Lately, I’ve been using it to automate my daily workflows with an AI agent harness like opencode. Dagu helps you manage local, private workflows. It gives you a flexible Kanban-style view, a declarative workflow language with validation, and the basics you expect from production workflow infrastructure: logs, retries, status tracking, and more.

The best part of having a workflow language is maintainability. Any AI agent can help create or debug your workflows. You can do something similar with plain Bash scripts, but Bash quickly becomes hard to debug and maintain.

Because Dagu is DAG-based, it automatically runs independent steps in parallel. Each step is validated and logged separately, which makes it much easier for an AI agent to understand what failed and fix it.

Give it a try!

There's no need for humans to write DAGs anymore, yet they remain human-readable. I truly believe this is the future of workflow orchestration.

https://github.com/dagucloud/dagu

Comments URL: https://news.ycombinator.com/item?id=47193863

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=45909763

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=45664791

Points: 25

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Dagu is designed for teams that find Airflow overkill but need more than cron. It's a single binary with zero dependencies that runs anywhere - from Raspberry Pi to cloud clusters.

What's new in v1.18:

- Distributed execution - Run workflow steps across multiple machines - OIDC authentication - Enterprise-ready auth for the Web UI - High availability - Redundant schedulers with automatic failover

Example workflow: ``` steps: - name: fetch data command: curl https://api.example.com/data output: DATA

      - name: process
        command: python process.py
        env:
          - INPUT: ${DATA}
        retryPolicy:
          limit: 3
          intervalSec: 30
          backoff: 2.0

No need for a database or complex setup. Just write your YAML workflow and run it with `dagu start my_workflow.yaml`.

Would love to hear your feedback!

Comments URL: https://news.ycombinator.com/item?id=44307323

Points: 3

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The core idea: define workflows in YAML, run them as scheduled jobs or on-demand. Single Go binary, no external dependencies, stores everything locally.

New in this release: - Nested DAGs - workflows can call other workflows - Refactored execution history storage (10x faster for large histories) - Better debugging UI showing precondition results and variable outputs - Proper job queue management

Example workflow: ```yaml name: data-pipeline schedule: "0 10 * * *" steps: - name: fetch-data command: curl https://api.example.com/data > raw.json - name: process command: jq '.items[] | select(.active)' raw.json > processed.json depends: fetch-data - name: load command: python load_to_db.py processed.json depends: process ```

Try it: ``` docker run --rm -p 8080:8080 ghcr.io/dagu-org/dagu:1.17.0-beta.1 dagu start-all ```

Then visit http://localhost:8080

We use it in production for ETL pipelines, report generation, and system maintenance tasks. It's not trying to be Airflow - no distributed execution, no Python dependency hell, just a reliable way to run workflows.

GitHub: https://github.com/dagu-org/dagu

Would love feedback on the beta, especially around the nested DAG implementation and any performance issues you encounter.

Comments URL: https://news.ycombinator.com/item?id=44142130

Points: 4

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Comments URL: https://news.ycombinator.com/item?id=42647403

Points: 3

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- Lightweight (450 LOC) with zero dependencies - Fast performance (benchmarks in README) - Full middleware support for cross-cutting concerns - 100% test coverage, production-ready

Dew implements the command-oriented interface pattern, helping separate concerns and reduce cognitive load in large Go projects. It's designed for developers who want a simple, efficient way to structure their application logic without heavy frameworks. We built Dew because we couldn't find a command bus library for Go that met all our needs. We'd love your feedback!

Comments URL: https://news.ycombinator.com/item?id=41279908

Points: 4

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Features: - Install by only a single binary - Zero dependencies - No database is required - Built-in Web UI and API Details: https://dagu.readthedocs.io/en/latest/web_interface.html

Quickstart: https://dagu.readthedocs.io/en/latest/quickstart.html

New features: 1. Added run and params field Now you can modularize DAGs. You can run another DAG from a DAG by specifying the name: ```yaml steps: - name: running sub_dag run: sub_dag params: "FOO=BAR" ``` 2. Accept JSON list to specify command and args You can make the DAG to be more readable by using list notation for specifying complex arguments to a command: ```yaml steps: - name: step1 description: print current time command: [python, "-c", "import sys; print(sys.argv)", "argument"] ```

Comments URL: https://news.ycombinator.com/item?id=40480019

Points: 4

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Comments URL: https://news.ycombinator.com/item?id=40412940

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=40255273

Points: 1

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This library builds upon the excellent foundations laid by pySBD and pragmatic_segmenter.

The roadmap includes language support expansion, text cleaning features, and improved testing. Contributions are greatly appreciated.

Repository: https://github.com/gosbd/gosbd

Comments URL: https://news.ycombinator.com/item?id=39380828

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=38849874

Points: 4

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