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Declarative
Workflow DSL
for

Define complex multi-agent pipelines in YAML. Let LLMs do creative work. Let duckflux handle the plumbing.

YAML-native Google CEL State machine Multi-runtime Agent-first Open source

# hello-world.duck.yaml
name: hello-world

participants:
  greet:
    type: exec
    command: echo "Hello from duckflux! 🦆"

steps:
  - run: greet

# ai-pipeline.duck.yaml
name: code-review-pipeline

inputs:
  repo:   { type: string, required: true }
  branch: { type: string, default: main }

participants:
  programmer: { type: exec, command: ai-agent code }
  reviewer:   { type: exec, command: ai-agent review }
  tester:     { type: exec, command: ai-agent test }

steps:
  - run: programmer
    output: code_result

  - run: reviewer
    input: code_result
    output: review_result
    retry:
      max: 3
      when: "!review_result.approved"

  - run: tester
    input: code_result
    when: "review_result.approved == true"

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Design Philosophy

duckflux isn't another "AI framework". It's an opinionated workflow engine with clear rules about what belongs where.

Declarative-first

Describe what your workflow should do, not how to do it. YAML is the source of truth — readable, version-controlled, diffable.

Deterministic

A state machine — not an LLM — controls flow. Your pipeline runs the same way every time. No hallucinated next steps.

Multi-runtime

Run workflows with the Go CLI for production deployments or the Bun JS/TS runtime for Node.js ecosystems. Same YAML, any runtime.

Powered by CEL

Google Common Expression Language for all conditional logic. Type-safe, sandboxed, and battle-tested in Kubernetes.

Agent-native

Built from the ground up for AI agent orchestration. Participants can call LLMs, MCP tools, HTTP APIs, or shell commands.

Human-readable

YAML you can read in a code review. Self-documenting workflows that any engineer can understand without a PhD in distributed systems.

Features

From simple scripts to complex multi-agent pipelines — duckflux speaks the same YAML regardless of complexity.

🔁

Loops

Iterate over collections with `for` loops. Process each item through a participant or nested sub-workflow.

steps:
  - for:
      var: item
      in: "{{ inputs.items }}"
      steps:
        - run: process_item
          input: item
          output: "results[item.id]"

⚡

Parallel execution

Run multiple participants concurrently and synchronize results. Fan-out, fan-in — built into the DSL.

steps:
  - parallel:
      - run: agent_a
        output: result_a
      - run: agent_b
        output: result_b
      - run: agent_c
        output: result_c
  - run: aggregator
    input: [result_a, result_b, result_c]

🔀

Conditionals

CEL-powered `when` guards on any step. Route execution based on previous outputs, inputs, or context.

steps:
  - run: analyze
    output: analysis

  - run: handle_success
    when: "analysis.score >= 0.8"

  - run: handle_failure
    when: "analysis.score < 0.8"
    input: analysis.reason

🛡️

Guards & retry

Automatic retry with configurable backoff, max attempts, and CEL conditions. Guards prevent steps from running when prerequisites aren't met.

steps:
  - run: reviewer
    output: review
    retry:
      max: 3
      delay: 2s
      when: "!review.approved"
    on_error: handle_review_error

📡

Events

Emit events to named channels and pause execution waiting for external events. Perfect for human-in-the-loop workflows.

steps:
  - run: draft_code
    output: draft

  - emit:
      channel: "approvals"
      data: { draft: draft, author: inputs.user }

  - wait:
      event: "approval.received"
      timeout: 24h
      output: decision

  - run: deploy
    when: "decision.approved == true"

📋

Input/Output schemas

Typed inputs and outputs with validation. CEL expressions reference inputs and previous outputs by name — fully type-aware.

inputs:
  repo:
    type: string
    required: true
  branch:
    type: string
    default: "main"
  dry_run:
    type: boolean
    default: false

outputs:
  deploy_url:
    type: string
  exit_code:
    type: integer

🪆

Nested workflows

Compose complex pipelines from smaller reusable sub-workflows. Pass context down and collect results back up.

participants:
  test_suite:
    type: workflow
    path: ./workflows/test-suite.duck.yaml
  deploy:
    type: workflow
    path: ./workflows/deploy.duck.yaml

steps:
  - run: test_suite
    input: "{{ context }}"
    output: test_results
    when: "!inputs.skip_tests"

🔧

Error handling

Declare error handlers per-step or globally. Capture, transform, or re-raise errors using CEL expressions.

steps:
  - run: risky_operation
    output: result
    on_error:
      run: error_handler
      input:
        error: "{{ error }}"
        context: "{{ context }}"

on_failure:
  - run: notify_slack
    input: "{{ error }}"

Powered by Google CEL

Google's Common Expression Language keeps your logic safe, predictable, and readable. The same engine that powers Kubernetes admission controllers.

Type-safeSandboxedNo side effectsFast evaluationKubernetes-provenHuman-readable

workflow.duck.yaml

steps:
  - run: analyze
    output: result

  - run: approve
    when: "result.score >= 0.9 && result.issues.size() == 0"

  - run: review
    when: "result.score >= 0.6 && result.score < 0.9"

  - run: reject
    when: "result.score < 0.6"

CEL expressions used

result.score >= 0.9

Direct field access with comparison

result.issues.size() == 0

CEL built-in: list length check

&&

Boolean AND — both conditions must be true

Full CEL reference

workflow.duck.yaml

steps:
  - run: llm_reviewer
    output: review
    retry:
      max: 5
      delay: 1s
      backoff: exponential
      when: >
        review.status == "incomplete" ||
        review.confidence < 0.7 ||
        "RETRY" in review.flags

CEL expressions used

review.status == "incomplete"

String equality check

review.confidence < 0.7

Numeric threshold condition

"RETRY" in review.flags

CEL `in` operator: list membership

Full CEL reference

workflow.duck.yaml

participants:
  deploy:
    type: exec
    command: >
      deploy.sh
      --env {{ inputs.environment }}
      --tag {{ inputs.tag }}

steps:
  - run: deploy
    input:
      target: >
        inputs.environment == "prod"
          ? inputs.prod_cluster
          : inputs.staging_cluster
      replicas: "inputs.replicas * (inputs.environment == 'prod' ? 2 : 1)"

CEL expressions used

inputs.environment == "prod" ? … : …

Ternary operator for dynamic values

inputs.replicas * 2

Arithmetic in expressions

{{ inputs.tag }}

Template interpolation in commands

Full CEL reference

Building Blocks

Every step in a duckflux workflow is powered by a participant. Six types cover everything from shell commands to event-driven coordination.

💻

exec

Shell commands & scripts

Execute any shell command, script, or binary. Perfect for AI agent CLIs, build tools, or custom runners.

Full exec reference

example.duck.yaml

participants:
  coder:
    type: exec
    command: >
      claude --model claude-opus-4-5
      --prompt "{{ inputs.task }}"
      --output-format json

🌐

http

HTTP/REST API calls

Make HTTP requests to any REST API. Supports GET, POST, PUT, DELETE with headers, auth, and body templating.

Full http reference

example.duck.yaml

participants:
  deploy_api:
    type: http
    method: POST
    url: "https://api.deploy.io/deploys"
    headers:
      Authorization: "Bearer {{ secrets.API_TOKEN }}"
    body:
      repo: "{{ inputs.repo }}"
      branch: "{{ inputs.branch }}"

🔌

mcp

Model Context Protocol

Call any MCP (Model Context Protocol) server tool. Bridges workflows with the broader MCP ecosystem.

Full mcp reference

example.duck.yaml

participants:
  github_tool:
    type: mcp
    server: "@modelcontextprotocol/server-github"
    tool: create_pull_request
    args:
      repo: "{{ inputs.repo }}"
      title: "{{ inputs.title }}"
      branch: "{{ inputs.branch }}"

🪆

workflow

Nested sub-workflows

Compose workflows from reusable sub-workflows. Build libraries of common patterns and share across teams.

Full workflow reference

example.duck.yaml

participants:
  test_suite:
    type: workflow
    path: ./shared/test-suite.duck.yaml

  security_scan:
    type: workflow
    path: ./shared/security.duck.yaml
    inputs:
      strict: true

📡

emit

Event emission

Emit named events to channels. Enable event-driven architectures and cross-workflow communication.

Full emit reference

example.duck.yaml

steps:
  - run: analyze
    output: result

  - emit:
      channel: "analysis.complete"
      data:
        result: "{{ result }}"
        workflow_id: "{{ context.id }}"
        timestamp: "{{ context.now }}"

⏸️

wait

Event & condition waits

Suspend execution until an event arrives or a condition is met. Supports timeouts and fallback handlers.

Full wait reference

example.duck.yaml

steps:
  - emit:
      channel: "approvals.pending"
      data: { pr_url: "{{ outputs.pr_url }}" }

  - wait:
      event: "approval.decision"
      timeout: 48h
      output: decision
      on_timeout: handle_expired

  - run: merge_pr
    when: "decision.approved"

Same YAML, any Runtime

One workflow definition runs anywhere. Use the Go CLI for production and edge deployments, or the JS/TS runtime to integrate with your Node/Bun ecosystem.

Go CLI

Production-ready binary

v0.1.0 stable

# Install with Go
go install github.com/your-org/duckflux/cmd/duck@latest

# Or download binary
curl -sSL https://install.duckflux.dev | sh

# Run a workflow
duck run ./my-workflow.duck.yaml

# Run with inputs
duck run ./pipeline.duck.yaml \
  --input repo=https://github.com/org/repo \
  --input branch=main

# Watch mode
duck run ./pipeline.duck.yaml --watch

🍞

JS/TS (Bun)

Bun & Node.js compatible

v0.1.0 beta

# Install with Bun
bun add -g duckflux

# Or with npm / pnpm
npm install -g duckflux
pnpm add -g duckflux

# Run a workflow
duck run ./my-workflow.duck.yaml

# Use as library in your Bun app
import { run } from 'duckflux';

const result = await run('./pipeline.duck.yaml', {
  inputs: { repo: 'https://github.com/org/repo' }
});

Not sure which to choose?

Read the runtime comparison guide

How duckflux Compares

An honest comparison with popular workflow orchestration tools. Different tools solve different problems — here's where duckflux fits.

Feature	duckflux YAML DSL for agents	Temporal Workflow platform	LangGraph LLM graph orchestration	n8n Visual automation	Step Functions AWS managed workflows
YAML DSL Define workflows in human-readable YAML	✓	✗	✗	✗	◑
Agent-native First-class support for AI agent orchestration	✓	✗	✓	◑	✗
CEL expressions Google CEL for safe, typed condition evaluation	✓	✗	✗	✗	✗
Multi-runtime Run on multiple language runtimes	✓	✓	✗	✗	✗
MCP support Native Model Context Protocol integration	✓	✗	◑	✗	✗
Loops Iterate over collections natively	✓	✓	✓	✓	✓
Parallel steps Fan-out parallel execution	✓	✓	✓	✓	✓
Event wait Pause execution for external events	✓	✓	✗	◑	✓
Self-hosted Run on your own infrastructure	✓	✓	✓	✓	✗
Open source Fully open-source license	✓	✓	✓	✓	✗

✓ Supported ✗ Not supported ◑ Partial / plugin required

Comparison based on publicly available documentation. Table reflects capabilities, not quality.

Ready to quack?

Stop orchestrating
imperatively.

Write your first workflow in 5 minutes. Run it with a single command. Let duckflux handle the state machine so you can focus on what matters.

Get started in 5 min View on GitHub

$ duck run ./hello.duck.yaml

Declarative Workflow DSL for

Design Philosophy

Declarative-first

Deterministic

Multi-runtime

Powered by CEL

Agent-native

Human-readable

Features

Loops

Parallel execution

Conditionals

Guards & retry

Events

Input/Output schemas

Nested workflows

Error handling

Powered by Google CEL

CEL expressions used

CEL expressions used

CEL expressions used

Building Blocks

Same YAML, any Runtime

Go CLI

JS/TS (Bun)

How duckflux Compares

Stop orchestrating imperatively.

Declarative
Workflow DSL
for

Stop orchestrating
imperatively.