# rilldata/rill

The fastest business intelligence tool for humans and agents.

Repository: https://github.com/rilldata/rill
Canonical: https://ross.abutalabs.com/products/rill
Homepage: https://www.rilldata.com
Language: Go
License: Apache-2.0
License Family: permissive
Topics: duckdb, sveltekit, dataviz, csv, parquet, parquet-tools, golang, s3, data-analysis, sql, bi, data, data-visualization, parquet-viewer, business-analytics, sql-editor, ai, ai-chatbot, gen-ai
Last push: 2026-08-26T17:13:54+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 1728, "days_push": 7, "days_rel": 7, "gap_med": 1, "n_releases_24m": 208}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2844, forks 196 (observed 2026-08-28T04:07:24.901892+00:00)

## What it is
Rill is an open-source business intelligence tool that combines a local developer application and cloud platform for building interactive dashboards from OLAP engines like DuckDB and ClickHouse. It uses a BI-as-code approach (YAML + SQL) with a semantic layer, natural-language agentic analytics, and an MCP server so AI coding agents can author and query projects.

## Use cases
- build interactive BI dashboards from CSV, Parquet, or data warehouses
- explore large datasets with sub-second SQL queries
- let business users query metrics in natural language
- scaffold and manage BI projects with AI coding agents like Claude Code or Cursor
- define a semantic layer of dimensions and measures as code
- embed analytics dashboards into your own application
- connect AI agents to metrics via an MCP server

## When to choose
- you want fast, code-driven BI on DuckDB or ClickHouse with dashboards as YAML and SQL
- your team wants AI agents to build and explore analytics end-to-end
- you need embedded, brandable dashboards with API access and alerts
- you want a local developer tool plus a managed cloud deployment path

## When to avoid
- you need a traditional drag-and-drop BI suite with no code
- your data lives only in transactional (OLTP) databases without an OLAP engine
- you require heavy custom pixel-perfect reporting rather than metric dashboards
- you want a fully offline tool with no cloud component for sharing

## Facets
- artifact type: application
- maturity: active
- function: data-visualization, analytics, mcp, chatbot, etl, search-engine
- domain: data-visualization, analytics, large-language-models
- platform: windows, cli, self-hosted, go
- tags: business-intelligence, bi-as-code, duckdb, clickhouse, semantic-layer, dashboards, sql, parquet, agentic-analytics, embedded-analytics, ai-agents, data-engineering, macos, linux, web-server

## Member repositories
- rilldata/rill (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:24.901892+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:37:10.478519+00:00, confidence not recorded.
  - readme: https://github.com/rilldata/rill (fetched 2026-08-28T04:07:24.901892+00:00, sha 4571a017bc7f)
  - homepage: https://www.rilldata.com (fetched 2026-08-29T09:52:45.530680+00:00, sha f3d7720b89b8)
  - site_page: https://docs.rilldata.com/ (fetched 2026-08-29T09:52:45.540137+00:00, sha ff7af920330e)
  - site_page: https://www.rilldata.com/pricing (fetched 2026-08-29T09:52:45.541917+00:00, sha 2bc3011f70cd)
- Data as of 2026-08-30T08:39:29.467469+00:00.