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| --- | ||
| title: Langflow integration | ||
| sidebar_label: Langflow | ||
| description: Learn how to integrate Apify with Langflow low-code tool to build powerful AI agents and workflows that can use any API, model, or database. | ||
| sidebar_position: 1 | ||
| slug: /integrations/langflow | ||
| --- | ||
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| **Learn how to integrate Apify with Langflow to run complex AI agent workflows.** | ||
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| ## What is Langflow | ||
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| [Langflow](https://langflow.org/) is a low-code, visual tool that enables developers to build powerful AI agents and workflows that can use any API, models, or databases. | ||
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| :::note Explore Langflow | ||
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| For more information on Langflow, visit its [documentation](https://docs.langflow.org/). | ||
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| ::: | ||
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| ## How to use Apify with Langflow | ||
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| This guide will first demonstrate how to call Apify Actors by using the [RAG Web Browser](https://apify.com/apify/rag-web-browser) Actor to search Google for a query and extract the search results. Then, it will provide an example of using an AI agent workflow to search the web for a company's social media profiles and extract data from them. We will use the [Google Search Results Scraper](https://apify.com/apify/google-search-scraper) Actor to search the web for social media profiles of a given company. Then, we will use the [TikTok Data Extractor](https://apify.com/clockworks/free-tiktok-scraper) Actor to extract data from the TikTok profiles. | ||
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| :::note Cloud vs local setup | ||
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| Langflow can either be installed locally or used in the cloud. The cloud version is available on the [Langflow](http://langflow.org/) website. If you are using the cloud version, you can skip the installation step, and go straight to [Creating a new flow](#creating-a-new-flow) | ||
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| ::: | ||
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| ### Installation | ||
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| First, we need to install the Langflow platform using [uv](https://docs.astral.sh/uv/): | ||
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| ```bash | ||
| uv pip install langflow | ||
| ``` | ||
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| After successfully installing Langflow, we can start the platform: | ||
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| ```bash | ||
| uv run langflow run | ||
| ``` | ||
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| When the platform is started, open the [Langflow UI](http://127.0.0.1:7860) in your browser. | ||
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| > Other installation methods can be found in the [Langflow documentation](https://docs.langflow.org/get-started-installation). | ||
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| ### Creating a new flow | ||
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| First, we need to create a new flow. | ||
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| On the Langflow welcome screen, click the **New Flow** button: | ||
|  | ||
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| On the **New Flow** screen, click **Blank Flow**: | ||
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| Now, we can start building our flow. | ||
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| ### Calling Apify Actors in Langflow | ||
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| To call Apify Actors in Langflow, we need to add the **Apify Actors** component to the flow. | ||
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| From the bundle menu, add **Apify Actors** component: | ||
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| Next, we need to configure the Apify Actors components. First, input your API token (learn how to get it [here](https://docs.apify.com/platform/integrations/api)). Then, set the Actor ID of the component to `apify/rag-web-browser` to use the [RAG Web Browser](https://apify.com/apify/rag-web-browser). Set the **Run input** field to pass arguments to the Actor run, allowing it to search Google with the query `"what is monero?"` (an example Actor input can be found [here](https://apify.com/apify/rag-web-browser/input-schema)): | ||
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| ```json | ||
| {"query": "what is monero?", "maxResults": 3} | ||
| ``` | ||
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| Now, we can run the component by clicking the **Run** button. | ||
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| Once the run is finished, we can view the output by clicking the **Output** button. | ||
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| The output should look similar to this: | ||
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| To filter only the `metadata` and `markdown` fields, set **Output fields** to `metadata,markdown`. Additionally, enable **Flatten output?** by setting it to `true`. This will output only the metadata and text content from the search results. | ||
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| > Flattening is necessary when you need to access nested dictionary fields in the output data object; they cannot be accessed directly otherwise in the Data object. | ||
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| Now, the output contains only the `markdown` and flattened `metadata` fields: | ||
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| Now that we understand how to call Apify Actors, let's build a practical example where we search for a company's social media profiles and extract data from them. | ||
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| ### Building a flow to search for company's social media profiles | ||
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| In this example, we will build a flow that searches the web for a company's social media profiles and extracts data from them. | ||
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| Create a new flow and add two **Apify Actors** components from the menu. | ||
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| Input your API token (learn how to get it [here](https://docs.apify.com/platform/integrations/api)) and set the Actor ID of the first component to `apify/google-search-scraper` and the second one to `clockworks/free-tiktok-scraper`: | ||
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| Now add the *Agent* component from the menu and set your OpenAI API key (get it [here](https://platform.openai.com/account/api-keys)): | ||
| :::tip Optimize Agent results | ||
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| For better results, switch the model to `gpt-4o` instead of `gpt-4o-mini` in the Agent configuration | ||
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| ::: | ||
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| Then connect both Apify Actor components **Tool** outputs to the Agent component **Tools** input: | ||
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| Now add **Chat Input** and **Chat Output** components from the menu and connect them to the Agent component **Input** and **Response**: | ||
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| The final flow that can search the web for a company's social media profiles and extract data from them: | ||
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| Click the **Playground** button and chat with the Agent to test the flow: | ||
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| Here is an example Agent output for the following query: | ||
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| ```text | ||
| find tiktok profile of company openai using google search and then show me the profile bio and their latest video | ||
| ``` | ||
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