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dagster

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library1.13.20pypypi✓ verified 26d ago

Dagster is an orchestration platform designed for the development, production, and observation of data assets. It emphasizes defining pipelines as data assets and provides a rich development experience with built-in UI and testing capabilities. The library is actively maintained with frequent minor releases, often on a weekly basis, incorporating new features, bug fixes, and improvements.

pip install dagster dagster-webserver
INSTALL
IMPORT
SIG · DAGSTER
D
dagster
workflowpythonv1.13.20
Install
17.4s avg
Import
2714ms
Disk
208MB
Pass rate
10/ 10
Env Coverage10 / 10
glibc
3.93.13
musl
3.93.13
Install & Compatibility
Where this runs
tested against v1.13.20 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
musl
py 3.103.910 runs
installs and imports cleanly · install 0.0s · import 2.791s · 201.4MB
glibc
py 3.103.910 runs
installs and imports cleanly · install 17.4s · import 2.637s · 201MB
208MB installed
● package 208MB
Code
Verified usage

Verified import paths — ran on the pinned version, not inferred.

asset
from dagster import asset
job
from dagster import job
op
from dagster import op
from dagster import solid
The `solid` decorator was replaced by `op` in Dagster 1.0.0.
graph
from dagster import graph
Definitions
from dagster import Definitions
Config
from dagster import Config
from dagster import config_schema
Modern Dagster uses Pydantic-style `Config` models for resource/op configuration instead of `config_schema`.
Schedule
from dagster import Schedule
Sensor
from dagster import Sensor

This quickstart defines a simple asset and a job that materializes it. It includes a directly runnable Python snippet to execute the asset in memory. For local development with the full Dagster UI (Dagit), save the commented `Definitions` block to a file and run `dagster dev`.

from dagster import Definitions, asset, job @asset def my_first_asset(): """A simple asset that prints a message and returns a string.""" print("Hello, Dagster!") return "hello" @job def my_asset_job(): my_first_asset() # To run this locally and see the output immediately: if __name__ == "__main__": from dagster import materialize_to_memory result = materialize_to_memory(assets=[my_first_asset]) assert result.success print(f"Asset output: {result.output_for_node('my_first_asset')}") # For UI (Dagit) integration, save the following `Definitions` to a file (e.g., `my_repo.py`): # defs = Definitions( # assets=[my_first_asset], # jobs=[my_asset_job], # ) # Then run `dagster dev -f my_repo.py` and navigate to http://localhost:3000
dagster --version
Debug
Known issues
breakingDagster 1.0.0 introduced a major API overhaul, deprecating `solid` and `pipeline` in favor of `op` and `job`/`graph` respectively. Code written for pre-1.0 versions is not compatible without migration.
fix
Replace `@solid` with `@op` and `PipelineDefinition` with `JobDefinition` or `@job` decorator. Refer to the Dagster 1.0 migration guide for full details.
affects: <1.0.0
gotchaThe Dagster UI (Dagit) is provided by the `dagster-webserver` package, which must be installed separately from `dagster` core for local development and observation.
fix
Ensure both `dagster` and `dagster-webserver` are installed: `pip install dagster dagster-webserver`.
affects: All versions
gotchaStarting with Dagster 1.12.18, `psycopg2-binary` is no longer a transitive dependency of `dagster-postgres`. If you use `dagster-postgres` for event log or run storage and rely on this dependency, your environment may break.
fix
Explicitly add `psycopg2-binary` (or `psycopg2`) to your project's dependencies if you use `dagster-postgres`: `pip install psycopg2-binary`.
affects: >=1.12.18
gotchaThere are two main CLIs: `dagster` for open-source Dagster environments and `dg` (from `dagster-cloud`) for Dagster Cloud deployments. Using the wrong CLI can lead to confusion or errors.
fix
For open-source Dagster, use `dagster dev`, `dagster job launch`, etc. For Dagster Cloud, install `dagster-cloud` and use `dg deploy`, `dg api`, etc.
affects: All versions
deprecatedOlder methods of defining resource and op configuration using `config_schema` or directly passing dicts are deprecated. The modern approach uses Pydantic-style `Config` classes.
fix
Define configuration using `class MyConfig(Config): ...` and type-hint your resource/op functions with it: `def my_resource(config: MyConfig): ...`.
affects: >=1.0.0
Upgrade
Version history
1.13.20latest on PyPI · released Aug 27, 2026
Audit
Dependencies
dagster-webserverrequiredProvides the local Dagster UI (Dagit), essential for development and observation.
dagster-cloudoptionalCLI for interacting with Dagster Cloud deployments.
psycopg2-binaryoptionalRequired for `dagster-postgres` if you use it for storage (removed as transitive dependency in 1.12.18).
Agent activity
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Resources
dagster — pip install dagster · libregistry