Customize Deep Agents

Learn how to customize deep agents with system prompts, tools, subagents, and more

Model

By default, deepagents uses claude-sonnet-4-5-20250929. You can customize the model used by passing any supported model identifier string or LangChain model object.

Model string

from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

model = init_chat_model(model="gpt-5")
agent = create_deep_agent(model=model)

from langchain_ollama import ChatOllama
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

model = init_chat_model(
    model=ChatOllama(
        model="llama3.1",
        temperature=0,
        # other params...
    )
)
agent = create_deep_agent(model=model)

System prompt

Deep agents come with a built-in system prompt inspired by Claude Code’s system prompt. The default system prompt contains detailed instructions for using the built-in planning tool, file system tools, and subagents.

Each deep agent tailored to a use case should include a custom system prompt specific to that use case.

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,
)

Tools

Just like tool-calling agents, a deep agent gets a set of top level tools that it has access to.

import os
from typing import Literal
from tavily import TavilyClient
from deepagents import create_deep_agent

tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])

def internet_search(
    query: str,
    max_results: int = 5,
    topic: Literal["general", "news", "finance"] = "general",
    include_raw_content: bool = False,
):
    """Run a web search"""
    return tavily_client.search(
        query,
        max_results=max_results,
        include_raw_content=include_raw_content,
        topic=topic,
    )

agent = create_deep_agent(
    tools=[internet_search]
)

In addition to any tools that you provide, deep agents also get access to a number of default tools:

• write_todos – Update the agent’s to-do list
• ls – List all files in the agent’s filesystem
• read_file – Read a file from the agent’s filesystem
• write_file – Write a new file in the agent’s filesystem
• edit_file – Edit an existing file in the agent’s filesystem
• task – Spawn a subagent to handle a specific task

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