Tag: llm

Agentic Orchestration Pattern: Gateway Pattern


    """
Pattern 20.02: Gateway
Single entry point with API aggregation
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class GatewayState(TypedDict):
    """State for Gateway pattern."""
    input: str
    output: str
    metadata: dict


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


def create_gateway():
    """Create Gateway workflow."""
    workflow = StateGraph(GatewayState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Gateway Pattern

02 Gateway Pattern – Detailed Documentation

Overview

File: 20_integration/02_gateway.py
Pattern Category: 20 Integration
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 20.02: Gateway Single entry point with API aggregation

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 GatewayState(TypedDict):
    """State for Gateway pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: GatewayState) -> GatewayState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Gateway agent implementation.

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


def create_gateway():

`create_gateway()`

Line: 28
Arguments: “

Create Gateway workflow.

def create_gateway():
    """Create Gateway workflow."""
    workflow = StateGraph(GatewayState)
    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 GatewayState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Gateway pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: GatewayState) -> GatewayState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Gateway agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_gateway definition

def create_gateway():

Defines the create_gateway function that processes the workflow state.

Line 29: Module docstring

    """Create Gateway workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 20.01: Adapter
Interface translation and legacy integration
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class AdapterState(TypedDict):
    """State for Adapter pattern."""
    input: str
    output: str
    metadata: dict


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


def create_adapter():
    """Create Adapter workflow."""
    workflow = StateGraph(AdapterState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Adapter Pattern

01 Adapter Pattern – Detailed Documentation

Overview

File: 20_integration/01_adapter.py
Pattern Category: 20 Integration
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 20.01: Adapter Interface translation and legacy integration

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 AdapterState(TypedDict):
    """State for Adapter pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: AdapterState) -> AdapterState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Adapter agent implementation.

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


def create_adapter():

`create_adapter()`

Line: 28
Arguments: “

Create Adapter workflow.

def create_adapter():
    """Create Adapter workflow."""
    workflow = StateGraph(AdapterState)
    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 AdapterState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Adapter pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: AdapterState) -> AdapterState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Adapter agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_adapter definition

def create_adapter():

Defines the create_adapter function that processes the workflow state.

Line 29: Module docstring

    """Create Adapter workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 19.04: Dashboard
Real-time visualization of agent activities
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class DashboardState(TypedDict):
    """State for Dashboard pattern."""
    input: str
    output: str
    metadata: dict


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


def create_dashboard():
    """Create Dashboard workflow."""
    workflow = StateGraph(DashboardState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Dashboard Pattern

04 Dashboard Pattern – Detailed Documentation

Overview

File: 19_observability/04_dashboard.py
Pattern Category: 19 Observability
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 19.04: Dashboard Real-time visualization of agent activities

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 DashboardState(TypedDict):
    """State for Dashboard pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: DashboardState) -> DashboardState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Dashboard agent implementation.

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


def create_dashboard():

`create_dashboard()`

Line: 28
Arguments: “

Create Dashboard workflow.

def create_dashboard():
    """Create Dashboard workflow."""
    workflow = StateGraph(DashboardState)
    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 DashboardState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Dashboard pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: DashboardState) -> DashboardState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Dashboard agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_dashboard definition

def create_dashboard():

Defines the create_dashboard function that processes the workflow state.

Line 29: Module docstring

    """Create Dashboard workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 19.03: Tracing
Distributed tracing and request correlation
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class TracingState(TypedDict):
    """State for Tracing pattern."""
    input: str
    output: str
    metadata: dict


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


def create_tracing():
    """Create Tracing workflow."""
    workflow = StateGraph(TracingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Tracing Pattern

03 Tracing Pattern – Detailed Documentation

Overview

File: 19_observability/03_tracing.py
Pattern Category: 19 Observability
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 19.03: Tracing Distributed tracing and request correlation

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 TracingState(TypedDict):
    """State for Tracing pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: TracingState) -> TracingState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Tracing agent implementation.

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


def create_tracing():

`create_tracing()`

Line: 28
Arguments: “

Create Tracing workflow.

def create_tracing():
    """Create Tracing workflow."""
    workflow = StateGraph(TracingState)
    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 TracingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Tracing pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: TracingState) -> TracingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Tracing agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_tracing definition

def create_tracing():

Defines the create_tracing function that processes the workflow state.

Line 29: Module docstring

    """Create Tracing workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 19.02: Metrics
Performance metrics and health indicators
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class MetricsState(TypedDict):
    """State for Metrics pattern."""
    input: str
    output: str
    metadata: dict


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


def create_metrics():
    """Create Metrics workflow."""
    workflow = StateGraph(MetricsState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Metrics Pattern

02 Metrics Pattern – Detailed Documentation

Overview

File: 19_observability/02_metrics.py
Pattern Category: 19 Observability
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 19.02: Metrics Performance metrics and health indicators

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 MetricsState(TypedDict):
    """State for Metrics pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: MetricsState) -> MetricsState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Metrics agent implementation.

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


def create_metrics():

`create_metrics()`

Line: 28
Arguments: “

Create Metrics workflow.

def create_metrics():
    """Create Metrics workflow."""
    workflow = StateGraph(MetricsState)
    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 MetricsState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Metrics pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: MetricsState) -> MetricsState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Metrics agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_metrics definition

def create_metrics():

Defines the create_metrics function that processes the workflow state.

Line 29: Module docstring

    """Create Metrics workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 19.01: Logging
Comprehensive structured logging
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class LoggingState(TypedDict):
    """State for Logging pattern."""
    input: str
    output: str
    metadata: dict


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


def create_logging():
    """Create Logging workflow."""
    workflow = StateGraph(LoggingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Logging Pattern

01 Logging Pattern – Detailed Documentation

Overview

File: 19_observability/01_logging.py
Pattern Category: 19 Observability
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 19.01: Logging Comprehensive structured logging

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 LoggingState(TypedDict):
    """State for Logging pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: LoggingState) -> LoggingState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Logging agent implementation.

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


def create_logging():

`create_logging()`

Line: 28
Arguments: “

Create Logging workflow.

def create_logging():
    """Create Logging workflow."""
    workflow = StateGraph(LoggingState)
    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 LoggingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Logging pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: LoggingState) -> LoggingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Logging agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_logging definition

def create_logging():

Defines the create_logging function that processes the workflow state.

Line 29: Module docstring

    """Create Logging workflow."""

Describes the pattern and its purpose.

Usage Example

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


    """
Pattern 18.04: Sandboxing
Isolated execution environment
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class SandboxingState(TypedDict):
    """State for Sandboxing pattern."""
    input: str
    output: str
    metadata: dict


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


def create_sandboxing():
    """Create Sandboxing workflow."""
    workflow = StateGraph(SandboxingState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Sandboxing Pattern

04 Sandboxing Pattern – Detailed Documentation

Overview

File: 18_security/04_sandboxing.py
Pattern Category: 18 Security
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 18.04: Sandboxing Isolated execution environment

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 SandboxingState(TypedDict):
    """State for Sandboxing pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: SandboxingState) -> SandboxingState:

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

Functions

`agent_function()`

Line: 18
Arguments: state

Sandboxing agent implementation.

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


def create_sandboxing():

`create_sandboxing()`

Line: 28
Arguments: “

Create Sandboxing workflow.

def create_sandboxing():
    """Create Sandboxing workflow."""
    workflow = StateGraph(SandboxingState)
    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 SandboxingState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Sandboxing pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: SandboxingState) -> SandboxingState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Sandboxing agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_sandboxing definition

def create_sandboxing():

Defines the create_sandboxing function that processes the workflow state.

Line 29: Module docstring

    """Create Sandboxing workflow."""

Describes the pattern and its purpose.

Usage Example

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


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


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


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


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


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

Agentic Orchestration Pattern: Encryption Pattern

03 Encryption Pattern – Detailed Documentation

Overview

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

Pattern Description

Pattern 18.03: Encryption Secure communication and data encryption

Code Structure

Imports

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

State Definition

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


def agent_function(state: EncryptionState) -> EncryptionState:

EncryptionState: State for Encryption pattern.

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

Functions

`agent_function()`

Line: 18
Arguments: state

Encryption agent implementation.

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


def create_encryption():

`create_encryption()`

Line: 28
Arguments: “

Create Encryption workflow.

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


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class EncryptionState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Encryption pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: EncryptionState) -> EncryptionState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Encryption agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_encryption definition

def create_encryption():

Defines the create_encryption function that processes the workflow state.

Line 29: Module docstring

    """Create Encryption workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

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

Pattern Benefits

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

Related Patterns

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

November 4, 2025

Agentic Orchestration Pattern: Authorization Pattern


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


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


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


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


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

Agentic Orchestration Pattern: Authorization Pattern

02 Authorization Pattern – Detailed Documentation

Overview

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

Pattern Description

Pattern 18.02: Authorization Permission checking and access control

Code Structure

Imports

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

State Definition

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


def agent_function(state: AuthorizationState) -> AuthorizationState:

AuthorizationState: State for Authorization pattern.

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

Functions

`agent_function()`

Line: 18
Arguments: state

Authorization agent implementation.

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


def create_authorization():

`create_authorization()`

Line: 28
Arguments: “

Create Authorization workflow.

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


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class AuthorizationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Authorization pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: AuthorizationState) -> AuthorizationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Authorization agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_authorization definition

def create_authorization():

Defines the create_authorization function that processes the workflow state.

Line 29: Module docstring

    """Create Authorization workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

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

Pattern Benefits

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

Related Patterns

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

November 4, 2025

Agentic Orchestration Pattern: Authentication Pattern


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


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


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


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


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

Agentic Orchestration Pattern: Authentication Pattern

01 Authentication Pattern – Detailed Documentation

Overview

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

Pattern Description

Pattern 18.01: Authentication Agent identity verification and credentials

Code Structure

Imports

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

State Definition

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


def agent_function(state: AuthenticationState) -> AuthenticationState:

AuthenticationState: State for Authentication pattern.

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

Functions

`agent_function()`

Line: 18
Arguments: state

Authentication agent implementation.

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


def create_authentication():

`create_authentication()`

Line: 28
Arguments: “

Create Authentication workflow.

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


if __name__ == "__main__":

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class AuthenticationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Authentication pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: AuthenticationState) -> AuthenticationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Authentication agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_authentication definition

def create_authentication():

Defines the create_authentication function that processes the workflow state.

Line 29: Module docstring

    """Create Authentication workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

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

Pattern Benefits

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

Related Patterns

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

November 4, 2025