> For the complete documentation index, see [llms.txt](https://docs.talordata.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.talordata.com/serp-api/integration/sdk-integration/how-to-set-up-talordata-with-langchain.md).

# How to Set Up TalorData with LangChain

Integrate TalorData with LangChain to provide LLM-based agents with reliable, anonymous, and scalable web access for executing real-world data tasks, covering 195 countries and regions.

You can use the `langchain-talordata` Python package to implement this integration, which supports the following features:

* TalorSerpTool – TalorData offers a powerful SERP API that allows you to query search engines (Google, Bing, Yandex, DuckDuckGo) using geolocation and advanced customization options—features particularly useful for AI agents requiring real-time web information.

Alternatively, via [TalorData's MCP (Model Context Protocol)](/serp-api/mcp-server/introduction.md)—a local server providing various scraping and automation tools — Although it is not part of the `langchain-talordata` package, it can be manually integrated using LangChain's `Tool` or `RequestsWrapper`.

### How to integrate TalorData with LangChain

{% stepper %}
{% step %}

#### **Get your TalorData API Token**

* Log in to your [TalorData dashboard](https://dashboard.talordata.com/).
* Go to [SERP API > API Token](https://dashboard.talordata.com/scraping/serp-api/api-token). If you haven't generated an API token yet, please generate one.

{% endstep %}

{% step %}

#### **Install the TalorData integration**

Run the following command to install the TalorData integration package for LangChain:

```
pip install langchain-talordata
```

{% endstep %}

{% step %}

#### **Set environment variables**

Set your TalorData API Token as an environment variable:

```
import os
os.environ["TALOR_API_KEY"] = "your-token"
```

{% endstep %}

{% step %}

#### **Using the TaylorData + LangChain integration**

API Reference: [SERP API Documentation](/serp-api/introduction.md)

Basic Usage

```
from langchain_talordata import TalorSerpTool

search_tool = TalorSerpTool.from_env()

result = search_tool.invoke({
    "query": "LangChain tutorial",
    "engine": "google",
    "params": {
        "gl": "us",
        "hl": "en",
        "device": "desktop",
    },
})

print(result)
```

{% endstep %}

{% step %}

#### **Use within an Agent**

```
from langchain_talordata import TalorSerpTool
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tool = TalorSerpTool.from_env()

# Tool calling without langchain_classic agents
model_with_tools = llm.bind_tools([tool])
response = model_with_tools.invoke("Search for the latest LangChain news")
print(response)
```

{% endstep %}
{% endstepper %}


---

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