workflow-use 0.2.11

Create, edit, run deterministic workflows

Workflow-Use

Semantic browser automation with deterministic workflow generation and variables.

Quick Start

1. Test Deterministic Workflow Generation (NEW!)

python examples/scripts/deterministic/run_complete_test.py

Generate workflows without LLM for step creation - 10-100x faster, guaranteed semantic steps.

2. Create Your Own Workflow

from workflow_use.healing.service import HealingService
from browser_use.llm import ChatBrowserUse

llm = ChatBrowserUse(model_name="bu-latest")
service = HealingService(llm=llm, use_deterministic_conversion=True)

workflow = await service.generate_workflow_from_prompt(
    prompt="Go to GitHub, search for browser-use, get star count",
    agent_llm=llm,
    extraction_llm=llm
)

3. Run a Workflow

cd /path/to/workflow-use/workflows
python cli.py run-workflow-no-ai my_workflow.json

# If the workflow has variables, the CLI will prompt you interactively:
# Enter value for repo_name (required, type: string): browser-use

---

Key Features

🚀 Deterministic Workflow Generation

• Direct Action Mapping: input_text → input step (no LLM)
• Guaranteed Semantic Steps: 0 agent steps (instant execution, $0/run)
• 10-100x Faster: 5-10s vs 20-40s for LLM-based
• 90% Cheaper: Minimal LLM usage

🎯 Semantic-Only Multi-Strategy Element Finding

• No CSS/XPath: 100% semantic strategies (text, role, ARIA, placeholder, etc.)
• 7 Fallback Strategies: text_exact → role_text → aria_label → placeholder → title → alt_text → text_fuzzy
• Works WITH Browser-Use: Finds element index in DOM state, then uses browser-use's controller
• Fast & Robust: Direct index lookup when strategies match, falls back to AI when needed
• Human-Readable: Workflow YAML contains semantic strategies, not brittle selectors

🔄 Variables in Workflows

• Reusable Workflows: Parameterize dynamic values
• Semantic Targeting: Use {variable} in target_text
• Auto-Extraction: LLM suggests variables automatically

📊 Real-time Progress Tracking (NEW!)

• Step-by-Step Visibility: See each browser action as it's recorded
• Status Updates: Track workflow processing phases in real-time
• Cloud Integration Ready: Store progress in database for live UI updates
• Debug Friendly: Know exactly where workflow generation fails
• Zero Overhead: Optional callbacks, fully backward compatible

# Track workflow generation progress in real-time
workflow = await service.generate_workflow_from_prompt(
    prompt="Search for Python docs",
    agent_llm=llm,
    extraction_llm=llm,
    on_step_recorded=lambda s: print(f"Step {s['step_number']}: {s['description']}"),
    on_status_update=lambda msg: print(f"Status: {msg}"),
)

---

Documentation

• docs/DETERMINISTIC.md - Deterministic workflow generation
• docs/VARIABLES.md - Variables guide
• docs/PROGRESS_TRACKING.md - Real-time progress tracking ⭐ NEW
• QUICK_START_PROGRESS_TRACKING.md - 5-minute integration guide
• examples/README.md - Example scripts

---

Project Structure

workflows/
├── workflow_use/              # Main package
│   ├── healing/              # Workflow generation & healing
│   │   ├── deterministic_converter.py   # NEW: Deterministic conversion
│   │   ├── variable_extractor.py        # Auto variable detection
│   │   └── service.py                   # Main workflow generation
│   ├── workflow/             # Workflow execution
│   │   └── semantic_executor.py         # Semantic step execution
│   ├── controller/           # Workflow controller
│   ├── recorder/             # Workflow recording
│   ├── storage/              # Storage logic
│   ├── mcp/                  # MCP integration
│   ├── schema/               # Schema definitions
│   └── builder/              # Workflow builder
│
├── backend/                  # FastAPI backend service
│   ├── api.py               # API entry point
│   ├── routers.py           # API routes
│   └── service.py           # Business logic
│
├── examples/                 # Examples organized by feature
│   ├── scripts/
│   │   ├── deterministic/   # Deterministic workflow examples
│   │   │   ├── run_complete_test.py        # ⭐ Test deterministic generation
│   │   │   └── create_deterministic_workflow.py
│   │   ├── variables/       # Variable feature examples
│   │   ├── demos/           # Advanced demos
│   │   └── runner.py        # Generic workflow runner
│   ├── progress_tracking_example.py  # ⭐ NEW: Real-time progress tracking
│   └── workflows/           # Example workflow JSON files
│       ├── basic/           # Basic workflow examples
│       ├── form_filling/    # Form filling examples
│       ├── parameterized/   # Parameterized workflows
│       └── advanced/        # Advanced workflows
│
├── tests/                    # Test files
│   ├── test_button_click.py
│   └── test_recorded_workflow.py
│
├── docs/                     # Documentation
│   ├── DETERMINISTIC.md     # Deterministic workflows
│   └── VARIABLES.md         # Variables guide
│
├── data/                     # Runtime & test data
│   └── test_data/           # Test data (tracked in git)
│       ├── form-filling/
│       └── flight-test/
│
├── cli.py                   # CLI entry point
├── pyproject.toml          # Project configuration
└── README.md               # This file

---

Comparison: Deterministic vs LLM-Based

Feature	Deterministic	LLM-Based
Generation Speed	⚡ 5-10s	🐌 20-40s
Generation Cost	💰 $0.01-0.05	💸 $0.10-0.30
Agent Steps	✅ 0 guaranteed	❌ Variable
Deterministic	✅ Yes	❌ No
Execution Speed	⚡ Instant	🐌 5-45s
Execution Cost	💰 $0/run	💸 $0.03-0.30/run

Recommendation: Use deterministic for most workflows (search, click, input, navigate).

---

Testing

# Test deterministic generation
python examples/scripts/deterministic/run_complete_test.py

# Test variables
python examples/scripts/variables/create_workflow_with_variables.py

# Compare approaches
python examples/scripts/deterministic/test_deterministic_workflow.py

---

Next Steps

1. ✅ Run examples/run_complete_test.py
2. ✅ Review the generated workflow JSON
3. ✅ Try creating your own workflow
4. ✅ Add variables to make it reusable