Customize Deep Agents ↗

langchain guide intermediate tool-use agents prompts workflows

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

Original Documentation

Documentation Index#
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt Use this file to discover all available pages before exploring further.

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
Subagents
Backends (virtual filesystems)
Human-in-the-loop
Skills
Memory

create_deep_agent(
    name: str | None = None,
    model: str | BaseChatModel | None = None,
    tools: Sequence[BaseTool | Callable | dict[str, Any]] | None = None,
    *,
    system_prompt: str | SystemMessage | None = None
) -> CompiledStateGraph

For more information, see create_deep_agent.

Connection resilience#

LangChain chat models automatically retry failed API requests with exponential backoff. By default, models retry up to 6 times for network errors, rate limits (429), and server errors (5xx). Client errors like 401 (unauthorized) or 404 are not retried.

You can adjust the max_retries parameter when creating a model to tune this behavior for your environment:

from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

agent = create_deep_agent(
    model=init_chat_model(
        model="claude-sonnet-4-5-20250929",
        max_retries=10,  # Increase for unreliable networks (default: 6)
        timeout=120,     # Increase timeout for slow connections
    ),
)

For long-running agent tasks on unreliable networks, consider increasing max_retries to 10–15 and pairing it with a checkpointer so that progress is preserved across failures.

Model#

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

Use the provider:model format (for example openai:gpt-5) to quickly switch between models.

👉 Read the OpenAI chat model integration docs

    pip install -U "langchain[openai]"
    ```


  ```python
      import os
      from deepagents import create_deep_agent

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

      agent = create_deep_agent(model="openai:gpt-5.2")
      # this calls init_chat_model for the specified model with default parameters
      # to use specific modele parameters, use init_chat_model directly
      ```

  ```python
      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-4.1")
      agent = create_deep_agent(model=model)
      ```

  ```python
      import os
      from langchain_openai import ChatOpenAI
      from deepagents import create_deep_agent

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

      model = ChatOpenAI(model="gpt-4.1")
      agent = create_deep_agent(model=model)
      ```

  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Anthropic"></span>
👉 Read the [Anthropic chat model integration docs](/oss/python/integrations/chat/anthropic/)

```shell
    pip install -U "langchain[anthropic]"
    ```


  ```python
      import os
      from deepagents import create_deep_agent

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

      agent = create_deep_agent(model="claude-sonnet-4-5-20250929")
      # this calls init_chat_model for the specified model with default parameters
      # to use specific modele parameters, use init_chat_model directly
      ```

  ```python
      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-4-5-20250929")
      agent = create_deep_agent(model=model)
      ```

  ```python
      import os
      from langchain_anthropic import ChatAnthropic
      from deepagents import create_deep_agent

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

      model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
      agent = create_deep_agent(model=model)
      ```

  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Azure"></span>
👉 Read the [Azure chat model integration docs](/oss/python/integrations/chat/azure_chat_openai/)

```shell
    pip install -U "langchain[openai]"
    ```


  ```python
      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-4.1")
      # this calls init_chat_model for the specified model with default parameters
      # to use specific modele parameters, use init_chat_model directly
      ```

  ```python
      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-4.1",
          azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
      )
      agent = create_deep_agent(model=model)
      ```

  ```python
      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-4.1",
          azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
      )
      agent = create_deep_agent(model=model)
      ```

  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Google Gemini"></span>
👉 Read the [Google GenAI chat model integration docs](/oss/python/integrations/chat/google_generative_ai/)

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


  ```python
      import os
      from deepagents import create_deep_agent

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

      agent = create_deep_agent(model="google_genai:gemini-2.5-flash-lite")
      # this calls init_chat_model for the specified model with default parameters
      # to use specific modele parameters, use init_chat_model directly
      ```

  ```python
      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-2.5-flash-lite")
      agent = create_deep_agent(model=model)
      ```

  ```python
      import os
      from langchain_google_genai import ChatGoogleGenerativeAI
      from deepagents import create_deep_agent

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

      model = ChatGoogleGenerativeAI(model="gemini-2.5-flash-lite")
      agent = create_deep_agent(model=model)
      ```

  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="AWS Bedrock"></span>
👉 Read the [AWS Bedrock chat model integration docs](/oss/python/integrations/chat/bedrock/)

```shell
    pip install -U "langchain[aws]"
    ```


  ```python
      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-3-5-sonnet-20240620-v1:0",
          model_provider="bedrock_converse",
      )
      # this calls init_chat_model for the specified model with default parameters
      # to use specific modele parameters, use init_chat_model directly
      ```

  ```python
      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-3-5-sonnet-20240620-v1:0",
          model_provider="bedrock_converse",
      )
      agent = create_deep_agent(model=model)
      ```

  ```python
      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-3-5-sonnet-20240620-v1:0")
      agent = create_deep_agent(model=model)
      ```

  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="HuggingFace"></span>
👉 Read the [HuggingFace chat model integration docs](/oss/python/integrations/chat/huggingface/)

```shell
    pip install -U "langchain[huggingface]"
    ```


  ```python
      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 modele parameters, use init_chat_model directly
      ```

  ```python
      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)
      ```

  ```python
      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)
      ```

  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Tools

In addition to [built-in tools](/oss/python/deepagents/overview#core-capabilities) for planning, file management, and subagent spawning, you can provide custom tools:

```python
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]
)

System prompt#

Deep agents come with a built-in system prompt. The default system prompt contains detailed instructions for using the built-in planning tool, file system tools, and subagents. 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:

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,
)

Middleware#

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 interrupt_on argument is provided

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

from langchain.tools import tool
from langchain.agents.middleware import wrap_tool_call
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(
    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:

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 CustomMiddleware(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 other 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.

Subagents#

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

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,
    )

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-4.1",  # Optional override, defaults to main agent model
}
subagents = [research_subagent]

agent = create_deep_agent(
    model="claude-sonnet-4-5-20250929",
    subagents=subagents
)

For more information, see Subagents.

{/* ## Context - You can persist agent state between runs to store information like user IDs. */}

Backends#

Deep agent tools 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.

An ephemeral filesystem backend stored in langgraph state.

This filesystem only persists for a single thread.

    # By default we provide a StateBackend
    agent = create_deep_agent()

    # Under the hood, it looks like
    from deepagents.backends import StateBackend

    agent = create_deep_agent(
        backend=(lambda rt: StateBackend(rt))   # Note that the tools access State through the runtime.state
    )
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="FilesystemBackend"></span>
The local machine's filesystem.

<span class="callout-start" data-callout-type="warning"></span>
  This backend grants agents direct filesystem read/write access.
  Use with caution and only in appropriate environments.
  For more information, see [`FilesystemBackend`](/oss/python/deepagents/backends#filesystembackend-local-disk).
<span class="callout-end"></span>

```python
    from deepagents.backends import FilesystemBackend

    agent = create_deep_agent(
        backend=FilesystemBackend(root_dir=".", virtual_mode=True)
    )
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="LocalShellBackend"></span>
A filesystem with shell execution directly on the host. Provides filesystem tools plus the `execute` tool for running commands.

<span class="callout-start" data-callout-type="warning"></span>
  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`](/oss/python/deepagents/backends#localshellbackend-local-shell).
<span class="callout-end"></span>

```python
    from deepagents.backends import LocalShellBackend

    agent = create_deep_agent(
        backend=LocalShellBackend(root_dir=".", env={"PATH": "/usr/bin:/bin"})
    )
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="StoreBackend"></span>
A filesystem that provides long-term storage that is *persisted across threads*.

```python
    from langgraph.store.memory import InMemoryStore
    from deepagents.backends import StoreBackend

    agent = create_deep_agent(
        backend=(lambda rt: StoreBackend(rt)),
        store=InMemoryStore()  # Good for local dev; omit for LangSmith Deployment
    )
    ```

<span class="callout-start" data-callout-type="note"></span>
  When deploying to [LangSmith Deployment](/langsmith/deployments), omit the `store` parameter. The platform automatically provisions a store for your agent.
<span class="callout-end"></span>
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="CompositeBackend"></span>
A flexible backend where you can specify different routes in the filesystem to point towards different backends.

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

    composite_backend = lambda rt: CompositeBackend(
        default=StateBackend(rt),
        routes={
            "/memories/": StoreBackend(rt),
        }
    )

    agent = create_deep_agent(
        backend=composite_backend,
        store=InMemoryStore()  # Store passed to create_deep_agent, not backend
    )
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

For more information, see [Backends](/oss/python/deepagents/backends).

### Sandboxes

Sandboxes are specialized [backends](/oss/python/deepagents/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:

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="Modal"></span>

  ```bash
      pip install langchain-modal
      ```

  ```bash
      uv add langchain-modal
      ```


```python
    import modal
    from langchain_anthropic import ChatAnthropic

    from deepagents import create_deep_agent
    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-4-20250514"),
        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()
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Runloop"></span>

  ```bash
      pip install langchain-runloop
      ```

  ```bash
      uv add langchain-runloop
      ```


```python
    import os

    from runloop_api_client import RunloopSDK
    from langchain_anthropic import ChatAnthropic

    from deepagents import create_deep_agent
    from langchain_runloop import RunloopSandbox

    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-4-20250514"),
        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()
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Daytona"></span>

  ```bash
      pip install langchain-daytona
      ```

  ```bash
      uv add langchain-daytona
      ```


```python
    from daytona import Daytona
    from langchain_anthropic import ChatAnthropic

    from deepagents import create_deep_agent
    from langchain_daytona import DaytonaSandbox

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

    agent = create_deep_agent(
        model=ChatAnthropic(model="claude-sonnet-4-20250514"),
        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()
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

For more information, see [Sandboxes](/oss/python/deepagents/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:

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

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

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

@tool
def send_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="claude-sonnet-4-5-20250929",
    tools=[delete_file, read_file, send_email],
    interrupt_on={
        "delete_file": True,  # Default: approve, edit, reject
        "read_file": False,   # No interrupts needed
        "send_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 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 deepagents.backends.utils import create_file_data
    from langgraph.checkpoint.memory import MemorySaver

    checkpointer = MemorySaver()

    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(
        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"}},
    )
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="StoreBackend"></span>
```python
    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


    store = InMemoryStore()

    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(
        backend=(lambda rt: StoreBackend(rt)),
        store=store,
        skills=["/skills/"]
    )

    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "What is langgraph?",
                }
            ]
        },
        config={"configurable": {"thread_id": "12345"}},
    )
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="FilesystemBackend"></span>
```python
    from deepagents import create_deep_agent
    from langgraph.checkpoint.memory import MemorySaver
    from deepagents.backends.filesystem import FilesystemBackend

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

    agent = create_deep_agent(
        backend=FilesystemBackend(root_dir="/Users/user/{project}"),
        skills=["/Users/user/{project}/skills/"],
        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!
    )

    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "What is langgraph?",
                }
            ]
        },
        config={"configurable": {"thread_id": "12345"}},
    )
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Memory

Use [`AGENTS.md` files](https://agents.md/) 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:

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="StateBackend"></span>
```python
    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(
        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"}},
    )
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="StoreBackend"></span>
```python
    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(
        backend=(lambda rt: StoreBackend(rt)),
        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"}},
    )
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="FilesystemBackend"></span>
```python
    from deepagents import create_deep_agent
    from langgraph.checkpoint.memory import MemorySaver
    from deepagents.backends import FilesystemBackend

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

    agent = create_deep_agent(
        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!
    )
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Structured ouput

Deep agents support [structured ouput](/oss/python/langchain/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.

```python
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(
    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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Link last verified June 7, 2026. View original ↗

Source: LangChain Docs

Link last verified: 2026-02-26