docglow

Next-generation documentation site generator for dbt™ Core projects.

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Why Docglow?

Thousands of teams use dbt Core without access to dbt Cloud's documentation features. The built-in dbt docs generate && dbt docs serve generates a useful, but somewhat dated static site, with limited functionality that doesn't scale well.

Docglow replaces it with a modern, interactive single-page application — and it works with any dbt Core project out of the box.

• No dbt Cloud required — generate and serve docs locally or deploy anywhere
• Unlimited models, unlimited viewers — no seat caps, no model limits
• Zero configuration — just point it at a dbt project with compiled artifacts and go
• Interactive lineage explorer — drag, filter, and trace upstream/downstream dependencies visually
• Project health scoring — get a coverage report for descriptions, tests, and documentation completeness

Switching from dbt docs serve? See the migration guide for a side-by-side comparison and step-by-step instructions.

Interactive lineage explorer — layer-grouped DAG with upstream/downstream filtering, depth control, and folder grouping

Column-level lineage — expand nodes to trace individual columns across models with transformation labels (direct, derived, aggregated) (guide)

Column table with lineage — view types, descriptions, tests, and upstream/downstream dependencies for every column. Click a lineage badge to jump directly to that column in the linked model.

Install

pip install docglow

Try It in 60 Seconds

pip install docglow
git clone https://github.com/docglow/docglow.git
cd docglow
docglow generate --project-dir examples/jaffle-shop --output-dir ./demo-site
docglow serve --dir ./demo-site

This uses the bundled jaffle_shop example project with pre-built dbt artifacts.

Quick Start

# Generate the site from your dbt project
docglow generate --project-dir /path/to/dbt/project --output-dir ./site

# Serve locally
docglow serve --dir ./site

Features

• Interactive lineage explorer — drag, filter, and explore upstream/downstream dependencies with configurable depth and layer visualization
• Column-level documentation — searchable column tables with descriptions, types, and test status
• Project health score — coverage metrics for descriptions, tests, and documentation completeness (details)
• Full-text search — instant search across all models, sources, and columns
• Single static site — no backend required, deploy anywhere (S3, GitHub Pages, Netlify, etc.)
• AI chat (BYOK) — ask natural language questions about your project using your own Anthropic API key
• Dark mode — auto, light, and dark themes (follows system preference by default)

CLI Commands

Command	Description
docglow generate	Generate the documentation site from dbt artifacts
docglow serve	Serve the generated site locally
docglow health	Show project health score and coverage metrics
docglow mcp-server	Start MCP server for AI editor integration
docglow init	Generate a starter docglow.yml configuration file
docglow profile	Run column-level profiling (requires docglow[profiling])

Single-File Mode

Generate a completely self-contained HTML file — no server needed:

docglow generate --project-dir /path/to/dbt --static
# Open target/docglow/index.html directly in your browser

The entire site (data, styles, JavaScript) is embedded in one file. Perfect for sharing via email, Slack, or committing to a repository.

Configuration

Add a docglow.yml to your dbt project root for optional customization (layer definitions, display settings, etc.). Docglow works out of the box without any configuration — just point it at a dbt project with compiled artifacts in target/.

Generate a starter config with all options documented:

docglow init

Theme

Docglow supports three themes: auto (follows system preference), light, and dark.

docglow generate --theme dark

Or in docglow.yml:

theme: dark  # auto | light | dark

AI Chat (Bring Your Own Key)

Docglow includes a built-in AI chat panel powered by Claude. Ask natural language questions about your dbt project and get answers grounded in your actual metadata — models, columns, lineage, tests, and health scores.

Enable it:

# Option 1: Pass your key as a flag (for local use only)
docglow generate --ai --ai-key sk-ant-...

# Option 2: Set the environment variable
export ANTHROPIC_API_KEY=sk-ant-...
docglow generate --ai

# Option 3: Enable in docglow.yml
# ai:
#   enabled: true

Open the chat panel with Ctrl+J (or click the chat icon in the header), enter your Anthropic API key, and start asking questions.

Example questions:

Question	What it does
What models depend on the orders source?	Traces the lineage graph to find all downstream consumers
Which columns might contain PII?	Scans column names and descriptions for personally identifiable information
What would break if I changed stg_customers?	Lists all downstream models that depend on stg_customers
Show me all models related to revenue	Searches model names, descriptions, and tags for revenue-related content
Which models have the most failing tests?	Cross-references test results with model metadata
What's the overall health of this project?	Summarizes the health score breakdown across all six dimensions
Explain what dim_employee does	Describes the model using its SQL, columns, upstream dependencies, and description
What's the difference between stg_orders and fct_orders?	Compares two models side-by-side using their metadata

How it works: When you generate with --ai, Docglow builds a compact project context (model names, descriptions, columns, lineage, test status, health scores) and embeds it in the site. The chat panel sends this context as a system prompt to the Anthropic API along with your question. Responses stream back in real-time with clickable model references.

Security: Your API key is never embedded in the generated site. It's stored in your browser's localStorage when you enter it in the chat panel, and sent directly to the Anthropic API from your browser. You can safely deploy AI-enabled sites — they contain the project context but not your key.

Limits: 20 requests per session (clear chat to reset). Uses Claude Sonnet 4 with streaming.

AI Editor Integration (MCP)

Docglow includes a Model Context Protocol server that exposes your dbt project to AI editors like Claude Code, Cursor, and Copilot.

Add to your editor's MCP config (e.g. ~/.claude.json):

{
  "mcpServers": {
    "docglow": {
      "command": "docglow",
      "args": ["mcp-server", "--project-dir", "/path/to/dbt/project"]
    }
  }
}

The server provides 9 tools: model/source lookup, lineage traversal, health scores, undocumented/untested discovery, cross-model column search, and full-text search. No API keys or network access required — it runs locally over stdio.

CI/CD Deployment

Use Docglow as a CI quality gate with the --fail-under flag:

# .github/workflows/docs.yml
- name: Check documentation health
  run: docglow health --project-dir . --fail-under 75

- name: Generate and deploy docs
  run: docglow generate --project-dir . --output-dir ./site

Column-level lineage runs by default. For large projects, use --skip-column-lineage to speed up generation, or --slim to strip SQL source from the output and reduce payload size by 40–60%.

See the CI/CD Deployment Guide for complete walkthroughs covering GitHub Pages, S3, GitLab CI, health score thresholds, and enterprise private Pages.

Ready-to-copy workflow files: GitHub Pages (recommended), S3, and PR health check.

Pre-commit

Add Docglow's health check to your existing pre-commit workflow:

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/docglow/docglow
    rev: v0.5.5
    hooks:
      - id: docglow-health
        args: ['--fail-under', '75']

Requirements

• Python 3.10+
• A dbt project with target/manifest.json (run dbt compile or dbt run first)
• See Compatibility for supported dbt versions and adapters

License

MIT

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dbt is a trademark of dbt Labs, Inc. Docglow is not affiliated with or endorsed by dbt Labs.