Tag: llm

Agentic Orchestration Pattern: Adaptive Fallback


    """
Pattern 07.04: Adaptive Fallback
Dynamic fallback chain based on context
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class AdaptiveFallbackState(TypedDict):
    """State for Adaptive Fallback pattern."""
    input: str
    output: str


def agent_function(state: AdaptiveFallbackState) -> AdaptiveFallbackState:
    """Adaptive Fallback agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Adaptive Fallback pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_adaptive_fallback():
    """Create Adaptive Fallback workflow."""
    workflow = StateGraph(AdaptiveFallbackState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_adaptive_fallback()
    result = workflow.invoke({"input": "Test Adaptive Fallback", "output": ""})
    print(f"\n=== Adaptive Fallback Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Adaptive Fallback

04 Adaptive Fallback Pattern – Detailed Documentation

Overview

File: 07_adaptive/04_adaptive_fallback.py
Pattern Category: 07 Adaptive
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 07.04: Adaptive Fallback Dynamic fallback chain based on context

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

Purpose of imports: – TypedDict: For type-safe state definitions – StateGraph: LangGraph’s graph-based workflow orchestration – ChatOpenAI: OpenAI LLM integration – HumanMessage: LangChain message formatting

State Definition

class AdaptiveFallbackState(TypedDict):
    """State for Adaptive Fallback pattern."""
    input: str
    output: str


def agent_function(state: AdaptiveFallbackState) -> AdaptiveFallbackState:

AdaptiveFallbackState: State for Adaptive Fallback pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Adaptive Fallback pattern: Stores prompt = f”Process this using Adaptive Fallback pattern – def create_adaptive_fallback(): Stores def create adaptive fallback()

Functions

`agent_function()`

Line: 17
Arguments: state

Adaptive Fallback agent implementation.

def agent_function(state: AdaptiveFallbackState) -> AdaptiveFallbackState:
    """Adaptive Fallback agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Adaptive Fallback pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_adaptive_fallback():

`create_adaptive_fallback()`

Line: 26
Arguments: “

Create Adaptive Fallback workflow.

def create_adaptive_fallback():
    """Create Adaptive Fallback workflow."""
    workflow = StateGraph(AdaptiveFallbackState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class AdaptiveFallbackState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Adaptive Fallback pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: AdaptiveFallbackState) -> AdaptiveFallbackState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Adaptive Fallback agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_adaptive_fallback definition

def create_adaptive_fallback():

Defines the create_adaptive_fallback function that processes the workflow state.

Line 27: Module docstring

    """Create Adaptive Fallback workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_adaptive_fallback()
    result = workflow.invoke({"input": "Test Adaptive Fallback", "output": ""})
    print(f"\n=== Adaptive Fallback Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: A/B Testing Pattern


    """
Pattern 07.03: AB Testing
Parallel execution of alternatives with comparison
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class AbTestingState(TypedDict):
    """State for AB Testing pattern."""
    input: str
    output: str


def agent_function(state: AbTestingState) -> AbTestingState:
    """AB Testing agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using AB Testing pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_ab_testing():
    """Create AB Testing workflow."""
    workflow = StateGraph(AbTestingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_ab_testing()
    result = workflow.invoke({"input": "Test AB Testing", "output": ""})
    print(f"\n=== AB Testing Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: A/B Testing Pattern

03 Ab Testing Pattern – Detailed Documentation

Overview

File: 07_adaptive/03_ab_testing.py
Pattern Category: 07 Adaptive
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 07.03: AB Testing Parallel execution of alternatives with comparison

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class AbTestingState(TypedDict):
    """State for AB Testing pattern."""
    input: str
    output: str


def agent_function(state: AbTestingState) -> AbTestingState:

AbTestingState: State for AB Testing pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using AB Testing pattern: Stores prompt = f”Process this using AB Testing pattern – def create_ab_testing(): Stores def create ab testing()

Functions

`agent_function()`

Line: 17
Arguments: state

AB Testing agent implementation.

def agent_function(state: AbTestingState) -> AbTestingState:
    """AB Testing agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using AB Testing pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_ab_testing():

`create_ab_testing()`

Line: 26
Arguments: “

Create AB Testing workflow.

def create_ab_testing():
    """Create AB Testing workflow."""
    workflow = StateGraph(AbTestingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class AbTestingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for AB Testing pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: AbTestingState) -> AbTestingState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """AB Testing agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_ab_testing definition

def create_ab_testing():

Defines the create_ab_testing function that processes the workflow state.

Line 27: Module docstring

    """Create AB Testing workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_ab_testing()
    result = workflow.invoke({"input": "Test AB Testing", "output": ""})
    print(f"\n=== AB Testing Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: Learning Pattern


    """
Pattern 07.02: Learning
Agents improve from experience
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class LearningState(TypedDict):
    """State for Learning pattern."""
    input: str
    output: str


def agent_function(state: LearningState) -> LearningState:
    """Learning agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Learning pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_learning():
    """Create Learning workflow."""
    workflow = StateGraph(LearningState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_learning()
    result = workflow.invoke({"input": "Test Learning", "output": ""})
    print(f"\n=== Learning Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Learning Pattern

02 Learning Pattern – Detailed Documentation

Overview

File: 07_adaptive/02_learning.py
Pattern Category: 07 Adaptive
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 07.02: Learning Agents improve from experience

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class LearningState(TypedDict):
    """State for Learning pattern."""
    input: str
    output: str


def agent_function(state: LearningState) -> LearningState:

LearningState: State for Learning pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Learning pattern: Stores prompt = f”Process this using Learning pattern – def create_learning(): Stores def create learning()

Functions

`agent_function()`

Line: 17
Arguments: state

Learning agent implementation.

def agent_function(state: LearningState) -> LearningState:
    """Learning agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Learning pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_learning():

`create_learning()`

Line: 26
Arguments: “

Create Learning workflow.

def create_learning():
    """Create Learning workflow."""
    workflow = StateGraph(LearningState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class LearningState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Learning pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: LearningState) -> LearningState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Learning agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_learning definition

def create_learning():

Defines the create_learning function that processes the workflow state.

Line 27: Module docstring

    """Create Learning workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_learning()
    result = workflow.invoke({"input": "Test Learning", "output": ""})
    print(f"\n=== Learning Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: Dynamic Routing


    """
Pattern 07.01: Dynamic Routing
Runtime determination of execution path
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class DynamicRoutingState(TypedDict):
    """State for Dynamic Routing pattern."""
    input: str
    output: str


def agent_function(state: DynamicRoutingState) -> DynamicRoutingState:
    """Dynamic Routing agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Dynamic Routing pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_dynamic_routing():
    """Create Dynamic Routing workflow."""
    workflow = StateGraph(DynamicRoutingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_dynamic_routing()
    result = workflow.invoke({"input": "Test Dynamic Routing", "output": ""})
    print(f"\n=== Dynamic Routing Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Dynamic Routing

01 Dynamic Routing Pattern – Detailed Documentation

Overview

File: 07_adaptive/01_dynamic_routing.py
Pattern Category: 07 Adaptive
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 07.01: Dynamic Routing Runtime determination of execution path

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class DynamicRoutingState(TypedDict):
    """State for Dynamic Routing pattern."""
    input: str
    output: str


def agent_function(state: DynamicRoutingState) -> DynamicRoutingState:

DynamicRoutingState: State for Dynamic Routing pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Dynamic Routing pattern: Stores prompt = f”Process this using Dynamic Routing pattern – def create_dynamic_routing(): Stores def create dynamic routing()

Functions

`agent_function()`

Line: 17
Arguments: state

Dynamic Routing agent implementation.

def agent_function(state: DynamicRoutingState) -> DynamicRoutingState:
    """Dynamic Routing agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Dynamic Routing pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_dynamic_routing():

`create_dynamic_routing()`

Line: 26
Arguments: “

Create Dynamic Routing workflow.

def create_dynamic_routing():
    """Create Dynamic Routing workflow."""
    workflow = StateGraph(DynamicRoutingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class DynamicRoutingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Dynamic Routing pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: DynamicRoutingState) -> DynamicRoutingState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Dynamic Routing agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_dynamic_routing definition

def create_dynamic_routing():

Defines the create_dynamic_routing function that processes the workflow state.

Line 27: Module docstring

    """Create Dynamic Routing workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_dynamic_routing()
    result = workflow.invoke({"input": "Test Dynamic Routing", "output": ""})
    print(f"\n=== Dynamic Routing Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: Self-Reflection Pattern


    """
Pattern 06.04: Self-Reflection
Agent evaluates its own output
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class SelfReflectionState(TypedDict):
    """State for Self-Reflection pattern."""
    input: str
    output: str


def agent_function(state: SelfReflectionState) -> SelfReflectionState:
    """Self-Reflection agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Self-Reflection pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_self_reflection():
    """Create Self-Reflection workflow."""
    workflow = StateGraph(SelfReflectionState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_self_reflection()
    result = workflow.invoke({"input": "Test Self-Reflection", "output": ""})
    print(f"\n=== Self-Reflection Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Self-Reflection Pattern

04 Self Reflection Pattern – Detailed Documentation

Overview

File: 06_feedback_control/04_self_reflection.py
Pattern Category: 06 Feedback Control
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 06.04: Self-Reflection Agent evaluates its own output

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class SelfReflectionState(TypedDict):
    """State for Self-Reflection pattern."""
    input: str
    output: str


def agent_function(state: SelfReflectionState) -> SelfReflectionState:

SelfReflectionState: State for Self-Reflection pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Self-Reflection pattern: Stores prompt = f”Process this using Self-Reflection pattern – def create_self_reflection(): Stores def create self reflection()

Functions

`agent_function()`

Line: 17
Arguments: state

Self-Reflection agent implementation.

def agent_function(state: SelfReflectionState) -> SelfReflectionState:
    """Self-Reflection agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Self-Reflection pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_self_reflection():

`create_self_reflection()`

Line: 26
Arguments: “

Create Self-Reflection workflow.

def create_self_reflection():
    """Create Self-Reflection workflow."""
    workflow = StateGraph(SelfReflectionState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class SelfReflectionState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Self-Reflection pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: SelfReflectionState) -> SelfReflectionState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Self-Reflection agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_self_reflection definition

def create_self_reflection():

Defines the create_self_reflection function that processes the workflow state.

Line 27: Module docstring

    """Create Self-Reflection workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_self_reflection()
    result = workflow.invoke({"input": "Test Self-Reflection", "output": ""})
    print(f"\n=== Self-Reflection Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: Monitor-Controller Pattern


    """
Pattern 06.03: Monitor-Controller
Monitoring agent observes, controller adjusts
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class MonitorControllerState(TypedDict):
    """State for Monitor-Controller pattern."""
    input: str
    output: str


def agent_function(state: MonitorControllerState) -> MonitorControllerState:
    """Monitor-Controller agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Monitor-Controller pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_monitor_controller():
    """Create Monitor-Controller workflow."""
    workflow = StateGraph(MonitorControllerState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_monitor_controller()
    result = workflow.invoke({"input": "Test Monitor-Controller", "output": ""})
    print(f"\n=== Monitor-Controller Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Monitor-Controller Pattern

03 Monitor Controller Pattern – Detailed Documentation

Overview

File: 06_feedback_control/03_monitor_controller.py
Pattern Category: 06 Feedback Control
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 06.03: Monitor-Controller Monitoring agent observes, controller adjusts

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class MonitorControllerState(TypedDict):
    """State for Monitor-Controller pattern."""
    input: str
    output: str


def agent_function(state: MonitorControllerState) -> MonitorControllerState:

MonitorControllerState: State for Monitor-Controller pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Monitor-Controller pattern: Stores prompt = f”Process this using Monitor-Controller pattern – def create_monitor_controller(): Stores def create monitor controller()

Functions

`agent_function()`

Line: 17
Arguments: state

Monitor-Controller agent implementation.

def agent_function(state: MonitorControllerState) -> MonitorControllerState:
    """Monitor-Controller agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Monitor-Controller pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_monitor_controller():

`create_monitor_controller()`

Line: 26
Arguments: “

Create Monitor-Controller workflow.

def create_monitor_controller():
    """Create Monitor-Controller workflow."""
    workflow = StateGraph(MonitorControllerState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class MonitorControllerState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Monitor-Controller pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: MonitorControllerState) -> MonitorControllerState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Monitor-Controller agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_monitor_controller definition

def create_monitor_controller():

Defines the create_monitor_controller function that processes the workflow state.

Line 27: Module docstring

    """Create Monitor-Controller workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_monitor_controller()
    result = workflow.invoke({"input": "Test Monitor-Controller", "output": ""})
    print(f"\n=== Monitor-Controller Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: Critic-Actor Pattern


    """
Pattern 06.02: Critic-Actor
One agent generates, another critiques
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class CriticActorState(TypedDict):
    """State for Critic-Actor pattern."""
    input: str
    output: str


def agent_function(state: CriticActorState) -> CriticActorState:
    """Critic-Actor agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Critic-Actor pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_critic_actor():
    """Create Critic-Actor workflow."""
    workflow = StateGraph(CriticActorState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_critic_actor()
    result = workflow.invoke({"input": "Test Critic-Actor", "output": ""})
    print(f"\n=== Critic-Actor Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Critic-Actor Pattern

02 Critic Actor Pattern – Detailed Documentation

Overview

File: 06_feedback_control/02_critic_actor.py
Pattern Category: 06 Feedback Control
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 06.02: Critic-Actor One agent generates, another critiques

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class CriticActorState(TypedDict):
    """State for Critic-Actor pattern."""
    input: str
    output: str


def agent_function(state: CriticActorState) -> CriticActorState:

CriticActorState: State for Critic-Actor pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Critic-Actor pattern: Stores prompt = f”Process this using Critic-Actor pattern – def create_critic_actor(): Stores def create critic actor()

Functions

`agent_function()`

Line: 17
Arguments: state

Critic-Actor agent implementation.

def agent_function(state: CriticActorState) -> CriticActorState:
    """Critic-Actor agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Critic-Actor pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_critic_actor():

`create_critic_actor()`

Line: 26
Arguments: “

Create Critic-Actor workflow.

def create_critic_actor():
    """Create Critic-Actor workflow."""
    workflow = StateGraph(CriticActorState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class CriticActorState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Critic-Actor pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: CriticActorState) -> CriticActorState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Critic-Actor agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_critic_actor definition

def create_critic_actor():

Defines the create_critic_actor function that processes the workflow state.

Line 27: Module docstring

    """Create Critic-Actor workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_critic_actor()
    result = workflow.invoke({"input": "Test Critic-Actor", "output": ""})
    print(f"\n=== Critic-Actor Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: Feedback Loop Pattern


    """
Pattern 06.01: Feedback Loop
Output fed back as input for self-correction
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class FeedbackLoopState(TypedDict):
    """State for Feedback Loop pattern."""
    input: str
    output: str


def agent_function(state: FeedbackLoopState) -> FeedbackLoopState:
    """Feedback Loop agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Feedback Loop pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_feedback_loop():
    """Create Feedback Loop workflow."""
    workflow = StateGraph(FeedbackLoopState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_feedback_loop()
    result = workflow.invoke({"input": "Test Feedback Loop", "output": ""})
    print(f"\n=== Feedback Loop Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Feedback Loop Pattern

01 Feedback Loop Pattern – Detailed Documentation

Overview

File: 06_feedback_control/01_feedback_loop.py
Pattern Category: 06 Feedback Control
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 06.01: Feedback Loop Output fed back as input for self-correction

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class FeedbackLoopState(TypedDict):
    """State for Feedback Loop pattern."""
    input: str
    output: str


def agent_function(state: FeedbackLoopState) -> FeedbackLoopState:

FeedbackLoopState: State for Feedback Loop pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Feedback Loop pattern: Stores prompt = f”Process this using Feedback Loop pattern – def create_feedback_loop(): Stores def create feedback loop()

Functions

`agent_function()`

Line: 17
Arguments: state

Feedback Loop agent implementation.

def agent_function(state: FeedbackLoopState) -> FeedbackLoopState:
    """Feedback Loop agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Feedback Loop pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_feedback_loop():

`create_feedback_loop()`

Line: 26
Arguments: “

Create Feedback Loop workflow.

def create_feedback_loop():
    """Create Feedback Loop workflow."""
    workflow = StateGraph(FeedbackLoopState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class FeedbackLoopState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Feedback Loop pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: FeedbackLoopState) -> FeedbackLoopState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Feedback Loop agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_feedback_loop definition

def create_feedback_loop():

Defines the create_feedback_loop function that processes the workflow state.

Line 27: Module docstring

    """Create Feedback Loop workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_feedback_loop()
    result = workflow.invoke({"input": "Test Feedback Loop", "output": ""})
    print(f"\n=== Feedback Loop Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: Consensus Pattern


    """
Pattern 05.05: Consensus
Agents negotiate to reach agreement
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ConsensusState(TypedDict):
    """State for Consensus pattern."""
    input: str
    output: str


def agent_function(state: ConsensusState) -> ConsensusState:
    """Consensus agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Consensus pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_consensus():
    """Create Consensus workflow."""
    workflow = StateGraph(ConsensusState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_consensus()
    result = workflow.invoke({"input": "Test Consensus", "output": ""})
    print(f"\n=== Consensus Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Consensus Pattern

05 Consensus Pattern – Detailed Documentation

Overview

File: 05_collaborative/05_consensus.py
Pattern Category: 05 Collaborative
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 05.05: Consensus Agents negotiate to reach agreement

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class ConsensusState(TypedDict):
    """State for Consensus pattern."""
    input: str
    output: str


def agent_function(state: ConsensusState) -> ConsensusState:

ConsensusState: State for Consensus pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Consensus pattern: Stores prompt = f”Process this using Consensus pattern – def create_consensus(): Stores def create consensus()

Functions

`agent_function()`

Line: 17
Arguments: state

Consensus agent implementation.

def agent_function(state: ConsensusState) -> ConsensusState:
    """Consensus agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Consensus pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_consensus():

`create_consensus()`

Line: 26
Arguments: “

Create Consensus workflow.

def create_consensus():
    """Create Consensus workflow."""
    workflow = StateGraph(ConsensusState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class ConsensusState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Consensus pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: ConsensusState) -> ConsensusState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Consensus agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_consensus definition

def create_consensus():

Defines the create_consensus function that processes the workflow state.

Line 27: Module docstring

    """Create Consensus workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_consensus()
    result = workflow.invoke({"input": "Test Consensus", "output": ""})
    print(f"\n=== Consensus Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025

Agentic Orchestration Pattern: Brainstorming Pattern


    """
Pattern 05.04: Brainstorming
Parallel ideation by multiple agents
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class BrainstormingState(TypedDict):
    """State for Brainstorming pattern."""
    input: str
    output: str


def agent_function(state: BrainstormingState) -> BrainstormingState:
    """Brainstorming agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Brainstorming pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_brainstorming():
    """Create Brainstorming workflow."""
    workflow = StateGraph(BrainstormingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_brainstorming()
    result = workflow.invoke({"input": "Test Brainstorming", "output": ""})
    print(f"\n=== Brainstorming Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Brainstorming Pattern

04 Brainstorming Pattern – Detailed Documentation

Overview

File: 05_collaborative/04_brainstorming.py
Pattern Category: 05 Collaborative
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 05.04: Brainstorming Parallel ideation by multiple agents

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

State Definition

class BrainstormingState(TypedDict):
    """State for Brainstorming pattern."""
    input: str
    output: str


def agent_function(state: BrainstormingState) -> BrainstormingState:

BrainstormingState: State for Brainstorming pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Brainstorming pattern: Stores prompt = f”Process this using Brainstorming pattern – def create_brainstorming(): Stores def create brainstorming()

Functions

`agent_function()`

Line: 17
Arguments: state

Brainstorming agent implementation.

def agent_function(state: BrainstormingState) -> BrainstormingState:
    """Brainstorming agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Brainstorming pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_brainstorming():

`create_brainstorming()`

Line: 26
Arguments: “

Create Brainstorming workflow.

def create_brainstorming():
    """Create Brainstorming workflow."""
    workflow = StateGraph(BrainstormingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class BrainstormingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Brainstorming pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: BrainstormingState) -> BrainstormingState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Brainstorming agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_brainstorming definition

def create_brainstorming():

Defines the create_brainstorming function that processes the workflow state.

Line 27: Module docstring

    """Create Brainstorming workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_brainstorming()
    result = workflow.invoke({"input": "Test Brainstorming", "output": ""})
    print(f"\n=== Brainstorming Pattern ===\nOutput: {result['output'][:200]}...")

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

✅ Type-safe state management
✅ Clear workflow visualization
✅ Modular and reusable components
✅ Easy to test and debug
✅ Scalable architecture

Related Patterns

See other patterns in the same category
Explore combination possibilities
Check the main documentation for pattern relationships

November 4, 2025