Agent에서의 context engineering

from langchain.agents import create_agent
from langchain.agents.middleware import dynamic_prompt, ModelRequest

@dynamic_prompt
def state_aware_prompt(request: ModelRequest) -> str:
    # request.messages is a shortcut for request.state["messages"]
    message_count = len(request.messages)

    base = "You are a helpful assistant."

    if message_count > 10:
        base += "\nThis is a long conversation - be extra concise."

    return base

agent = create_agent(
    model="openai:gpt-4o",
    tools=[...],
    middleware=[state_aware_prompt]
)

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from typing import Callable

@wrap_model_call
def inject_file_context(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Inject context about files user has uploaded this session."""
    # Read from State: get uploaded files metadata
    uploaded_files = request.state.get("uploaded_files", [])  

    if uploaded_files:
        # Build context about available files
        file_descriptions = []
        for file in uploaded_files:
            file_descriptions.append(
                f"- {file['name']} ({file['type']}): {file['summary']}"
            )

        file_context = f"""Files you have access to in this conversation:
{chr(10).join(file_descriptions)}

Reference these files when answering questions."""

        # Inject file context before recent messages
        messages = [  
            *request.messages
            {"role": "user", "content": file_context},
        ]
        request = request.override(messages=messages)  

    return handler(request)

agent = create_agent(
    model="openai:gpt-4o",
    tools=[...],
    middleware=[inject_file_context]
)

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from typing import Callable

@wrap_model_call
def state_based_tools(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Filter tools based on conversation State."""
    # Read from State: check if user has authenticated
    state = request.state  
    is_authenticated = state.get("authenticated", False)  
    message_count = len(state["messages"])

    # Only enable sensitive tools after authentication
    if not is_authenticated:
        tools = [t for t in request.tools if t.name.startswith("public_")]
        request = request.override(tools=tools)  
    elif message_count < 5:
        # Limit tools early in conversation
        tools = [t for t in request.tools if t.name != "advanced_search"]
        request = request.override(tools=tools)  

    return handler(request)

agent = create_agent(
    model="openai:gpt-4o",
    tools=[public_search, private_search, advanced_search],
    middleware=[state_based_tools]
)

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from langchain.chat_models import init_chat_model
from typing import Callable

# Initialize models once outside the middleware
large_model = init_chat_model("anthropic:claude-sonnet-4-5")
standard_model = init_chat_model("openai:gpt-4o")
efficient_model = init_chat_model("openai:gpt-4o-mini")

@wrap_model_call
def state_based_model(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Select model based on State conversation length."""
    # request.messages is a shortcut for request.state["messages"]
    message_count = len(request.messages)  

    if message_count > 20:
        # Long conversation - use model with larger context window
        model = large_model
    elif message_count > 10:
        # Medium conversation
        model = standard_model
    else:
        # Short conversation - use efficient model
        model = efficient_model

    request = request.override(model=model)  

    return handler(request)

agent = create_agent(
    model="openai:gpt-4o-mini",
    tools=[...],
    middleware=[state_based_model]
)

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from pydantic import BaseModel, Field
from typing import Callable

class SimpleResponse(BaseModel):
    """Simple response for early conversation."""
    answer: str = Field(description="A brief answer")

class DetailedResponse(BaseModel):
    """Detailed response for established conversation."""
    answer: str = Field(description="A detailed answer")
    reasoning: str = Field(description="Explanation of reasoning")
    confidence: float = Field(description="Confidence score 0-1")

@wrap_model_call
def state_based_output(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Select output format based on State."""
    # request.messages is a shortcut for request.state["messages"]
    message_count = len(request.messages)  

    if message_count < 3:
        # Early conversation - use simple format
        request = request.override(response_format=SimpleResponse)  
    else:
        # Established conversation - use detailed format
        request = request.override(response_format=DetailedResponse)  

    return handler(request)

agent = create_agent(
    model="openai:gpt-4o",
    tools=[...],
    middleware=[state_based_output]
)

from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent

@tool
def check_authentication(
    runtime: ToolRuntime
) -> str:
    """Check if user is authenticated."""
    # Read from State: check current auth status
    current_state = runtime.state
    is_authenticated = current_state.get("authenticated", False)

    if is_authenticated:
        return "User is authenticated"
    else:
        return "User is not authenticated"

agent = create_agent(
    model="openai:gpt-4o",
    tools=[check_authentication]
)

from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent
from langgraph.types import Command

@tool
def authenticate_user(
    password: str,
    runtime: ToolRuntime
) -> Command:
    """Authenticate user and update State."""
    # Perform authentication (simplified)
    if password == "correct":
        # Write to State: mark as authenticated using Command
        return Command(
            update={"authenticated": True},
        )
    else:
        return Command(update={"authenticated": False})

agent = create_agent(
    model="openai:gpt-4o",
    tools=[authenticate_user]
)