yflow 0.2.1

The Makefile for AI workflows — 1 dependency, zero daemons, provider-agnostic

yflow

The Makefile for AI workflows — 1 dependency, zero daemons, provider-agnostic.

Define multi-agent workflows in YAML. Run them anywhere. No Docker, no servers, no lock-in.

Why yflow?

AI agent platforms are powerful but heavyweight — dozens of dependencies, daemons, Docker, and vendor lock-in. yflow is different:

Feature	yflow	Claude Code Workflow	awf	AutoTeam	Animus	AQM
Dependencies	1 (PyYAML)	Node.js + Anthropic API	20+	30+	15+	25+
Daemon required	❌	❌	✅	✅	✅	✅
Docker required	❌	❌	✅	✅	❌	✅
Provider-agnostic	✅	❌ (Anthropic-only)	✅	❌	✅	❌
Definition format	YAML (~20 lines)	Generated code (300+ lines)	YAML	Python DSL	YAML	YAML
Variable passing	✅ $step-id.output	❌	✅	❌	❌	❌
Sub-workflows	✅	❌	❌	❌	❌	❌
Template system	✅ auto-classify	❌	❌	❌	❌	❌
Persistence	Permanent	3-day auto-delete	Permanent	Permanent	Permanent	Permanent

yflow vs Claude Code Workflow

Claude Code added a Workflow feature in v2.1.47 (/workflow, --ultraworkflow) — a major step forward for multi-agent orchestration. But it's a different design philosophy:

Definition format: Claude Code generates 300+ line code scripts. yflow uses declarative YAML (~20 lines). Same power, less ceremony.

Vendor lock-in: Claude Code Workflow requires Node.js + an Anthropic API key. yflow is pure Python with one dependency (PyYAML). Use any LLM provider.

Persistence: Claude Code Workflow scripts auto-delete after 3 days (unless manually saved to ~/.claude/workflows/). yflow workflows are permanent files you control.

Complementary, not competing. Claude Code Workflow excels at Anthropic-native, single-session coding pipelines. yflow excels at cross-provider, persistent, multi-tool orchestration. Use yflow to define the pipeline, and Claude Code (or any agent) as one of the executors.

Install

pip install yflow

That's it. No Docker, no daemon, no API keys. Just yflow on your PATH.

Quick Start

1. Create a workflow

yflow create hello-world

2. Edit it

name: "Hello World"
description: "My first yflow pipeline"

steps:
  - id: greet
    name: "Say hello"
    type: command
    command: "echo 'Hello from yflow!'"

  - id: verify
    name: "Verify output"
    type: command
    command: "echo 'Previous step said: $greet.output'"
    depends_on: greet

3. Run it

yflow run hello-world --native

⚡ Native mode: executing locally...
   Local steps: 2 completed
   ✅ All steps executed natively!

Step Types

Type	Description	Needs external agent?
command	Shell command (native execution)	No
reasonix	One-shot reasoning / coding agent	Yes (reasonix CLI)
opencode	Coding agent	Yes (opencode CLI)
gbrain	Knowledge memory query/store	Optional (gbrain CLI)
subagent	Delegated AI task	Yes
skill	Reusable skill/capability	Yes
workflow	Reference another workflow	No

gbrain — Optional Knowledge Memory

yflow integrates with gbrain as an optional tool backend. gbrain is Garry Tan's knowledge memory system — a vector database for storing and retrieving structured knowledge across sessions.

steps:
  # Query past knowledge before coding
  - id: check_known
    type: gbrain
    action: query
    query: "LanceDB dimension mismatch fix"
    output_as: past_solution

  # Save new knowledge
  - id: record_fix
    type: gbrain
    action: put
    slug: "new-bug-pattern"
    content: |
      # Bug: $check_known.output

  # Full-text search
  - id: find_patterns
    type: gbrain
    action: search
    query: "Riverpod context loss"

  # Read a page
  - id: read_page
    type: gbrain
    action: get
    slug: "lancedb-dimension-mismatch"

Installation: gbrain is NOT a pip dependency. Install it separately:

git clone https://github.com/garrytan/gbrain ~/gbrain
cd ~/gbrain && bun install

Set GBRAIN_BIN env var if gbrain is not on $PATH. yflow auto-detects ~/.local/bin/bun run ~/gbrain/src/cli.ts as fallback.

Variable Passing

reasonix — DeepSeek-Native Agent (Run + Code)

yflow integrates with Reasonix, a DeepSeek-native agent framework with 91%+ cache hit rates. Two modes:

Run mode (default): Read-only analysis, ultra-cheap (~$0.00003 per call):

- id: analyze
  type: reasonix
  prompt: "Review this code for security issues"
  model: flash  # flash or pro (default: flash)

ACP mode: Full coding agent — read, write, edit files, run terminal commands:

- id: fix_bug
  type: reasonix
  mode: acp
  prompt: "Fix the race condition in worker.py"
  workdir: /home/user/project
  model: flash
  timeout: 600

Fields for reasonix steps:

• prompt — task description
• mode — run (default, read-only) or acp (coding with filesystem access)
• model — flash (default) or pro
• workdir — working directory for acp mode (default: cwd)
• timeout — seconds (default: 300 run / 600 acp)

Requires Reasonix CLI and DEEPSEEK_API_KEY in environment.

Variable Passing

Steps can reference outputs from previous steps:

- id: build
  type: command
  command: "npm run build"

- id: test
  type: command
  command: "echo 'Build output: $build.output'"
  depends_on: build

Templates

Bootstrap common workflows from templates:

yflow create my-fix --from backend-bug-fix --set TASK_DESCRIPTION="Fix timeout in /api/search"

Built-in templates: backend-bug-fix, backend-feature, flutter-bug-fix, flutter-feature.

Use with Any AI Agent

yflow is agent-agnostic. Pipe prompts to your agent of choice:

export YFLOW_EXEC="hermes -p"
yflow run my-pipeline --exec

Or use with any agent that can consume a prompt string.

Learn More

• Examples
• GitHub Repository
• Report an Issue

License

MIT © Guo-luen Huang