Customize Deep Agents

Learn how to customize Deep Agents with system prompts, tools, subagents, and more

create_deep_agent has the following core configuration options:

• Model
• Tools
• System Prompt
• Middleware, including prebuilt middleware and custom middleware
• Interpreters
• Subagents
• Backends (virtual filesystems)
• Human-in-the-loop
• Skills
• Memory
• Profiles

create_deep_agent(
    model: str | BaseChatModel | None = None,
    tools: Sequence[BaseTool | Callable | dict[str, Any]] | None = None,
    *,
    system_prompt: str | SystemMessage | None = None,
    middleware: Sequence[AgentMiddleware] = (),
    subagents: Sequence[SubAgent | CompiledSubAgent | AsyncSubAgent] | None = None,
    skills: list[str] | None = None,
    memory: list[str] | None = None,
    permissions: list[FilesystemPermission] | None = None,
    backend: BackendProtocol | BackendFactory | None = None,
    interrupt_on: dict[str, bool | InterruptOnConfig] | None = None,
    response_format: ResponseFormat[ResponseT] | type[ResponseT] | dict[str, Any] | None = None,
    context_schema: type[ContextT] | None = None,
    checkpointer: Checkpointer | None = None,
    store: BaseStore | None = None,
    debug: bool = False,
    name: str | None = None,
    cache: BaseCache | None = None
) -> CompiledStateGraph[AgentState[ResponseT], ContextT, _InputAgentState, _OutputAgentState[ResponseT]]

For the full parameter list, see the create_deep_agent API reference.

Model

Pass a model string in provider:model format, or an initialized model instance. See supported models for all providers and suggested models for tested recommendations.

Use the provider:model format (for example openai:gpt-5.4) to quickly switch between models.

OpenAI

👉 Read the OpenAI chat model integration docs

pip install -U "langchain[openai]"

default parameters

import os
from deepagents import create_deep_agent

os.environ["OPENAI_API_KEY"] = "sk-..."

agent = create_deep_agent(model="openai:gpt-5.4")
# this calls init_chat_model for the specified model with default parameters
# to use specific model parameters, use init_chat_model directly

import os
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

os.environ["OPENAI_API_KEY"] = "sk-..."

model = init_chat_model(model="openai:gpt-5.4")
agent = create_deep_agent(model=model)

import os
from langchain_openai import ChatOpenAI
from deepagents import create_deep_agent

os.environ["OPENAI_API_KEY"] = "sk-..."

model = ChatOpenAI(model="gpt-5.4")
agent = create_deep_agent(model=model)

Anthropic

👉 Read the Anthropic chat model integration docs

pip install -U "langchain[anthropic]"

default parameters

import os
from deepagents import create_deep_agent

os.environ["ANTHROPIC_API_KEY"] = "sk-..."

agent = create_deep_agent(model="anthropic:claude-sonnet-4-6")
# this calls init_chat_model for the specified model with default parameters
# to use specific model parameters, use init_chat_model directly

import os
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

os.environ["ANTHROPIC_API_KEY"] = "sk-..."

model = init_chat_model(model="claude-sonnet-4-6")
agent = create_deep_agent(model=model)

import os
from langchain_anthropic import ChatAnthropic
from deepagents import create_deep_agent

os.environ["ANTHROPIC_API_KEY"] = "sk-..."

model = ChatAnthropic(model="claude-sonnet-4-6")
agent = create_deep_agent(model=model)

Azure

👉 Read the Azure chat model integration docs

pip install -U "langchain[openai]"

default parameters

import os
from deepagents import create_deep_agent

os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"

agent = create_deep_agent(model="azure_openai:gpt-5.4")
# this calls init_chat_model for the specified model with default parameters
# to use specific model parameters, use init_chat_model directly

import os
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"

model = init_chat_model(
    model="azure_openai:gpt-5.4",
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)
agent = create_deep_agent(model=model)

import os
from langchain_openai import AzureChatOpenAI
from deepagents import create_deep_agent

os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"

model = AzureChatOpenAI(
    model="gpt-5.4",
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)
agent = create_deep_agent(model=model)

Google Gemini

👉 Read the Google GenAI chat model integration docs

pip install -U "langchain[google-genai]"

default parameters

import os
from deepagents import create_deep_agent

os.environ["GOOGLE_API_KEY"] = "..."

agent = create_deep_agent(model="google_genai:gemini-3.1-pro-preview")
# this calls init_chat_model for the specified model with default parameters
# to use specific model parameters, use init_chat_model directly

import os
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

os.environ["GOOGLE_API_KEY"] = "..."

model = init_chat_model(model="google_genai:gemini-3.1-pro-preview")
agent = create_deep_agent(model=model)

import os
from langchain_google_genai import ChatGoogleGenerativeAI
from deepagents import create_deep_agent

os.environ["GOOGLE_API_KEY"] = "..."

model = ChatGoogleGenerativeAI(model="gemini-3.1-pro-preview")
agent = create_deep_agent(model=model)

AWS Bedrock

👉 Read the AWS Bedrock chat model integration docs

pip install -U "langchain[aws]"

default parameters

from deepagents import create_deep_agent

# Follow the steps here to configure your credentials:
# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

agent = create_deep_agent(
    model="anthropic.claude-sonnet-4-6",
    model_provider="bedrock_converse",
)
# this calls init_chat_model for the specified model with default parameters
# to use specific model parameters, use init_chat_model directly

from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

# Follow the steps here to configure your credentials:
# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

model = init_chat_model(
    model="anthropic.claude-sonnet-4-6",
    model_provider="bedrock_converse",
)
agent = create_deep_agent(model=model)

from langchain_aws import ChatBedrock
from deepagents import create_deep_agent

# Follow the steps here to configure your credentials:
# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

model = ChatBedrock(model="anthropic.claude-sonnet-4-6")
agent = create_deep_agent(model=model)

HuggingFace

👉 Read the HuggingFace chat model integration docs

pip install -U "langchain[huggingface]"

default parameters

import os
from deepagents import create_deep_agent

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..."

agent = create_deep_agent(
    model="microsoft/Phi-3-mini-4k-instruct",
    model_provider="huggingface",
    temperature=0.7,
    max_tokens=1024,
)
# this calls init_chat_model for the specified model with default parameters
# to use specific model parameters, use init_chat_model directly

import os
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..."

model = init_chat_model(
    model="microsoft/Phi-3-mini-4k-instruct",
    model_provider="huggingface",
    temperature=0.7,
    max_tokens=1024,
)
agent = create_deep_agent(model=model)

import os
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from deepagents import create_deep_agent

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..."

llm = HuggingFaceEndpoint(
    repo_id="microsoft/Phi-3-mini-4k-instruct",
    temperature=0.7,
    max_length=1024,
)
model = ChatHuggingFace(llm=llm)
agent = create_deep_agent(model=model)

Other

Pass any supported model string, or an initialized model instance:

model string

from deepagents import create_deep_agent

agent = create_deep_agent(model="provider:model-name")

from deepagents import create_deep_agent
from langchain.chat_models import init_chat_model

model = init_chat_model("provider:model-name")
agent = create_deep_agent(model=model)

from langchain_<provider> import Chat<Provider>
from deepagents import create_deep_agent

model = Chat<Provider>(model="model-name")
agent = create_deep_agent(model=model)

Chat models automatically retry transient API failures (with exponential backoff). For defaults, limits, and code samples for tuning max_retries / timeout live on the LangChain Models page.

Tools

In addition to built-in tools for planning, file management, and subagent spawning, you can provide custom tools:

Google

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(
    model="google_genai:gemini-3.1-pro-preview",
    tools=[internet_search],
)

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(
    model="openai:gpt-5.4",
    tools=[internet_search],
)

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(
    model="anthropic:claude-sonnet-4-6",
    tools=[internet_search],
)

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(
    model="openrouter:anthropic/claude-sonnet-4-6",
    tools=[internet_search],
)

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(
    model="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    tools=[internet_search],
)

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(
    model="baseten:zai-org/GLM-5",
    tools=[internet_search],
)

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(
    model="ollama:devstral-2",
    tools=[internet_search],
)

System prompt

Deep Agents come with a built-in system prompt. A deep agent’s value comes from the orchestration layer the SDK provides on top of the model—planning, virtual-filesystem tools, and subagents—and the model needs to know those exist and when to reach for them. The built-in prompt teaches the agent how to use that scaffolding so you don’t have to re-derive it for every project; tweak it through a profile or your own system_prompt= rather than copying it verbatim.

When middleware add special tools, like the filesystem tools, it appends them to the system prompt.

Each deep agent should also include a custom system prompt specific to its specific use case:

Google

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(
    model="google_genai:gemini-3.1-pro-preview",
    system_prompt=research_instructions,
)

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(
    model="openai:gpt-5.4",
    system_prompt=research_instructions,
)

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(
    model="anthropic:claude-sonnet-4-6",
    system_prompt=research_instructions,
)

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(
    model="openrouter:anthropic/claude-sonnet-4-6",
    system_prompt=research_instructions,
)

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(
    model="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    system_prompt=research_instructions,
)

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(
    model="baseten:zai-org/GLM-5",
    system_prompt=research_instructions,
)

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(
    model="ollama:devstral-2",
    system_prompt=research_instructions,
)

Prompt assembly

Deep Agents builds the system prompt from up to four named parts so that caller-supplied instructions, the SDK’s built-in agent guidance, and any model-specific profile overrides can coexist with predictable precedence. Without this layering, a profile suffix tuned for Claude (for example) could overwrite or be overwritten by your system_prompt= argument depending on call order; the named slots make the ordering explicit and stable.

In practice, most callers only encounter two slots: USER (your system_prompt=) and BASE (the SDK default). Selecting a model with a built-in profile—Anthropic or OpenAI today—adds a SUFFIX. The full four-part assembly is mainly relevant when you author a custom HarnessProfile or debug why a profile’s text appears where it does.

The four named parts (each may be absent):

Name	Source	Notes
USER	system_prompt= argument to create_deep_agent	str or SystemMessage; omitted when unset.
BASE	The SDK default (BASE_AGENT_PROMPT)	Always present unless replaced by a profile’s CUSTOM.
CUSTOM	HarnessProfile.base_system_prompt	Replaces BASE outright when a matching profile sets it.
SUFFIX	HarnessProfile.system_prompt_suffix	Appended last when a matching profile sets it.

The order is always USER -> (BASE or CUSTOM) -> SUFFIX, joined by blank lines (\n\n). Two invariants follow:

1. USER is always at the front. The caller’s text precedes any SDK or profile content, so persona/instructions take precedence regardless of which model is selected.
2. SUFFIX is always at the end. Profile suffixes sit closest to the conversation history, where model-tuning guidance lands most reliably.

Assembled shapes (✓ = field is set, - = field is unset):

system_prompt=	profile base_system_prompt (CUSTOM)	profile system_prompt_suffix (SUFFIX)	Final assembled system prompt
None	-	-	BASE
None	-	✓	BASE + SUFFIX
None	✓	-	CUSTOM
None	✓	✓	CUSTOM + SUFFIX
str	-	-	USER + BASE
str	-	✓	USER + BASE + SUFFIX
str	✓	-	USER + CUSTOM
str	✓	✓	USER + CUSTOM + SUFFIX

Worked example—built-in profiles (Anthropic, OpenAI) ship only a system_prompt_suffix, so a typical call lands in the str + - + ✓ row:

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    system_prompt="You are a customer-support agent for ACME Corp.",
)
# Final = USER + BASE + SUFFIX
#       = "You are a customer-support agent for ACME Corp."
#         + "\n\n"
#         + BASE_AGENT_PROMPT
#         + "\n\n"
#         + <Claude-specific guidance>

Passing a SystemMessage (rather than a string) triggers a different concatenation path: the right-hand assembly (BASE-or-CUSTOM plus any SUFFIX) is appended as an additional text content block onto the message’s existing content_blocks. The same logical ordering applies (caller blocks first), and any cache_control markers on the caller’s blocks are preserved — useful for placing explicit Anthropic prompt-cache breakpoints.

Subagent prompts

The same overlay rules apply to declarative subagents — each subagent re-runs profile resolution against its own model, then applies the resolved profile’s base_system_prompt / system_prompt_suffix to its authored system_prompt. The subagent’s system_prompt plays the BASE role; CUSTOM and SUFFIX come from the profile that matches the subagent’s model (which may differ from the main agent’s profile).

spec["system_prompt"]	profile base_system_prompt (CUSTOM)	profile system_prompt_suffix (SUFFIX)	Final subagent system prompt
authored	-	-	authored
authored	-	✓	authored + SUFFIX
authored	✓	-	CUSTOM
authored	✓	✓	CUSTOM + SUFFIX

There is no USER segment for subagents — the spec’s authored system_prompt is the closest analog and stays in the BASE slot. A profile that ships only a system_prompt_suffix (the common case for built-in Anthropic / OpenAI profiles) just appends to whatever the subagent author wrote; a profile that sets base_system_prompt will replace the authored prompt outright, so reach for that field deliberately.

General-purpose subagent prompt

The auto-added general-purpose subagent follows the same overlay rules with one extra layer: the GP base prompt is resolved as general_purpose_subagent.system_prompt (if set) -> HarnessProfile.base_system_prompt (if set) -> SDK GP default. The profile suffix layers on top either way.

The two override fields can both carry a base-prompt replacement, but they are not interchangeable. general_purpose_subagent.system_prompt is GP-specific configuration; base_system_prompt is a global override that primarily targets the main agent. When both are set, the GP-specific intent wins for the GP subagent so a user tuning both fields never sees their GP override silently dropped:

register_harness_profile(
    "anthropic",
    HarnessProfile(
        base_system_prompt="You are ACME's support orchestrator.",  # main agent
        general_purpose_subagent=GeneralPurposeSubagentProfile(
            system_prompt="You are a research subagent. Cite sources.",  # GP subagent
        ),
        system_prompt_suffix="Always think step by step.",
    ),
)

Stack	Final system prompt
Main agent	"You are ACME's support orchestrator." + SUFFIX
GP subagent	"You are a research subagent. Cite sources." + SUFFIX

If general_purpose_subagent.system_prompt is unset, the GP subagent falls back to base_system_prompt (when set) and finally to the SDK GP default.

Middleware

Deep Agents support any middleware, including the built-in middleware listed below, prebuilt middleware from LangChain, provider-specific middleware, and custom middleware you write yourself. Pass middleware to the middleware argument of create_deep_agent.

By default, Deep Agents have access to the following middleware:

• TodoListMiddleware: Tracks and manages todo lists for organizing agent tasks and work
• FilesystemMiddleware: Handles file system operations such as reading, writing, and navigating directories
• SubAgentMiddleware: Spawns and coordinates subagents for delegating tasks to specialized agents
• SummarizationMiddleware: Condenses message history to stay within context limits when conversations grow long
• AnthropicPromptCachingMiddleware: Automatic reduction of redundant token processing when using Anthropic models
• PatchToolCallsMiddleware: Automatic message history fixes when tool calls are interrupted or cancelled before receiving results

If you are using memory, skills, or human-in-the-loop, the following middleware is also included:

• MemoryMiddleware: Persists and retrieves conversation context across sessions when the memory argument is provided
• SkillsMiddleware: Enables custom skills when the skills argument is provided
• HumanInTheLoopMiddleware: Pauses for human approval or input at specified points when the interruptOn argument is provided

Prebuilt middleware

LangChain exposes additional prebuilt middleware that let you add-on various features, such as retries, fallbacks, or PII detection. See Prebuilt middleware for more.

The deepagents library also exposes create_summarization_tool_middleware, enabling agents to trigger summarization at opportune times—such as between tasks—instead of at fixed token intervals. For more detail, see Summarization.

Provider-specific middleware

For provider-specific middleware that is optimized for specific LLM providers, see Official integrations and Community integrations.

Custom middleware

You can provide additional middleware to extend functionality, add tools, or implement custom hooks:

Google

from langchain.agents.middleware import wrap_tool_call
from langchain.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."


call_count = [0]  # Use list to allow modification in nested function


@wrap_tool_call
def log_tool_calls(request, handler):
    """Intercept and log every tool call - demonstrates cross-cutting concern."""
    call_count[0] += 1
    tool_name = request.name if hasattr(request, "name") else str(request)

    print(f"[Middleware] Tool call #{call_count[0]}: {tool_name}")
    print(f"[Middleware] Arguments: {request.args if hasattr(request, 'args') else 'N/A'}")

    # Execute the tool call
    result = handler(request)

    # Log the result
    print(f"[Middleware] Tool call #{call_count[0]} completed")

    return result


agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    tools=[get_weather],
    middleware=[log_tool_calls],
)

from langchain.agents.middleware import wrap_tool_call
from langchain.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."


call_count = [0]  # Use list to allow modification in nested function


@wrap_tool_call
def log_tool_calls(request, handler):
    """Intercept and log every tool call - demonstrates cross-cutting concern."""
    call_count[0] += 1
    tool_name = request.name if hasattr(request, "name") else str(request)

    print(f"[Middleware] Tool call #{call_count[0]}: {tool_name}")
    print(f"[Middleware] Arguments: {request.args if hasattr(request, 'args') else 'N/A'}")

    # Execute the tool call
    result = handler(request)

    # Log the result
    print(f"[Middleware] Tool call #{call_count[0]} completed")

    return result


agent = create_deep_agent(
    model="openai:gpt-5.4",
    tools=[get_weather],
    middleware=[log_tool_calls],
)

from langchain.agents.middleware import wrap_tool_call
from langchain.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."


call_count = [0]  # Use list to allow modification in nested function


@wrap_tool_call
def log_tool_calls(request, handler):
    """Intercept and log every tool call - demonstrates cross-cutting concern."""
    call_count[0] += 1
    tool_name = request.name if hasattr(request, "name") else str(request)

    print(f"[Middleware] Tool call #{call_count[0]}: {tool_name}")
    print(f"[Middleware] Arguments: {request.args if hasattr(request, 'args') else 'N/A'}")

    # Execute the tool call
    result = handler(request)

    # Log the result
    print(f"[Middleware] Tool call #{call_count[0]} completed")

    return result


agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[get_weather],
    middleware=[log_tool_calls],
)

from langchain.agents.middleware import wrap_tool_call
from langchain.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."


call_count = [0]  # Use list to allow modification in nested function


@wrap_tool_call
def log_tool_calls(request, handler):
    """Intercept and log every tool call - demonstrates cross-cutting concern."""
    call_count[0] += 1
    tool_name = request.name if hasattr(request, "name") else str(request)

    print(f"[Middleware] Tool call #{call_count[0]}: {tool_name}")
    print(f"[Middleware] Arguments: {request.args if hasattr(request, 'args') else 'N/A'}")

    # Execute the tool call
    result = handler(request)

    # Log the result
    print(f"[Middleware] Tool call #{call_count[0]} completed")

    return result


agent = create_deep_agent(
    model="openrouter:anthropic/claude-sonnet-4-6",
    tools=[get_weather],
    middleware=[log_tool_calls],
)

from langchain.agents.middleware import wrap_tool_call
from langchain.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."


call_count = [0]  # Use list to allow modification in nested function


@wrap_tool_call
def log_tool_calls(request, handler):
    """Intercept and log every tool call - demonstrates cross-cutting concern."""
    call_count[0] += 1
    tool_name = request.name if hasattr(request, "name") else str(request)

    print(f"[Middleware] Tool call #{call_count[0]}: {tool_name}")
    print(f"[Middleware] Arguments: {request.args if hasattr(request, 'args') else 'N/A'}")

    # Execute the tool call
    result = handler(request)

    # Log the result
    print(f"[Middleware] Tool call #{call_count[0]} completed")

    return result


agent = create_deep_agent(
    model="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    tools=[get_weather],
    middleware=[log_tool_calls],
)

from langchain.agents.middleware import wrap_tool_call
from langchain.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."


call_count = [0]  # Use list to allow modification in nested function


@wrap_tool_call
def log_tool_calls(request, handler):
    """Intercept and log every tool call - demonstrates cross-cutting concern."""
    call_count[0] += 1
    tool_name = request.name if hasattr(request, "name") else str(request)

    print(f"[Middleware] Tool call #{call_count[0]}: {tool_name}")
    print(f"[Middleware] Arguments: {request.args if hasattr(request, 'args') else 'N/A'}")

    # Execute the tool call
    result = handler(request)

    # Log the result
    print(f"[Middleware] Tool call #{call_count[0]} completed")

    return result


agent = create_deep_agent(
    model="baseten:zai-org/GLM-5",
    tools=[get_weather],
    middleware=[log_tool_calls],
)

from langchain.agents.middleware import wrap_tool_call
from langchain.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."


call_count = [0]  # Use list to allow modification in nested function


@wrap_tool_call
def log_tool_calls(request, handler):
    """Intercept and log every tool call - demonstrates cross-cutting concern."""
    call_count[0] += 1
    tool_name = request.name if hasattr(request, "name") else str(request)

    print(f"[Middleware] Tool call #{call_count[0]}: {tool_name}")
    print(f"[Middleware] Arguments: {request.args if hasattr(request, 'args') else 'N/A'}")

    # Execute the tool call
    result = handler(request)

    # Log the result
    print(f"[Middleware] Tool call #{call_count[0]} completed")

    return result


agent = create_deep_agent(
    model="ollama:devstral-2",
    tools=[get_weather],
    middleware=[log_tool_calls],
)

Do not mutate attributes after initialization

If you need to track values across hook invocations (for example, counters or accumulated data), use graph state. Graph state is scoped to a thread by design, so updates are safe under concurrency.

Do this:

from langchain.agents.middleware import AgentMiddleware


class CustomMiddleware(AgentMiddleware):
    def __init__(self):
        pass

    def before_agent(self, state, runtime):
        return {"x": state.get("x", 0) + 1}  # Update graph state instead

Do not do this:

class CustomMiddlewareBad(AgentMiddleware):
    def __init__(self):
        self.x = 1

    def before_agent(self, state, runtime):
        self.x += 1  # Mutation causes race conditions

Mutation in place, such as modifying self.x in before_agent or changing other shared values in hooks, can lead to subtle bugs and race conditions because many operations run concurrently (subagents, parallel tools, and parallel invocations on different threads).

For full details on extending state with custom properties, see Custom middleware - Custom state schema. If you must use mutation in custom middleware, consider what happens when subagents, parallel tools, or concurrent agent invocations run at the same time.

Interpreters

Use interpreters to add an eval tool that runs JavaScript in a scoped QuickJS runtime. Interpreters are useful when the agent needs to compose tools programmatically, batch work, handle errors in code, or transform structured data without a full shell environment.

Google

from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    middleware=[CodeInterpreterMiddleware()],
)

from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware

agent = create_deep_agent(
    model="openai:gpt-5.4",
    middleware=[CodeInterpreterMiddleware()],
)

from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    middleware=[CodeInterpreterMiddleware()],
)

from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware

agent = create_deep_agent(
    model="openrouter:anthropic/claude-sonnet-4-6",
    middleware=[CodeInterpreterMiddleware()],
)

from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware

agent = create_deep_agent(
    model="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    middleware=[CodeInterpreterMiddleware()],
)

from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware

agent = create_deep_agent(
    model="baseten:zai-org/GLM-5",
    middleware=[CodeInterpreterMiddleware()],
)

from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware

agent = create_deep_agent(
    model="ollama:devstral-2",
    middleware=[CodeInterpreterMiddleware()],
)

For setup, programmatic tool calling, interpreter skills, and limits, see Interpreters.

Subagents

To isolate detailed work and avoid context bloat, use subagents:

import os
from typing import Literal

from deepagents import create_deep_agent
from tavily import TavilyClient

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,
    )


research_subagent = {
    "name": "research-agent",
    "description": "Used to research more in depth questions",
    "system_prompt": "You are a great researcher",
    "tools": [internet_search],
    "model": "openai:gpt-5.4",  # Optional override, defaults to main agent model
}
subagents = [research_subagent]

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    subagents=subagents,
)

For more information, see Subagents.

Backends

Tools for a deep agent can make use of virtual file systems to store, access, and edit files. By default, deep agents use a StateBackend.

If you are using skills or memory, you must add the expected skill or memory files to the backend before creating the agent.

StateBackend

A thread-scoped filesystem backend stored in langgraph state.

Files persist across turns within a thread (via your checkpointer) and are not shared across threads.

from deepagents import create_deep_agent
from deepagents.backends import StateBackend

# By default we provide a StateBackend
agent = create_deep_agent(model="google_genai:gemini-3.1-pro-preview")

# Under the hood, it looks like
agent2 = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=StateBackend(),
)

FilesystemBackend

The local machine’s filesystem.

This backend grants agents direct filesystem read/write access. Use with caution and only in appropriate environments. For more information, see FilesystemBackend.

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=FilesystemBackend(root_dir=".", virtual_mode=True),
)

LocalShellBackend

A filesystem with shell execution directly on the host. Provides filesystem tools plus the execute tool for running commands.

This backend grants agents direct filesystem read/write access and unrestricted shell execution on your host. Use with extreme caution and only in appropriate environments. For more information, see LocalShellBackend.

from deepagents import create_deep_agent
from deepagents.backends import LocalShellBackend

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=LocalShellBackend(root_dir=".", virtual_mode=True, env={"PATH": "/usr/bin:/bin"}),
)

StoreBackend

A filesystem that provides long-term storage that is persisted across threads.

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from langgraph.store.memory import InMemoryStore

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=StoreBackend(
        namespace=lambda rt: (rt.server_info.user.identity,),
    ),
    store=InMemoryStore(),  # Good for local dev; omit for LangSmith Deployment
)

When deploying to LangSmith Deployment, omit the store parameter. The platform automatically provisions a store for your agent.

The namespace parameter controls data isolation. For multi-user deployments, always set a namespace factory to isolate data per user or tenant.

ContextHubBackend

Durable filesystem storage in a LangSmith Hub repo.

from deepagents import create_deep_agent
from deepagents.backends import ContextHubBackend

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=ContextHubBackend("my-agent"),
)

For more details, see ContextHubBackend.

CompositeBackend

A flexible backend where you can specify different routes in the filesystem to point towards different backends.

from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, StateBackend, StoreBackend
from langgraph.store.memory import InMemoryStore

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=CompositeBackend(
        default=StateBackend(),
        routes={
            "/memories/": StoreBackend(namespace=lambda _rt: ("memories",)),
        },
    ),
    store=InMemoryStore(),  # Store passed to create_deep_agent, not backend
)

For more information, see Backends.

Sandboxes

Sandboxes are specialized backends that run agent code in an isolated environment with their own filesystem and an execute tool for shell commands. Use a sandbox backend when you want your deep agent to write files, install dependencies, and run commands without changing anything on your local machine.

You configure sandboxes by passing a sandbox backend to backend when creating your deep agent:

Modal

pip

pip install langchain-modal

uv add langchain-modal

import modal
from deepagents import create_deep_agent
from langchain_anthropic import ChatAnthropic
from langchain_modal import ModalSandbox

app = modal.App.lookup("your-app")
modal_sandbox = modal.Sandbox.create(app=app)
backend = ModalSandbox(sandbox=modal_sandbox)

agent = create_deep_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    system_prompt="You are a Python coding assistant with sandbox access.",
    backend=backend,
)
try:
    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "Create a small Python package and run pytest",
                }
            ]
        }
    )
finally:
    modal_sandbox.terminate()

Runloop

pip

pip install langchain-runloop

uv add langchain-runloop

import os

from deepagents import create_deep_agent
from langchain_anthropic import ChatAnthropic
from langchain_runloop import RunloopSandbox
from runloop_api_client import RunloopSDK

client = RunloopSDK(bearer_token=os.environ["RUNLOOP_API_KEY"])

devbox = client.devbox.create()
backend = RunloopSandbox(devbox=devbox)

agent = create_deep_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    system_prompt="You are a Python coding assistant with sandbox access.",
    backend=backend,
)

try:
    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "Create a small Python package and run pytest",
                }
            ]
        }
    )
finally:
    devbox.shutdown()

Daytona

pip

pip install langchain-daytona

uv add langchain-daytona

from daytona import Daytona
from deepagents import create_deep_agent
from langchain_anthropic import ChatAnthropic
from langchain_daytona import DaytonaSandbox

sandbox = Daytona().create()
backend = DaytonaSandbox(sandbox=sandbox)

agent = create_deep_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    system_prompt="You are a Python coding assistant with sandbox access.",
    backend=backend,
)

try:
    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "Create a small Python package and run pytest",
                }
            ]
        }
    )
finally:
    sandbox.stop()

LangSmith

LangSmith sandboxes are currently in private beta.

pip

pip install "langsmith[sandbox]"

uv add "langsmith[sandbox]"

from deepagents import create_deep_agent
from deepagents.backends import LangSmithSandbox
from langchain_anthropic import ChatAnthropic
from langsmith.sandbox import SandboxClient

client = SandboxClient()
ls_sandbox = client.create_sandbox()
backend = LangSmithSandbox(sandbox=ls_sandbox)

agent = create_deep_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    system_prompt="You are a Python coding assistant with sandbox access.",
    backend=backend,
)
try:
    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "Create a small Python package and run pytest",
                }
            ]
        }
    )
finally:
    client.delete_sandbox(ls_sandbox.name)

For more information, see Sandboxes.

Human-in-the-loop

Some tool operations may be sensitive and require human approval before execution. You can configure the approval for each tool:

from langchain.tools import tool
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver


@tool
def remove_file(path: str) -> str:
    """Delete a file from the filesystem."""
    return f"Deleted {path}"


@tool
def fetch_file(path: str) -> str:
    """Read a file from the filesystem."""
    return f"Contents of {path}"


@tool
def notify_email(to: str, subject: str, body: str) -> str:
    """Send an email."""
    return f"Sent email to {to}"


# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    tools=[remove_file, fetch_file, notify_email],
    interrupt_on={
        "remove_file": True,  # Default: approve, edit, reject, respond
        "fetch_file": False,  # No interrupts needed
        "notify_email": {"allowed_decisions": ["approve", "reject"]},  # No editing
    },
    checkpointer=checkpointer,  # Required!
)

You can configure interrupt for agents and subagents on tool call as well as from within tool calls. For more information, see Human-in-the-loop.

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 Agents example skills.

To add skills to your deep agent, pass them as an argument to create_deep_agent:

StateBackend

Google

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StateBackend
from deepagents.backends.utils import create_file_data
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
backend = StateBackend()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/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": create_file_data(skill_content),
}

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=backend,
    skills=["/skills/"],
    checkpointer=checkpointer,
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)], # for interpreter skills
)

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"}},
)

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StateBackend
from deepagents.backends.utils import create_file_data
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
backend = StateBackend()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/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": create_file_data(skill_content),
}

agent = create_deep_agent(
    model="openai:gpt-5.4",
    backend=backend,
    skills=["/skills/"],
    checkpointer=checkpointer,
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)], # for interpreter skills
)

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"}},
)

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StateBackend
from deepagents.backends.utils import create_file_data
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
backend = StateBackend()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/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": create_file_data(skill_content),
}

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    backend=backend,
    skills=["/skills/"],
    checkpointer=checkpointer,
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)], # for interpreter skills
)

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"}},
)

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StateBackend
from deepagents.backends.utils import create_file_data
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
backend = StateBackend()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/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": create_file_data(skill_content),
}

agent = create_deep_agent(
    model="openrouter:anthropic/claude-sonnet-4-6",
    backend=backend,
    skills=["/skills/"],
    checkpointer=checkpointer,
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)], # for interpreter skills
)

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"}},
)

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StateBackend
from deepagents.backends.utils import create_file_data
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
backend = StateBackend()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/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": create_file_data(skill_content),
}

agent = create_deep_agent(
    model="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    backend=backend,
    skills=["/skills/"],
    checkpointer=checkpointer,
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)], # for interpreter skills
)

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"}},
)

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StateBackend
from deepagents.backends.utils import create_file_data
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
backend = StateBackend()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/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": create_file_data(skill_content),
}

agent = create_deep_agent(
    model="baseten:zai-org/GLM-5",
    backend=backend,
    skills=["/skills/"],
    checkpointer=checkpointer,
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)], # for interpreter skills
)

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"}},
)

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StateBackend
from deepagents.backends.utils import create_file_data
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
backend = StateBackend()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/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": create_file_data(skill_content),
}

agent = create_deep_agent(
    model="ollama:devstral-2",
    backend=backend,
    skills=["/skills/"],
    checkpointer=checkpointer,
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)], # for interpreter skills
)

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"}},
)

StoreBackend

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.store.memory import InMemoryStore

store = InMemoryStore()
backend = StoreBackend(namespace=lambda _rt: ("filesystem",))

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

store.put(
    namespace=("filesystem",),
    key="/skills/langgraph-docs/SKILL.md",
    value=create_file_data(skill_content),
)

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=backend,
    store=store,
    skills=["/skills/"],
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)],
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What is langgraph?"}]},
    config={"configurable": {"thread_id": "12345"}},
)

FilesystemBackend

from deepagents import create_deep_agent
from deepagents.backends.filesystem import FilesystemBackend
from langchain_quickjs import CodeInterpreterMiddleware
from langgraph.checkpoint.memory import MemorySaver

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()
root_dir = "/Users/user/{project}"
backend = FilesystemBackend(root_dir=root_dir)

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=backend,
    skills=[str(Path(root_dir) / "skills")],
    interrupt_on={
        "write_file": True,
        "read_file": False,
        "edit_file": True,
    },
    checkpointer=checkpointer, # Required!
    middleware=[CodeInterpreterMiddleware(skills_backend=backend)], # for interpreter skills
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What is langgraph?"}]},
    config={"configurable": {"thread_id": "12345"}},
)

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:

StateBackend

Google

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/main/examples/text-to-sql-agent/AGENTS.md"
) as response:
    agents_md = response.read().decode("utf-8")
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    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"}},
)

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/main/examples/text-to-sql-agent/AGENTS.md"
) as response:
    agents_md = response.read().decode("utf-8")
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="openai:gpt-5.4",
    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"}},
)

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/main/examples/text-to-sql-agent/AGENTS.md"
) as response:
    agents_md = response.read().decode("utf-8")
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    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"}},
)

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/main/examples/text-to-sql-agent/AGENTS.md"
) as response:
    agents_md = response.read().decode("utf-8")
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="openrouter:anthropic/claude-sonnet-4-6",
    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"}},
)

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/main/examples/text-to-sql-agent/AGENTS.md"
) as response:
    agents_md = response.read().decode("utf-8")
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    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"}},
)

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/main/examples/text-to-sql-agent/AGENTS.md"
) as response:
    agents_md = response.read().decode("utf-8")
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="baseten:zai-org/GLM-5",
    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"}},
)

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/main/examples/text-to-sql-agent/AGENTS.md"
) as response:
    agents_md = response.read().decode("utf-8")
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="ollama:devstral-2",
    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"}},
)

StoreBackend

Google

from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

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

# Create the store and add the file to it
store = InMemoryStore()
file_data = create_file_data(agents_md)
store.put(
    namespace=("filesystem",),
    key="/AGENTS.md",
    value=file_data,
)

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=StoreBackend(namespace=lambda _rt: ("filesystem",)),
    store=store,
    memory=["/AGENTS.md"],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me what's in your memory files.",
            }
        ],
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "12345"}},
)

from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

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

# Create the store and add the file to it
store = InMemoryStore()
file_data = create_file_data(agents_md)
store.put(
    namespace=("filesystem",),
    key="/AGENTS.md",
    value=file_data,
)

agent = create_deep_agent(
    model="openai:gpt-5.4",
    backend=StoreBackend(namespace=lambda _rt: ("filesystem",)),
    store=store,
    memory=["/AGENTS.md"],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me what's in your memory files.",
            }
        ],
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "12345"}},
)

from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

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

# Create the store and add the file to it
store = InMemoryStore()
file_data = create_file_data(agents_md)
store.put(
    namespace=("filesystem",),
    key="/AGENTS.md",
    value=file_data,
)

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    backend=StoreBackend(namespace=lambda _rt: ("filesystem",)),
    store=store,
    memory=["/AGENTS.md"],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me what's in your memory files.",
            }
        ],
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "12345"}},
)

from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

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

# Create the store and add the file to it
store = InMemoryStore()
file_data = create_file_data(agents_md)
store.put(
    namespace=("filesystem",),
    key="/AGENTS.md",
    value=file_data,
)

agent = create_deep_agent(
    model="openrouter:anthropic/claude-sonnet-4-6",
    backend=StoreBackend(namespace=lambda _rt: ("filesystem",)),
    store=store,
    memory=["/AGENTS.md"],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me what's in your memory files.",
            }
        ],
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "12345"}},
)

from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

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

# Create the store and add the file to it
store = InMemoryStore()
file_data = create_file_data(agents_md)
store.put(
    namespace=("filesystem",),
    key="/AGENTS.md",
    value=file_data,
)

agent = create_deep_agent(
    model="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    backend=StoreBackend(namespace=lambda _rt: ("filesystem",)),
    store=store,
    memory=["/AGENTS.md"],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me what's in your memory files.",
            }
        ],
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "12345"}},
)

from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

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

# Create the store and add the file to it
store = InMemoryStore()
file_data = create_file_data(agents_md)
store.put(
    namespace=("filesystem",),
    key="/AGENTS.md",
    value=file_data,
)

agent = create_deep_agent(
    model="baseten:zai-org/GLM-5",
    backend=StoreBackend(namespace=lambda _rt: ("filesystem",)),
    store=store,
    memory=["/AGENTS.md"],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me what's in your memory files.",
            }
        ],
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "12345"}},
)

from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

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

# Create the store and add the file to it
store = InMemoryStore()
file_data = create_file_data(agents_md)
store.put(
    namespace=("filesystem",),
    key="/AGENTS.md",
    value=file_data,
)

agent = create_deep_agent(
    model="ollama:devstral-2",
    backend=StoreBackend(namespace=lambda _rt: ("filesystem",)),
    store=store,
    memory=["/AGENTS.md"],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Please tell me what's in your memory files.",
            }
        ],
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "12345"}},
)

FilesystemBackend

Google

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    memory=[
        "./AGENTS.md"
    ],
    interrupt_on={
        "write_file": True,  # Default: approve, edit, reject
        "read_file": False,  # No interrupts needed
        "edit_file": True,   # Default: approve, edit, reject
    },
    checkpointer=checkpointer,  # Required!
)

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="openai:gpt-5.4",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    memory=[
        "./AGENTS.md"
    ],
    interrupt_on={
        "write_file": True,  # Default: approve, edit, reject
        "read_file": False,  # No interrupts needed
        "edit_file": True,   # Default: approve, edit, reject
    },
    checkpointer=checkpointer,  # Required!
)

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    memory=[
        "./AGENTS.md"
    ],
    interrupt_on={
        "write_file": True,  # Default: approve, edit, reject
        "read_file": False,  # No interrupts needed
        "edit_file": True,   # Default: approve, edit, reject
    },
    checkpointer=checkpointer,  # Required!
)

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="openrouter:anthropic/claude-sonnet-4-6",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    memory=[
        "./AGENTS.md"
    ],
    interrupt_on={
        "write_file": True,  # Default: approve, edit, reject
        "read_file": False,  # No interrupts needed
        "edit_file": True,   # Default: approve, edit, reject
    },
    checkpointer=checkpointer,  # Required!
)

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    memory=[
        "./AGENTS.md"
    ],
    interrupt_on={
        "write_file": True,  # Default: approve, edit, reject
        "read_file": False,  # No interrupts needed
        "edit_file": True,   # Default: approve, edit, reject
    },
    checkpointer=checkpointer,  # Required!
)

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="baseten:zai-org/GLM-5",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    memory=[
        "./AGENTS.md"
    ],
    interrupt_on={
        "write_file": True,  # Default: approve, edit, reject
        "read_file": False,  # No interrupts needed
        "edit_file": True,   # Default: approve, edit, reject
    },
    checkpointer=checkpointer,  # Required!
)

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="ollama:devstral-2",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    memory=[
        "./AGENTS.md"
    ],
    interrupt_on={
        "write_file": True,  # Default: approve, edit, reject
        "read_file": False,  # No interrupts needed
        "edit_file": True,   # Default: approve, edit, reject
    },
    checkpointer=checkpointer,  # Required!
)

Profiles

A harness profile packages per-provider or per-model tweaks (system prompt suffixes, tool description overrides, excluded tools or middleware, extra middleware, and general-purpose subagent edits) so create_deep_agent applies them automatically when the matching model is selected.

from deepagents import HarnessProfile, register_harness_profile

# Append a system-prompt suffix whenever gpt-5.4 is selected.
register_harness_profile(
    "openai:gpt-5.4",
    HarnessProfile(system_prompt_suffix="Respond in under 100 words."),
)

See Profiles for registration keys, merge semantics, and plugin packaging. A narrower companion API, provider profiles, packages model-construction arguments for a provider.

Structured output

Deep Agents support structured output. You can set a desired structured output schema by passing it as the response_format argument to the call to create_deep_agent(). When the model generates the structured data, it’s captured, validated, and returned in the ‘structured_response’ key of the deep agent’s state.

import os
from typing import Literal

from pydantic import BaseModel, Field
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,
    )


class WeatherReport(BaseModel):
    """A structured weather report with current conditions and forecast."""
    location: str = Field(description="The location for this weather report")
    temperature: float = Field(description="Current temperature in Celsius")
    condition: str = Field(
        description="Current weather condition (e.g., sunny, cloudy, rainy)"
    )
    humidity: int = Field(description="Humidity percentage")
    wind_speed: float = Field(description="Wind speed in km/h")
    forecast: str = Field(description="Brief forecast for the next 24 hours")


agent = create_deep_agent(
    model=model,
    response_format=WeatherReport,
    tools=[internet_search],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "What's the weather like in San Francisco?",
            }
        ]
    }
)

print(result["structured_response"])
# location='San Francisco, California' temperature=18.3 condition='Sunny' humidity=48 wind_speed=7.6 forecast='Pleasant sunny conditions expected to continue with temperatures around 64°F (18°C) during the day, dropping to around 52°F (11°C) at night. Clear skies with minimal precipitation expected.'

For more information and examples, see response format.

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