The official documentation is pretty good. In a nutshell, you need to create a zip file of a directory containing both the code of your lambda function and all external libraries you use at the top level.

You can simulate that by deactivating your virtualenv, copying all your required libraries into the working directory (which is always in sys.path if you invoke a script on the command line), and checking whether your script still works.

It sounds like you're encountering some common issues with Lambda layers. Let's go through a few potential reasons why your Lambda function might not be finding the correct folder for your dependencies: 1. Incorrect folder structure: When creating a layer for Python dependencies, the correct structure is crucial. The packages should be in a folder named 'python' at the root of your zip file, and within that, they should be in the appropriate site-packages directory. For example: python/lib/python3.x/site-packages/. 2. Python version mismatch: Ensure that the Python version you're using to install the packages matches the runtime of your Lambda function. If you're using Python 3.13 in your layer structure, make sure your Lambda function is also set to use Python 3.13. 3. Layer size limits: Lambda has a maximum size limit for all layers combined when extracted. If your layer is too large, it might not be applied correctly. 4. Layer attachment: Make sure you've actually attached the layer to your Lambda function after creating it. 5. Permissions: Ensure your Lambda function has the necessary permissions to access the layer. To troubleshoot: 1. Double-check your folder structure in the zip file. 2. Verify that the Python version in your layer matches your Lambda runtime. 3. Check the size of your layer - if it's very large, consider optimizing it. 4. Confirm that the layer is attached to your function. 5. Review the function's execution role to ensure it has permissions to use the layer. If you're still having issues, you might consider alternative approaches: 1. Include the dependencies directly in your Lambda deployment package instead of using a layer. 2. For larger dependencies, consider using Lambda container images, which have a much higher size limit (up to 10GB). Remember, while layers can be useful for managing dependencies, they're not always necessary. For simpler use cases, including dependencies directly in your deployment package can be more straightforward. **Sources** Unable to import module 'lambda_function': No module named 'requests | AWS re:Post Lambda Package Exceeds 60MB: Solutions for Large Dependencies? | AWS re:Post Packaging your layer content - AWS Lambda Lambda function package not found error | AWS re:Post