Data and AI Pipeline · Public

dagster

Data and AI pipeline project for checking inputs, outputs, state, latency, recovery, and deployment boundaries.

Data pipelineAI workflowState managementLatencyRecovery

Best fitDevelopers who need data streams, realtime processing, or AI pipelines inside a verifiable engineering workflow.

Check whether this project matches your task before installing it.

What it can doInput/output contracts, state checks, latency budgets, failure recovery, and acceptance checks

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot16k stars

2.2k forks · 680 contributors

Doramagic.ai Last verification date: 2026-06-22 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-06-22

What is dagster?

dagster helps build, run, or validate data and AI pipelines.
Best fit: Developers who need data streams, realtime processing, or AI pipelines inside a verifiable engineering workflow.
Not for: Not for users without clear inputs/outputs, isolated data sources, or those who only need a one-off script.
Capability added to an AI workflow: Input/output contracts, state checks, latency budgets, failure recovery, and acceptance checks
First safe verification step: Verify inputs, outputs, state, and recovery with a small sample dataset first.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/dagster-io/dagster, https://github.com/dagster-io/dagster#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers who need data streams, realtime processing, or AI pipelines inside a verifiable engineering workflow.

Match the project to your task before installing it.

CapabilityInput/output contracts, state checks, latency budgets, failure recovery, and acceptance checks

Data and AI pipeline project for checking inputs, outputs, state, latency, recovery, and deployment boundaries.

Repositorydagster-io/dagster

16k stars · 2.2k forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Dagster Overview and Core Concepts

Related topics: System Architecture and Deployment, Integrations and Extensibility, Operations, Observability, and Community Roadmap

Source: https://github.com/dagster-io/dagster / Human Manual

System Architecture and Deployment

Related topics: Dagster Overview and Core Concepts, Integrations and Extensibility, Operations, Observability, and Community Roadmap

Source: https://github.com/dagster-io/dagster / Human Manual

Integrations and Extensibility

Related topics: Dagster Overview and Core Concepts, System Architecture and Deployment, Operations, Observability, and Community Roadmap

Source: https://github.com/dagster-io/dagster / Human Manual

Operations, Observability, and Community Roadmap

Related topics: Dagster Overview and Core Concepts, System Architecture and Deployment, Integrations and Extensibility

Source: https://github.com/dagster-io/dagster / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/dagster-io/dagster, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars16k stars

Forks2.2k forks

Contributors680 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using dagster with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
A single un-deserializable bulk_actions row breaks the entire get_backfi
github / github_issue
02
[UI] Searching for "true" or "false" in Jobs and Schedules throws an err
github / github_issue
03
Add executor_def for AutomationConditionSensorDefinition
github / github_issue
04
dg scaffold project my_proj does not create virtual environment.
github / github_issue
05
Enhancement - Update to tomlkit 0.15.0, Remove dagster-dg-core pin "toml
github / github_issue
06
Assign ECS cpu/memory in tags on a per-asset basis
github / github_issue
07
Sqlalchemy Errors in Dagster
github / github_issue
08
Customize Dagster UI logo and colors / whitelabeling
github / github_issue
09
Dynamic partition definitions leaking data across code locations
github / github_issue
10
1.13.10 (core) / 0.29.10 (libraries)
github / github_release
11
1.13.9 (core) / 0.29.9 (libraries)
github / github_release
12
1.13.8 (core) / 0.29.8 (libraries)
github / github_release

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install dagster

Official start command · https://github.com/dagster-io/dagster#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

dagster Manual

An orchestration platform for the development, production, and observation of data assets.

Open the full manual

https://github.com/dagster-io/dagster Project Manual
Table of Contents
Dagster Overview and Core Concepts
Related Pages
Purpose and Scope
Core Concepts
Assets, Ops, Jobs, and Resources
Schedules, Sensors, and Partitions

Dagster Overview and Core Concepts

Related topics: System Architecture and Deployment, Integrations and Extensibility, Operations, Observability, and Community Roadmap

Source: https://github.com/dagster-io/dagster / Human Manual

System Architecture and Deployment

Related topics: Dagster Overview and Core Concepts, Integrations and Extensibility, Operations, Observability, and Community Roadmap

Source: https://github.com/dagster-io/dagster / Human Manual

Integrations and Extensibility

Related topics: Dagster Overview and Core Concepts, System Architecture and Deployment, Operations, Observability, and Community Roadmap

Source: https://github.com/dagster-io/dagster / Human Manual

Operations, Observability, and Community Roadmap

Related topics: Dagster Overview and Core Concepts, System Architecture and Deployment, Integrations and Extensibility

Source: https://github.com/dagster-io/dagster / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Configuration risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Configuration risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Capability evidence risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.