Tag: orchestration

Agentic Orchestration Pattern: Visualization Pattern


    """
Pattern 30.04: Visualization
Workflow and execution visualization
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class VisualizationState(TypedDict):
    """State for Visualization pattern."""
    input: str
    output: str
    metadata: dict


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


def create_visualization():
    """Create Visualization workflow."""
    workflow = StateGraph(VisualizationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Visualization Pattern

04 Visualization Pattern – Detailed Documentation

Overview

File: 30_documentation/04_visualization.py
Pattern Category: 30 Documentation
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 30.04: Visualization Workflow and execution visualization

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 VisualizationState(TypedDict):
    """State for Visualization pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: VisualizationState) -> VisualizationState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Visualization agent implementation.

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


def create_visualization():

`create_visualization()`

Line: 28
Arguments: “

Create Visualization workflow.

def create_visualization():
    """Create Visualization workflow."""
    workflow = StateGraph(VisualizationState)
    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 VisualizationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Visualization pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: VisualizationState) -> VisualizationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Visualization agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_visualization definition

def create_visualization():

Defines the create_visualization function that processes the workflow state.

Line 29: Module docstring

    """Create Visualization workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 30.03: Documentation
Self-documenting behavior and knowledge capture
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class DocumentationState(TypedDict):
    """State for Documentation pattern."""
    input: str
    output: str
    metadata: dict


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


def create_documentation():
    """Create Documentation workflow."""
    workflow = StateGraph(DocumentationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Documentation Pattern

03 Documentation Pattern – Detailed Documentation

Overview

File: 30_documentation/03_documentation.py
Pattern Category: 30 Documentation
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 30.03: Documentation Self-documenting behavior and knowledge capture

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 DocumentationState(TypedDict):
    """State for Documentation pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: DocumentationState) -> DocumentationState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Documentation agent implementation.

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


def create_documentation():

`create_documentation()`

Line: 28
Arguments: “

Create Documentation workflow.

def create_documentation():
    """Create Documentation workflow."""
    workflow = StateGraph(DocumentationState)
    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 DocumentationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Documentation pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: DocumentationState) -> DocumentationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Documentation agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_documentation definition

def create_documentation():

Defines the create_documentation function that processes the workflow state.

Line 29: Module docstring

    """Create Documentation workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 30.02: Explanation
Generate explanations for transparency
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ExplanationState(TypedDict):
    """State for Explanation pattern."""
    input: str
    output: str
    metadata: dict


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


def create_explanation():
    """Create Explanation workflow."""
    workflow = StateGraph(ExplanationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Explanation Pattern

02 Explanation Pattern – Detailed Documentation

Overview

File: 30_documentation/02_explanation.py
Pattern Category: 30 Documentation
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 30.02: Explanation Generate explanations for transparency

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 ExplanationState(TypedDict):
    """State for Explanation pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ExplanationState) -> ExplanationState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Explanation agent implementation.

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


def create_explanation():

`create_explanation()`

Line: 28
Arguments: “

Create Explanation workflow.

def create_explanation():
    """Create Explanation workflow."""
    workflow = StateGraph(ExplanationState)
    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 ExplanationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Explanation pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ExplanationState) -> ExplanationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Explanation agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_explanation definition

def create_explanation():

Defines the create_explanation function that processes the workflow state.

Line 29: Module docstring

    """Create Explanation workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 30.01: Provenance
Track data lineage and decision attribution
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ProvenanceState(TypedDict):
    """State for Provenance pattern."""
    input: str
    output: str
    metadata: dict


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


def create_provenance():
    """Create Provenance workflow."""
    workflow = StateGraph(ProvenanceState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Provenance Pattern

01 Provenance Pattern – Detailed Documentation

Overview

File: 30_documentation/01_provenance.py
Pattern Category: 30 Documentation
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 30.01: Provenance Track data lineage and decision attribution

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 ProvenanceState(TypedDict):
    """State for Provenance pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ProvenanceState) -> ProvenanceState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Provenance agent implementation.

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


def create_provenance():

`create_provenance()`

Line: 28
Arguments: “

Create Provenance workflow.

def create_provenance():
    """Create Provenance workflow."""
    workflow = StateGraph(ProvenanceState)
    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 ProvenanceState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Provenance pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ProvenanceState) -> ProvenanceState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Provenance agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_provenance definition

def create_provenance():

Defines the create_provenance function that processes the workflow state.

Line 29: Module docstring

    """Create Provenance workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 29.04: Shadow
Parallel execution for testing
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ShadowState(TypedDict):
    """State for Shadow pattern."""
    input: str
    output: str
    metadata: dict


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


def create_shadow():
    """Create Shadow workflow."""
    workflow = StateGraph(ShadowState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Shadow Pattern

04 Shadow Pattern – Detailed Documentation

Overview

File: 29_testing_simulation/04_shadow.py
Pattern Category: 29 Testing Simulation
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 29.04: Shadow Parallel execution for testing

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 ShadowState(TypedDict):
    """State for Shadow pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ShadowState) -> ShadowState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Shadow agent implementation.

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


def create_shadow():

`create_shadow()`

Line: 28
Arguments: “

Create Shadow workflow.

def create_shadow():
    """Create Shadow workflow."""
    workflow = StateGraph(ShadowState)
    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 ShadowState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Shadow pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ShadowState) -> ShadowState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Shadow agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_shadow definition

def create_shadow():

Defines the create_shadow function that processes the workflow state.

Line 29: Module docstring

    """Create Shadow workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 29.03: Canary
Gradual rollout with risk mitigation
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class CanaryState(TypedDict):
    """State for Canary pattern."""
    input: str
    output: str
    metadata: dict


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


def create_canary():
    """Create Canary workflow."""
    workflow = StateGraph(CanaryState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Canary Pattern

03 Canary Pattern – Detailed Documentation

Overview

File: 29_testing_simulation/03_canary.py
Pattern Category: 29 Testing Simulation
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 29.03: Canary Gradual rollout with risk mitigation

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 CanaryState(TypedDict):
    """State for Canary pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: CanaryState) -> CanaryState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Canary agent implementation.

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


def create_canary():

`create_canary()`

Line: 28
Arguments: “

Create Canary workflow.

def create_canary():
    """Create Canary workflow."""
    workflow = StateGraph(CanaryState)
    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 CanaryState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Canary pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: CanaryState) -> CanaryState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Canary agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_canary definition

def create_canary():

Defines the create_canary function that processes the workflow state.

Line 29: Module docstring

    """Create Canary workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_canary()
    result = workflow.invoke({"input": "Test Canary", "output": "", "metadata": {}})
    print(f"\n=== Canary 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: Chaos Engineering Pattern


    """
Pattern 29.02: Chaos Engineering
Intentional failure injection
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ChaosEngineeringState(TypedDict):
    """State for Chaos Engineering pattern."""
    input: str
    output: str
    metadata: dict


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


def create_chaos_engineering():
    """Create Chaos Engineering workflow."""
    workflow = StateGraph(ChaosEngineeringState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Chaos Engineering Pattern

02 Chaos Engineering Pattern – Detailed Documentation

Overview

File: 29_testing_simulation/02_chaos_engineering.py
Pattern Category: 29 Testing Simulation
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 29.02: Chaos Engineering Intentional failure injection

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 ChaosEngineeringState(TypedDict):
    """State for Chaos Engineering pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ChaosEngineeringState) -> ChaosEngineeringState:

ChaosEngineeringState: State for Chaos Engineering 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 Chaos Engineering: Stores prompt = f”Process using Chaos Engineering – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Chaos Engineering agent implementation.

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


def create_chaos_engineering():

`create_chaos_engineering()`

Line: 28
Arguments: “

Create Chaos Engineering workflow.

def create_chaos_engineering():
    """Create Chaos Engineering workflow."""
    workflow = StateGraph(ChaosEngineeringState)
    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 ChaosEngineeringState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Chaos Engineering pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ChaosEngineeringState) -> ChaosEngineeringState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Chaos Engineering agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_chaos_engineering definition

def create_chaos_engineering():

Defines the create_chaos_engineering function that processes the workflow state.

Line 29: Module docstring

    """Create Chaos Engineering workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_chaos_engineering()
    result = workflow.invoke({"input": "Test Chaos Engineering", "output": "", "metadata": {}})
    print(f"\n=== Chaos Engineering 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: Mock Pattern


    """
Pattern 29.01: Mock
Simulated agents for testing
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class MockState(TypedDict):
    """State for Mock pattern."""
    input: str
    output: str
    metadata: dict


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


def create_mock():
    """Create Mock workflow."""
    workflow = StateGraph(MockState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Mock Pattern

01 Mock Pattern – Detailed Documentation

Overview

File: 29_testing_simulation/01_mock.py
Pattern Category: 29 Testing Simulation
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 29.01: Mock Simulated agents for testing

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 MockState(TypedDict):
    """State for Mock pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: MockState) -> MockState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Mock agent implementation.

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


def create_mock():

`create_mock()`

Line: 28
Arguments: “

Create Mock workflow.

def create_mock():
    """Create Mock workflow."""
    workflow = StateGraph(MockState)
    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 MockState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Mock pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: MockState) -> MockState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Mock agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_mock definition

def create_mock():

Defines the create_mock function that processes the workflow state.

Line 29: Module docstring

    """Create Mock workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

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

Pattern Benefits

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

Related Patterns

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

November 4, 2025

Agentic Orchestration Pattern: Feedback Collection Pattern


    """
Pattern 28.04: Feedback Collection
User feedback integration and learning
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class FeedbackCollectionState(TypedDict):
    """State for Feedback Collection pattern."""
    input: str
    output: str
    metadata: dict


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


def create_feedback_collection():
    """Create Feedback Collection workflow."""
    workflow = StateGraph(FeedbackCollectionState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Feedback Collection Pattern

04 Feedback Collection Pattern – Detailed Documentation

Overview

File: 28_user_interaction/04_feedback_collection.py
Pattern Category: 28 User Interaction
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 28.04: Feedback Collection User feedback integration and learning

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 FeedbackCollectionState(TypedDict):
    """State for Feedback Collection pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: FeedbackCollectionState) -> FeedbackCollectionState:

FeedbackCollectionState: State for Feedback Collection 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 Feedback Collection: Stores prompt = f”Process using Feedback Collection – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Feedback Collection agent implementation.

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


def create_feedback_collection():

`create_feedback_collection()`

Line: 28
Arguments: “

Create Feedback Collection workflow.

def create_feedback_collection():
    """Create Feedback Collection workflow."""
    workflow = StateGraph(FeedbackCollectionState)
    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 FeedbackCollectionState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Feedback Collection pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: FeedbackCollectionState) -> FeedbackCollectionState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Feedback Collection agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_feedback_collection definition

def create_feedback_collection():

Defines the create_feedback_collection function that processes the workflow state.

Line 29: Module docstring

    """Create Feedback Collection workflow."""

Describes the pattern and its purpose.

Usage Example

if __name__ == "__main__":
    workflow = create_feedback_collection()
    result = workflow.invoke({"input": "Test Feedback Collection", "output": "", "metadata": {}})
    print(f"\n=== Feedback Collection 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: Progressive Disclosure


    """
Pattern 28.03: Progressive Disclosure
Gradual information reveal
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ProgressiveDisclosureState(TypedDict):
    """State for Progressive Disclosure pattern."""
    input: str
    output: str
    metadata: dict


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


def create_progressive_disclosure():
    """Create Progressive Disclosure workflow."""
    workflow = StateGraph(ProgressiveDisclosureState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Progressive Disclosure

03 Progressive Disclosure Pattern – Detailed Documentation

Overview

File: 28_user_interaction/03_progressive_disclosure.py
Pattern Category: 28 User Interaction
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 28.03: Progressive Disclosure Gradual information reveal

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 ProgressiveDisclosureState(TypedDict):
    """State for Progressive Disclosure pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ProgressiveDisclosureState) -> ProgressiveDisclosureState:

ProgressiveDisclosureState: State for Progressive Disclosure 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 Progressive Disclosure: Stores prompt = f”Process using Progressive Disclosure – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Progressive Disclosure agent implementation.

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


def create_progressive_disclosure():

`create_progressive_disclosure()`

Line: 28
Arguments: “

Create Progressive Disclosure workflow.

def create_progressive_disclosure():
    """Create Progressive Disclosure workflow."""
    workflow = StateGraph(ProgressiveDisclosureState)
    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 ProgressiveDisclosureState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Progressive Disclosure pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ProgressiveDisclosureState) -> ProgressiveDisclosureState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Progressive Disclosure agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_progressive_disclosure definition

def create_progressive_disclosure():

Defines the create_progressive_disclosure function that processes the workflow state.

Line 29: Module docstring

    """Create Progressive Disclosure workflow."""

Describes the pattern and its purpose.

Usage Example

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