and the likely path is a vector database they run themselves — Weaviate, Qdrant, or pgvector — rather than this service.
- Memory and query caps on lower tiers create a hard ceiling for teams with moderate-to-high query volume. Unlimited memories and queries are a paid-only feature, which means a small team that hits the ceiling mid-sprint faces an unplanned upgrade decision or a gap in retrieval coverage.
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About
- Platforms
- Web, VS Code, Cursor, JetBrains, Microsoft Visual Studio, Claude Desktop, ChatGPT Desktop, Copilot Desktop
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-07-14T17:23:26.511Z
Best For
Who it's for
- Teams using multiple AI tools simultaneously
- Developers and consultants requiring cross-session memory
What it does well
- Retain organizational decisions and fixes across AI sessions
- Share memories between team members and multiple AI clients
- Reduce repeated context loading in repeated tasks
- Maintain searchable historical solutions for code and ops
Integrations
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Frequently Asked Questions
- Is ContextVault free?
- ContextVault has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is ContextVault open source?
- No — ContextVault is a closed-source tool. Source code is not publicly available.
- Does ContextVault have an API?
- Yes. ContextVault exposes a developer API. See the official documentation at https://contextvault.dev for details.
- What platforms does ContextVault support?
- ContextVault is available on: Web, VS Code, Cursor, JetBrains, Microsoft Visual Studio, Claude Desktop, ChatGPT Desktop, Copilot Desktop.
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Most AI memory solutions stop at the individual: one browser, one chat, one session. ContextVault shifts the unit to the organization. The vendor describes it as a single vault that every agent reads from and writes to, scoped per user, per agent, and per tenant, with memories that survive session resets, model switches, and tool changes. The workflow is MCP-based: you connect your existing AI clients — Claude Desktop, Claude Code, ChatGPT, Codex, Copilot, Gemini, or any MCP-compatible editor — to the vault, and queries pull ranked results from a hybrid vector-plus-full-text retrieval layer without requiring you to reframe your prompt for each tool.
The differentiating claim is organizational durability. Local memory files get stale, personal prompt docs don’t travel, and scattered Markdown repos become maintenance burdens. ContextVault centralizes historical solutions, decisions, and preferences in one searchable store with database-level isolation between groups — meaning a security team’s context doesn’t leak into a product team’s queries. The docs describe group-scoped access and audit trails, which positions this as a compliance-aware choice for teams where memory visibility is not a free-for-all.
The tool fits best when the problem is retrieval: re-explaining architecture to a new AI session, reconstructing debugging context, or onboarding a new team member whose AI client knows nothing. It does not fit when the problem is execution — ContextVault is passive storage and retrieval, not an agent that acts on what it finds. There is no self-hosted option, which means teams with hard data-residency requirements will hit a wall before they finish the security review. The memory cap on the entry tiers is a real constraint for teams with high query volume; the docs indicate unlimited queries and memories are a paid-only feature gated to the top tier.
Compatibility spans OpenAI Desktop, Claude Code, Claude Desktop, Microsoft Copilot, Google Gemini, and the major code editors including VSCode, Cursor, Windsurf, Visual Studio, and JetBrains IDEs. The vendor notes Google Gemini Desktop support is planned rather than current. Integration is MCP-based, so any client that speaks MCP can connect without bespoke adapters.