Tag: orchestration

Agentic Orchestration Pattern: Quantum-Inspired Pattern


    """
Pattern 25.05: Quantum-Inspired
Superposition and parallel exploration
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class QuantumInspiredState(TypedDict):
    """State for Quantum-Inspired pattern."""
    input: str
    output: str
    metadata: dict


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


def create_quantum_inspired():
    """Create Quantum-Inspired workflow."""
    workflow = StateGraph(QuantumInspiredState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Quantum-Inspired Pattern

05 Quantum Inspired Pattern – Detailed Documentation

Overview

File: 25_experimental/05_quantum_inspired.py
Pattern Category: 25 Experimental
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 25.05: Quantum-Inspired Superposition and parallel exploration

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 QuantumInspiredState(TypedDict):
    """State for Quantum-Inspired pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: QuantumInspiredState) -> QuantumInspiredState:

QuantumInspiredState: State for Quantum-Inspired 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 Quantum-Inspired: Stores prompt = f”Process using Quantum-Inspired – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Quantum-Inspired agent implementation.

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


def create_quantum_inspired():

`create_quantum_inspired()`

Line: 28
Arguments: “

Create Quantum-Inspired workflow.

def create_quantum_inspired():
    """Create Quantum-Inspired workflow."""
    workflow = StateGraph(QuantumInspiredState)
    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 QuantumInspiredState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Quantum-Inspired pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: QuantumInspiredState) -> QuantumInspiredState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Quantum-Inspired agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_quantum_inspired definition

def create_quantum_inspired():

Defines the create_quantum_inspired function that processes the workflow state.

Line 29: Module docstring

    """Create Quantum-Inspired workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_quantum_inspired()
    result = workflow.invoke({"input": "Test Quantum-Inspired", "output": "", "metadata": {}})
    print(f"\n=== Quantum-Inspired 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: Multi-Agent Simulation


    """
Pattern 25.04: Multi-Agent Simulation
Simulated environments and interactions
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class MultiAgentSimulationState(TypedDict):
    """State for Multi-Agent Simulation pattern."""
    input: str
    output: str
    metadata: dict


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


def create_multi_agent_simulation():
    """Create Multi-Agent Simulation workflow."""
    workflow = StateGraph(MultiAgentSimulationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Multi-Agent Simulation

04 Multi Agent Simulation Pattern – Detailed Documentation

Overview

File: 25_experimental/04_multi_agent_simulation.py
Pattern Category: 25 Experimental
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 25.04: Multi-Agent Simulation Simulated environments and interactions

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 MultiAgentSimulationState(TypedDict):
    """State for Multi-Agent Simulation pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: MultiAgentSimulationState) -> MultiAgentSimulationState:

MultiAgentSimulationState: State for Multi-Agent Simulation 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 Multi-Agent Simulation: Stores prompt = f”Process using Multi-Agent Simulation – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Multi-Agent Simulation agent implementation.

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


def create_multi_agent_simulation():

`create_multi_agent_simulation()`

Line: 28
Arguments: “

Create Multi-Agent Simulation workflow.

def create_multi_agent_simulation():
    """Create Multi-Agent Simulation workflow."""
    workflow = StateGraph(MultiAgentSimulationState)
    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 MultiAgentSimulationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Multi-Agent Simulation pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: MultiAgentSimulationState) -> MultiAgentSimulationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Multi-Agent Simulation agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_multi_agent_simulation definition

def create_multi_agent_simulation():

Defines the create_multi_agent_simulation function that processes the workflow state.

Line 29: Module docstring

    """Create Multi-Agent Simulation workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_multi_agent_simulation()
    result = workflow.invoke({"input": "Test Multi-Agent Simulation", "output": "", "metadata": {}})
    print(f"\n=== Multi-Agent Simulation 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: Reinforcement Learning Pattern


    """
Pattern 25.03: Reinforcement Learning
Learn from rewards and policy optimization
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ReinforcementLearningState(TypedDict):
    """State for Reinforcement Learning pattern."""
    input: str
    output: str
    metadata: dict


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


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


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

Agentic Orchestration Pattern: Reinforcement Learning Pattern

03 Reinforcement Learning Pattern – Detailed Documentation

Overview

File: 25_experimental/03_reinforcement_learning.py
Pattern Category: 25 Experimental
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 25.03: Reinforcement Learning Learn from rewards and policy optimization

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 ReinforcementLearningState(TypedDict):
    """State for Reinforcement Learning pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ReinforcementLearningState) -> ReinforcementLearningState:

ReinforcementLearningState: State for Reinforcement 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 Reinforcement Learning: Stores prompt = f”Process using Reinforcement Learning – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Reinforcement Learning agent implementation.

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


def create_reinforcement_learning():

`create_reinforcement_learning()`

Line: 28
Arguments: “

Create Reinforcement Learning workflow.

def create_reinforcement_learning():
    """Create Reinforcement Learning workflow."""
    workflow = StateGraph(ReinforcementLearningState)
    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 ReinforcementLearningState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Reinforcement Learning pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ReinforcementLearningState) -> ReinforcementLearningState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Reinforcement Learning agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_reinforcement_learning definition

def create_reinforcement_learning():

Defines the create_reinforcement_learning function that processes the workflow state.

Line 29: Module docstring

    """Create Reinforcement Learning workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_reinforcement_learning()
    result = workflow.invoke({"input": "Test Reinforcement Learning", "output": "", "metadata": {}})
    print(f"\n=== Reinforcement 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: Genetic Algorithm Pattern


    """
Pattern 25.02: Genetic Algorithm
Evolution of solutions
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class GeneticAlgorithmState(TypedDict):
    """State for Genetic Algorithm pattern."""
    input: str
    output: str
    metadata: dict


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


def create_genetic_algorithm():
    """Create Genetic Algorithm workflow."""
    workflow = StateGraph(GeneticAlgorithmState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Genetic Algorithm Pattern

02 Genetic Algorithm Pattern – Detailed Documentation

Overview

File: 25_experimental/02_genetic_algorithm.py
Pattern Category: 25 Experimental
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 25.02: Genetic Algorithm Evolution of solutions

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 GeneticAlgorithmState(TypedDict):
    """State for Genetic Algorithm pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: GeneticAlgorithmState) -> GeneticAlgorithmState:

GeneticAlgorithmState: State for Genetic Algorithm 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 Genetic Algorithm: Stores prompt = f”Process using Genetic Algorithm – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Genetic Algorithm agent implementation.

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


def create_genetic_algorithm():

`create_genetic_algorithm()`

Line: 28
Arguments: “

Create Genetic Algorithm workflow.

def create_genetic_algorithm():
    """Create Genetic Algorithm workflow."""
    workflow = StateGraph(GeneticAlgorithmState)
    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 GeneticAlgorithmState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Genetic Algorithm pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: GeneticAlgorithmState) -> GeneticAlgorithmState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Genetic Algorithm agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_genetic_algorithm definition

def create_genetic_algorithm():

Defines the create_genetic_algorithm function that processes the workflow state.

Line 29: Module docstring

    """Create Genetic Algorithm workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_genetic_algorithm()
    result = workflow.invoke({"input": "Test Genetic Algorithm", "output": "", "metadata": {}})
    print(f"\n=== Genetic Algorithm 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: Swarm Pattern


    """
Pattern 25.01: Swarm
Swarm intelligence and collective behavior
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class SwarmState(TypedDict):
    """State for Swarm pattern."""
    input: str
    output: str
    metadata: dict


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


def create_swarm():
    """Create Swarm workflow."""
    workflow = StateGraph(SwarmState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Swarm Pattern

01 Swarm Pattern – Detailed Documentation

Overview

File: 25_experimental/01_swarm.py
Pattern Category: 25 Experimental
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 25.01: Swarm Swarm intelligence and collective behavior

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 SwarmState(TypedDict):
    """State for Swarm pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: SwarmState) -> SwarmState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Swarm agent implementation.

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


def create_swarm():

`create_swarm()`

Line: 28
Arguments: “

Create Swarm workflow.

def create_swarm():
    """Create Swarm workflow."""
    workflow = StateGraph(SwarmState)
    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 SwarmState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Swarm pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: SwarmState) -> SwarmState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Swarm agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_swarm definition

def create_swarm():

Defines the create_swarm function that processes the workflow state.

Line 29: Module docstring

    """Create Swarm workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 24.04: Isolation
Failure isolation and blast radius limitation
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class IsolationState(TypedDict):
    """State for Isolation pattern."""
    input: str
    output: str
    metadata: dict


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


def create_isolation():
    """Create Isolation workflow."""
    workflow = StateGraph(IsolationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Isolation Pattern

04 Isolation Pattern – Detailed Documentation

Overview

File: 24_resilience/04_isolation.py
Pattern Category: 24 Resilience
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 24.04: Isolation Failure isolation and blast radius limitation

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 IsolationState(TypedDict):
    """State for Isolation pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: IsolationState) -> IsolationState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Isolation agent implementation.

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


def create_isolation():

`create_isolation()`

Line: 28
Arguments: “

Create Isolation workflow.

def create_isolation():
    """Create Isolation workflow."""
    workflow = StateGraph(IsolationState)
    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 IsolationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Isolation pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: IsolationState) -> IsolationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Isolation agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_isolation definition

def create_isolation():

Defines the create_isolation function that processes the workflow state.

Line 29: Module docstring

    """Create Isolation workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_isolation()
    result = workflow.invoke({"input": "Test Isolation", "output": "", "metadata": {}})
    print(f"\n=== Isolation 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: Health Check Pattern


    """
Pattern 24.03: Health Check
Regular health monitoring and recovery
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class HealthCheckState(TypedDict):
    """State for Health Check pattern."""
    input: str
    output: str
    metadata: dict


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


def create_health_check():
    """Create Health Check workflow."""
    workflow = StateGraph(HealthCheckState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Health Check Pattern

03 Health Check Pattern – Detailed Documentation

Overview

File: 24_resilience/03_health_check.py
Pattern Category: 24 Resilience
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 24.03: Health Check Regular health monitoring and recovery

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 HealthCheckState(TypedDict):
    """State for Health Check pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: HealthCheckState) -> HealthCheckState:

HealthCheckState: State for Health Check 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 Health Check: Stores prompt = f”Process using Health Check – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Health Check agent implementation.

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


def create_health_check():

`create_health_check()`

Line: 28
Arguments: “

Create Health Check workflow.

def create_health_check():
    """Create Health Check workflow."""
    workflow = StateGraph(HealthCheckState)
    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 HealthCheckState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Health Check pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: HealthCheckState) -> HealthCheckState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Health Check agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_health_check definition

def create_health_check():

Defines the create_health_check function that processes the workflow state.

Line 29: Module docstring

    """Create Health Check workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_health_check()
    result = workflow.invoke({"input": "Test Health Check", "output": "", "metadata": {}})
    print(f"\n=== Health Check 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: Graceful Degradation


    """
Pattern 24.02: Graceful Degradation
Reduced functionality on failure
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class GracefulDegradationState(TypedDict):
    """State for Graceful Degradation pattern."""
    input: str
    output: str
    metadata: dict


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


def create_graceful_degradation():
    """Create Graceful Degradation workflow."""
    workflow = StateGraph(GracefulDegradationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Graceful Degradation

02 Graceful Degradation Pattern – Detailed Documentation

Overview

File: 24_resilience/02_graceful_degradation.py
Pattern Category: 24 Resilience
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 24.02: Graceful Degradation Reduced functionality on failure

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 GracefulDegradationState(TypedDict):
    """State for Graceful Degradation pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: GracefulDegradationState) -> GracefulDegradationState:

GracefulDegradationState: State for Graceful Degradation 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 Graceful Degradation: Stores prompt = f”Process using Graceful Degradation – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Graceful Degradation agent implementation.

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


def create_graceful_degradation():

`create_graceful_degradation()`

Line: 28
Arguments: “

Create Graceful Degradation workflow.

def create_graceful_degradation():
    """Create Graceful Degradation workflow."""
    workflow = StateGraph(GracefulDegradationState)
    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 GracefulDegradationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Graceful Degradation pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: GracefulDegradationState) -> GracefulDegradationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Graceful Degradation agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_graceful_degradation definition

def create_graceful_degradation():

Defines the create_graceful_degradation function that processes the workflow state.

Line 29: Module docstring

    """Create Graceful Degradation workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_graceful_degradation()
    result = workflow.invoke({"input": "Test Graceful Degradation", "output": "", "metadata": {}})
    print(f"\n=== Graceful Degradation 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: Redundancy Pattern


    """
Pattern 24.01: Redundancy
Multiple redundant agents for failover
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class RedundancyState(TypedDict):
    """State for Redundancy pattern."""
    input: str
    output: str
    metadata: dict


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


def create_redundancy():
    """Create Redundancy workflow."""
    workflow = StateGraph(RedundancyState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Redundancy Pattern

01 Redundancy Pattern – Detailed Documentation

Overview

File: 24_resilience/01_redundancy.py
Pattern Category: 24 Resilience
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 24.01: Redundancy Multiple redundant agents for failover

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 RedundancyState(TypedDict):
    """State for Redundancy pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: RedundancyState) -> RedundancyState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Redundancy agent implementation.

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


def create_redundancy():

`create_redundancy()`

Line: 28
Arguments: “

Create Redundancy workflow.

def create_redundancy():
    """Create Redundancy workflow."""
    workflow = StateGraph(RedundancyState)
    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 RedundancyState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Redundancy pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: RedundancyState) -> RedundancyState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Redundancy agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_redundancy definition

def create_redundancy():

Defines the create_redundancy function that processes the workflow state.

Line 29: Module docstring

    """Create Redundancy workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 23.04: Reasoning
Chain-of-thought and logical inference
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ReasoningState(TypedDict):
    """State for Reasoning pattern."""
    input: str
    output: str
    metadata: dict


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


def create_reasoning():
    """Create Reasoning workflow."""
    workflow = StateGraph(ReasoningState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Reasoning Pattern

04 Reasoning Pattern – Detailed Documentation

Overview

File: 23_domain_specific/04_reasoning.py
Pattern Category: 23 Domain Specific
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 23.04: Reasoning Chain-of-thought and logical inference

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 ReasoningState(TypedDict):
    """State for Reasoning pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ReasoningState) -> ReasoningState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Reasoning agent implementation.

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


def create_reasoning():

`create_reasoning()`

Line: 28
Arguments: “

Create Reasoning workflow.

def create_reasoning():
    """Create Reasoning workflow."""
    workflow = StateGraph(ReasoningState)
    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 ReasoningState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Reasoning pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ReasoningState) -> ReasoningState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Reasoning agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_reasoning definition

def create_reasoning():

Defines the create_reasoning function that processes the workflow state.

Line 29: Module docstring

    """Create Reasoning workflow."""

Describes the pattern and its purpose.

Usage Example

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