The context layer every AI you use is missing.
Structure what your AI draws from — versioned, verified, and auditable. Works with Claude, Cursor, and any MCP-compatible AI. Open source and free forever for individuals.
What is ContextNest?
Most AI systems work like this: you give the model a question, it searches through whatever documents you've connected, and returns an answer. The problem is that retrieval finds relevant — it doesn't guarantee current, approved, or auditable. An AI can confidently cite a policy that was revised 18 months ago. It has no way of knowing.
ContextNest adds the governance layer beneath retrieval. Every document in your vault is versioned with a cryptographic hash chain. Every change is recorded. Every query is traceable. Before any document reaches your AI, it passes through steward approval — so only current, human-approved content feeds your model.
And because ContextNest runs entirely locally on your machine or private infrastructure, your files, edits, and queries never leave your environment. Your context stays yours.
The result: your AI answers from what you actually know, not what you used to know.
Versioned
Every document change is hash-chained and tamper-evident. The complete history is always reconstructible.
Governed
Documents require steward approval before reaching your AI. Draft content stays in draft — it never feeds a model.
Auditable
Every query produces a complete trace: what your AI consumed, from which version, at what time.
Start in minutes.
The fastest way to start. Download ContextNest Desktop and your vault is running in minutes — no terminal, no configuration. ContextNest runs invisibly in the background, working with AI code editors and agents like Cursor, Claude, or Antigravity to keep your context grounded.
Download ContextNest Desktop
Ready-to-use prompts.
Copy these directly into Claude, Cursor, or any AI. They work better once ContextNest is installed and your vault is connected.
Set up a new project context
Query your vault
Capture a decision
Onboard to a project
Build a starter vault
Don't start from scratch.
Pick a vault template and give your AI instant, structured context.
Executive / Leadership
Strategy, operations, and leadership playbooks for senior leaders.
Developer / Engineering
Architecture decisions, coding standards, and fast onboarding for engineering teams.
Sales / Revenue
Objection handling, competitive intel, and enablement playbooks for sales teams.
How ContextNest works.
Three steps from raw knowledge to governed AI context.
Structure your knowledge
Write in Markdown. Use [[wiki links]] to connect related concepts. Tag documents with #topics. Add YAML frontmatter to define type, author, status, and relationships. ContextNest turns your documents into a navigable knowledge graph — not a flat file dump.
--- title: Q3 Pricing Strategy type: document tags: [#pricing, #strategy] status: draft --- We are shifting focus toward the [[Enterprise-Tier-Pricing]] model proposed by @Sarah.
Govern what reaches your AI
Documents don't reach your AI automatically. In governed mode, every document passes through steward approval before it's compiled into the active context. Draft content stays in draft. Outdated content gets versioned, not deleted. Your AI only works from what's been explicitly approved.
Connect your AI
Once approved, your context is queryable via the Model Context Protocol (MCP). Any MCP-compatible AI client — Claude, Cursor, Claude Code — connects to your vault directly. No custom integrations. No re-uploading. No context window paste-and-pray.
{
"mcpServers": {
"contextnest": {
"command": "node",
"args": ["node_modules/@promptowl/contextnest-mcp-server/dist/index.js"],
"env": {
"CONTEXTNEST_VAULT_PATH": "/path/to/your/vault"
}
}
}
}The open specification.
ContextNest is a fully open specification. Everything below is publicly documented and freely implementable. The reference implementation — CLI, engine, MCP server — is open source.
Typed document model
Markdown + YAML frontmatter. Human-readable, machine-parseable, portable. No proprietary format. Obsidian-compatible.
Deterministic selector grammar
Set-algebraic queries over document metadata. The same query returns the same documents, every run. Not similarity search — precision retrieval over a governed subset.
SHA-256 hash-chained versioning
Every document change is recorded in a cryptographic hash chain. Tamper-evident. The full knowledge state at any point in time is reconstructible from any checkpoint.
Graph-level checkpoints
Point-in-time snapshots of the entire vault. Enables complete reconstruction of what your AI knew at the moment of any query.
Context injection and audit trace
Every agent query produces a complete audit record: which documents were consumed, from which versions, at which timestamps. Full provenance chain.
MCP-native
The context engine exposes a standard Model Context Protocol interface. Compatible with Claude Desktop, Claude Code, Cursor, and any MCP client.
Source nodes
Live data integration via MCP. External services — calendars, CRMs, databases — can be connected as source nodes that feed governed context alongside static documents.
Everything ContextNest.
Documentation, research, blog posts, and community — all in one place.
Read
Community & Code
Frequently asked.
What is ContextNest?
ContextNest is an open-source governance layer for AI knowledge. It structures your documents into a versioned, cryptographically verified vault that AI agents draw from via the Model Context Protocol (MCP). It gives your AI a permanent, governed context — so it works from what you actually know, not from similarity guesses or expired documents.
Isn't this just RAG?
No — and the distinction matters. RAG (Retrieval-Augmented Generation) finds relevant passages from a corpus. ContextNest governs what's in the corpus before retrieval happens. The natural composition is ContextNest governing which documents are approved and current, with RAG layered on top for semantic search over that governed substrate. They answer different questions and work together. ContextNest is the governance frame beneath retrieval — not a replacement for it.
How is this different from a vector database?
A vector database handles similarity search. ContextNest handles governance — which documents are approved, versioned, and auditable. You can use both: ContextNest governs the corpus, your vector database searches within it.
Does ContextNest fix AI hallucinations?
Partially. ContextNest fixes the governance failure mode — AI citing outdated, unapproved, or incorrect content because the context layer had no governance. It does not fix model-level hallucinations where the model invents facts not present in any document. What it guarantees: your AI works from approved, current, auditable knowledge.
Where does my data go?
Nowhere. ContextNest runs locally — your vault is a directory of Markdown files on your own machine or infrastructure. No data leaves your environment. No external API calls from the governance layer. No vendor dependency.
Is ContextNest free?
Yes. The core specification, CLI, and MCP server are free and open source. PromptOwl offers a managed commercial layer — agents, workflows, RBAC, enterprise governance, and support SLAs — on top of ContextNest. ContextNest itself is free forever.
What AI tools does ContextNest work with?
Any MCP-compatible AI client. Currently: Claude Desktop, Claude Code, Cursor. The MCP server exposes a standard interface — any tool that supports Model Context Protocol can connect to your ContextNest vault.
Context is one piece.
PromptOwl governs all of them.
ContextNest is one type of AI artifact — persistent, wiki-style memory for your models. PromptOwl governs all four AI artifacts your team uses: context, agents, RAG, and tools. While the ContextNest CLI remains a free, standalone open-source engine, PromptOwl adds the control plane, user roles, and audit trails to govern your entire AI operation.