Graphene – Data analysis toolkit for your coding agent

My friend and I have worked at a number of BI companies and thought: Can’t a coding agent do most of this now?Almost. They just need a context/semantic layer to ensure query correctness and some kind of artifact for publishing findings and visualizations.We built an open source project called Graphene that provides these tools:- Semantic layer that’s more token-efficient than YAML, more deterministic than Markdown (eg, composable, callable metric macros), with a query API that’s well in-distribution (SQL). - MDX-like files for dashboards (Markdown with inlined SQL + HTML components for viz with support for CSS and Javascript) - Connects to popular data warehouses or local DuckDBYour coding agent will build really in-depth reports and can ofc leverage any other skills or context that you've made available to it. We dogfood it inside a monorepo with our website, app source code, planning docs, etc so the agent has access to a ton of context. It’s also nice that the agent can add instrumentation, adjust pipelines and transformations, and add dashboards all in one PR.If you want to try it out, just point your coding agent at https://github.com/graphene-data/graphene/blob/main/docs/set... and ask it to set up Graphene.If you don’t have data to play with, you can clone our example project:1. `git clone <https://github.com/graphene-data/example-flights.git`> 2. `cd example-flights && npm install`Would love any/all feedback!

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75

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Graphene is an open source semantic layer and dashboard toolkit that enables coding agents to generate accurate, in-depth BI reports and visualizations.

The pain

BI teams spend significant time ensuring query correctness, building semantic layers, and publishing dashboards, which is repetitive and error-prone.

The gap

Existing coding agents lack a robust, token-efficient semantic layer and integrated dashboard artifacts to fully automate BI workflows.

Build angle

Leverage open source to build a composable semantic layer and dashboard framework that integrates with popular data warehouses and coding agents.

Strengths

  • Founders have direct BI industry experience
  • Open source lowers adoption friction
  • Technical innovation in semantic layer design
  • Integration with coding agents and data warehouses

Risks

  • Unclear willingness to pay or monetize
  • Competition from established BI tools and platforms
  • Complexity of onboarding and user education
  • Dependence on coding agent adoption and maturity

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