Author: admin

Agentic Orchestration Pattern: Temporal Workflow


    """
Pattern 15.04: Temporal Workflow
Time-aware orchestration with delays
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class TemporalWorkflowState(TypedDict):
    """State for Temporal Workflow pattern."""
    input: str
    output: str
    metadata: dict


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


def create_temporal_workflow():
    """Create Temporal Workflow workflow."""
    workflow = StateGraph(TemporalWorkflowState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Temporal Workflow

04 Temporal Workflow Pattern – Detailed Documentation

Overview

File: 15_temporal/04_temporal_workflow.py
Pattern Category: 15 Temporal
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 15.04: Temporal Workflow Time-aware orchestration with delays

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 TemporalWorkflowState(TypedDict):
    """State for Temporal Workflow pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: TemporalWorkflowState) -> TemporalWorkflowState:

TemporalWorkflowState: State for Temporal Workflow 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 Temporal Workflow: Stores prompt = f”Process using Temporal Workflow – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Temporal Workflow agent implementation.

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


def create_temporal_workflow():

`create_temporal_workflow()`

Line: 28
Arguments: “

Create Temporal Workflow workflow.

def create_temporal_workflow():
    """Create Temporal Workflow workflow."""
    workflow = StateGraph(TemporalWorkflowState)
    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 TemporalWorkflowState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Temporal Workflow pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: TemporalWorkflowState) -> TemporalWorkflowState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Temporal Workflow agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_temporal_workflow definition

def create_temporal_workflow():

Defines the create_temporal_workflow function that processes the workflow state.

Line 29: Module docstring

    """Create Temporal Workflow workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_temporal_workflow()
    result = workflow.invoke({"input": "Test Temporal Workflow", "output": "", "metadata": {}})
    print(f"\n=== Temporal Workflow 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: Deadline Pattern


    """
Pattern 15.03: Deadline
Time-bounded execution with timeout handling
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class DeadlineState(TypedDict):
    """State for Deadline pattern."""
    input: str
    output: str
    metadata: dict


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


def create_deadline():
    """Create Deadline workflow."""
    workflow = StateGraph(DeadlineState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Deadline Pattern

03 Deadline Pattern – Detailed Documentation

Overview

File: 15_temporal/03_deadline.py
Pattern Category: 15 Temporal
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 15.03: Deadline Time-bounded execution with timeout handling

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 DeadlineState(TypedDict):
    """State for Deadline pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: DeadlineState) -> DeadlineState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Deadline agent implementation.

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


def create_deadline():

`create_deadline()`

Line: 28
Arguments: “

Create Deadline workflow.

def create_deadline():
    """Create Deadline workflow."""
    workflow = StateGraph(DeadlineState)
    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 DeadlineState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Deadline pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: DeadlineState) -> DeadlineState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Deadline agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_deadline definition

def create_deadline():

Defines the create_deadline function that processes the workflow state.

Line 29: Module docstring

    """Create Deadline workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

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

Pattern Benefits

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

Related Patterns

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

November 4, 2025

Agentic Orchestration Pattern: Event-Driven Pattern


    """
Pattern 15.02: Event-Driven
Trigger-based reactive execution
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class EventDrivenState(TypedDict):
    """State for Event-Driven pattern."""
    input: str
    output: str
    metadata: dict


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


def create_event_driven():
    """Create Event-Driven workflow."""
    workflow = StateGraph(EventDrivenState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Event-Driven Pattern

02 Event Driven Pattern – Detailed Documentation

Overview

File: 15_temporal/02_event_driven.py
Pattern Category: 15 Temporal
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 15.02: Event-Driven Trigger-based reactive execution

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 EventDrivenState(TypedDict):
    """State for Event-Driven pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: EventDrivenState) -> EventDrivenState:

EventDrivenState: State for Event-Driven pattern.

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

Functions

`agent_function()`

Line: 18
Arguments: state

Event-Driven agent implementation.

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


def create_event_driven():

`create_event_driven()`

Line: 28
Arguments: “

Create Event-Driven workflow.

def create_event_driven():
    """Create Event-Driven workflow."""
    workflow = StateGraph(EventDrivenState)
    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 EventDrivenState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Event-Driven pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: EventDrivenState) -> EventDrivenState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Event-Driven agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_event_driven definition

def create_event_driven():

Defines the create_event_driven function that processes the workflow state.

Line 29: Module docstring

    """Create Event-Driven workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_event_driven()
    result = workflow.invoke({"input": "Test Event-Driven", "output": "", "metadata": {}})
    print(f"\n=== Event-Driven 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: Scheduled Pattern


    """
Pattern 15.01: Scheduled
Time-based execution with cron-like scheduling
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ScheduledState(TypedDict):
    """State for Scheduled pattern."""
    input: str
    output: str
    metadata: dict


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


def create_scheduled():
    """Create Scheduled workflow."""
    workflow = StateGraph(ScheduledState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Scheduled Pattern

01 Scheduled Pattern – Detailed Documentation

Overview

File: 15_temporal/01_scheduled.py
Pattern Category: 15 Temporal
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 15.01: Scheduled Time-based execution with cron-like scheduling

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 ScheduledState(TypedDict):
    """State for Scheduled pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ScheduledState) -> ScheduledState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Scheduled agent implementation.

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


def create_scheduled():

`create_scheduled()`

Line: 28
Arguments: “

Create Scheduled workflow.

def create_scheduled():
    """Create Scheduled workflow."""
    workflow = StateGraph(ScheduledState)
    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 ScheduledState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Scheduled pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ScheduledState) -> ScheduledState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Scheduled agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_scheduled definition

def create_scheduled():

Defines the create_scheduled function that processes the workflow state.

Line 29: Module docstring

    """Create Scheduled workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 14.04: Sharding
Data and task partitioning across agents
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ShardingState(TypedDict):
    """State for Sharding pattern."""
    input: str
    output: str
    metadata: dict


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


def create_sharding():
    """Create Sharding workflow."""
    workflow = StateGraph(ShardingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Sharding Pattern

04 Sharding Pattern – Detailed Documentation

Overview

File: 14_scalability/04_sharding.py
Pattern Category: 14 Scalability
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 14.04: Sharding Data and task partitioning across 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 ShardingState(TypedDict):
    """State for Sharding pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ShardingState) -> ShardingState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Sharding agent implementation.

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


def create_sharding():

`create_sharding()`

Line: 28
Arguments: “

Create Sharding workflow.

def create_sharding():
    """Create Sharding workflow."""
    workflow = StateGraph(ShardingState)
    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 ShardingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Sharding pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ShardingState) -> ShardingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Sharding agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_sharding definition

def create_sharding():

Defines the create_sharding function that processes the workflow state.

Line 29: Module docstring

    """Create Sharding workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 14.03: Elastic
Dynamic scaling based on load
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ElasticState(TypedDict):
    """State for Elastic pattern."""
    input: str
    output: str
    metadata: dict


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


def create_elastic():
    """Create Elastic workflow."""
    workflow = StateGraph(ElasticState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Elastic Pattern

03 Elastic Pattern – Detailed Documentation

Overview

File: 14_scalability/03_elastic.py
Pattern Category: 14 Scalability
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 14.03: Elastic Dynamic scaling based on load

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 ElasticState(TypedDict):
    """State for Elastic pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ElasticState) -> ElasticState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Elastic agent implementation.

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


def create_elastic():

`create_elastic()`

Line: 28
Arguments: “

Create Elastic workflow.

def create_elastic():
    """Create Elastic workflow."""
    workflow = StateGraph(ElasticState)
    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 ElasticState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Elastic pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ElasticState) -> ElasticState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Elastic agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_elastic definition

def create_elastic():

Defines the create_elastic function that processes the workflow state.

Line 29: Module docstring

    """Create Elastic workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_elastic()
    result = workflow.invoke({"input": "Test Elastic", "output": "", "metadata": {}})
    print(f"\n=== Elastic 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: Vertical Scaling Pattern


    """
Pattern 14.02: Vertical Scaling
Enhance agent capabilities and resources
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class VerticalScalingState(TypedDict):
    """State for Vertical Scaling pattern."""
    input: str
    output: str
    metadata: dict


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


def create_vertical_scaling():
    """Create Vertical Scaling workflow."""
    workflow = StateGraph(VerticalScalingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Vertical Scaling Pattern

02 Vertical Scaling Pattern – Detailed Documentation

Overview

File: 14_scalability/02_vertical_scaling.py
Pattern Category: 14 Scalability
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 14.02: Vertical Scaling Enhance agent capabilities and resources

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 VerticalScalingState(TypedDict):
    """State for Vertical Scaling pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: VerticalScalingState) -> VerticalScalingState:

VerticalScalingState: State for Vertical Scaling 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 Vertical Scaling: Stores prompt = f”Process using Vertical Scaling – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Vertical Scaling agent implementation.

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


def create_vertical_scaling():

`create_vertical_scaling()`

Line: 28
Arguments: “

Create Vertical Scaling workflow.

def create_vertical_scaling():
    """Create Vertical Scaling workflow."""
    workflow = StateGraph(VerticalScalingState)
    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 VerticalScalingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Vertical Scaling pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: VerticalScalingState) -> VerticalScalingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Vertical Scaling agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_vertical_scaling definition

def create_vertical_scaling():

Defines the create_vertical_scaling function that processes the workflow state.

Line 29: Module docstring

    """Create Vertical Scaling workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_vertical_scaling()
    result = workflow.invoke({"input": "Test Vertical Scaling", "output": "", "metadata": {}})
    print(f"\n=== Vertical Scaling 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: Horizontal Scaling Pattern


    """
Pattern 14.01: Horizontal Scaling
Add more agent instances dynamically
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class HorizontalScalingState(TypedDict):
    """State for Horizontal Scaling pattern."""
    input: str
    output: str
    metadata: dict


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


def create_horizontal_scaling():
    """Create Horizontal Scaling workflow."""
    workflow = StateGraph(HorizontalScalingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Horizontal Scaling Pattern

01 Horizontal Scaling Pattern – Detailed Documentation

Overview

File: 14_scalability/01_horizontal_scaling.py
Pattern Category: 14 Scalability
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 14.01: Horizontal Scaling Add more agent instances dynamically

Code Structure

Imports

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

State Definition

class HorizontalScalingState(TypedDict):
    """State for Horizontal Scaling pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: HorizontalScalingState) -> HorizontalScalingState:

HorizontalScalingState: State for Horizontal Scaling 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 Horizontal Scaling: Stores prompt = f”Process using Horizontal Scaling – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Horizontal Scaling agent implementation.

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


def create_horizontal_scaling():

`create_horizontal_scaling()`

Line: 28
Arguments: “

Create Horizontal Scaling workflow.

def create_horizontal_scaling():
    """Create Horizontal Scaling workflow."""
    workflow = StateGraph(HorizontalScalingState)
    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 HorizontalScalingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Horizontal Scaling pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: HorizontalScalingState) -> HorizontalScalingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Horizontal Scaling agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_horizontal_scaling definition

def create_horizontal_scaling():

Defines the create_horizontal_scaling function that processes the workflow state.

Line 29: Module docstring

    """Create Horizontal Scaling workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_horizontal_scaling()
    result = workflow.invoke({"input": "Test Horizontal Scaling", "output": "", "metadata": {}})
    print(f"\n=== Horizontal Scaling 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: Audit Pattern


    """
Pattern 13.04: Audit
Comprehensive audit trail and compliance checking
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class AuditState(TypedDict):
    """State for Audit pattern."""
    input: str
    output: str
    metadata: dict


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


def create_audit():
    """Create Audit workflow."""
    workflow = StateGraph(AuditState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Audit Pattern

04 Audit Pattern – Detailed Documentation

Overview

File: 13_quality_assurance/04_audit.py
Pattern Category: 13 Quality Assurance
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 13.04: Audit Comprehensive audit trail and compliance checking

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 AuditState(TypedDict):
    """State for Audit pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: AuditState) -> AuditState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Audit agent implementation.

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


def create_audit():

`create_audit()`

Line: 28
Arguments: “

Create Audit workflow.

def create_audit():
    """Create Audit workflow."""
    workflow = StateGraph(AuditState)
    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 AuditState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Audit pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: AuditState) -> AuditState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Audit agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_audit definition

def create_audit():

Defines the create_audit function that processes the workflow state.

Line 29: Module docstring

    """Create Audit workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 13.03: Verification
Multiple verification steps with cross-validation
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class VerificationState(TypedDict):
    """State for Verification pattern."""
    input: str
    output: str
    metadata: dict


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


def create_verification():
    """Create Verification workflow."""
    workflow = StateGraph(VerificationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Verification Pattern

03 Verification Pattern – Detailed Documentation

Overview

File: 13_quality_assurance/03_verification.py
Pattern Category: 13 Quality Assurance
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 13.03: Verification Multiple verification steps with cross-validation

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 VerificationState(TypedDict):
    """State for Verification pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: VerificationState) -> VerificationState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Verification agent implementation.

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


def create_verification():

`create_verification()`

Line: 28
Arguments: “

Create Verification workflow.

def create_verification():
    """Create Verification workflow."""
    workflow = StateGraph(VerificationState)
    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 VerificationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Verification pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: VerificationState) -> VerificationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Verification agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_verification definition

def create_verification():

Defines the create_verification function that processes the workflow state.

Line 29: Module docstring

    """Create Verification workflow."""

Describes the pattern and its purpose.

Usage Example

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