Tag: orchestration-pattern

Agentic Orchestration Pattern: Encryption Pattern


    """
Pattern 18.03: Encryption
Secure communication and data encryption
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class EncryptionState(TypedDict):
    """State for Encryption pattern."""
    input: str
    output: str
    metadata: dict


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


def create_encryption():
    """Create Encryption workflow."""
    workflow = StateGraph(EncryptionState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Encryption Pattern

03 Encryption Pattern – Detailed Documentation

Overview

File: 18_security/03_encryption.py
Pattern Category: 18 Security
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 18.03: Encryption Secure communication and data encryption

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 EncryptionState(TypedDict):
    """State for Encryption pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: EncryptionState) -> EncryptionState:

EncryptionState: State for Encryption 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 Encryption: Stores prompt = f”Process using Encryption – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Encryption agent implementation.

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


def create_encryption():

`create_encryption()`

Line: 28
Arguments: “

Create Encryption workflow.

def create_encryption():
    """Create Encryption workflow."""
    workflow = StateGraph(EncryptionState)
    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 EncryptionState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Encryption pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: EncryptionState) -> EncryptionState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Encryption agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_encryption definition

def create_encryption():

Defines the create_encryption function that processes the workflow state.

Line 29: Module docstring

    """Create Encryption workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 18.02: Authorization
Permission checking and access control
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class AuthorizationState(TypedDict):
    """State for Authorization pattern."""
    input: str
    output: str
    metadata: dict


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


def create_authorization():
    """Create Authorization workflow."""
    workflow = StateGraph(AuthorizationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Authorization Pattern

02 Authorization Pattern – Detailed Documentation

Overview

File: 18_security/02_authorization.py
Pattern Category: 18 Security
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 18.02: Authorization Permission checking and access control

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 AuthorizationState(TypedDict):
    """State for Authorization pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: AuthorizationState) -> AuthorizationState:

AuthorizationState: State for Authorization 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 Authorization: Stores prompt = f”Process using Authorization – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Authorization agent implementation.

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


def create_authorization():

`create_authorization()`

Line: 28
Arguments: “

Create Authorization workflow.

def create_authorization():
    """Create Authorization workflow."""
    workflow = StateGraph(AuthorizationState)
    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 AuthorizationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Authorization pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: AuthorizationState) -> AuthorizationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Authorization agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_authorization definition

def create_authorization():

Defines the create_authorization function that processes the workflow state.

Line 29: Module docstring

    """Create Authorization workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 18.01: Authentication
Agent identity verification and credentials
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class AuthenticationState(TypedDict):
    """State for Authentication pattern."""
    input: str
    output: str
    metadata: dict


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


def create_authentication():
    """Create Authentication workflow."""
    workflow = StateGraph(AuthenticationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Authentication Pattern

01 Authentication Pattern – Detailed Documentation

Overview

File: 18_security/01_authentication.py
Pattern Category: 18 Security
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 18.01: Authentication Agent identity verification and credentials

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 AuthenticationState(TypedDict):
    """State for Authentication pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: AuthenticationState) -> AuthenticationState:

AuthenticationState: State for Authentication 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 Authentication: Stores prompt = f”Process using Authentication – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Authentication agent implementation.

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


def create_authentication():

`create_authentication()`

Line: 28
Arguments: “

Create Authentication workflow.

def create_authentication():
    """Create Authentication workflow."""
    workflow = StateGraph(AuthenticationState)
    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 AuthenticationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Authentication pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: AuthenticationState) -> AuthenticationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Authentication agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_authentication definition

def create_authentication():

Defines the create_authentication function that processes the workflow state.

Line 29: Module docstring

    """Create Authentication workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_authentication()
    result = workflow.invoke({"input": "Test Authentication", "output": "", "metadata": {}})
    print(f"\n=== Authentication 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: Chain of Responsibility


    """
Pattern 17.04: Chain of Responsibility
Request passed through processing chain
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ChainOfResponsibilityState(TypedDict):
    """State for Chain of Responsibility pattern."""
    input: str
    output: str
    metadata: dict


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


def create_chain_of_responsibility():
    """Create Chain of Responsibility workflow."""
    workflow = StateGraph(ChainOfResponsibilityState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Chain of Responsibility

04 Chain Of Responsibility Pattern – Detailed Documentation

Overview

File: 17_composition/04_chain_of_responsibility.py
Pattern Category: 17 Composition
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 17.04: Chain of Responsibility Request passed through processing chain

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 ChainOfResponsibilityState(TypedDict):
    """State for Chain of Responsibility pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ChainOfResponsibilityState) -> ChainOfResponsibilityState:

ChainOfResponsibilityState: State for Chain of Responsibility 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 Chain of Responsibility: Stores prompt = f”Process using Chain of Responsibility – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Chain of Responsibility agent implementation.

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


def create_chain_of_responsibility():

`create_chain_of_responsibility()`

Line: 28
Arguments: “

Create Chain of Responsibility workflow.

def create_chain_of_responsibility():
    """Create Chain of Responsibility workflow."""
    workflow = StateGraph(ChainOfResponsibilityState)
    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 ChainOfResponsibilityState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Chain of Responsibility pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ChainOfResponsibilityState) -> ChainOfResponsibilityState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Chain of Responsibility agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_chain_of_responsibility definition

def create_chain_of_responsibility():

Defines the create_chain_of_responsibility function that processes the workflow state.

Line 29: Module docstring

    """Create Chain of Responsibility workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_chain_of_responsibility()
    result = workflow.invoke({"input": "Test Chain of Responsibility", "output": "", "metadata": {}})
    print(f"\n=== Chain of Responsibility 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: Splitter Pattern


    """
Pattern 17.03: Splitter
Decompose complex tasks for distribution
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class SplitterState(TypedDict):
    """State for Splitter pattern."""
    input: str
    output: str
    metadata: dict


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


def create_splitter():
    """Create Splitter workflow."""
    workflow = StateGraph(SplitterState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Splitter Pattern

03 Splitter Pattern – Detailed Documentation

Overview

File: 17_composition/03_splitter.py
Pattern Category: 17 Composition
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 17.03: Splitter Decompose complex tasks for distribution

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 SplitterState(TypedDict):
    """State for Splitter pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: SplitterState) -> SplitterState:

SplitterState: State for Splitter 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 Splitter: Stores prompt = f”Process using Splitter – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Splitter agent implementation.

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


def create_splitter():

`create_splitter()`

Line: 28
Arguments: “

Create Splitter workflow.

def create_splitter():
    """Create Splitter workflow."""
    workflow = StateGraph(SplitterState)
    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 SplitterState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Splitter pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: SplitterState) -> SplitterState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Splitter agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_splitter definition

def create_splitter():

Defines the create_splitter function that processes the workflow state.

Line 29: Module docstring

    """Create Splitter workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 17.02: Aggregator
Combine multiple agent outputs
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class AggregatorState(TypedDict):
    """State for Aggregator pattern."""
    input: str
    output: str
    metadata: dict


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


def create_aggregator():
    """Create Aggregator workflow."""
    workflow = StateGraph(AggregatorState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Aggregator Pattern

02 Aggregator Pattern – Detailed Documentation

Overview

File: 17_composition/02_aggregator.py
Pattern Category: 17 Composition
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 17.02: Aggregator Combine multiple agent outputs

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 AggregatorState(TypedDict):
    """State for Aggregator pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: AggregatorState) -> AggregatorState:

AggregatorState: State for Aggregator 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 Aggregator: Stores prompt = f”Process using Aggregator – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Aggregator agent implementation.

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


def create_aggregator():

`create_aggregator()`

Line: 28
Arguments: “

Create Aggregator workflow.

def create_aggregator():
    """Create Aggregator workflow."""
    workflow = StateGraph(AggregatorState)
    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 AggregatorState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Aggregator pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: AggregatorState) -> AggregatorState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Aggregator agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_aggregator definition

def create_aggregator():

Defines the create_aggregator function that processes the workflow state.

Line 29: Module docstring

    """Create Aggregator workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_aggregator()
    result = workflow.invoke({"input": "Test Aggregator", "output": "", "metadata": {}})
    print(f"\n=== Aggregator 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: Composite Agent Pattern


    """
Pattern 17.01: Composite Agent
Agents composed of sub-agents
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class CompositeAgentState(TypedDict):
    """State for Composite Agent pattern."""
    input: str
    output: str
    metadata: dict


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


def create_composite_agent():
    """Create Composite Agent workflow."""
    workflow = StateGraph(CompositeAgentState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Composite Agent Pattern

01 Composite Agent Pattern – Detailed Documentation

Overview

File: 17_composition/01_composite_agent.py
Pattern Category: 17 Composition
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 17.01: Composite Agent Agents composed of sub-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 CompositeAgentState(TypedDict):
    """State for Composite Agent pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: CompositeAgentState) -> CompositeAgentState:

CompositeAgentState: State for Composite Agent 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 Composite Agent: Stores prompt = f”Process using Composite Agent – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Composite Agent agent implementation.

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


def create_composite_agent():

`create_composite_agent()`

Line: 28
Arguments: “

Create Composite Agent workflow.

def create_composite_agent():
    """Create Composite Agent workflow."""
    workflow = StateGraph(CompositeAgentState)
    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 CompositeAgentState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Composite Agent pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: CompositeAgentState) -> CompositeAgentState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Composite Agent agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_composite_agent definition

def create_composite_agent():

Defines the create_composite_agent function that processes the workflow state.

Line 29: Module docstring

    """Create Composite Agent workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_composite_agent()
    result = workflow.invoke({"input": "Test Composite Agent", "output": "", "metadata": {}})
    print(f"\n=== Composite Agent 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: Priority Queue Pattern


    """
Pattern 16.04: Priority Queue
Task prioritization with queue management
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class PriorityQueueState(TypedDict):
    """State for Priority Queue pattern."""
    input: str
    output: str
    metadata: dict


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


def create_priority_queue():
    """Create Priority Queue workflow."""
    workflow = StateGraph(PriorityQueueState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Priority Queue Pattern

04 Priority Queue Pattern – Detailed Documentation

Overview

File: 16_resource_management/04_priority_queue.py
Pattern Category: 16 Resource Management
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 16.04: Priority Queue Task prioritization with queue management

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 PriorityQueueState(TypedDict):
    """State for Priority Queue pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: PriorityQueueState) -> PriorityQueueState:

PriorityQueueState: State for Priority Queue 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 Priority Queue: Stores prompt = f”Process using Priority Queue – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Priority Queue agent implementation.

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


def create_priority_queue():

`create_priority_queue()`

Line: 28
Arguments: “

Create Priority Queue workflow.

def create_priority_queue():
    """Create Priority Queue workflow."""
    workflow = StateGraph(PriorityQueueState)
    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 PriorityQueueState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Priority Queue pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: PriorityQueueState) -> PriorityQueueState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Priority Queue agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_priority_queue definition

def create_priority_queue():

Defines the create_priority_queue function that processes the workflow state.

Line 29: Module docstring

    """Create Priority Queue workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_priority_queue()
    result = workflow.invoke({"input": "Test Priority Queue", "output": "", "metadata": {}})
    print(f"\n=== Priority Queue 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: Quota Pattern


    """
Pattern 16.03: Quota
Resource quota enforcement and tracking
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class QuotaState(TypedDict):
    """State for Quota pattern."""
    input: str
    output: str
    metadata: dict


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


def create_quota():
    """Create Quota workflow."""
    workflow = StateGraph(QuotaState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Quota Pattern

03 Quota Pattern – Detailed Documentation

Overview

File: 16_resource_management/03_quota.py
Pattern Category: 16 Resource Management
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 16.03: Quota Resource quota enforcement and tracking

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 QuotaState(TypedDict):
    """State for Quota pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: QuotaState) -> QuotaState:

QuotaState: State for Quota 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 Quota: Stores prompt = f”Process using Quota – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Quota agent implementation.

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


def create_quota():

`create_quota()`

Line: 28
Arguments: “

Create Quota workflow.

def create_quota():
    """Create Quota workflow."""
    workflow = StateGraph(QuotaState)
    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 QuotaState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Quota pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: QuotaState) -> QuotaState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Quota agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_quota definition

def create_quota():

Defines the create_quota function that processes the workflow state.

Line 29: Module docstring

    """Create Quota workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 16.02: Throttling
Rate limiting and resource usage control
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ThrottlingState(TypedDict):
    """State for Throttling pattern."""
    input: str
    output: str
    metadata: dict


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


def create_throttling():
    """Create Throttling workflow."""
    workflow = StateGraph(ThrottlingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Throttling Pattern

02 Throttling Pattern – Detailed Documentation

Overview

File: 16_resource_management/02_throttling.py
Pattern Category: 16 Resource Management
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 16.02: Throttling Rate limiting and resource usage control

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 ThrottlingState(TypedDict):
    """State for Throttling pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ThrottlingState) -> ThrottlingState:

ThrottlingState: State for Throttling 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 Throttling: Stores prompt = f”Process using Throttling – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Throttling agent implementation.

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


def create_throttling():

`create_throttling()`

Line: 28
Arguments: “

Create Throttling workflow.

def create_throttling():
    """Create Throttling workflow."""
    workflow = StateGraph(ThrottlingState)
    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 ThrottlingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Throttling pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ThrottlingState) -> ThrottlingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Throttling agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_throttling definition

def create_throttling():

Defines the create_throttling function that processes the workflow state.

Line 29: Module docstring

    """Create Throttling workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_throttling()
    result = workflow.invoke({"input": "Test Throttling", "output": "", "metadata": {}})
    print(f"\n=== Throttling 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