AI agent orchestration

Orchestrate agent CLIs like production workflows.

Dagu gives AI agent commands the operational wrapper they need: scheduling, dependency order, retries, logs, artifacts, and human checkpoints.

Try an agent recipe Install Dagu

Agent orchestration as YAML

name: agent-release-notes
schedule: "0 9 * * 1"

steps:
  - name: collect_changes
    command: git log --since="7 days ago" --oneline
    output: GIT_LOG

  - name: draft_notes
    command: codex exec "Draft release notes from stdin"
    script: |
      ${GIT_LOG}
    depends: collect_changes

  - name: human_review
    command: ./scripts/request-approval.sh
    depends: draft_notes

Works with agent CLIs instead of replacing them

Human review steps fit naturally in the graph

Logs and artifacts stay under your control

Model and provider choices remain portable

Keep the harness under your control

Agent workflows are more than one prompt. They collect context, call tools, validate output, request approval, and publish artifacts. Dagu keeps that harness in your own repo and runtime.

Treat agent calls as workflow steps with dependencies and retries.
Capture stdout, stderr, artifacts, and run history for review.
Add validation and approval steps before generated output reaches users.

Use the agent tools your team already trusts

Dagu is not an agent framework. It runs the command-line tools and scripts your team already uses, which keeps the orchestration layer portable.

Call Claude, Codex, Gemini, OpenCode, Aider, or internal tools.
Switch providers by editing commands and environment variables.
Avoid locking production workflows to one vendor harness.

Schedule recurring agent work

Release notes, triage summaries, cleanup passes, reporting, and QA checks become scheduled workflows instead of manual prompts.

Run agent workflows on cron schedules.
Fan out over repositories, teams, or environments.
Record every run so humans can inspect what happened.

Dagu's role in an agent stack

Dimension

Dagu

Typical alternative

Agent model

Bring any CLI or API-backed agent.

Vendor platforms often tie orchestration to their own model surface.

Operations

Schedules, retries, logs, artifacts, approvals, and reruns.

Prompt-only automation leaves these controls to custom glue code.

Ownership

Workflow definitions, logs, and runtime live in your environment.

Hosted harnesses can make workflow state and pricing harder to control.

FAQ

Practical questions before adopting Dagu

Is Dagu an AI agent framework?

No. Dagu is the workflow engine underneath agent work. It schedules and observes commands, so you can use the agent CLI, model, and prompt system that fits each job.

How do human approvals work?

Approval can be modeled as its own workflow step. Teams commonly add review scripts, notification steps, or manual gates before publishing agent-generated output.

Can Dagu run multiple agent providers?

Yes. Because steps are commands, a workflow can call different CLIs or scripts in different steps and use fallback logic around them.

Start with one workflow.

Install Dagu, move one fragile script or agent task into YAML, and decide from a real run history.

Install Dagu