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Sluice
An execution of the flow sales/nightly-load while it runs, with the waterfall of its tasks and the live log.An execution of the flow sales/nightly-load while it runs, with the waterfall of its tasks and the live log.

Sluice

Flows are YAML files. Tasks are your scripts. Sluice runs them on a schedule, a webhook or a click, on a process, a container or a Kubernetes Job, and shows every run live. One Go binary with Postgres.
Run your first flowUse Sluice with coding agents

Sluice is one binary: the server, the web UI, the CLI and an MCP server. It needs only Postgres.

Terminal window
export SLUICE_BOOTSTRAP_ADMIN_PASSWORD=change-me-now-1
export SLUICE_MASTER_KEYS="k1:$(openssl rand -base64 32)"
docker compose -f deploy/compose/compose.yml up -d

Open http://localhost:8080, sign in as admin@local.test, and run a flow.

A failed execution: the failure triage with its cause and fix, the failed task selected in the waterfall, and its error in the log.A failed execution: the failure triage with its cause and fix, the failed task selected in the waterfall, and its error in the log.

Flows are files

A flow is a YAML file next to the scripts it runs. Edit it in the browser, sync it from git, or push it from CI with sluice namespaces push. Every save is a version.

Watch every run

The execution page shows a live waterfall of the tasks. Select a task to see its attempts, outputs, metrics and logs. A failed run takes you to the failed task in one click.

Built for agents

sluice init gives a coding agent the flow rules. The CLI prints JSON and exits with the end state. The MCP server reads, runs and triages executions. The docs are in llms.txt.

Runs where your code runs

Tasks run as a local process, in a Docker container or as a Kubernetes Job. Pools route tasks to the instances that can run them.

A flow

Section titled “A flow”
id: nightly-load
description: Load orders, then build the report.
triggers:
  - { id: nightly, type: schedule, cron: "0 2 * * *", timezone: Europe/Zurich }
tasks:
  - id: extract
    type: command
    command: ["python3", "pipelines/extract.py"]
    env:
      DB_URL: ${{ secret('DB_URL') }}
  - id: report
    type: command
    depends_on: [extract]
    command: ["echo", "rows: ${{ tasks.extract.outputs.rows }}"]

Run it from a terminal, a CI job or an agent, and get its end state as the exit code:

Terminal window
sluice run sales/nightly-load --wait

Where to go next

Section titled “Where to go next”
TutorialsStart Sluice, write a flow, run it and read the execution. Then build an ELT pipeline.
How-to guidesSchedules, webhooks, secrets, git sync, Docker and Kubernetes, CI, coding agents and MCP.
ConceptsHow flows, executions, namespaces, executors and the assistant work, and why.
ReferenceThe flow file, the CLI, the environment variables, the MCP tools and the HTTP API.