> ## Documentation Index > Fetch the complete documentation index at: https://docs.llmcontrols.ai/llms.txt > Use this file to discover all available pages before exploring further. # Publish flows LLM Controls provides flexible ways to publish your flows and integrate them into external applications. Whether you want to expose your flow as a secure API, embed it on a website, or share it with your team or organization, this guide walks you through all options. ### **API Access** You can access your flow programmatically using REST API calls. LLM Controls supports integration using Python, JavaScript (fetch), and cURL. API ### **Generating Your API Key** To authenticate API requests, you need a valid LLM Controls API key. Follow the steps below to create one: 1. Go to the **Settings** panel in your LLM Controls dashboard. 2. In the left sidebar, click on **“LLM Controls API Keys”**. 3. Click the **“Add New”** button in the top-right corner. 4. Give your key a name (e.g., my\_project\_key) and save it. 5. Your new API key will appear in the list. Make sure to **copy it immediately** ,this is the only time it will be fully visible. **Important** Keep your API key secure. Do not expose it in frontend code or share it publicly. Use environment variables or server-side storage to manage keys safely. Once created, you can use this key in your API requests by setting it as the x-api-key header. ### How to Use the API Depending on your preferred language, follow the example below ```python theme={null} import requests import os import json # Get API key from environment variable api_key = os.getenv("LLMC_API_KEY") if not api_key: raise ValueError("LLMC_API_KEY environment variable not found. Please set your API key in the environment variables.") url = "https://api.llmcontrols.ai/api/v1/run/9a453fad-2bb2-474f-a541-78dad816d45f?stream=false" headers = { "Content-Type": "application/json", "x-api-key": os.environ["LLMC_API_KEY"] # Authentication key from environment variable } payload = { "input_value": "hello world!", "output_type": "chat", "input_type": "chat" } try: # Make the API request response = requests.post(url, json=payload, headers=headers) response.raise_for_status() # Raise an exception for HTTP errors # Process the response data = response.json() print("Initial response:", json.dumps(data, indent=2)) except requests.exceptions.RequestException as e: print("Error making request:", e) except json.JSONDecodeError as e: print("Error decoding response:", e) ``` ```javascript theme={null} // Get API key from environment variable const apiKey = process.env.LLMC_API_KEY; if (!apiKey) { throw new Error("LLMC_API_KEY environment variable not found. Please set your API key in the environment variables."); } const url = "https://api.llmcontrols.ai/api/v1/run/9a453fad-2bb2-474f-a541-78dad816d45f?stream=false"; const headers = { "Content-Type": "application/json", "x-api-key": process.env.LLMC_API_KEY // Authentication key from environment variable }; const payload = { "input_value": "hello world!", "output_type": "chat", "input_type": "chat" }; fetch(url, { method: "POST", headers: headers, body: JSON.stringify(payload) }) .then(response => response.json()) .then(data => { console.log("Initial response:", data); }) .catch(error => console.error("Error:", error)); ``` ```bash theme={null} curl --request POST \ --url 'https://api.llmcontrols.ai/api/v1/run/9a453fad-2bb2-474f-a541-78dad816d45f?stream=false' \ --header 'Content-Type: application/json' \ --header "x-api-key: $LLMC_API_KEY" \ --data '{ "input_value": "hello world!", "output_type": "chat", "input_type": "chat" }' ``` ## Temporary Overrides Each flow has customizable input parameters that you can override: * These input fields are part of the payload (e.g. input\_value) * You can override them via the API or directly through the UI * To add more override options, add input boxes in the UI and connect them to relevant components Overrides are not persistent, but they are applied to that particular API run. ## **Execution Modes: Static vs. Polling** LLM Controls lets you run flows in two modes based on your needs: **Static (synchronous)** or **Polling (asynchronous)**. You can control this using the sync query parameter in the URL. ### **Overview** | **Mode** | **Query Param** | **Use Case** | **Simplicity** | **Suitable For Complex Workflow** | | :---------- | :-------------- | :----------------------------------- | :------------- | :-------------------------------- | | **Static** | stream=false | Short flows, real-time responses | Yes | No | | **Polling** | sync=false | Long-running flows, background tasks | No | Yes | ### **Static Mode (stream=false)** In static mode, the API **waits for the full result** before returning the response. **When to use:** * Short or fast-executing flows * Real-time user interaction (e,.g. chat widgets) **Example Request:** ```text theme={null} POST https://api.llmcontrols.ai/api/v1/run/YOUR_FLOW_ID?stream=false Content-Type: application/json x-api-key: YOUR_API_KEY { "input_value": "your message", // For chat inputs "input_type": "chat", // or "text" based on input type "output_type": "chat", // or "text" based on output type "tweaks": { // Optional tweaks object "component_id": { "parameter": "value" } } } ``` **Example Response:** ```text theme={null} { "result": "Here's the response...", // Additional response data based on your flow's output } ``` ### **Polling Mode (sync=false)** In polling mode, the API returns immediately with a task\_id and task\_url. You must poll this URL to retrieve the final result once it's ready. **When to use:** * Long-running flows * Flows that perform multi-step background operations * When using external data sources or embeddings **Initial Request:** ```text theme={null} POST https://api.llmcontrols.ai/api/v1/run/YOUR_FLOW_ID?sync=false Content-Type: application/json x-api-key: YOUR_API_KEY { "input_value": "your message", // For chat inputs "input_type": "chat", // or "text" based on input type "output_type": "chat", // or "text" based on output type "tweaks": { // Optional tweaks object "component_id": { "parameter": "value" } } } ``` **Initial Response:** ```text theme={null} { "task_id": "abc123", "message": "Task started. Check task_url for updates.", "task_url": "https://api.llmcontrols.ai/api/v1/task/abc123" } ``` **Polling Request:** ```text theme={null} GET https://api.llmcontrols.ai/api/v1/task/abc123 x-api-key: YOUR_API_KEY ``` **Polling Response:** ```text theme={null} { "status": "COMPLETED", // or "PENDING", "FAILED" "result": { // Flow output data } } ``` ### **Best Practices** * Use **static mode** for user-facing interactions. * Use **polling mode** when a flow takes more than 10 seconds. * Always check status in polling responses (PENDING, RUNNING, COMPLETED, FAILED). ### Embed into Site You can embed your flow as a chat widget or a full-screen application. #### Features: * Supports two-way communication between frontend and backend * Can be embedded in HTML, React, or other frontend frameworks * Full control over the input/output elements * Styling customization available (light/dark themes, widget position) #### Usage Example: ```text theme={null} ```