YAML based Workflows - Wavefront AI

YAML Workflow Configuration

Flo AI supports creating entire multi-agent workflows through YAML configuration files, making it easy to version control, share, and manage complex workflow configurations.

Basic YAML Workflow

Create a simple workflow using YAML:

workflow.yaml

metadata:
  name: "content-analysis-workflow"
  version: "1.0.0"
  description: "Multi-agent content analysis pipeline"

arium:
  agents:
    - name: "analyzer"
      role: "Content Analyst"
      job: "Analyze the input content and extract key insights."
      model:
        provider: "openai"
        name: "gpt-4o-mini"
        temperature: 0.7

    - name: "summarizer"
      role: "Content Summarizer"
      job: "Create a concise summary based on the analysis."
      model:
        provider: "anthropic"
        name: "claude-3-5-sonnet-20240620"
        temperature: 0.3

  workflow:
    start: "analyzer"
    edges:
      - from: "analyzer"
        to: ["summarizer"]
    end: ["summarizer"]

Load YAML Workflow

from flo_ai.arium import AriumBuilder

# Load workflow from YAML
arium_builder = AriumBuilder.from_yaml(yaml_file='workflow.yaml')
result = await arium_builder.build_and_run(["Analyze this quarterly business report..."])

Advanced YAML Configuration

Workflow with Function Nodes

Function nodes allow you to execute custom Python functions within workflows:

function-workflow.yaml

metadata:
  name: "data-processing-workflow"
  version: "1.0.0"

arium:
  agents:
    - name: "analyzer"
      job: "Analyze the processed data."
      model:
        provider: "openai"
        name: "gpt-4o-mini"

  function_nodes:
    - name: "data_processor"
      function_name: "process_data"  # Must exist in function_registry
      description: "Processes input data"
      input_filter: ["node1", "node2"]  # Optional: filter inputs from specific nodes
      prefilled_params:  # Optional: pre-fill function parameters
        format: "json"

  workflow:
    start: "data_processor"
    edges:
      - from: "data_processor"
        to: ["analyzer"]
    end: ["analyzer"]

Workflow with Routers

Use routers for intelligent routing decisions:

router-workflow.yaml

metadata:
  name: "routing-workflow"
  version: "1.0.0"

arium:
  agents:
    - name: "classifier"
      job: "Classify the input content."
      model:
        provider: "openai"
        name: "gpt-4o-mini"

    - name: "technical_writer"
      job: "Write technical content."
      model:
        provider: "openai"
        name: "gpt-4o"

    - name: "creative_writer"
      job: "Write creative content."
      model:
        provider: "anthropic"
        name: "claude-3-5-sonnet-20240620"

  routers:
    - name: "content_type_router"
      type: "smart"  # Uses LLM for intelligent routing
      routing_options:
        technical_writer: "Technical content, documentation, tutorials"
        creative_writer: "Creative writing, storytelling, fiction"
      model:
        provider: "openai"
        name: "gpt-4o-mini"
        temperature: 0.3

  workflow:
    start: "classifier"
    edges:
      - from: "classifier"
        to: ["technical_writer", "creative_writer"]
        router: "content_type_router"
    end: ["technical_writer", "creative_writer"]

Workflow with Nested Arium (Sub-workflows)

Create nested workflows for complex scenarios:

nested-workflow.yaml

metadata:
  name: "nested-workflow"
  version: "1.0.0"

arium:
  agents:
    - name: "coordinator"
      job: "Coordinate the workflow."
      model:
        provider: "openai"
        name: "gpt-4o-mini"

  ariums:
    - name: "sub_workflow"
      inherit_variables: true  # Inherit variables from parent
      yaml_file: "sub-workflow.yaml"  # Reference external YAML file
      # Or use inline configuration:
      # agents:
      #   - name: "sub_agent"
      #     job: "Process in sub-workflow."
      #     model:
      #       provider: "openai"
      #       name: "gpt-4o-mini"
      # workflow:
      #   start: "sub_agent"
      #   edges: []
      #   end: ["sub_agent"]

  workflow:
    start: "coordinator"
    edges:
      - from: "coordinator"
        to: ["sub_workflow"]
    end: ["sub_workflow"]

Workflow with ForEach Nodes

Process items in batches using ForEach nodes:

foreach-workflow.yaml

metadata:
  name: "batch-processing-workflow"
  version: "1.0.0"

arium:
  agents:
    - name: "processor"
      job: "Process each item in the batch."
      model:
        provider: "openai"
        name: "gpt-4o-mini"

  iterators:  # or use 'foreach_nodes'
    - name: "batch_processor"
      execute_node: "processor"  # Node to execute on each item

  workflow:
    start: "batch_processor"
    edges:
      - from: "batch_processor"
        to: ["end"]  # Special 'end' keyword
    end: ["end"]

Agent Configuration in Workflows

Agents in workflows support multiple configuration methods:

Method 1: Direct Configuration

agents:
  - name: "agent_name"
    job: "Agent's system prompt"
    role: "Agent Role"  # Optional
    model:
      provider: "openai"
      name: "gpt-4o-mini"
    settings:
      max_retries: 3
      reasoning_pattern: "REACT"

Method 2: Reference Pre-built Agent

agents:
  - name: "pre_built_agent"  # Only name - agent must be provided in agents dict

Method 3: Inline YAML Config

agents:
  - name: "agent_name"
    yaml_config: |
      agent:
        name: "Agent Name"
        prompt: "Agent prompt"
        model:
          provider: "openai"
          name: "gpt-4o-mini"

Method 4: External YAML File Reference

agents:
  - name: "agent_name"
    yaml_file: "agent-config.yaml"

Router Types

Smart Router

Uses LLM to intelligently route based on content:

routers:
  - name: "smart_router"
    type: "smart"
    routing_options:
      agent1: "Description of when to route to agent1"
      agent2: "Description of when to route to agent2"
    model:
      provider: "openai"
      name: "gpt-4o-mini"
    settings:
      temperature: 0.3
      fallback_strategy: "first"  # first, random, or all

Task Classifier Router

Routes based on task categories:

routers:
  - name: "task_router"
    type: "task_classifier"
    task_categories:
      coding:
        description: "Programming, debugging, code review tasks"
        keywords: ["code", "debug", "programming"]
        examples: ["Fix this bug", "Review this code"]
      writing:
        description: "Content writing, documentation tasks"
        keywords: ["write", "document", "content"]
        examples: ["Write a blog post", "Document this API"]
    model:
      provider: "openai"
      name: "gpt-4o-mini"

Conversation Analysis Router

Routes based on conversation analysis:

routers:
  - name: "conversation_router"
    type: "conversation_analysis"
    routing_logic:
      agent1: "Route to agent1 when conversation indicates X"
      agent2: "Route to agent2 when conversation indicates Y"
    model:
      provider: "openai"
      name: "gpt-4o-mini"

Reflection Router

For A→B→A→C feedback patterns:

routers:
  - name: "reflection_router"
    type: "reflection"
    flow_pattern: ["writer", "critic", "writer", "editor"]  # A → B → A → C pattern
    model:
      provider: "openai"
      name: "gpt-4o-mini"
    settings:
      allow_early_exit: true  # Allow early exit if criteria met

YAML Schema Reference

Metadata Section

metadata:
  name: "workflow-name" # Required: Unique workflow identifier
  version: "1.0.0" # Required: Semantic version
  description: "Workflow description" # Optional: Human-readable description
  author: "Your Name" # Optional: Workflow author
  tags: ["tag1", "tag2"] # Optional: Categorization tags

Arium Configuration

arium:
  agents: [] # Optional: List of agents (see agent configuration)
  function_nodes: [] # Optional: List of function nodes (see function node configuration)
  routers: [] # Optional: List of routers (see router configuration)
  ariums: [] # Optional: List of nested arium nodes (see arium node configuration)
  iterators: [] # Optional: List of foreach nodes (alias: foreach_nodes)
  workflow: # Required: Workflow configuration
    start: "node_name" # Required: Starting node name
    edges: [] # Required: List of edges (see edge configuration)
    end: ["node_name"] # Required: List of end node names

Agent Configuration

agents:
  - name: "agent_name" # Required: Agent name
    job: "System prompt" # Required (if using direct config): Agent's system prompt
    prompt: "System prompt" # Alternative to 'job'
    role: "Agent Role" # Optional: Internal role description
    act_as: "assistant" # Optional: Message role
    base_url: "https://api.example.com" # Optional: Base URL override
    input_filter: ["node1", "node2"] # Optional: Filter inputs from specific workflow nodes
    model: # Optional: LLM configuration (required for direct config)
      provider: "openai" # Required: LLM provider
      name: "gpt-4o-mini" # Required: Model name
      temperature: 0.7 # Optional: Temperature setting
      max_tokens: 1000 # Optional: Maximum tokens
    settings: # Optional: Agent settings
      max_retries: 3 # Optional: Maximum retries
      reasoning_pattern: "DIRECT" # Optional: DIRECT, REACT, or COT
    # Alternative: Reference pre-built agent (only name)
    # - name: "pre_built_agent"
    # Alternative: Inline YAML config
    # yaml_config: "agent yaml string"
    # Alternative: External YAML file
    # yaml_file: "agent.yaml"

Note: input_filter for agents specifies which nodes’ outputs should be passed as inputs to this agent. Only results from the specified node names will be included. If not specified, all available memory items are passed.

Function Node Configuration

function_nodes:
  - name: "function_node_name" # Required: Function node name
    function_name: "function_name" # Required: Name in function_registry
    description: "Function description" # Optional: Description
    input_filter: ["node1", "node2"] # Optional: Filter inputs from specific workflow nodes
    prefilled_params: # Optional: Pre-filled parameters
      param1: "value1"
      param2: 42

Note: input_filter specifies which nodes’ outputs should be passed as inputs to this node. Only results from the specified node names will be included. If not specified, all available memory items are passed.

Router Configuration

routers:
  - name: "router_name" # Required: Router name
    type: "smart" # Required: smart, task_classifier, conversation_analysis, reflection, plan_execute
    model: # Optional: LLM configuration for router
      provider: "openai"
      name: "gpt-4o-mini"
    settings: # Optional: Router settings
      temperature: 0.3
      fallback_strategy: "first" # first, random, or all
      allow_early_exit: true # For reflection router
      planner_agent: "planner" # For plan_execute router
      executor_agent: "executor" # For plan_execute router
      reviewer_agent: "reviewer" # For plan_execute router
    # Smart router
    routing_options: # Required for smart router
      agent1: "Description for agent1"
      agent2: "Description for agent2"
    # Task classifier router
    task_categories: # Required for task_classifier router
      category1:
        description: "Category description"
        keywords: ["keyword1", "keyword2"]
        examples: ["example1", "example2"]
    # Conversation analysis router
    routing_logic: # Required for conversation_analysis router
      agent1: "Routing logic for agent1"
    # Reflection router
    flow_pattern: ["agent1", "agent2", "agent1"] # Required for reflection router
    # Plan-execute router
    agents: # Required for plan_execute router
      planner: "Planner description"
      executor: "Executor description"

Edge Configuration

edges:
  - from: "source_node" # Required: Source node name
    to: ["target_node1", "target_node2"] # Required: List of target node names
    router: "router_name" # Optional: Router name to use for routing

Arium Node Configuration (Nested Workflows)

ariums:
  - name: "nested_arium_name" # Required: Nested arium name
    inherit_variables: true # Optional: Inherit variables from parent (default: true)
    input_filter: ["node1", "node2"] # Optional: Filter inputs from specific workflow nodes
    yaml_file: "nested-workflow.yaml" # Optional: External YAML file reference
    # Or use inline configuration:
    agents: [] # Optional: List of agents for nested arium
    function_nodes: [] # Optional: List of function nodes
    routers: [] # Optional: List of routers
    ariums: [] # Optional: Nested arium nodes (supports nesting)
    iterators: [] # Optional: List of foreach nodes
    workflow: # Required if using inline config
      start: "node_name"
      edges: []
      end: ["node_name"]

Note: input_filter for nested ariums works the same way - it filters which parent workflow nodes’ outputs are passed to the nested workflow.

ForEach Node Configuration

iterators: # or 'foreach_nodes'
  - name: "foreach_node_name" # Required: ForEach node name
    execute_node: "node_name" # Required: Name of node to execute on each item
    input_filter: ["node1", "node2"] # Optional: Filter inputs from specific workflow nodes

Note: input_filter for ForEach nodes filters which nodes’ outputs are used as the collection to iterate over.

Loading and Using YAML Workflows

Basic Loading

from flo_ai.arium import AriumBuilder

# Load from file
arium_builder = AriumBuilder.from_yaml(yaml_file='workflow.yaml')
arium = arium_builder.build()

# Load from string
yaml_content = """
arium:
  agents:
    - name: "agent1"
      job: "Process input."
      model:
        provider: "openai"
        name: "gpt-4o-mini"
  workflow:
    start: "agent1"
    edges: []
    end: ["agent1"]
"""

arium_builder = AriumBuilder.from_yaml(yaml_str=yaml_content)
arium = arium_builder.build()

Loading with Registries

When using function nodes or tool-enabled agents, provide registries:

from flo_ai.arium import AriumBuilder
from flo_ai.tool import flo_tool

# Define functions for function nodes
def process_data(inputs, variables=None, **kwargs):
    return f"Processed: {inputs}"

# Define tools for agents
@flo_tool(description="Get weather")
async def get_weather(city: str) -> str:
    return f"Weather in {city}: sunny"

# Create registries
function_registry = {
    "process_data": process_data
}

tool_registry = {
    "get_weather": get_weather.tool
}

# Load with registries
arium_builder = AriumBuilder.from_yaml(
    yaml_file='workflow.yaml',
    function_registry=function_registry,
    tool_registry=tool_registry
)
arium = arium_builder.build()

Loading with Pre-built Agents

Reference pre-built agents in YAML:

from flo_ai.arium import AriumBuilder
from flo_ai.agent import AgentBuilder
from flo_ai.llm import OpenAI

# Create pre-built agents
agent1 = (
    AgentBuilder()
    .with_name('pre_built_agent')
    .with_prompt('You are a helpful assistant.')
    .with_llm(OpenAI(model='gpt-4o-mini'))
    .build()
)

# Provide agents dictionary
agents_dict = {
    'pre_built_agent': agent1
}

# YAML can reference it:
# agents:
#   - name: "pre_built_agent"

arium_builder = AriumBuilder.from_yaml(
    yaml_file='workflow.yaml',
    agents=agents_dict
)
arium = arium_builder.build()

Loading with Custom Routers

Provide custom router functions:

from flo_ai.arium import AriumBuilder
from flo_ai.arium.memory import MessageMemory

def custom_router(memory: MessageMemory) -> str:
    # Custom routing logic
    messages = memory.get()
    if messages and "urgent" in str(messages[-1].result).lower():
        return "urgent_handler"
    return "normal_handler"

routers_dict = {
    'custom_router': custom_router
}

arium_builder = AriumBuilder.from_yaml(
    yaml_file='workflow.yaml',
    routers=routers_dict
)
arium = arium_builder.build()

Running Workflows

# Build and run in one step
result = await (
    AriumBuilder()
    .from_yaml(yaml_file='workflow.yaml')
    .build_and_run(["Input message"])
)

# Or build first, then run
arium_builder = AriumBuilder.from_yaml(yaml_file='workflow.yaml')
arium = arium_builder.build()
result = await arium.run(["Input message"])

# With variables
variables = {'user_name': 'John', 'company': 'TechCorp'}
result = await arium.run(["Input message"], variables=variables)

Writing/Saving YAML Workflow Configuration

To save a workflow configuration to YAML, you can manually construct the YAML structure:

import yaml
from flo_ai.arium import AriumBuilder

# Create a workflow programmatically
arium_builder = (
    AriumBuilder()
    .add_agent(agent1)
    .add_agent(agent2)
    .start_with(agent1)
    .connect(agent1, agent2)
    .end_with(agent2)
)

# Create YAML configuration dictionary
yaml_config = {
    'metadata': {
        'name': 'my-workflow',
        'version': '1.0.0',
        'description': 'My workflow description'
    },
    'arium': {
        'agents': [
            {
                'name': 'agent1',
                'job': 'First agent prompt',
                'model': {
                    'provider': 'openai',
                    'name': 'gpt-4o-mini'
                }
            },
            {
                'name': 'agent2',
                'job': 'Second agent prompt',
                'model': {
                    'provider': 'openai',
                    'name': 'gpt-4o-mini'
                }
            }
        ],
        'workflow': {
            'start': 'agent1',
            'edges': [
                {
                    'from': 'agent1',
                    'to': ['agent2']
                }
            ],
            'end': ['agent2']
        }
    }
}

# Write to file
with open('exported-workflow.yaml', 'w') as f:
    yaml.dump(yaml_config, f, default_flow_style=False, sort_keys=False)

print("✅ Workflow configuration saved to exported-workflow.yaml")

Validation and Testing

Schema Validation

Use the AriumYamlModel for proper validation of YAML configurations:

from flo_ai.arium import AriumBuilder
from flo_ai.models.arium import AriumYamlModel
import yaml

# Validate YAML structure using AriumYamlModel
def validate_workflow_yaml(file_path):
    """Validate YAML configuration using AriumYamlModel.
    
    Args:
        file_path: Path to YAML file to validate
        
    Returns:
        bool: True if valid, False otherwise
    """
    try:
        with open(file_path, 'r') as f:
            config = yaml.safe_load(f)

        # Use AriumYamlModel for validation
        # This will validate all fields, types, and constraints
        validated_config = AriumYamlModel(**config)
        
        print("✅ YAML configuration is valid")
        print(f"   Workflow start: {validated_config.arium.workflow.start}")
        print(f"   Number of agents: {len(validated_config.arium.agents or [])}")
        if validated_config.metadata:
            print(f"   Metadata: {validated_config.metadata.name} v{validated_config.metadata.version}")
        return True
    except ValueError as e:
        # AriumBuilder._validate_yaml_config raises ValueError with formatted errors
        print(f"❌ YAML validation failed: {e}")
        return False
    except Exception as e:
        print(f"❌ YAML validation failed: {e}")
        return False

# Validate a YAML file
validate_workflow_yaml('workflow.yaml')

You can also use AriumBuilder.from_yaml() which automatically validates the configuration:

from flo_ai.arium import AriumBuilder

def validate_and_load_workflow(yaml_file):
    """Validate and load workflow from YAML file.
    
    Args:
        yaml_file: Path to YAML file
        
    Returns:
        AriumBuilder or None if validation fails
    """
    try:
        # from_yaml automatically validates using AriumYamlModel
        arium_builder = AriumBuilder.from_yaml(yaml_file=yaml_file)
        print("✅ YAML configuration is valid and workflow builder created")
        return arium_builder
    except ValueError as e:
        # Validation errors are raised as ValueError with detailed messages
        print(f"❌ YAML validation failed:\n{e}")
        return None
    except Exception as e:
        print(f"❌ Error loading workflow: {e}")
        return None

# Validate and load
arium_builder = validate_and_load_workflow('workflow.yaml')
if arium_builder:
    arium = arium_builder.build()

Testing YAML Workflows

import asyncio
from flo_ai.arium import AriumBuilder

async def test_yaml_workflow():
    """Test a YAML workflow configuration."""
    try:
        # Load and validate workflow (validation happens automatically)
        arium_builder = AriumBuilder.from_yaml(yaml_file='workflow.yaml')
        arium = arium_builder.build()

        # Test workflow execution
        result = await arium.run(["Test input message"])
        assert result is not None
        print(f"✅ Workflow executed: {len(result)} message(s)")

        # Test with variables
        variables = {'user_name': 'Test User', 'company': 'TestCorp'}
        result = await arium.run(["Test message"], variables=variables)
        print(f"✅ Workflow with variables: {len(result)} message(s)")
        
        return True
    except ValueError as e:
        print(f"❌ Validation error: {e}")
        return False
    except Exception as e:
        print(f"❌ Error: {e}")
        return False

# Run tests
asyncio.run(test_yaml_workflow())

Best Practices

Workflow Structure

Use meaningful names: Choose descriptive agent, node, and router names
Version your configurations: Always include version numbers in metadata
Document thoroughly: Add descriptions for all components
Validate schemas: Use YAML schema validation tools

Performance Optimization

# Optimize for performance
arium:
  agents:
    - name: "fast_agent"
      job: "Quick processing"
      model:
        provider: "openai"
        name: "gpt-4o-mini" # Use faster model
        temperature: 0.3
        max_tokens: 500
      settings:
        max_retries: 2 # Limit retries
  workflow:
    start: "fast_agent"
    edges: []
    end: ["fast_agent"]

Security Considerations

# Secure configuration
arium:
  agents:
    - name: "secure_agent"
      job: |
        You are a secure assistant. Never:
        - Share sensitive information
        - Execute dangerous commands
      model:
        provider: "openai"
        name: "gpt-4o"
        temperature: 0.1
        max_tokens: 200
        timeout: 10 # Short timeout
      settings:
        max_retries: 1
  workflow:
    start: "secure_agent"
    edges: []
    end: ["secure_agent"]

Examples

Content Analysis Workflow

content-analysis.yaml

metadata:
  name: "content-analysis"
  version: "1.0.0"
  description: "Analyzes and summarizes content"

arium:
  agents:
    - name: "analyzer"
      role: "Content Analyst"
      job: |
        Analyze the input content and:
        1. Extract key insights
        2. Identify main themes
        3. Note important details
      model:
        provider: "openai"
        name: "gpt-4o"
        temperature: 0.2

    - name: "summarizer"
      role: "Content Summarizer"
      job: "Create a concise summary based on the analysis."
      model:
        provider: "anthropic"
        name: "claude-3-5-sonnet-20240620"
        temperature: 0.3

  workflow:
    start: "analyzer"
    edges:
      - from: "analyzer"
        to: ["summarizer"]
    end: ["summarizer"]

Multi-Agent Routing Workflow

routing-workflow.yaml

metadata:
  name: "routing-workflow"
  version: "1.0.0"
  description: "Routes content to specialized agents"

arium:
  agents:
    - name: "classifier"
      job: "Classify the input content type."
      model:
        provider: "openai"
        name: "gpt-4o-mini"

    - name: "technical_writer"
      job: "Write technical documentation."
      model:
        provider: "openai"
        name: "gpt-4o"

    - name: "creative_writer"
      job: "Write creative content."
      model:
        provider: "anthropic"
        name: "claude-3-5-sonnet-20240620"

  routers:
    - name: "content_router"
      type: "smart"
      routing_options:
        technical_writer: "Technical content, documentation, code"
        creative_writer: "Creative writing, stories, fiction"
      model:
        provider: "openai"
        name: "gpt-4o-mini"
        temperature: 0.3

  workflow:
    start: "classifier"
    edges:
      - from: "classifier"
        to: ["technical_writer", "creative_writer"]
        router: "content_router"
    end: ["technical_writer", "creative_writer"]

Reflection Workflow

reflection-workflow.yaml

metadata:
  name: "reflection-workflow"
  version: "1.0.0"
  description: "Writer-critic feedback loop"

arium:
  agents:
    - name: "writer"
      job: "Write content based on feedback."
      model:
        provider: "openai"
        name: "gpt-4o"

    - name: "critic"
      job: "Review and provide constructive feedback."
      model:
        provider: "anthropic"
        name: "claude-3-5-sonnet-20240620"

    - name: "editor"
      job: "Finalize the content."
      model:
        provider: "openai"
        name: "gpt-4o"

  routers:
    - name: "reflection_router"
      type: "reflection"
      flow_pattern: ["writer", "critic", "writer", "editor"]
      model:
        provider: "openai"
        name: "gpt-4o-mini"
      settings:
        allow_early_exit: true

  workflow:
    start: "writer"
    edges:
      - from: "writer"
        to: ["critic"]
        router: "reflection_router"
    end: ["editor"]

This YAML-based approach makes workflow configuration declarative, versionable, and easily shareable across teams!

Getting started

Core Features

Advanced

Examples

​YAML Workflow Configuration

​Basic YAML Workflow

​Advanced YAML Configuration

​Workflow with Function Nodes

​Workflow with Routers

​Workflow with Nested Arium (Sub-workflows)

​Workflow with ForEach Nodes

​Agent Configuration in Workflows

​Method 1: Direct Configuration

​Method 2: Reference Pre-built Agent

​Method 3: Inline YAML Config

​Method 4: External YAML File Reference

​Router Types

​Smart Router

​Task Classifier Router

​Conversation Analysis Router

​Reflection Router

​YAML Schema Reference

​Metadata Section

​Arium Configuration

​Agent Configuration

​Function Node Configuration

​Router Configuration

​Edge Configuration

​Arium Node Configuration (Nested Workflows)

​ForEach Node Configuration

​Loading and Using YAML Workflows

​Basic Loading

​Loading with Registries

​Loading with Pre-built Agents

​Loading with Custom Routers

​Running Workflows

​Writing/Saving YAML Workflow Configuration

​Validation and Testing

​Schema Validation

​Testing YAML Workflows

​Best Practices

​Workflow Structure

​Performance Optimization

​Security Considerations

​Examples

​Content Analysis Workflow

​Multi-Agent Routing Workflow

​Reflection Workflow

YAML Workflow Configuration

Basic YAML Workflow

Advanced YAML Configuration

Workflow with Function Nodes

Workflow with Routers

Workflow with Nested Arium (Sub-workflows)

Workflow with ForEach Nodes

Agent Configuration in Workflows

Method 1: Direct Configuration

Method 2: Reference Pre-built Agent

Method 3: Inline YAML Config

Method 4: External YAML File Reference

Router Types

Smart Router

Task Classifier Router

Conversation Analysis Router

Reflection Router

YAML Schema Reference

Metadata Section

Arium Configuration

Agent Configuration

Function Node Configuration

Router Configuration

Edge Configuration

Arium Node Configuration (Nested Workflows)

ForEach Node Configuration

Loading and Using YAML Workflows

Basic Loading

Loading with Registries

Loading with Pre-built Agents

Loading with Custom Routers

Running Workflows

Writing/Saving YAML Workflow Configuration

Validation and Testing

Schema Validation

Testing YAML Workflows

Best Practices

Workflow Structure

Performance Optimization

Security Considerations

Examples

Content Analysis Workflow

Multi-Agent Routing Workflow

Reflection Workflow