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Model

By default, deepagents uses "claude-sonnet-4-5-20250929". You can customize this by passing any LangChain model object. 
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,
)

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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,
)

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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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