import ApiLink from '@site/src/components/ApiLink';

import CodeBlock from '@theme/CodeBlock';

import BeautifulSoupCrawlerLambda from '!!raw-loader!./code_examples/aws/beautifulsoup_crawler_lambda.py'; import PlaywrightCrawlerLambda from '!!raw-loader!./code_examples/aws/playwright_crawler_lambda.py'; import PlaywrightCrawlerDockerfile from '!!raw-loader!./code_examples/aws/playwright_dockerfile';

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. This guide covers deploying BeautifulSoupCrawler and PlaywrightCrawler.

The code examples are based on the BeautifulSoupCrawler example.

For simple crawlers that don't require browser rendering, you can deploy using a ZIP archive.

When instantiating a crawler, use MemoryStorageClient. By default, Crawlee uses file-based storage, but the Lambda filesystem is read-only (except for /tmp). Using MemoryStorageClient tells Crawlee to use in-memory storage instead.

Wrap the crawler logic in a lambda_handler function. This is the entry point that AWS will execute.

:::important

Make sure to always instantiate a new crawler for every Lambda invocation. AWS keeps the environment running for some time after the first execution (to reduce cold-start times), so subsequent calls may access an already-used crawler instance.

TL;DR: Keep your Lambda stateless.

:::

Finally, return the scraped data from the Lambda when the crawler run ends.

Lambda requires all dependencies to be included in the deployment package. Create a virtual environment and install dependencies:

python3.14 -m venv .venv
source .venv/bin/activate
pip install 'crawlee[beautifulsoup]' 'boto3' 'aws-lambda-powertools'

boto3 is the AWS SDK for Python. Including it in your dependencies is recommended to avoid version misalignment issues with the Lambda runtime.

Create a ZIP archive from your project, including dependencies from the virtual environment:

cd .venv/lib/python3.14/site-packages
zip -r ../../../../package.zip .
cd ../../../../
zip package.zip lambda_function.py

:::note Large dependencies?

AWS has a limit of 50 MB for direct upload and 250 MB for unzipped deployment package size.

A better way to manage dependencies is by using Lambda Layers. With Layers, you can share files between multiple Lambda functions and keep the actual code as slim as possible.

To create a Lambda Layer:

1. Create a python/ folder and copy dependencies from site-packages into it
2. Create a zip archive: zip -r layer.zip python/
3. Create a new Lambda Layer from the archive (you may need to upload it to S3 first)
4. Attach the Layer to your Lambda function

:::

Create the Lambda function in the AWS Lambda Console:

1. Navigate to Lambda in AWS Management Console.
2. Click Create function.
3. Select Author from scratch.
4. Enter a Function name, for example BeautifulSoupTest.
5. Choose a Python runtime that matches the version used in your virtual environment (for example, Python 3.14).
6. Click Create function to finish.

Once created, upload package.zip as the code source in the AWS Lambda Console using the "Upload from" button.

In Lambda Runtime Settings, set the handler. Since the file is named lambda_function.py and the function is lambda_handler, you can use the default value lambda_function.lambda_handler.

:::tip Configuration

In the Configuration tab, you can adjust:

• Memory: Memory size can greatly affect execution speed. A minimum of 256-512 MB is recommended.
• Timeout: Set according to the size of the website you are scraping (1 minute for the example code).
• Ephemeral storage: Size of the /tmp directory.

See the official documentation to learn how performance and cost scale with memory.

:::

After the Lambda deploys, you can test it by clicking the "Test" button. The event contents don't matter for a basic test, but you can parameterize your crawler by parsing the event object that AWS passes as the first argument to the handler.

For crawlers that require browser rendering, you need to deploy using Docker container images because Playwright and browser binaries exceed Lambda's ZIP deployment size limits.

As with BeautifulSoupCrawler, use MemoryStorageClient and wrap the logic in a lambda_handler function. Additionally, configure browser_launch_options with flags optimized for serverless environments. These flags disable sandboxing and GPU features that aren't available in Lambda's containerized runtime.

Install AWS CLI following the official documentation according to your operating system.

Authenticate by running:

aws login

Initialize the project by running uvx 'crawlee[cli]' create.

Or use a single command if you don't need interactive mode:

uvx 'crawlee[cli]' create aws_playwright --crawler-type playwright --http-client impit --package-manager uv --no-apify --start-url 'https://crawlee.dev' --install

Add the following dependencies:

uv add awslambdaric aws-lambda-powertools boto3

boto3 is the AWS SDK for Python. Use it if your function integrates with any other AWS services.

The project is created with a Dockerfile that needs to be modified for AWS Lambda by adding ENTRYPOINT and updating CMD:

Create a repository lambda/aws-playwright in Amazon Elastic Container Registry in the same region where your Lambda functions will run. To learn more, refer to the official documentation.

Navigate to the created repository and click the "View push commands" button. This will open a window with console commands for uploading the Docker image to your repository. Execute them.

Example:

aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin {user-specific-data}
docker build --platform linux/amd64 --provenance=false -t lambda/aws-playwright .
docker tag lambda/aws-playwright:latest {user-specific-data}/lambda/aws-playwright:latest
docker push {user-specific-data}/lambda/aws-playwright:latest

1. Navigate to Lambda in AWS Management Console.
2. Click Create function.
3. Select Container image.
4. Browse and select your ECR image.
5. Click Create function to finish.

:::tip Configuration

In the Configuration tab, you can adjust resources. Playwright crawlers require more resources than BeautifulSoup crawlers:

• Memory: Minimum 1024 MB recommended. Browser operations are memory-intensive, so 2048 MB or more may be needed for complex pages.
• Timeout: Set according to crawl size. Browser startup adds overhead, so allow at least 5 minutes even for simple crawls.
• Ephemeral storage: Default 512 MB is usually sufficient unless downloading large files.

See the official documentation to learn how performance and cost scale with memory.

:::

After the Lambda deploys, click the "Test" button to invoke it. The event contents don't matter for a basic test, but you can parameterize your crawler by parsing the event object that AWS passes as the first argument to the handler.