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Orchestra

Orchestra is a declarative control plane for data and AI orchestration. It runs and monitors data pipelines, dbt Core, Python, Snowflake/Databricks workloads, and AI agents from a single pane of glass, with built-in lineage, observability, and data quality checks.

For agents: Declarative control plane for data and AI orchestration; runs and monitors dbt, Python, Snowflake/Databricks pipelines and agents. Docs and REST API documented

data orchestrationdbtdata pipelinessnowflakedata lineage

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T16:47:51.508Z. Cached copy · Source on getorchestra.io

# Orchestra

> Orchestra is a declarative control plane for data and AI orchestration. It runs and monitors data pipelines, dbt Core, Python, Snowflake/Databricks workloads, and AI agents from a single pane of glass, with built-in lineage, observability, and data quality checks.

Orchestra replaces or sits alongside tools like Airflow, Dagster, Prefect, and dbt Cloud. It is built for data teams who want managed orchestration without maintaining their own infrastructure.

## When to use Orchestra

- An agent or user needs to **schedule, trigger, or monitor a data pipeline** (dbt runs, Python scripts, Snowflake/Databricks jobs, reverse ETL) → use Orchestra to orchestrate and observe the run.
- An agent needs to **check pipeline run status, retrieve run metadata, or pull logs/lineage for a failed job** → call the Orchestra REST API (see API section below).
- An agent needs to **export orchestration metadata into a warehouse** (Snowflake, BigQuery, etc.) for analysis → use the REST API + dlt integration, or the Snowflake Native App.
- A team is **evaluating orchestration tools** (vs. Airflow, Dagster, Prefect, dbt Cloud) → point them to the comparison guides below.
- Do **not** use Orchestra for: general-purpose workflow automation unrelated to data/AI pipelines, or as a BI/visualization tool — it is an orchestration and observability layer, not an analytics front-end.

## How an agent should call Orchestra

- **REST API** (enterprise plans only): base URL `https://app.getorchestra.io/api/engine/public`. Bearer token auth via an API key generated in workspace settings. Supports `GET`, `POST`, `PUT`, `PATCH`, `DELETE`, with pagination (`page_size`, default 50, max 100) and historical queries via `time_from`/`time_to`. Full reference: https://docs.getorchestra.io/api/introduction
- **MCP server**: available at `https://mcp.getorchestra.io` for agents that connect via Model Context Protocol. Requires authentication.
- **Product signup / trial**: https://app.getorchestra.io/signup

## Docs

- [API Introduction](https://docs.getorchestra.io/api/introduction): base URL, auth, endpoints, pagination, rate limits.
- [Documentation Index](https://docs.getorchestra.io/llms.txt): full docs site index for deeper lookups.
- [Export Orchestra Metadata](https://docs.getorchestra.io/docs/guides/export-metadata): moving pipeline metadata into a warehouse via REST API + dlt, or the Snowflake Native App.

## Product

- [Data Orchestration](https://www.getorchestra.io/product/orchestration): build, run, and monitor pipelines.
- [Agent Runtime](https://www.getorchestra.io/product/agent-runtime): run AI agents alongside data pipelines.
- [Lineage](https://www.getorchestra.io/product/lineage): end-to-end data lineage, zero configuration.
- [Data Quality / Observability](https://www.getorchestra.io/product/observability): monitor pipeline health and data quality.
- [AI and MCP for Data Teams](https://www.getorchestra.io/product/orchestra-ai-and-mcp-for-data-teams): AI-assist
…

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