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Releases: aws-powertools/powertools-lambda-python

v3.20.0

09 Sep 16:57
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v3.20.0
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Summary

In this release we are excited to announce the new build recipes documentation for Powertools for AWS Lambda Python, which explains how to handle Rust and C bindings libraries, GLIBC compatibility, cross-platform builds, and architecture-specific dependencies.

We also improved the Parser utility models by adding detailed descriptions and examples illustrating AWS Lambda event structures.

⭐️ Congrats to @dcabib, @sreejaaryahi18, @Liam-DeVoe and @shrivarshapoojari for contributing to this project for the first time!

The build recipes documentation

Docs

The Python ecosystem is evolving with more Rust and C bindings for better performance, introducing new Lambda deployment challenges like GLIBC compatibility and cross-platform builds.

We've published updated Powertools build recipes that cover:

  • Build tools: pip, Poetry, SAM, CDK, Pants
  • Cross-platform deployment for macOS/Windows → Linux Lambda
  • GLIBC compatibility across Amazon Linux runtime versions
  • Troubleshooting common issues
  • Performance tips to reduce cold starts and package size
  • CI/CD examples with GitHub Actions and CodeBuild
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Changes

  • refactor(event_handler): allow to pass dict as argument to exception classes (#7341) by @shrivarshapoojari
  • refactor(parser): improves S3 models with examples and descriptions (#7292) by @ivica-k
  • refactor(parser): Improve Transfer Family models with examples and descriptions (#7294) by @dcabib
  • refactor(parser): Improve Kafka models with examples and descriptions (#7293) by @dcabib
  • refactor(parser): Improve SQS models with examples and descriptions (#7286) by @dcabib
  • refactor(parser): Improve SNS models with examples and descriptions (#7287) by @dcabib
  • refactor(parser): Improve VPC Lattice with examples and descriptions (#7234) by @anafalcao
  • refactor(parser): Improve DynamoDB models with examples and descriptions (#7146) by @sreejaaryahi18

🌟New features and non-breaking changes

📜 Documentation updates

🐛 Bug and hot fixes

  • fix(shared/functions): fix slice_dictionary implementation (#7246) by @Liam-DeVoe

🔧 Maintenance

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Contributors

leandrodamascena, phipag, and 9 other contributors
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v3.19.0

14 Aug 09:19
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v3.19.0
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v3.19.0

Summary

This release adds support for fetching multiple secrets from AWS Secrets Manager in a single API call.

We also fixed a critical bug when working with Data validation and Middleware in our Event Handler utility.

⭐️ Congrats to @dap0am and @trevmt for contributing to this project for the first time!

Fetching multiple secrets

Docs

You can fetch multiple secrets from Secrets Manager in a single API call using get_secrets_by_name. This reduces the number of API calls and improves performance when you need to retrieve several secrets at once. This new method allows you to retrieve up to 20 secrets per call with filtering, lower AWS costs, and improved error resilience.

secrets

Working with Data Validation and Middleware

We have split OpenAPI validation into two phases, so you now benefit from:

  • Request validation runs before any business logic, checking only incoming requests.
  • Response validation runs only on actual endpoint responses.

This change ensures that only real endpoint responses go through schema validation, preventing errors from control flow responses (401, 403, 429) returned by other middlewares.

Read more about this change here

Changes

🌟New features and non-breaking changes

📜 Documentation updates

  • docs(logger): fix typo in sampling examples (#7133) by @trevmt
  • docs(maintainers): improve release process documentation (#7088) by @leandrodamascena
  • docs(event_handler): improve routing rules syntax documentation (#7094) by @dap0am

🐛 Bug and hot fixes

  • fix(parameters): fix _transform_and_cache_get_parameters_response (#7083) by @ericbn
  • fix(event_handler): split OpenAPI validation to respect middleware returns (#7050) by @leandrodamascena

🔧 Maintenance

This release was made possible by the following contributors:

@dap0am, @dependabot[bot], @ericbn, @github-actions[bot], @leandrodamascena, @trevmt, dependabot[bot] and github-actions[bot]

Contributors

ericbn, leandrodamascena, and 3 other contributors
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v3.18.0

29 Jul 16:59
@github-actions github-actions
v3.17.1
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v3.18.0

Summary

This release adds support for files media types in our OpenAPI utility. We also improved our Parser utility by adding new models for AppSync Events.

⭐️ Congrats to @oyiz-michael and @sdangol for contributing to this project for the first time!

Working with form data

Docs

You can use the Form type to tell the Event Handler that a parameter expects form data. This automatically sets the correct OpenAPI schema for application/x-www-form-urlencoded requests.

carbon (13)

Changes

🌟New features and non-breaking changes

📜 Documentation updates

🔧 Maintenance

This release was made possible by the following contributors:

@DustinAlandzes, @dependabot[bot], @github-actions[bot], @kattakaha, @leandrodamascena, @oyiz-michael, @sdangol, dependabot[bot] and github-actions[bot]

Contributors

leandrodamascena, DustinAlandzes, and 4 other contributors
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v3.17.0

15 Jul 08:47
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v3.17.0
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v3.17.0

Summary

This release enhances the Parser experience with new schemas for Amazon Cognito User Pool triggers. We also added support for externalDocs parameter in our OpenAPI utility.

⭐️ Thanks to @VatsalGoel3 for working on all the new schemas added in this release, as well as @mhindery and @kimsappi for their first contributions!

New Cognito Trigger Schemas

Docs

You can now use one of the new built-in schemas from Parser to easily work with events coming from Amazon Cognito to your Lambda function. With Amazon Cognito User Pools, you can configure triggers to invoke AWS Lambda functions, modifying the authentication behavior.

Below is a list of schemas we’ve added:

  • CognitoCreateAuthChallengeTriggerModel
  • CognitoCustomEmailSenderTriggerModel
  • CognitoCustomMessageTriggerModel
  • CognitoCustomSMSSenderTriggerModel
  • CognitoDefineAuthChallengeTriggerModel
  • CognitoMigrateUserTriggerModel
  • CognitoPostAuthenticationTriggerModel
  • CognitoPostConfirmationTriggerModel
  • CognitoPreAuthenticationTriggerModel
  • CognitoPreSignupTriggerModel
  • CognitoPreTokenGenerationTriggerModelV1
  • CognitoPreTokenGenerationTriggerModelV2AndV3
  • CognitoVerifyAuthChallengeTriggerModel

For more information, refer to the Amazon Cognito User Pools documentation.

Changes

🌟New features and non-breaking changes

  • feat(parser): Added Cognito trigger schemas (#6737) by @VatsalGoel3
  • feat(event_handler): add support for externalDocs attribute in OpenAPI schema (#6945) by @mhindery

📜 Documentation updates

🐛 Bug and hot fixes

  • fix: enable response compression when using multi-value headers (#6936) by @kimsappi

🔧 Maintenance

This release was made possible by the following contributors:

@VatsalGoel3, @dependabot[bot], @dreamorosi, @github-actions[bot], @kimsappi, @leandrodamascena, @mhindery, @ran-isenberg, dependabot[bot] and github-actions[bot]

Contributors

leandrodamascena, dreamorosi, and 5 other contributors
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v3.16.0

04 Jul 12:46
@github-actions github-actions
v3.16.0
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v3.16.0

Summary

We are thrilled to announce that Powertools for AWS Lambda (Python) now offers AWS Lambda layers in the AWS China region cn-north-1.

New AWS China Lambda Layer ARNs

You can now use Powertools for AWS Lambda (Python) Lambda layers in the AWS China regions, enhancing customer experience by making our pre-packaged Lambda layers available in this AWS partition.

To take advantage of the new layers, you need to update your functions or deployment setup to include one of the new Lambda layer ARN from the table below:

AWS China (cn-north-1)

Architecture Layer ARN
x86_64 arn:aws-cn:lambda:cn-north-1:498634801083:layer:AWSLambdaPowertoolsPythonV3-{python_version}-x86_64:{version}
ARM arn:aws-cn:lambda:cn-north-1:498634801083:layer:AWSLambdaPowertoolsPythonV3-{python_version}-arm64:{version}

Changes

🌟New features and non-breaking changes

  • feat(ci): Partition workflow scripts updated to work (#6900) by @sthulb
  • feat(ci): Deploy to AWS China partitions (#6867) by @sthulb

📜 Documentation updates

🐛 Bug and hot fixes

🔧 Maintenance

This release was made possible by the following contributors:

@amin-farjadi, @dependabot[bot], @dreamorosi, @github-actions[bot], @kimsappi, @leandrodamascena, @sthulb, @tonnico, dependabot[bot] and github-actions[bot]

Contributors

sthulb, leandrodamascena, and 5 other contributors
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v3.15.1

02 Jul 09:43
@leandrodamascena leandrodamascena
v3.15.1
062fcc4
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v3.15.1

Summary

In this release we fixed a problem when deserializing protobuf records with complex schemas.

Changes

🌟New features and non-breaking changes

📜 Documentation updates

🐛 Bug and hot fixes

🔧 Maintenance

This release was made possible by the following contributors:

@leandrodamascena

Contributors

leandrodamascena
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v3.15.0

20 Jun 16:27
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v3.15.0
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v3.15.0

Summary

We're excited to announce the Kafka Consumer utility, which transparently handles message deserialization, provides an intuitive developer experience, and integrates seamlessly with the rest of the Powertools for AWS Lambda ecosystem.

Key features

  • Automatic deserialization of Kafka messages (JSON, Avro, and Protocol Buffers)
  • Simplified event record handling with intuitive interface
  • Support for key and value deserialization
  • Support for Pydantic models and Dataclass output
  • Support for Event Source Mapping (ESM) with and without Schema Registry integration
  • Out of the box error handling for deserialization issues

Getting Started

To get started, depending on the schema types you want to use, install the library and the corresponding libraries:

For JSON schemas:

pip install aws-lambda-powertools

For Avro schemas:

pip install 'aws-lambda-powertools[kafka-consumer-avro]'

For Protobuf schemas:

pip install 'aws-lambda-powertools[kafka-consumer-protobuf]'

Additionally, if you want to use output serialization with Pydantic Models or Dataclases

Processing Kafka events

Docs

You can use Kafka consumer utility to transform raw Kafka events into an intuitive format for processing.

The @kafka_consumer decorator can deserialize both keys and values independently based on your schema configuration. This flexibility allows you to work with different data formats in the same message.

Working with Avro
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Working with Protobuf
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Working with JSON
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Custom output serializers

Docs

You can transform deserialized data into your preferred object types using output serializers. This can help you integrate Kafka data with your domain models and application architecture, providing type hints, validation, and structured data access.

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Error handling

Docs

You can handle errors when processing Kafka messages to ensure your application maintains resilience and provides clear diagnostic information.

We lazily decode fields like value, key, and headers only when accessed. This allows you to handle deserialization errors at the point of access rather than when the record is first processed.

carbon (12)

Changes

🌟New features and non-breaking changes

📜 Documentation updates

🐛 Bug and hot fixes

🔧 Maintenance

This release was made possible by the following contributors:

@dependabot[bot], @github-actions[bot], @leandrodamascena, @matteofigus, dependabot[bot] and github-actions[bot]

Contributors

matteofigus, leandrodamascena, and dependabot
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v3.14.0

04 Jun 08:28
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v3.14.0
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v3.14.0

Summary

This release introduces a new BedrockAgentFunctionResolver to Event Handler that simplifies connecting AWS Lambda functions to Amazon Bedrock Agents. This feature eliminates the need to write boilerplate code for parsing requests and formatting responses, allowing you to focus on your agent's business logic.

We would also like to extend a huge thank you to our new contributor @LucasCMFBraga ⭐

Creating Amazon Bedrock Agents

Docs

You can now use the new BedrockAgentFunctionResolver to register tools and handle requests in your Lambda functions. The resolver will automatically parse the request, route it to the appropriate function, and return a well-formed response that includes the tool's output and any existing session attributes.

bedrock_agent1

By default, errors are handled gracefully and returned to the agent with error type and message information, allowing the conversation to continue. This is useful when you want to let the LLM handle errors and reduce boilerplate error-handling code.

If you need more control over error scenarios, you can use BedrockFunctionResponse to customize the response and determine if the conversation should continue:

bedrock_error_response

You can also use the BedrockFunctionResponse when you want to enrich the response with session attributes or knowledge base configurations, or when you want the agent to re-prompt the user to provide additional information.

Changes

🌟New features and non-breaking changes

  • feat(bedrock_agent): add new Amazon Bedrock Agents Functions Resolver (#6564) by @anafalcao
  • feat(event_handler): enable support for custom deserializer to parse the request body (#6601) by @LucasCMFBraga

📜 Documentation updates

🐛 Bug and hot fixes

🔧 Maintenance

This release was made possible by the following contributors:

@LucasCMFBraga, @anafalcao, @dependabot[bot], @github-actions[bot], @hjgraca, @leandrodamascena, dependabot[bot] and github-actions[bot]

Contributors

hjgraca, leandrodamascena, and 3 other contributors
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v3.13.0

20 May 10:31
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v3.13.0
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v3.13.0

Summary

In this release, we renamed the Redis class to Cache in our Idempotency utility and added support for the valkey-glide library.

Thanks to our new contributors @AlisonVilela, @Artur-T-Malas, and @kiitosu, we also fixed bugs in our Event Source Data Class utility. ⭐🏅

Working with the new CachePersistenceLayer class

Docs

You can now use our CachePersistenceLayer classes, which are more generically named, in place of the previous Redis-specific classes when using the Idempotency feature.

For backward compatibility, we've maintained the old Redis class names. However, these are now marked as deprecated and will be removed in the next major version.

We've also added support for valkey-glide, providing more flexibility for your caching implementation needs.

glide

If you are using the RedisPersistenceLayer class in your codebase, you can use the new CachePersistenceLayer as a drop-in replacement.

Changes

🌟New features and non-breaking changes

  • feat(parser): add support to decompress Kinesis CloudWatch logs in Kinesis envelope (#6656) by @Artur-T-Malas
  • feat(event_source): add support for tumbling windows in Kinesis and DynamoDB events (#6658) by @kiitosu
  • feat(event_source): export SQSRecord in data_classes module (#6639) by @AlisonVilela

🔧 Maintenance

This release was made possible by the following contributors:

@AlisonVilela, @Artur-T-Malas, @dependabot[bot], @github-actions[bot], @kiitosu, @leandrodamascena, dependabot[bot] and github-actions[bot]

Contributors

leandrodamascena, AlisonVilela, and 3 other contributors
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v3.12.0

06 May 10:10
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v3.12.0
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v3.12.0

Summary

Thanks to @anafalcao, in this release we added support for additional response fields supported by Bedrock Agents when using the BedrockAgentResolver.

Additional response fields with BedrockAgentResolver

Docs

You can use the BedrockResponse class to add additional fields as needed, such as session attributes, prompt session attributes, and knowledge base configurations. These fields are useful when you want to persist attributes across multiple sessions, for example

bedrock_response

Changes

🌟New features and non-breaking changes

  • feat(bedrock_agents): add optional fields to response payload (#6336) by @anafalcao

📜 Documentation updates

🔧 Maintenance

This release was made possible by the following contributors:

@anafalcao, @dependabot[bot], @dreamorosi, @github-actions[bot], @leandrodamascena, @ran-isenberg, dependabot[bot] and github-actions[bot]

Contributors

leandrodamascena, dreamorosi, and 3 other contributors
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