glyf

Open source visualization build tool for your data pipeline.

Ship charts from the same pipeline as your data.
Define charts in SQL, compose dashboards in YAML, publish anywhere.

PyPI Tests Python Rust License

Install · Quickstart · How it works · CLI · Examples · Docs

  ┌──────────────────────┐   ┌──────────────────────┐   ┌──────────────────────┐
  │     dbt project      │   │     glyf sources     │   │      glyf build      │
  ├──────────────────────┤   ├──────────────────────┤   ├──────────────────────┤
  │ models/*.sql         │   │ charts     *.ggsql   │   │ resolve  ref()       │
  │ target/manifest.json │──▶│ dashboards *.yml     │──▶│ execute  DuckDB      │
  │ warehouse relations  │   │ macros     *.py      │   │ render   Altair      │
  └──────────────────────┘   └──────────────────────┘   └───────────┬──────────┘
                                                                    │
      ┌─────────────────────────┬───────────────────────┬───────────┘
      ▼                         ▼                       ▼
      site/index.html           charts/*.png *.svg      export --zip
      static dashboard          chart artifacts         zip for CI

The last mile

Every stage of the modern data stack has declarative, version-controlled, testable artifacts. Every stage except visualization.

Your models are versioned. Your jobs are automated. Your data quality is tested. Visualization is still the last artifact outside the pipeline.

  • Dashboards live outside the workflow. Your dbt models are in Git. Your charts are usually configured in a browser, stored elsewhere, and maintained by whoever last touched the UI.
  • Columns rename, charts break silently. When dbt models change, dashboard failures show up late. glyf moves chart definitions into a build step that validates earlier.
  • Publishing should not require a vendor. For internal portals and product dashboards, rendered HTML and chart assets are usually enough.

glyf is artifact-driven, not dbt-runtime-driven: run dbt first, then run glyf against the resulting artifacts and relations.

Install

glyf is published on PyPI as glyf-core. The package installs the glyf command and the glyf Python module; only the distribution name differs.

uv tool install glyf-core
glyf --version
Other ways to install — one-line script, Homebrew, pipx, pip, offline

One-line script (macOS and Linux). Installs uv first if it is missing, never uses sudo, and accepts --update, --version X, and --help:

curl -fsSL https://raw.githubusercontent.com/glyf-data/glyf/main/install.sh | sh

Homebrew. brew trust is required, not optional — Homebrew 6.0 refuses to load formulae from untrusted third-party taps:

brew tap glyf-data/glyf
brew trust --tap glyf-data/glyf
brew install glyf

pipx or pip. Prefer uv or pipx for a CLI tool; use pip when you want glyf inside an existing project virtualenv, next to dbt-core:

pipx install glyf-core
python -m pip install glyf-core

Offline or air-gapped. Every release ships platform wheels, an sdist, and a checksums.txt:

sha256sum --check --ignore-missing checksums.txt
uv tool install ./glyf_core-<version>-cp311-abi3-<platform>.whl

Prebuilt wheels cover Linux (x86_64, aarch64), macOS (Intel, Apple Silicon), and Windows (x86_64). They target Python 3.11+ through the stable ABI, so one wheel per platform covers every supported Python version and no Rust toolchain is needed. Upgrade with uv tool upgrade glyf-core.

Quickstart

In an existing dbt project:

glyf init      # scaffold glyf.yml, visualisations/, dashboards/
dbt build      # produce the dbt artifacts glyf reads
glyf doctor    # check artifacts, charts, and DuckDB execution
glyf build     # compile, render, and export the site
glyf serve     # preview it locally

doctor reports whether the dbt artifacts, chart files, and DuckDB execution are ready before your first build, so the first failure is a readable message rather than a stack trace.

How it works

01 — Write charts in GGSQL

SQL you already know, extended with a visualization grammar. Use ref() to reference dbt models directly, exactly as a dbt model would:

SELECT month, revenue
FROM {{ ref('fct_orders') }}

VISUALISE month AS x, revenue AS y
DRAW line
LABEL title => 'Monthly Revenue'
LABEL subtitle => 'Revenue trend from dbt model'
CONFIG width => 900

glyf resolves each reference to its schema path from target/manifest.json and validates the query before it renders anything.

02 — Compose dashboards in YAML

Lay charts out into sections. Use Python macros for labels, thresholds, and reusable components, so a dashboard change is a one-line diff in review:

name: executive
title: Executive Dashboard

summary:
  - "{{ ui.label_value('Owner', 'Analytics Engineering') }}"
  - "{{ ui.label_value('Generated', time.now('%Y-%m-%d %H:%M')) }}"

layout:
  columns: 3

sections:
  - title: Revenue overview
    columns: 3
    items:
      - metric:
          label: Sample revenue
          value: "$7.6k"
      - chart: revenue
        title: Monthly revenue
        width: 2

03 — Build once, publish anywhere

One command resolves dbt artifacts, validates chart specs, executes chart SQL with DuckDB, renders charts with Altair, and emits files you can publish:

target/glyf/
├── compiled/     resolved SQL
├── charts/       rendered PNG / SVG
├── dashboards/   dashboard specs
└── site/         self-contained static site  ← publish this

No BI server to maintain. Drop site/ into S3, GitHub Pages, a docs site, or a CI artifact.

CLI

Command What it does
glyf init Scaffold glyf config, chart, and dashboard directories
glyf doctor Check dbt artifacts, chart files, and DuckDB execution
glyf build Full pipeline: compile, render, and export
glyf serve Serve the generated site locally
glyf list List discovered charts and dashboards
glyf validate Validate chart and dashboard specs without rendering
glyf render Render charts only
glyf dashboard Build dashboards only
glyf export Export the publishable site (--clean, --zip)

Point any command at another project with --project-dir:

glyf build --project-dir examples/sales_dashboard

Examples

Four runnable projects live in examples/: simple_dbt, sales_dashboard, product_analytics, and finance_metrics.

uv sync
cd examples/simple_dbt
uv run dbt seed --profiles-dir . --full-refresh --no-partial-parse
uv run dbt run --profiles-dir .
uv run dbt compile --profiles-dir .
uv run glyf build
uv run glyf serve

Then open examples/simple_dbt/target/glyf/site/index.html.

Who it's for
Analytics Engineer You work in dbt and version-control everything. You should not need LookML or a BI platform UI to publish a declared dashboard artifact.
Data Scientist Write SQL-style chart definitions that run in the pipeline and stay current, instead of one-off notebooks that drift.
Data Leader Open source, runs locally, and produces outputs your team already knows how to deploy and review.
Application Engineer The data team owns the spec; you consume rendered output without negotiating with an embedded analytics vendor.

Status

Capability
dbt ref() and source() resolution from manifest.json shipped
GGSQL chart definitions, DuckDB execution, Altair rendering shipped
Dashboard YAML, Python macros, self-contained static site shipped
PNG / SVG chart artifacts and --zip export shipped
Generated typed React components planned
MCP server so agents can reason about the chart graph planned
Visual diff between builds as a CI artifact planned

See ROADMAP.md for the longer view.

Documentation

The full docs site is built from docs-site/ and published at glyf.pages.dev. These guides are also readable directly in the repository:
Quickstart First build, end to end
Configuration glyf.yml reference
Visualisation syntax The GGSQL grammar
Dashboard YAML Layout, sections, macros
dbt integration Artifacts, ref(), adapters
CI/CD Building glyf in a pipeline
Troubleshooting Common failures

Contributing

Developing from this repository 
uv sync

Dev dependencies include dbt-core and dbt-duckdb for the bundled examples.

The Makefile runs the same checks locally and in GitHub Actions:

make ci                      # the full pipeline
make ci PYTHON_VERSION=3.12  # against a specific Python

make on its own lists every target. Individual steps: make install, make test, make coverage, make build, make dashboard-ci. make test runs pytest with coverage and writes coverage.xml, which CI uploads to Codecov.

Run the docs site locally with Node.js installed:

cd docs-site
npm install
npm start

Issues and pull requests are welcome. Paths in glyf.yml and dashboard YAML use forward slashes on every platform.

License

Apache 2.0

Project links

Data verified by PyPI on Aug 29, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.6.0)

1 maintainer

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Credits

Author: Kannan Kalidasan

GitHub Statistics

Data verified by PyPI on Aug 29, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.11

Tags

visualization dashboards charts analytics semantic-layer static-site dbt

Classifiers

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