# Bing Autocomplete Actor (`scraperoka/bing-autocomplete-actor`) Actor 🎭 Bing Autocomplete Actor predicts and fetches autocomplete suggestions from Bing—fast, accurate, and automation-ready. 🚀 Perfect for SEO research, keyword discovery, content planning, and building smarter search workflows. - **URL**: https://apify.com/scraperoka/bing-autocomplete-actor.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** SEO tools, AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### Bing Autocomplete Scraper 🎯 Manually guessing what people type into Bing is slow and inconsistent. **Bing Autocomplete Scraper** pulls Bing autocomplete suggestions for your query in bulk—perfect for marketers, keyword researchers, and growth teams—so you can mine hundreds to thousands of suggestion ideas in minutes. --- ### What You Get: Sample Output Here's a sample record from a single run: ```json { "query": "apple watch", "suggestion_01": "apple watch series 9", "suggestion_02": "apple watch deals", "suggestion_03": "apple watch vs", "suggestion_04": "apple watch bands", "suggestion_05": "apple watch battery life" } ```` In a single run, the actor returns an array of records like the one above for the original query, and—if enabled—for prefix and/or suffix variations. #### Output Fields | Field | Type | What It Tells You | |---|---|---| | `query` | string | The exact query that was used to fetch autocomplete suggestions. | | `suggestion_01` | string | The first autocomplete suggestion for that query. | | `suggestion_02` | string | The second suggestion (useful for quickly expanding keyword lists). | | `suggestion_03` | string | A third suggestion you can add to content or campaign topic research. | | `suggestion_04` | string | Another suggestion variant for ideation. | | `suggestion_05` | string | A suggestion that often reveals intent phrasing from real user inputs. | | `suggestion_06` | string | Additional suggestion entries (the actor generates multiple suggestion fields). | | `suggestion_07` | string | More autocomplete ideas for broader keyword mining. | | `suggestion_08` | string | Further suggestion options to expand research coverage. | | `suggestion_09` | string | More suggestions to power clustering and prioritization. | | `suggestion_10` | string | More results you can use for topic mapping and briefs. | | `error_message` | (not included) | The actor logs failures internally and returns empty suggestions on request issues rather than adding an `error_message` field to each record. | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Bing Autocomplete Scraper? There are a lot of ways to pull autocomplete-style keyword ideas from web sources—here’s what sets **Bing Autocomplete Scraper** apart. #### Built for autocomplete keyword mining **Bing Autocomplete Scraper** is designed specifically to pull autocomplete suggestions for a given query, making it ideal for “Bing suggest keywords extraction” workflows and faster “autocomplete list extractor” research. #### Input flexibility with query expansion You can enable `use_prefix` and/or `use_suffix` to generate additional queries around your base term, which helps uncover more “related searches autocomplete” style variations without manual trial-and-error. #### Structured, analysis-friendly output The output records keep the originating `query` together with suggestion fields (`suggestion_01`, `suggestion_02`, etc.), so it’s easy to load into spreadsheets, BI tools, or downstream keyword clustering pipelines. #### Built-in proxy support for reliable scraping This Bing suggest scraper includes configurable proxy support (via `proxyConfiguration`) to help keep collection stable for repeated or higher-volume runs—useful when you’re scraping Bing autocomplete lists at scale. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "query": "apple watch", "use_prefix": false, "use_suffix": false, "proxyConfiguration": {} } ``` #### Input Parameters | Parameter | Required | What It Does | |---|---:|---| | `query` | ✅ | The search term you want autocomplete suggestions for (example default: `"apple watch"`). | | `use_prefix` | ⬜ | If enabled, the actor adds alphabetic prefixes to your query to generate additional suggestion runs. | | `use_suffix` | ⬜ | If enabled, the actor adds alphabetic suffixes to your query to generate additional suggestion runs. | | `proxyConfiguration` | ⬜ | Configure proxies for this Actor run (you can supply proxy settings here). | | ↳ `proxy support` | ⬜ | When set to `true` inside `proxyConfiguration`, the actor uses Apify Proxy support for requests. | *** ### Core Capabilities #### Query-based autocomplete extraction You provide a `query`, and **Bing Autocomplete Scraper** fetches the autocomplete suggestions for that term, giving you a clean starting list for “Bing autocomplete data scraper” style keyword research. #### Optional prefix and suffix expansion With `use_prefix` and `use_suffix`, the actor generates additional queries by adding alphabetic prefixes/suffixes to your original term—helpful for “Bing autocomplete scraping software” use cases where you want broader coverage. #### Output completeness as structured records Each record includes the originating `query` and multiple suggestion fields in a consistent schema (`suggestion_01`, `suggestion_02`, etc.), so your results are easy to transform and analyze. #### Resilience with request handling If a request fails for any query, the actor logs the error and returns an empty list of suggestions for that particular query run—so you still get a predictable output structure for successful queries. #### Automation-ready dataset writing Results are pushed to the default Apify dataset, making it straightforward to export, visualize, or feed into your automation pipeline after each run. *** ### Who Gets the Most Out of This Here’s how different teams put **Bing Autocomplete Scraper** to work: **SEO strategists and content marketers** — Use the scraped suggestions to discover real-world phrasing people type into Bing, then turn those ideas into search-intent-focused outlines and FAQ sections. **Keyword researchers** — When you need “Bing autosuggest scraper tool” outputs for ideation and clustering, you can expand beyond a single seed query with optional prefix and suffix runs to grow a richer keyword set faster. **Growth teams running experiments** — Build hypotheses based on autocomplete variants and related intent signals, then prioritize the most promising topics for landing pages, ads, or internal campaigns. **Freelance researchers and analysts** — Deliver structured autocomplete keyword datasets to clients, then export results from the Apify dataset as JSON, CSV, or Excel for further processing. **Developers and data engineers** — Integrate this “Bing suggest API scraper” style workflow into their pipeline by triggering the actor and consuming the dataset output programmatically via the Apify API. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run **Bing Autocomplete Scraper** from start to finish: 1. **Open the actor on Apify** — go to [console.apify.com](https://console.apify.com) and search for **Bing Autocomplete Scraper**. 2. **Enter your inputs** — set `query`, and optionally toggle `use_prefix` and `use_suffix` in the input panel. 3. **Configure proxy settings** — use the `proxyConfiguration` input if you want proxy support for more reliable runs. 4. **Hit Run and watch the live log** — review the progress messages while the actor fetches suggestions. 5. **View results in the dataset tab** — collected suggestions appear as records in the dataset. 6. **Export as JSON, CSV, or Excel** — download your results directly from the Apify dashboard. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, **Bing Autocomplete Scraper** plugs directly into your existing workflow. You can export the dataset as **JSON, CSV, or Excel** from the Apify dataset tab, which makes this Bing suggest crawler output easy to use in spreadsheets and analysis tools. For automation and programmatic pipelines, you can pull results via the **Apify API** (documentation: https://apify.com/docs/api), connect runs with no-code tools like **Zapier** or **Make**, and use **webhooks** to trigger downstream actions when a run completes. *** ### Pricing & Free Trial **Bing Autocomplete Scraper** runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You’ll typically pay for usage based on Apify platform compute units (CU), and you can scale up when your keyword mining needs grow. For exact pricing details, check the Apify pricing page for the most up-to-date information. Start for free at [apify.com](https://apify.com) and scale when you're ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Rate limiting and stability | Built with reliable request execution and resilient handling. | | Proxy reliability (optional) | Supports proxy configuration to improve run stability for higher volume scraping. | | Failed requests | Request issues are handled by logging and returning empty suggestions for the affected query. | | Output usability | Results are pushed to the dataset in a structured format for easy export and analysis. | | Scale across queries | Supports expanding beyond a single query via `use_prefix` and `use_suffix`. | Limitations: this actor only extracts autocomplete suggestions and does not provide deeper page-level scraping beyond what’s returned as suggestions. If a request fails for a given query, that query will return no suggestions. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial for Bing Autocomplete Scraper? Yes. Apify offers a free tier with credits on sign-up, which is enough for several test runs depending on your usage. #### Do I need to log in to Bing to use this? No. You only need to provide a `query` (and optionally `use_prefix`, `use_suffix`, and `proxyConfiguration`) to run Bing autocomplete keyword extraction. #### How accurate is the autocomplete data? The results reflect the autocomplete suggestions returned for the queries you submit. Accuracy depends on what the source provides at the time of the run. #### How many results can I get per run? The actor generates multiple `suggestion_XX` fields per query based on the returned suggestions list. The exact number depends on what the autocomplete source returns for each query. #### How often is the data updated / how fresh is it? Freshness depends on when you run the actor. Each run fetches suggestions live for the given `query` (and any prefix/suffix expansions you enable). #### Is this legal? Does it comply with GDPR / CCPA? Using **publicly available data** is generally the right approach, but compliance (including GDPR/CCPA and platform terms) is ultimately your responsibility. Only use the results for lawful purposes and ensure your workflows match your legal requirements. #### Can I export results to Google Sheets or Excel? Yes. You can export from the Apify dashboard as JSON, CSV, or Excel, and then import into tools like spreadsheets as needed. #### Can I run this on a schedule automatically? Yes. You can automate runs using Apify scheduling and trigger-based workflows so your Bing autocomplete scraper runs on a cadence without manual effort. #### Can I access Bing Autocomplete Scraper via API? Yes. You can trigger the actor and retrieve results programmatically via the Apify API (documentation: https://apify.com/docs/api). #### What happens if the actor hits an error? If a request fails for a given query, the actor logs the error and returns an empty suggestions list for that query, while continuing to handle the overall run flow. *** ### Need Help or Have a Request? Got a question about Bing Autocomplete Scraper or want a new feature added? Reach out at . We respond quickly and actively maintain this actor based on user feedback. If you want ideas like webhook notifications on completion or expanded query controls, tell us what you’re trying to accomplish. *** ### Disclaimer & Responsible Use *Bing Autocomplete Scraper is the fastest, most reliable way to collect Bing autocomplete suggestions for keyword research — start your free run today.* This actor collects **publicly available data** and does not access private accounts, login-gated content, or password-protected pages. You are responsible for ensuring your use complies with GDPR, CCPA, and applicable platform Terms of Service. For data-removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `query` (type: `string`): The search term to get suggestions for. ## `use_prefix` (type: `boolean`): Whether to add alphabetic prefixes to the query. ## `use_suffix` (type: `boolean`): Whether to add alphabetic suffixes to the query. ## `proxyConfiguration` (type: `object`): Configure proxies for this Actor. ## Actor input object example ```json { "query": "apple watch", "use_prefix": false, "use_suffix": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/bing-autocomplete-actor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("scraperoka/bing-autocomplete-actor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call scraperoka/bing-autocomplete-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/bing-autocomplete-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Bing Autocomplete Actor", "description": "🎭 Bing Autocomplete Actor predicts and fetches autocomplete suggestions from Bing—fast, accurate, and automation-ready. 🚀 Perfect for SEO research, keyword discovery, content planning, and building smarter search workflows.", "version": "1.0", "x-build-id": "DvbBQmjqYkvk1FMbt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~bing-autocomplete-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-bing-autocomplete-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~bing-autocomplete-actor/runs": { "post": { "operationId": "runs-sync-scraperoka-bing-autocomplete-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~bing-autocomplete-actor/run-sync": { "post": { "operationId": "run-sync-scraperoka-bing-autocomplete-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "query" ], "properties": { "query": { "title": "Query", "type": "string", "description": "The search term to get suggestions for.", "default": "apple watch" }, "use_prefix": { "title": "Use Prefix", "type": "boolean", "description": "Whether to add alphabetic prefixes to the query.", "default": false }, "use_suffix": { "title": "Use Suffix", "type": "boolean", "description": "Whether to add alphabetic suffixes to the query.", "default": false }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxies for this Actor." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```