> 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/mcp-integration/langchain-integration.md).

# LangChain Integration

Connect LangChain and LangGraph agents to the TalorData MCP server to add real-time web search, scraping, and structured data tools to your AI workflows.

### Hosted MCP

{% stepper %}
{% step %}

#### **Get your API token**

* Go to [TalorData SERP API - API Token](https://dashboard.talordata.com/scraping/serp-api/api-token) to create an API token;
* Copy your API token (format as follows: `sk_7qBRe***************************`）
  {% endstep %}

{% step %}

#### **Install the required software packages**

```
pip install langchain-mcp-adapters
```

{% endstep %}

{% step %}

#### **Configure your MCP server**

```
import asyncio
from langchain_openai import ChatOpenAI
from langchain.agents import create_agent
from langchain_mcp_adapters.client import MultiServerMCPClient
from dotenv import load_dotenv
import os

load_dotenv()

async def main():
    # Configure MCP client
    client = MultiServerMCPClient({
        "talor_data": {
            "url": "https://mcp.talordata.net/<API_TOKEN>/mcp",
            "transport": "http",
        }
    })

    # Get available tools
    tools = await client.get_tools()
    print("Available tools:", [tool.name for tool in tools])

    # Configure LLM
    llm = ChatOpenAI(
        openai_api_key=os.getenv("OPENROUTER_API_KEY"),
        openai_api_base="https://openrouter.ai/api/v1",
        model_name="moonshotai/kimi-k2"
    )

    # System prompt for web search agent
    system_prompt = """
    You are a web search agent with comprehensive scraping capabilities. Your tools include:
    - **search_engine**: Get search results from Google/Bing/Yandex
    - **scrape_as_markdown**: Extract content from any webpage with bot detection bypass
    - **Structured extractors**: Fast, reliable data from major platforms (Amazon, LinkedIn, Instagram, Facebook, X, TikTok, YouTube, Reddit, Zillow, etc.)
    - **Browser automation**: Navigate, click, type, screenshot for complex interactions

    Guidelines:
    - Use structured web_data_* tools for supported platforms when possible (faster/more reliable)
    - Use general scraping for other sites
    - Handle errors gracefully and respect rate limits
    - Think step by step about what information you need and which tools to use
    - Be thorough in your research and provide comprehensive answers

    When responding, follow this pattern:
    1. Think about what information is needed
    2. Choose the appropriate tool(s)
    3. Execute the tool(s)
    4. Analyze the results
    5. Provide a clear, comprehensive answer
    """

    # Create ReAct agent
    agent = create_agent(
        model=llm,
        tools=tools,
        system_prompt=system_prompt
    )

    # Test the agent
    print("Testing ReAct Agent with available tools...")
    print("=" * 50)

    result = await agent.ainvoke({
        "messages": [("human", "Search for the latest news about AI developments")]
    })

    print("\nAgent Response:")
    print(result["messages"][-1].content)

if __name__ == "__main__":
    asyncio.run(main())
```

{% endstep %}

{% step %}

#### **Set environment variables**

Create a `.env` file in your project directory:

```
OPENROUTER_API_KEY=your_openrouter_api_key_here
```

{% endstep %}

{% step %}

#### **Testing to see if it works**

* Please replace <kbd>\<API\_TOKEN></kbd> with your actual TalorData API token.
* Run your LangChain script
* You should see the agent perform a web search and provide a comprehensive response.
  {% endstep %}

{% step %}

#### **Monitor usage**

* View your API usage in [TalorData SERP API – Usage Records](https://dashboard.talordata.com/scraping/serp-api/history).
  {% endstep %}
  {% endstepper %}

For further assistance, please contact us via **online customer support** or email **<support@talordata.com>**.


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.talordata.com/serp-api/integration/mcp-integration/langchain-integration.md?ask=<question>&goal=<endgoal>
```

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