Delvyn Studio Documentation

The product thinking layer for AI-native teams. Define vision, strategy, and guardrails — then push agent specifications to GitHub, Linear, or Jira for AI coding agents to implement.

Explore the Documentation

Find guides, references, and examples for every feature

Getting Started
Learn the basics and get up and running quickly
Product
Manage your product vision, strategy, and discovery
OKR
Plan, track, and evaluate objectives and key results
Team
Manage your teams and members
Integrations
Push agent specs to GitHub, Linear, Jira. OKR notifications in Slack.

Quick Start Guide

Get up and running with Delvyn Studio in minutes

1

Sign Up

Create your account and set up your organization

2

Set Vision & Strategy

Define your product vision, strategy, and strategic guardrails

3

Generate Agent Specs

Create structured specs from ideas, discovery, or key results

4

Push & Build

Push specs to your issue tracker and let AI agents implement

New: MCP Server

Give your AI assistant product context

Connect GitHub Copilot, Claude Code, or Cursor to your Delvyn Studio data via the Model Context Protocol. Your AI assistant can instantly access OKRs, agent specs, product vision, and discovery insights — no copy-pasting needed.

GitHub Copilot

Works in VS Code agent mode. Add one config file and Copilot can query your product context directly.

Claude Code

Run one CLI command to register the server. Claude can retrieve and log learnings mid-session.

17 Tools

Products, OKRs, agent specs, discovery, learning captures, and teams — all queryable by your AI.

Read-only & Secure

Bearer token auth, per-device API keys, and no write access to critical data.