ContextVault – Shared Memory Layer for AI Teams

TL;DR

TL;DR: ContextVault is a shared memory layer for AI systems that lets teams store, retrieve, and reuse context across sessions — eliminating the need to re-prompt AI clients with the same background information repeatedly.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: contextvault.dev
• Source: product website only (no public source repository)
• License: proprietary SaaS (no open-source license available)
• HN launch thread: news.ycombinator.com/item?id=48900288

What Is ContextVault?

ContextVault is a team-scoped shared memory layer for AI systems. Instead of pasting the same project context, API docs, team conventions, or background information into every AI prompt, you store it once in ContextVault and retrieve it on demand across any AI client your team uses.

The core problem it solves: AI context fragmentation. When a team uses multiple AI clients (Claude, GPT, Gemini, local models), each conversation starts from scratch. Institutional knowledge lives in people’s heads or scattered documents — ContextVault centralizes it as a reusable memory layer that survives individual sessions.

Key Features

Persistent Memory Stores memories that persist beyond individual AI sessions. What your team teaches the AI stays available for the whole team.

Hybrid Retrieval Supports both exact-match and semantic search to surface the right context when needed, without manual lookup.

Group-Scoped Access Team-level memory isolation — group-scoped access means memories are shared within a team but separated across groups.

Access-Controlled Memory Fine-grained access controls so sensitive information stays protected while still being available to authorized team members.

Works with Your AI Clients Designed as a retrieval layer that plugs into existing AI clients rather than replacing them.

Practical Evaluation Checklist

• Sign up for free trial at contextvault.dev
• Define a team group and invite members
• Add a shared memory entry (e.g., project conventions, API overview)
• Query from an AI client and verify context is injected
• Test access controls by creating a restricted memory entry

Security Notes

• Access controls are group-scoped, not global
• No information about encryption or data residency is publicized on the marketing site — verify with the team before using for highly sensitive internal data
• Memory data is stored in ContextVault’s cloud (proprietary product, not self-hosted)

FAQ

Q: Does ContextVault replace the AI model? A: No. It sits in front of your existing AI clients as a context retrieval layer. You still use your preferred AI (Claude, GPT, etc.) — ContextVault just feeds it relevant memory automatically.

Q: How does retrieval work? A: ContextVault uses hybrid retrieval — both keyword and semantic vector search — to match incoming queries to stored memories. You get the right context without manually searching and pasting it.

Q: Can I self-host ContextVault? A: ContextVault is a hosted SaaS product. There is no self-hosted version mentioned on the current product site.

Q: What AI clients does it support? A: ContextVault is designed as a client-agnostic retrieval layer. It should work with any AI client that can call an external retrieval API.

Q: What is the pricing? A: Free trial available. Enterprise plan exists for larger teams. Refer to contextvault.dev for current pricing.

Conclusion

ContextVault addresses a real pain point in team AI workflows: context fragmentation. If your team repeatedly re-explains project background, API conventions, or domain knowledge to AI clients session after session, a shared memory layer like ContextVault can save significant time and reduce inconsistency. The group-scoped access model keeps memory organized by team while hybrid retrieval means you do not need to manually search and paste context. Try the free trial at contextvault.dev if your team uses AI clients regularly.