WrenAI GenBI

What does this agent do, and when should you use it?

WrenAI is an open-source generative BI engine for AI agents, spanning natural-language questions, SQL, charts, and shareable dashboards. Its context layer combines MDL semantic models with versioned instructions.md and queries.yml files plus a local LanceDB memory index for business definitions, examples, and query recall. The core tree includes a Rust semantic engine built on Apache DataFusion, Python bindings, a Python SDK and CLI, and the browser-side wren-core-wasm build. Agents can run MDL-mediated queries with wren query and use the GenBI workflow to build browser-side dashboards for deployment to the user’s Vercel or Cloudflare Pages account. It is a fit for teams that want governed, Git-friendly business context behind their existing agents, rather than a one-off chart from a CSV.

After installing wrenai, an agent can fetch the setup workflow with wren skills get onboarding, check the environment, create a connection profile, scaffold a project, and run an initial query. It works from MDL definitions for models, columns, relationships, views, cubes, metrics, and row- and column-level access control, alongside instructions.md, queries.yml, and a LanceDB memory index. Agents can shape questions with wren ask "<question>" --guided or --direct, or execute SQL through the MDL layer with wren query --sql '...'; the repository also names schema-aware retrieval, MDL planning, dry-plan validation, structured errors, functions, and row limits. For dashboard delivery, an agent fetches wren skills get genbi, builds a browser-side GenBI app powered by wren-core-wasm, previews it locally, and deploys it to the user’s Vercel or Cloudflare Pages account.

1. A data team using Codex, Claude Code, or another connected agent needs answers to quarterly sales questions based on approved Postgres business definitions.
2. A warehouse team on BigQuery, Snowflake, ClickHouse, or Databricks wants approved metrics, joins, and enum meanings maintained as versioned context.
3. An analytics engineering team wants an agent to turn a validated query into an interactive, filterable dashboard and publish it on its own Vercel or Cloudflare Pages account.
4. A company whose business logic is scattered across documents, wikis, and chat threads wants to encode that knowledge in MDL, instructions.md, queries.yml, and memory records.
5. A Python team building a data assistant with LangChain or LangGraph wants to use the repository’s wren-langchain reference SDK integration.

What are this agent's strengths and limitations?

How do you install or deploy this agent?

Use an environment with shell access, network access, and connectivity to a target data source:

pip install wrenai

For Postgres and memory support:

pip install "wrenai[postgres,memory]"

Then install the AI-client discovery stub:

npx skills add Canner/WrenAI

The supplied material says this command auto-detects Claude Code, Cursor, Cline, Codex, and similar clients. The required data-source credential fields and connection parameters are not specified in the supplied material; create the connection profile through the onboarding workflow.

How do you use this agent?

From a project directory, begin with:

wren skills get onboarding

Have an AI client with the discovery stub follow that workflow to set up the database and run an initial query. To add business context, run:

wren skills get enrich-context

For routine querying, use:

wren query --sql '...'

Or have the agent use:

wren ask "Who are our top 10 customers by sales this quarter?" --guided

For dashboards, run wren skills get genbi and have the agent generate, preview, and deploy the browser-side app. Deployment requires the user’s own Vercel or Cloudflare Pages account.

How does this agent compare with similar options?

FAQ