feat: Add S3 support for artifacts

[wmsnp]

Link to Issue or Description of Change

• Closes: Add S3 support to manage artifacts. adk-python#555
• Related:
  • feat: Add S3 support for artifacts adk-python#556
  • feat: Add S3 support for artifacts adk-python#3563

Description

Introduce S3ArtifactService to provide an self-hosted Artifact storage solution

Solution

• Supports asynchronous upload and download of S3 artifacts
• Includes unit tests covering core functionality
• Depends on aioboto3

Testing Plan

• I have added or updated unit tests for my change.
• All unit tests pass locally.

[gemini-code-assist]

Summary of Changes

Hello @wmsnp, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the artifact storage capabilities by integrating S3-compatible object storage. It provides a robust, self-hosted option for managing application artifacts, ensuring data persistence and version control. The new service is designed for high performance through asynchronous operations and includes comprehensive testing to guarantee reliability.

Highlights

• S3 Artifact Service Introduction: Introduced a new S3ArtifactService class to provide a self-hosted solution for storing and retrieving artifacts using Amazon S3 or S3-compatible services.
• Asynchronous Operations: The S3ArtifactService supports asynchronous upload and download of artifacts, leveraging aioboto3 for non-blocking I/O operations.
• Artifact Versioning and Metadata: Implemented atomic versioning for artifacts using S3's If-None-Match header, along with support for custom metadata which is JSON-serialized.
• Comprehensive Unit Tests: Added a new test file with extensive unit tests covering the core functionality of the S3ArtifactService, including saving, loading, deleting, and listing artifacts and their versions, as well as handling user-scoped artifacts.
• Dependency Update: Added aioboto3>=15.5.0 as a new dependency to pyproject.toml to enable asynchronous interaction with S3.

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[gemini-code-assist]

Code Review

This pull request introduces an S3ArtifactService to support artifact storage on S3-compatible services. The implementation is robust, featuring asynchronous operations and atomic versioning with a retry mechanism. The accompanying unit tests are comprehensive and effectively mock the S3 interactions. I've identified a few areas for improvement, including a performance optimization for listing artifact versions, adding a test case for the version conflict retry logic, and some minor code cleanup in the project dependencies and tests. Overall, this is a solid contribution.

[thedayisntgray]

Can we git this PR or the other one referenced merged?

Is there anything I can do to help speed this along?

[miyannishar]

Hey @wmsnp — I've opened #115 which supersedes both this PR and my earlier #36.

Your implementation here was a big influence — I adopted both the aioboto3 pattern for native async I/O and the IfNoneMatch conditional writes for atomic versioning. The key additions in the new PR are:

• Optional [s3] dependency group (so aioboto3 isn't required if you're not using S3)
• User-scoped artifact key listing with user: prefix preservation
• Comprehensive standalone test suite with full async mock infrastructure
• README and documentation

Thanks for pioneering the async approach — it clearly belongs in the final version. 🙏

[wmsnp]

Hey @wmsnp — I've opened #115 which supersedes both this PR and my earlier #36.

Your implementation here was a big influence — I adopted both the aioboto3 pattern for native async I/O and the IfNoneMatch conditional writes for atomic versioning. The key additions in the new PR are:

• Optional [s3] dependency group (so aioboto3 isn't required if you're not using S3)
• User-scoped artifact key listing with user: prefix preservation
• Comprehensive standalone test suite with full async mock infrastructure
• README and documentation

Thanks for pioneering the async approach — it clearly belongs in the final version. 🙏

Thanks — really appreciate that. One thing I found later, though, is that not all S3-compatible implementations support If-None-Match, so that part may need a bit of care for compatibility.

[DeanChensj]

@gemini-cli /review

[github-actions]

🤖 Hi @DeanChensj, I've received your request, and I'm working on it now! You can track my progress in the logs for more details.

[github-actions]

Thanks for the contribution! The S3ArtifactService implementation is solid and follows the established patterns. I've left a few comments regarding:

1. Performance: The list_artifact_versions method performs a head_object call for every version, which could be a bottleneck.
2. Metadata Limits: S3 has a 2KB limit on metadata that we should be aware of when flattening custom metadata.
3. Robustness: A finite default for retries in save_artifact might be safer than infinite.

Overall, great work!

[github-actions]

S3 metadata has a total size limit of 2 KB (including keys and values). Since custom_metadata is flattened into JSON strings here, large metadata dictionaries might cause put_object to fail. It might be worth adding a check or a note about this limit.

[github-actions]

With save_artifact_max_retries set to -1 (infinite), this loop could theoretically run forever if there's a consistent race condition or a logic error in version calculation. A high but finite default might be safer.

[github-actions]

Calling head_object for every version in a loop will be very slow if an artifact has many versions (O(N) network calls). S3 doesn't return custom metadata in list_objects_v2, so this might be necessary if metadata is required, but we should consider if there's a way to cache or avoid this for large version sets.