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Chapter 1: Getting Started
1
Langflow Tutorial

Chapter 1: Getting Started

Welcome to Chapter 1: Getting Started. In this part of Langflow Tutorial: Visual AI Agent and Workflow Platform, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter gets Langflow running locally so you can build and test flows immediately.

Learning Goals

  • install Langflow using the recommended package path
  • run the local UI and create first flow
  • validate basic node execution and output correctness
  • understand key runtime prerequisites

Quick Start

uv pip install langflow -U
uv run langflow run

Langflow starts at http://127.0.0.1:7860 by default.

First Validation Checklist

  1. UI loads and you can create a new flow
  2. at least one model node executes successfully
  3. flow output is visible in playground
  4. save/export path works

Source References

Summary

You now have a working Langflow environment ready for architecture and workflow design.

Next: Chapter 2: Platform Architecture

Depth Expansion Playbook

Source Code Walkthrough

scripts/generate_migration.py

The upgrade function in scripts/generate_migration.py handles a key part of this chapter's functionality:

def upgrade() -> None:
    """
    EXPAND PHASE: Add new schema elements (backward compatible)
    - All new columns must be nullable or have defaults
    - No breaking changes to existing schema
    - Services using old schema continue to work
    """
    bind = op.get_bind()
    inspector = inspect(bind)

    # Get existing columns for idempotency
        columns = [col['name'] for col in inspector.get_columns('{table_name}')]
    }

    # Add new nullable column (always check existence first)
    if '{column_name}' not in columns:
        op.add_column('{table_name}',
            sa.Column('{column_name}', sa.{column_type}(), nullable=True{default_value})
        )

        print(f"✅ Added column '{column_name}' to table '{table_name}'")

        # Optional: Add index for performance
        # op.create_index('ix_{table_name}_{column_name}', '{table_name}', ['{column_name}'])
    else:
        print(f"⏭️  Column '{column_name}' already exists in table '{table_name}'")

    # Verify the change
    result = bind.execute(text(
        "SELECT COUNT(*) as cnt FROM {table_name}"

This function is important because it defines how Langflow Tutorial: Visual AI Agent and Workflow Platform implements the patterns covered in this chapter.

scripts/generate_migration.py

The downgrade function in scripts/generate_migration.py handles a key part of this chapter's functionality:

def downgrade() -> None:
    """
    Rollback EXPAND phase
    - Safe to rollback as it only removes additions
    - Check for data loss before dropping
    """
    bind = op.get_bind()
    inspector = inspect(bind)
    columns = [col['name'] for col in inspector.get_columns('{table_name}')]

    if '{column_name}' in columns:
        # Check if column has data
        result = bind.execute(text("""
            SELECT COUNT(*) as cnt FROM {table_name}
            WHERE {column_name} IS NOT NULL
        """)).first()

        if result and result.cnt > 0:
            print(f"⚠️  Warning: Dropping column '{column_name}' with {{result.cnt}} non-null values")

            # Optional: Create backup table
            backup_table = '_{table_name}_{column_name}_backup_' + datetime.now().strftime('%Y%m%d_%H%M%S')
            bind.execute(text(f"""
                CREATE TABLE {{backup_table}} AS
                SELECT id, {column_name}, NOW() as backed_up_at
                FROM {table_name}
                WHERE {column_name} IS NOT NULL
            """))
            print(f"💾 Created backup table: {{backup_table}}")

This function is important because it defines how Langflow Tutorial: Visual AI Agent and Workflow Platform implements the patterns covered in this chapter.

scripts/generate_migration.py

The upgrade function in scripts/generate_migration.py handles a key part of this chapter's functionality:

def upgrade() -> None:
    """
    EXPAND PHASE: Add new schema elements (backward compatible)
    - All new columns must be nullable or have defaults
    - No breaking changes to existing schema
    - Services using old schema continue to work
    """
    bind = op.get_bind()
    inspector = inspect(bind)

    # Get existing columns for idempotency
        columns = [col['name'] for col in inspector.get_columns('{table_name}')]
    }

    # Add new nullable column (always check existence first)
    if '{column_name}' not in columns:
        op.add_column('{table_name}',
            sa.Column('{column_name}', sa.{column_type}(), nullable=True{default_value})
        )

        print(f"✅ Added column '{column_name}' to table '{table_name}'")

        # Optional: Add index for performance
        # op.create_index('ix_{table_name}_{column_name}', '{table_name}', ['{column_name}'])
    else:
        print(f"⏭️  Column '{column_name}' already exists in table '{table_name}'")

    # Verify the change
    result = bind.execute(text(
        "SELECT COUNT(*) as cnt FROM {table_name}"

This function is important because it defines how Langflow Tutorial: Visual AI Agent and Workflow Platform implements the patterns covered in this chapter.

scripts/generate_migration.py

The downgrade function in scripts/generate_migration.py handles a key part of this chapter's functionality:

def downgrade() -> None:
    """
    Rollback EXPAND phase
    - Safe to rollback as it only removes additions
    - Check for data loss before dropping
    """
    bind = op.get_bind()
    inspector = inspect(bind)
    columns = [col['name'] for col in inspector.get_columns('{table_name}')]

    if '{column_name}' in columns:
        # Check if column has data
        result = bind.execute(text("""
            SELECT COUNT(*) as cnt FROM {table_name}
            WHERE {column_name} IS NOT NULL
        """)).first()

        if result and result.cnt > 0:
            print(f"⚠️  Warning: Dropping column '{column_name}' with {{result.cnt}} non-null values")

            # Optional: Create backup table
            backup_table = '_{table_name}_{column_name}_backup_' + datetime.now().strftime('%Y%m%d_%H%M%S')
            bind.execute(text(f"""
                CREATE TABLE {{backup_table}} AS
                SELECT id, {column_name}, NOW() as backed_up_at
                FROM {table_name}
                WHERE {column_name} IS NOT NULL
            """))
            print(f"💾 Created backup table: {{backup_table}}")

This function is important because it defines how Langflow Tutorial: Visual AI Agent and Workflow Platform implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[upgrade]
    B[downgrade]
    C[upgrade]
    D[downgrade]
    A --> B
    B --> C
    C --> D
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