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Model

By default, deepagents uses claude-sonnet-4-5-20250929. You can customize the model used by passing any supported or LangChain model object.
Use the provider:model format (e.g., openai:gpt-5) to quickly switch between models.
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

model = init_chat_model(model="openai: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

In addition to custom tools you provide, deep agents include built-in tools for planning, file management, and subagent spawning. 
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]
)

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Skills

You can use skills to provide your deep agent with new capabilities and expertise. While tools tend to cover lower level functionality like native file system actions or planning, skills can contain detailed instructions on how to complete tasks, reference info, and other assets, such as templates. These files are only loaded by the agent when the agent has determined that the skill is useful for the current prompt. This progressive disclosure reduces the amount of tokens and context the agent has to consider upon startup. For example skills, see Deep Agent example skills. To add skills to your deep agent, pass them as an argument to create_deep_agent: 
from urllib.request import urlopen
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagentsjs/refs/heads/main/examples/skills/langgraph-docs/SKILL.md"
with urlopen(skill_url) as response:
    skill_content = response.read().decode('utf-8')

skills_files = {
    "/skills/langgraph-docs/SKILL.md": skill_content
}

agent = create_deep_agent(
    skills=["./skills/"],
    checkpointer=checkpointer,
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "What is langgraph?",
            }
        ],
        # Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
        "files": skills_files
    },
    config={"configurable": {"thread_id": "12345"}},
)

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Memory

Use AGENTS.md files to provide extra context to your deep agent. You can pass one or more file paths to the memory parameter when creating your deep agent: 
from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends.utils import create_file_data
from langgraph.checkpoint.memory import MemorySaver

with urlopen("https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/master/examples/text-to-sql-agent/AGENTS.md") as response:
    agents_md = response.read().decode("utf-8")
checkpointer = MemorySaver()

agent = create_deep_agent(
    memory=[
        "/AGENTS.md"
    ],
    checkpointer=checkpointer,
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me what's in your memory files.",
            }
        ],
        # Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "123456"}},
)
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