> ## 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.

# Add custom tools to Managed Deep Agents

> Define authored tools for Managed Deep Agents projects.

Managed Deep Agents support the normal Deep Agents `tools` configuration surface.

Define LangChain tools in your project, import them into `agent.py`, and pass them to `define_deep_agent`.

<Note>
  Managed Deep Agents is in **public [beta](/langsmith/release-stages)** and available on [LangSmith Cloud](/langsmith/cloud) in the US region only.
</Note>

## Project structure

Keep the agent entry point at the project root and authored tools under `tools/`:

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
my-agent/
  agent.py
  tools/
    customer.py
```

## Add authored tools

Use authored tools for business logic, private APIs, database access, and other code that belongs in your agent project. Managed Deep Agents copies the source into the compiled build and passes the tools to Deep Agents.

For more about LangChain tool definitions, see [Tools](/oss/python/langchain/tools).

## Add a tool module

```python tools/customer.py theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain.tools import tool


@tool(parse_docstring=True)
def lookup_customer(customer_id: str) -> str:
    """Look up a customer record by ID.

    Args:
        customer_id: Customer ID from the CRM.
    """
    return f"Customer {customer_id} is on the enterprise plan."
```

## Attach tools to the agent

Import the tools into the project-root agent entry and pass them in the `tools` list.

```python agent.py theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from managed_deepagents import define_deep_agent

from tools.customer import lookup_customer

agent = define_deep_agent(
    name="support-agent",
    model="openai:gpt-5.5",
    tools=[lookup_customer],
)
```

`mda dev` and `mda deploy` copy the project files into the compiled build.

Your imports should work the same way they do in a normal local Python project.

Use clear, unique tool names to avoid collisions.

## Human-in-the-loop

Pause the agent before sensitive tool calls so a person can approve, edit, or reject them.

Set `interrupt_on` in the agent definition, and optionally set `permissions` to gate tool and filesystem access.

```python agent.py theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from managed_deepagents import define_deep_agent

from tools.customer import lookup_customer

agent = define_deep_agent(
    name="support-agent",
    model="openai:gpt-5.5",
    tools=[lookup_customer],
    interrupt_on={"lookup_customer": True},
)
```

The `interrupt_on` field applies the same interrupt behavior as LangChain's [human-in-the-loop middleware](/oss/python/langchain/guardrails#human-in-the-loop).

For decision types (approve, edit, reject), conditional interrupts, and permission rules, see the Deep Agents [Human-in-the-loop](/oss/python/deepagents/human-in-the-loop) and [Permissions](/oss/python/deepagents/permissions) guides.

### Respond to an interrupt

When a run hits an interrupt, it pauses and waits for a human response before continuing.

* **During local development**, `mda dev` runs the agent in LangSmith Studio, which surfaces the interrupt so you can inspect the pending tool call and resume the run.
* **On a deployed agent**, resume the paused run through the LangGraph server API with a `Command(resume=...)` payload. See [Human-in-the-loop using server API](/langsmith/add-human-in-the-loop).

<Note>
  During public beta, Managed Deep Agents is CLI-first and programmatic invocation is not yet documented. To resume runs programmatically from your own application, contact your LangChain team.
</Note>

Human-in-the-loop needs durable thread state to pause and resume. The managed runtime owns the checkpointer, so no extra setup is required.

## Use secrets and context

Tools can read deployment secrets from environment variables. Put local values in `.env` for `mda dev`; `mda deploy` forwards non-reserved `.env` values as hosted deployment secrets.

For per-run values such as request metadata or feature flags, use the normal LangChain runtime context patterns for tools. See [how to access context from within your tools](/oss/python/langchain/tools#access-context).

***

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