Canner/WrenAI
GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more. observed · 2026-08-28
Health v2 · maintenance only
87/100
- Activity 99
- Release rhythm 86
- Longevity 64
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 4.0
- age_days: 903
- days_rel: 15
- days_push: 7
- n_releases_24m: 75
Adoption not part of the score
17401 stars · 1970 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
WrenAI is an open-source generative BI (GenBI) engine that turns natural-language questions into governed SQL, charts, and dashboards across 20+ data sources like BigQuery, Snowflake, PostgreSQL, and ClickHouse. It provides an open AI context layer with a semantic layer (MDL) so AI agents and humans get trustworthy, business-grounded answers instead of schema guesses.
Use cases
- ask business questions in plain English and get SQL answers
- generate dashboards and charts from natural language
- text-to-SQL over Snowflake, BigQuery, Postgres, ClickHouse, Databricks
- give AI agents a governed semantic layer over warehouse data
- expose business data to agents via MCP
- define metrics and business logic once in a version-controlled MDL
- self-host a governed AI analytics platform
When to choose
- you want open-source, self-hostable text-to-SQL with governance
- your team needs natural-language BI on existing warehouses without migrating data
- you're building AI agents that must query business data reliably
- you need a semantic layer with approved metric definitions in git
When to avoid
- you need a traditional hand-built BI tool without LLM involvement
- you want a fully closed-source managed product with vendor SLAs only
- your use case is simple SQL editing rather than natural-language analytics
Facets
application · maturity active
rag llm-inference agent-framework mcp data-visualization search-engine api-framework cli large-language-models databases analytics data-visualization self-hosted python self-hosted cloud cli text-to-sql genbi semantic-layer mdl context-layer business-intelligence nl2sql data-connectors open-core ai-agents retrieval-augmented-generation docker web-server
7 sources
- readme: https://github.com/Canner/WrenAI · fetched 2026-08-28 · aa1086e7df48
- homepage: https://www.getwren.ai/en/open-core · fetched 2026-08-29 · 101e2ad891f3
- site_page: https://docs.getwren.ai/oss/introduction · fetched 2026-08-29 · d594af47fd5b
- site_page: https://docs.getwren.ai/cp/overview · fetched 2026-08-29 · 73bd0b5f8193
- registry_pypi: https://pypi.org/pypi/wrenai/json · fetched 2026-08-29 · 214440668d70
- site_page: https://www.getwren.ai/pricing · fetched 2026-08-29 · e9cbab881f02
- site_page: https://www.getwren.ai/product · fetched 2026-08-29 · 27aa13999c00
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Canner/WrenAI | main | 87 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem