ContextVault
Shared memory layer for your AI and your team
Details
- External ID
- 48900288
- Source
- HN
- Company
- —
- Product
- ContextVault
- Website domain
- contextvault.dev
- Launched
- July 13, 2026
- Cohort
- —
- Upvotes
- 12
- Upvotes percentile
- 0.6039426523297491
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Hi HN,I'm Kevin. I built ContextVault because I kept running into the same problem with AI tools.Every project accumulated prompts, coding conventions, architectural decisions, examples, and other pieces of context that made the models significantly more useful. The problem was that this information quickly became fragmented. Some lived in ChatGPT Projects, some in Claude, some in Markdown files, some in internal documentation, and some only existed in previous conversations.Late last year, I realized several people on our team were solving the same problems independently because previous work was difficult to discover. I assumed this problem existed in other large organizations, so I started experimenting with a shared context store.I started with a local proof of concept and a rough MCP server. If I asked questions like "have we done this before?", the AI could search the database and find the most relevant item to review. If a conversation produced something worth remembering, I could say "save what we learned to the vault." After using that workflow for a few months, I found myself relying on it every day.I decided to make it available to others. I've never built a product before, and I thought it would be a valuable learning exercise to do.ContextVault is a a product for storing and organizing reusable context that can be shared across people, projects, and AI tools. Instead of copying the same instructions into every conversation, you can store them once and retrieve them through our MCP server. It is not limited to any one AI client. Your team can use ChatGPT, Codex, Claude, and Gemini and save/read from the vault all the same.It currently supports:- OAuth support for GitHub, Google, Microsoft, and GitLab- Structured context records with metadata- Multi-user organizations with role-based access- MCP server for all AI clients that support MCP- Organization-scoped storage keeps tenant data separated- Group visibility rules decide which memories each member can search- Authenticated MCP access ties every request back to a real user and workspace- Feedback signals can be captured now and used to improve ranking later- Supports desktop versions of AI clients, not just their CLI versions (mobile app support should also work)The backend is built with PostgreSQL, pgvector, Node.js, and TypeScript. The frontend uses Next.js, React, Tailwind CSS, and shadcn/ui (frontend is not my strong suit, please be kind). Authentication is handled with Clerk and billing with Stripe.I started building this for my own workflow, but after relying on it for several months I decided to make it available to others. We soft launched a few weeks ago, and I find it useful as a daily tool.Essentially, ContextVault offers a way to track memories and context, distribute them instantly to your team, and help reduce duplicated work.I'd be interested in feedback on a few things:- How are you managing reusable AI context today?- Are you relying on similar tools, or do you keep everything in Git or Markdown?- If you've built something similar, what did you learn that you would do differently?You can see the product here:https://www.contextvault.dev
Enrichment
- Theme
- database infrastructure and developer tools
- Vertical
- Horizontal
- Function
- Agent / copilot
- Audience
- B2B
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- shared memory system for ai agents and teams
- Manually corrected
- False
Could you build this?
Yes Building a central repository for shared context and prompts using standard vector embeddings, hybrid full-text search, and an MCP (Model Context Protocol) server is well within vibe-coding scope.
Discussion
11 comments analyzed.
Competitors mentioned: Shared RAG knowledge bases, Document stores with MCP access, Markdown files in repos
Concerns raised: Core mechanics and retrieval logic not clearly explained, Data security and privacy - how sensitive data is stored, Data deletion controls and processes unclear, Whether this is substantially different from existing solutions, Query limits (15,000/month) may not be sufficient for some organizations
Feature requests: Account deletion without destroying shared workspace memories, Workspace admin controls for data deletion, Automatic context injection suggestions to other team members, Flexible pricing/usage limits based on actual usage patterns
Competitors
Other products that read as similar to this one — 139 launches clear the similarity bar, closest 8 shown.
Attention rank: #57 of 140 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 249 days after the earliest competitor.
- LLM Memory Storage that scales, easily integrates, and is smart · hn · 2026-03-16 · 6 upvotes · similarity 0.51
- Vilix AI · ph · 2026-09-11 · 3 upvotes · similarity 0.48
- Omnesis · ph · 2026-09-29 · 2 upvotes · similarity 0.47
- Context Hub · ph · 2026-09-29 · 3 upvotes · similarity 0.46
- Modular · hn · 2026-04-20 · 7 upvotes · similarity 0.46
- Skald · hn · 2025-12-10 · 5 upvotes · similarity 0.46
- My first SaaS to help human developers and AI agents share context · hn · 2026-07-02 · 5 upvotes · similarity 0.46
- Neither · ph · 2026-09-22 · 1 upvotes · similarity 0.45
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a agent / copilot tool for Agriculture yet.