> ## 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.
# Unstructured Transform integration
> Integrate with the Unstructured Transform tools using LangChain Python.
This guide provides a quick overview for getting started with the Unstructured Transform
[tools](/oss/python/langchain/tools). The [Unstructured Transform](https://docs.unstructured.io/transform/overview)
MCP server ingests and transforms files (PDF, DOCX, images, and 70+ file types) into partitioned,
enriched, chunked, and embedded data for RAG and AI pipelines. The `langchain-unstructured-transform`
package loads those tools as native LangChain tools.
## Overview
### Details
| Class | Package | Serializable | JS support | Downloads | Version |
| :----------------------------- | :--------------------------------------------------------------------------------------------- | :----------: | :--------: | :---------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------: |
| `UnstructuredTransformToolkit` | [langchain-unstructured-transform](https://pypi.org/project/langchain-unstructured-transform/) | ❌ | ❌ |  |  |
### Features
* Loads the hosted Transform MCP server's tools as native LangChain tools.
* Drives the full parsing lifecycle: request an upload URL, transform files, poll status, and fetch results.
* Works with any LangChain or LangGraph agent.
* Remote and hosted — no local server or bridge to run.
***
## Setup
To access the Unstructured Transform tools, you'll need an Unstructured account and an API key, and
you'll need to install the `langchain-unstructured-transform` package.
### Credentials
Get an API key from the [Unstructured docs](https://docs.unstructured.io/transform/overview), then
set it as an environment variable:
```python Set API key icon="key" theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import getpass
import os
if "UNSTRUCTURED_API_KEY" not in os.environ:
os.environ["UNSTRUCTURED_API_KEY"] = getpass.getpass("Enter your Unstructured API key: ")
```
It's also helpful (but not needed) to set up LangSmith for best-in-class observability/tracing of your tool calls. To enable automated tracing, set your [LangSmith](/langsmith/observability) API key:
```python Enable tracing icon="flask" theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
```
### Installation
The Unstructured Transform tools live in the `langchain-unstructured-transform` package:
```python pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -U langchain-unstructured-transform
```
```python uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
uv add langchain-unstructured-transform
```
***
## Instantiation
The Transform tools are loaded over MCP, which is an asynchronous operation. Use the toolkit's
`aget_tools()` method (or the `aget_transform_tools()` helper) to load them:
```python Load the tools icon="robot" theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_unstructured_transform import UnstructuredTransformToolkit
toolkit = UnstructuredTransformToolkit() # reads UNSTRUCTURED_API_KEY
tools = await toolkit.aget_tools()
```
The loaded `tools` include `request_file_upload_url`, `transform_files`, `check_transform_status`,
and `get_transform_results`.
***
## Invocation
### Within an agent
The tools are designed to be orchestrated by an agent, which chains them to run a full parsing job.
```python Agent with tools icon="robot" theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# pip install -qU "langchain[anthropic]" to call the model
from langchain.agents import create_agent
from langchain_unstructured_transform import aget_transform_tools
tools = await aget_transform_tools()
agent = create_agent(
model="claude-sonnet-4-6",
tools=tools,
)
response = await agent.ainvoke(
{
"messages": [
{
"role": "user",
"content": (
"Use the Unstructured Transform tools to parse ./report.pdf. "
"Provide the results as JSON."
),
}
]
}
)
print(response["messages"][-1].content)
```
The agent requests an upload URL, uploads each file, starts the transform, polls for status, and
returns the results — one output per input file.
***
## Limits
Parsing requests have the following limits:
* Each file must be a [supported file type](https://docs.unstructured.io/transform/supported-file-types).
* Each file must be 50 MB or less in size.
* Each request must have 10 files or fewer.
* Only 5 requests can run at a time.
The Transform MCP server reports these limits back to the agent through its tool responses, so you can
instruct your agent to batch files and back off accordingly in its system prompt.
***
## API reference
For detailed documentation of the Unstructured Transform MCP server, its tools, and how to control
its output, head to the [Unstructured Transform documentation](https://docs.unstructured.io/transform/overview).
***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/tools/unstructured_transform.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).