rilldata/rill

Agent-first, human-friendly business intelligence

Docs · Data Talks

---

Rill is the fastest BI tool for humans and agents, powered by OLAP engines like ClickHouse and DuckDB.

Get Started

curl https://rill.sh | sh        # install
rill start my-project            # create a project and open the UI

Scaffold a project with agent context

Use rill init to scaffold a project interactively:

➜ rill init
? Project name my-rill-project
? OLAP engine duckdb
? Agent instructions claude

Created a new Rill project at ~/my-rill-project
Added Claude instructions in .claude and .mcp.json

Success! Run the following command to start the project:

  rill start my-rill-project

Why Rill?

• Build with agents — BI-as-code (YAML + SQL) means coding agents like Claude Code and Cursor can author projects, dashboards, and security policies end-to-end
• Semantic layer — Single source of truth for dimensions, measures, and time grains — defined in YAML, generating SQL at query time against your OLAP engine
• Explore with agents — Conversational BI lets business users query metrics in natural language; the MCP server connects AI agents directly to your semantic layer
• Real-time performance — Sub-second queries at any scale; ClickHouse for billions of rows, DuckDB for smaller datasets and fast iteration
• Embeddable — Dashboards, APIs, and agent interfaces you can ship in your product

Capabilities

Rill Developer (local)

• Connectors — S3, GCS, databases, and 20+ sources
• OLAP Engines — Managed ClickHouse or DuckDB included, or connect an external engine (ClickHouse Cloud, Druid, Pinot, MotherDuck)
• SQL Models — Transform raw data with SQL, join models together
• Data Profiling — Instant column stats and distributions
• Incremental Ingestion — Load only new data on each run to keep large datasets current without full refreshes
• Semantic Layer — Dimensions, measures, and time grains in YAML
• Row Access Policies — Per-user, per-group data access control
• Local Dashboards — Preview and explore dashboards locally

Rill Cloud

• Deploy — Git-backed, versioned deployments — push with rill deploy or connect a repo for automatic CI/CD
• Explore & Canvas Dashboards — Interactive dashboards, embeddable in your product
• Conversational BI — Ask questions in natural language
• MCP Server — Connect Claude, ChatGPT, or any AI agent to your metrics
• Custom APIs & Embedding — Expose metrics via REST or embed dashboards
• Alerts & Reports — Threshold alerting, code-defined or UI-defined

How It Works

Define everything in code — models, metrics, dashboards — and Rill handles the rest.

1. Connect data — models/events.yaml

type: model
connector: duckdb
materialize: true

sql: |
  select * from read_parquet('gs://rilldata-public/auction_data.parquet')

2. Define metrics — metrics/events_metrics.yaml

version: 1
type: metrics_view
model: events
timeseries: timestamp

dimensions:
  - name: country
    column: country
  - name: device
    column: device_type

measures:
  - name: total_events
    expression: count(*)
  - name: revenue
    expression: sum(price * quantity)
    description: Total revenue

3. Create a dashboard — dashboards/events_explore.yaml

type: explore

display_name: "Events Dashboard"
metrics_view: events_metrics

dimensions: "*"
measures: "*"

4. Deploy

rill deploy                      # push to Rill Cloud

Your metrics view is immediately queryable on Rill Cloud — add YAML files to configure dashboards, alerts, and custom APIs.

Learn More

Getting Started with Rill Developer • Exploring Data with Rill • Data Talks on the Rocks • Agentic Analytics with Claude Code and Rill

Examples

Example	Description	Links
Programmatic Ads	Bidstream data for pricing and campaign performance	GitHub · Demo
Cost Monitoring	Cloud infra merged with customer data	GitHub · Demo
GitHub Analytics	Contributor activity and commit patterns	GitHub · Demo

Or explore a live embedded dashboard.

Community

Contributing

We welcome contributions! See our Contributing Guide to get started.