Tag: orchestration-pattern

Agentic Orchestration Pattern: Planning Pattern


    """
Pattern 23.03: Planning
Generate and optimize execution plans
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class PlanningState(TypedDict):
    """State for Planning pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: PlanningState) -> PlanningState:
    """Planning agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Planning: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Planning", "status": "completed"}
    return state


def create_planning():
    """Create Planning workflow."""
    workflow = StateGraph(PlanningState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Planning Pattern

03 Planning Pattern – Detailed Documentation

Overview

File: 23_domain_specific/03_planning.py
Pattern Category: 23 Domain Specific
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 23.03: Planning Generate and optimize execution plans

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 PlanningState(TypedDict):
    """State for Planning pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: PlanningState) -> PlanningState:

PlanningState: State for Planning pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Planning: Stores prompt = f”Process using Planning – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Planning agent implementation.

def agent_function(state: PlanningState) -> PlanningState:
    """Planning agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Planning: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Planning", "status": "completed"}
    return state


def create_planning():

`create_planning()`

Line: 28
Arguments: “

Create Planning workflow.

def create_planning():
    """Create Planning workflow."""
    workflow = StateGraph(PlanningState)
    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 PlanningState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Planning pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: PlanningState) -> PlanningState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Planning agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_planning definition

def create_planning():

Defines the create_planning function that processes the workflow state.

Line 29: Module docstring

    """Create Planning workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_planning()
    result = workflow.invoke({"input": "Test Planning", "output": "", "metadata": {}})
    print(f"\n=== Planning 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: Tool-Use Pattern


    """
Pattern 23.02: Tool-Use
Agents use external tools and APIs
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ToolUseState(TypedDict):
    """State for Tool-Use pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ToolUseState) -> ToolUseState:
    """Tool-Use agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Tool-Use: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Tool-Use", "status": "completed"}
    return state


def create_tool_use():
    """Create Tool-Use workflow."""
    workflow = StateGraph(ToolUseState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Tool-Use Pattern

02 Tool Use Pattern – Detailed Documentation

Overview

File: 23_domain_specific/02_tool_use.py
Pattern Category: 23 Domain Specific
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 23.02: Tool-Use Agents use external tools and APIs

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 ToolUseState(TypedDict):
    """State for Tool-Use pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ToolUseState) -> ToolUseState:

ToolUseState: State for Tool-Use pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Tool-Use: Stores prompt = f”Process using Tool-Use – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Tool-Use agent implementation.

def agent_function(state: ToolUseState) -> ToolUseState:
    """Tool-Use agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Tool-Use: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Tool-Use", "status": "completed"}
    return state


def create_tool_use():

`create_tool_use()`

Line: 28
Arguments: “

Create Tool-Use workflow.

def create_tool_use():
    """Create Tool-Use workflow."""
    workflow = StateGraph(ToolUseState)
    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 ToolUseState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Tool-Use pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ToolUseState) -> ToolUseState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Tool-Use agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_tool_use definition

def create_tool_use():

Defines the create_tool_use function that processes the workflow state.

Line 29: Module docstring

    """Create Tool-Use workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_tool_use()
    result = workflow.invoke({"input": "Test Tool-Use", "output": "", "metadata": {}})
    print(f"\n=== Tool-Use 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: Retrieval-Augmented Generation (RAG)


    """
Pattern 23.01: RAG
Retrieval-Augmented Generation pattern
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class RagState(TypedDict):
    """State for RAG pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: RagState) -> RagState:
    """RAG agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using RAG: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "RAG", "status": "completed"}
    return state


def create_rag():
    """Create RAG workflow."""
    workflow = StateGraph(RagState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Retrieval-Augmented Generation (RAG)

01 Rag Pattern – Detailed Documentation

Overview

File: 23_domain_specific/01_rag.py
Pattern Category: 23 Domain Specific
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 23.01: RAG Retrieval-Augmented Generation pattern

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 RagState(TypedDict):
    """State for RAG pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: RagState) -> RagState:

RagState: State for RAG pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using RAG: Stores prompt = f”Process using RAG – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

RAG agent implementation.

def agent_function(state: RagState) -> RagState:
    """RAG agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using RAG: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "RAG", "status": "completed"}
    return state


def create_rag():

`create_rag()`

Line: 28
Arguments: “

Create RAG workflow.

def create_rag():
    """Create RAG workflow."""
    workflow = StateGraph(RagState)
    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 RagState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for RAG pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: RagState) -> RagState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """RAG agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_rag definition

def create_rag():

Defines the create_rag function that processes the workflow state.

Line 29: Module docstring

    """Create RAG workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_rag()
    result = workflow.invoke({"input": "Test RAG", "output": "", "metadata": {}})
    print(f"\n=== RAG 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: Hybrid Pattern


    """
Pattern 22.04: Hybrid
Combine multiple orchestration patterns
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class HybridState(TypedDict):
    """State for Hybrid pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: HybridState) -> HybridState:
    """Hybrid agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Hybrid: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Hybrid", "status": "completed"}
    return state


def create_hybrid():
    """Create Hybrid workflow."""
    workflow = StateGraph(HybridState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Hybrid Pattern

04 Hybrid Pattern – Detailed Documentation

Overview

File: 22_meta_orchestration/04_hybrid.py
Pattern Category: 22 Meta Orchestration
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 22.04: Hybrid Combine multiple orchestration patterns

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 HybridState(TypedDict):
    """State for Hybrid pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: HybridState) -> HybridState:

HybridState: State for Hybrid pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Hybrid: Stores prompt = f”Process using Hybrid – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Hybrid agent implementation.

def agent_function(state: HybridState) -> HybridState:
    """Hybrid agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Hybrid: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Hybrid", "status": "completed"}
    return state


def create_hybrid():

`create_hybrid()`

Line: 28
Arguments: “

Create Hybrid workflow.

def create_hybrid():
    """Create Hybrid workflow."""
    workflow = StateGraph(HybridState)
    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 HybridState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Hybrid pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: HybridState) -> HybridState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Hybrid agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_hybrid definition

def create_hybrid():

Defines the create_hybrid function that processes the workflow state.

Line 29: Module docstring

    """Create Hybrid workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_hybrid()
    result = workflow.invoke({"input": "Test Hybrid", "output": "", "metadata": {}})
    print(f"\n=== Hybrid 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: Pattern Selection Pattern


    """
Pattern 22.03: Pattern Selection
Choose orchestration pattern dynamically
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class PatternSelectionState(TypedDict):
    """State for Pattern Selection pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: PatternSelectionState) -> PatternSelectionState:
    """Pattern Selection agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Pattern Selection: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Pattern Selection", "status": "completed"}
    return state


def create_pattern_selection():
    """Create Pattern Selection workflow."""
    workflow = StateGraph(PatternSelectionState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Pattern Selection Pattern

03 Pattern Selection Pattern – Detailed Documentation

Overview

File: 22_meta_orchestration/03_pattern_selection.py
Pattern Category: 22 Meta Orchestration
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 22.03: Pattern Selection Choose orchestration pattern dynamically

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 PatternSelectionState(TypedDict):
    """State for Pattern Selection pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: PatternSelectionState) -> PatternSelectionState:

PatternSelectionState: State for Pattern Selection pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Pattern Selection: Stores prompt = f”Process using Pattern Selection – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Pattern Selection agent implementation.

def agent_function(state: PatternSelectionState) -> PatternSelectionState:
    """Pattern Selection agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Pattern Selection: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Pattern Selection", "status": "completed"}
    return state


def create_pattern_selection():

`create_pattern_selection()`

Line: 28
Arguments: “

Create Pattern Selection workflow.

def create_pattern_selection():
    """Create Pattern Selection workflow."""
    workflow = StateGraph(PatternSelectionState)
    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 PatternSelectionState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Pattern Selection pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: PatternSelectionState) -> PatternSelectionState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Pattern Selection agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_pattern_selection definition

def create_pattern_selection():

Defines the create_pattern_selection function that processes the workflow state.

Line 29: Module docstring

    """Create Pattern Selection workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_pattern_selection()
    result = workflow.invoke({"input": "Test Pattern Selection", "output": "", "metadata": {}})
    print(f"\n=== Pattern Selection 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: Meta-Learning Pattern


    """
Pattern 22.02: Meta-Learning
Learn orchestration strategies
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class MetaLearningState(TypedDict):
    """State for Meta-Learning pattern."""
    input: str
    output: str
    metadata: dict


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


def create_meta_learning():
    """Create Meta-Learning workflow."""
    workflow = StateGraph(MetaLearningState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Meta-Learning Pattern

02 Meta Learning Pattern – Detailed Documentation

Overview

File: 22_meta_orchestration/02_meta_learning.py
Pattern Category: 22 Meta Orchestration
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 22.02: Meta-Learning Learn orchestration strategies

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 MetaLearningState(TypedDict):
    """State for Meta-Learning pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: MetaLearningState) -> MetaLearningState:

MetaLearningState: State for Meta-Learning pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Meta-Learning: Stores prompt = f”Process using Meta-Learning – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Meta-Learning agent implementation.

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


def create_meta_learning():

`create_meta_learning()`

Line: 28
Arguments: “

Create Meta-Learning workflow.

def create_meta_learning():
    """Create Meta-Learning workflow."""
    workflow = StateGraph(MetaLearningState)
    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 MetaLearningState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Meta-Learning pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: MetaLearningState) -> MetaLearningState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Meta-Learning agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_meta_learning definition

def create_meta_learning():

Defines the create_meta_learning function that processes the workflow state.

Line 29: Module docstring

    """Create Meta-Learning workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_meta_learning()
    result = workflow.invoke({"input": "Test Meta-Learning", "output": "", "metadata": {}})
    print(f"\n=== Meta-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: Self-Organizing Pattern


    """
Pattern 22.01: Self-Organizing
Agents organize autonomously
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class SelfOrganizingState(TypedDict):
    """State for Self-Organizing pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: SelfOrganizingState) -> SelfOrganizingState:
    """Self-Organizing agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Self-Organizing: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Self-Organizing", "status": "completed"}
    return state


def create_self_organizing():
    """Create Self-Organizing workflow."""
    workflow = StateGraph(SelfOrganizingState)
    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_organizing()
    result = workflow.invoke({"input": "Test Self-Organizing", "output": "", "metadata": {}})
    print(f"\n=== Self-Organizing Pattern ===\nOutput: {result['output'][:200]}...")

Agentic Orchestration Pattern: Self-Organizing Pattern

01 Self Organizing Pattern – Detailed Documentation

Overview

File: 22_meta_orchestration/01_self_organizing.py
Pattern Category: 22 Meta Orchestration
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 22.01: Self-Organizing Agents organize autonomously

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 SelfOrganizingState(TypedDict):
    """State for Self-Organizing pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: SelfOrganizingState) -> SelfOrganizingState:

SelfOrganizingState: State for Self-Organizing pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Self-Organizing: Stores prompt = f”Process using Self-Organizing – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Self-Organizing agent implementation.

def agent_function(state: SelfOrganizingState) -> SelfOrganizingState:
    """Self-Organizing agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Self-Organizing: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Self-Organizing", "status": "completed"}
    return state


def create_self_organizing():

`create_self_organizing()`

Line: 28
Arguments: “

Create Self-Organizing workflow.

def create_self_organizing():
    """Create Self-Organizing workflow."""
    workflow = StateGraph(SelfOrganizingState)
    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 SelfOrganizingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Self-Organizing pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: SelfOrganizingState) -> SelfOrganizingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Self-Organizing agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_self_organizing definition

def create_self_organizing():

Defines the create_self_organizing function that processes the workflow state.

Line 29: Module docstring

    """Create Self-Organizing workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_self_organizing()
    result = workflow.invoke({"input": "Test Self-Organizing", "output": "", "metadata": {}})
    print(f"\n=== Self-Organizing 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: CQRS Pattern


    """
Pattern 21.04: CQRS
Command-query separation pattern
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class CqrsState(TypedDict):
    """State for CQRS pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: CqrsState) -> CqrsState:
    """CQRS agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using CQRS: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "CQRS", "status": "completed"}
    return state


def create_cqrs():
    """Create CQRS workflow."""
    workflow = StateGraph(CqrsState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: CQRS Pattern

04 Cqrs Pattern – Detailed Documentation

Overview

File: 21_advanced_coordination/04_cqrs.py
Pattern Category: 21 Advanced Coordination
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 21.04: CQRS Command-query separation pattern

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 CqrsState(TypedDict):
    """State for CQRS pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: CqrsState) -> CqrsState:

CqrsState: State for CQRS pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using CQRS: Stores prompt = f”Process using CQRS – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

CQRS agent implementation.

def agent_function(state: CqrsState) -> CqrsState:
    """CQRS agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using CQRS: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "CQRS", "status": "completed"}
    return state


def create_cqrs():

`create_cqrs()`

Line: 28
Arguments: “

Create CQRS workflow.

def create_cqrs():
    """Create CQRS workflow."""
    workflow = StateGraph(CqrsState)
    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 CqrsState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for CQRS pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: CqrsState) -> CqrsState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """CQRS agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_cqrs definition

def create_cqrs():

Defines the create_cqrs function that processes the workflow state.

Line 29: Module docstring

    """Create CQRS workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_cqrs()
    result = workflow.invoke({"input": "Test CQRS", "output": "", "metadata": {}})
    print(f"\n=== CQRS 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: Event Sourcing Pattern


    """
Pattern 21.03: Event Sourcing
Event log as source of truth
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class EventSourcingState(TypedDict):
    """State for Event Sourcing pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: EventSourcingState) -> EventSourcingState:
    """Event Sourcing agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Event Sourcing: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Event Sourcing", "status": "completed"}
    return state


def create_event_sourcing():
    """Create Event Sourcing workflow."""
    workflow = StateGraph(EventSourcingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Event Sourcing Pattern

03 Event Sourcing Pattern – Detailed Documentation

Overview

File: 21_advanced_coordination/03_event_sourcing.py
Pattern Category: 21 Advanced Coordination
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 21.03: Event Sourcing Event log as source of truth

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 EventSourcingState(TypedDict):
    """State for Event Sourcing pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: EventSourcingState) -> EventSourcingState:

EventSourcingState: State for Event Sourcing pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Event Sourcing: Stores prompt = f”Process using Event Sourcing – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Event Sourcing agent implementation.

def agent_function(state: EventSourcingState) -> EventSourcingState:
    """Event Sourcing agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Event Sourcing: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Event Sourcing", "status": "completed"}
    return state


def create_event_sourcing():

`create_event_sourcing()`

Line: 28
Arguments: “

Create Event Sourcing workflow.

def create_event_sourcing():
    """Create Event Sourcing workflow."""
    workflow = StateGraph(EventSourcingState)
    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 EventSourcingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Event Sourcing pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: EventSourcingState) -> EventSourcingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Event Sourcing agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_event_sourcing definition

def create_event_sourcing():

Defines the create_event_sourcing function that processes the workflow state.

Line 29: Module docstring

    """Create Event Sourcing workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_event_sourcing()
    result = workflow.invoke({"input": "Test Event Sourcing", "output": "", "metadata": {}})
    print(f"\n=== Event Sourcing 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: Orchestration vs Choreography


    """
Pattern 21.02: Orchestration vs Choreography
Central vs decentralized coordination
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class OrchestrationVsChoreographyState(TypedDict):
    """State for Orchestration vs Choreography pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: OrchestrationVsChoreographyState) -> OrchestrationVsChoreographyState:
    """Orchestration vs Choreography agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Orchestration vs Choreography: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Orchestration vs Choreography", "status": "completed"}
    return state


def create_orchestration_vs_choreography():
    """Create Orchestration vs Choreography workflow."""
    workflow = StateGraph(OrchestrationVsChoreographyState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Orchestration vs Choreography

02 Orchestration Vs Choreography Pattern – Detailed Documentation

Overview

File: 21_advanced_coordination/02_orchestration_vs_choreography.py
Pattern Category: 21 Advanced Coordination
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 21.02: Orchestration vs Choreography Central vs decentralized coordination

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 OrchestrationVsChoreographyState(TypedDict):
    """State for Orchestration vs Choreography pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: OrchestrationVsChoreographyState) -> OrchestrationVsChoreographyState:

OrchestrationVsChoreographyState: State for Orchestration vs Choreography pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Orchestration vs Choreography: Stores prompt = f”Process using Orchestration vs Choreography – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Orchestration vs Choreography agent implementation.

def agent_function(state: OrchestrationVsChoreographyState) -> OrchestrationVsChoreographyState:
    """Orchestration vs Choreography agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Orchestration vs Choreography: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Orchestration vs Choreography", "status": "completed"}
    return state


def create_orchestration_vs_choreography():

`create_orchestration_vs_choreography()`

Line: 28
Arguments: “

Create Orchestration vs Choreography workflow.

def create_orchestration_vs_choreography():
    """Create Orchestration vs Choreography workflow."""
    workflow = StateGraph(OrchestrationVsChoreographyState)
    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 OrchestrationVsChoreographyState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Orchestration vs Choreography pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: OrchestrationVsChoreographyState) -> OrchestrationVsChoreographyState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Orchestration vs Choreography agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_orchestration_vs_choreography definition

def create_orchestration_vs_choreography():

Defines the create_orchestration_vs_choreography function that processes the workflow state.

Line 29: Module docstring

    """Create Orchestration vs Choreography workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_orchestration_vs_choreography()
    result = workflow.invoke({"input": "Test Orchestration vs Choreography", "output": "", "metadata": {}})
    print(f"\n=== Orchestration vs Choreography 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