Configuration

Deep agents are highly configurable. This page covers the main configuration options for customizing agent behavior.

Learn more: For comprehensive customization guides, see the Customization documentation.

Model Configuration

By default, deep agents use claude-sonnet-4-5-20250929. You can use any LangChain-compatible chat model.

from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

# Using the provider:model format
model = init_chat_model(model="openai:gpt-5")
agent = create_deep_agent(model=model)

# Or directly with a model instance
from langchain_anthropic import ChatAnthropic
model = ChatAnthropic(model="claude-sonnet-4-20250514")
agent = create_deep_agent(model=model)

System Prompt

Deep agents include a comprehensive built-in system prompt. Add use-case specific instructions to guide agent behavior.

from deepagents import create_deep_agent

research_instructions = """You are an expert researcher.
Your job is to conduct thorough research, and then write a polished report.
"""

agent = create_deep_agent(
    system_prompt=research_instructions,
)

Interrupt Configuration (Human-in-the-Loop)

Configure which tools require human approval before execution.

Learn more: See Human-in-the-Loop for detailed patterns.

from langchain_core.tools import tool
from deepagents import create_deep_agent

@tool
def get_weather(city: str) -> str:
    """Get the weather in a city."""
    return f"The weather in {city} is sunny."

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-20250514",
    tools=[get_weather],
    interrupt_on={
        "get_weather": {
            "allowed_decisions": ["approve", "edit", "reject"]
        },
    }
)

Backend Configuration

Control how the agent stores files and manages state.

from deepagents import create_deep_agent
from deepagents.backends import StateBackend, StoreBackend, FilesystemBackend

# Default: StateBackend (in-memory, ephemeral)
agent = create_deep_agent()

# StoreBackend: Persistent storage
from langgraph.store.memory import InMemoryStore
agent = create_deep_agent(
    backend=StoreBackend,
    store=InMemoryStore(),
)

# FilesystemBackend: Real filesystem
agent = create_deep_agent(
    backend=FilesystemBackend(root_dir="./agent-workspace"),
)