CrystalSpec vs Keeborg
From bundle to living spec.

Give Keeborg credit first: it is fast, and it is broad. You describe your app in plain English, choose a tech stack, and in roughly ninety seconds it returns eight cross-referenced documents — a requirements spec, a technical architecture, an OpenAPI contract, a database schema, UX and UI specifications, an implementation guide, and an agent workflow — then lets you export the set or push it straight into a fresh GitHub repository with CLAUDE.md and .cursorrules already in place. Specialized agents write each document and, per Keeborg, reconcile them for consistency, so features in the requirements line up with endpoints and tables. As a way to turn a blank page into a coherent starting spec for Claude Code, Cursor, or Windsurf, it is genuinely impressive — see keeborg.com.

The shape of that value is the whole story, though: Keeborg is one-shot generation. It says so plainly — the tool is "focused on new project bootstrapping," and "if you need to add requirements to an existing, mature project, you may need to adapt the output." Once the bundle lands you own eight documents outright, and the way you change them is to refine through AI chat or regenerate a document wholesale. As of mid-2026 the site describes no versioning, no revision history, no field-level diffs, no team permissions, no shared workspace, and no step where a human signs off on a change before it lands. For a weekend greenfield build that is exactly enough. For a product that keeps moving, week two is where it strains.

CrystalSpec is built for week two and every week after. Rather than a folder of generated documents, the spec is a single typed system, all of it cross-linked into one vocabulary: roles, test cases, a glossary, data models whose typed fields link out to other models, and flows whose labelled branches render as clickable diagrams. AI helps author every part, yet it is barred from writing to the spec directly — it puts forward create, update, and delete proposals, each vetted for appliability first (missing fields and broken references caught before a byte is written), and a human accepts or declines them one row at a time, every decision kept on record. A publish cuts a versioned revision that carries an AI-written change summary and diffs at field granularity, and any version can be rolled back with its lineage preserved.

Both products greet coding agents over MCP, but the two servers field different questions. Query Keeborg's and you get a generated document back, or you regenerate one; query CrystalSpec's and you draw structure from a single published source — the steps inside a flow, the fields on a model, which revision is currently live, the exact delta between two versions — across a hosted MCP server, a scoped GraphQL API, and HMAC-signed webhooks. On publish, CrystalSpec breaks the change into atomic tasks and files them in GitHub, Linear, or ClickUp, one-way and back-linked, and a re-push never files the same one twice. Generate the first draft wherever that goes fastest. Keep the living spec — versioned, approved, queryable — wherever it has to stay true.