aioboto3

aioboto3 lets Python asyncio applications use boto3’s familiar high-level resource APIs (the DynamoDB Table object, S3 Bucket helpers, and similar) in an async/await style, rather than only the lower-level client calls that aiobotocore already asyncifies. It wraps boto3’s resource layer, patching the parts that don’t naturally translate to async — S3 upload/download/copy (normally handled by the s3transfer module), DynamoDB’s batch_writer, resource waiters, and lazily-loaded resource properties — so consumers get boto3-shaped code paths (Bucket.objects.all(), Table.query()) that await cleanly inside an event loop.

Sessions, clients, and resources must be created and used as async context managers (async with session.resource("s3") as s3), a requirement introduced once the underlying aiobotocore dependency needed to support things like bucket-region redirection and web-identity-based role assumption. The library also adds basic Amazon S3 client-side encryption support (KMS-managed keys, AES/GCM or AES/CBC) that boto3 itself doesn’t provide, generating output compatible with the Java Encryption SDK.

What You Get

• Async context-manager-based session/client/resource creation matching boto3’s resource API shape
• Async-patched S3 transfer operations (upload_file, download_file, copy) that normally rely on the sync s3transfer module
• An async DynamoDB batch_writer context manager and awaitable resource waiters
• Basic S3 client-side encryption (KMS-managed keys, AES/GCM or AES/CBC) not available in boto3 itself
• A documented table of tested-and-working AWS resources (DynamoDB, S3, Kinesis, SSM, Athena)

Common Use Cases

• Using boto3’s DynamoDB Table resource object (query, put_item, batch_writer) inside async microservices
• Streaming large S3 uploads/downloads from an asyncio application without blocking the event loop
• Adding client-side encryption to S3 objects from Python when boto3 alone doesn’t support it
• Migrating existing boto3-resource-based code to an asyncio codebase with minimal API-shape changes

Under The Hood

Architecture: aioboto3 is structured as a thin wrapper layer over aiobotocore, exposing an aioboto3.Session whose .client()/.resource() calls must be used as async context managers so that connection lifecycle (and things like bucket-region redirection or web-identity role assumption) can be handled correctly. Where boto3’s resource layer depends on synchronous helper modules that don’t translate directly to async — most notably s3transfer for S3 upload/download/copy — aioboto3 monkey-patches those code paths with its own async-compatible implementations (custom multipart upload, get_object-based copy) rather than reimplementing the AWS API surface from scratch.

Tech Stack: Pure Python, layered directly on boto3 and aiobotocore as its core dependencies, with an optional s3cse extra pulling in the cryptography library for client-side encryption. Packaging uses pyproject.toml with a uv.lock file, indicating a modern Python packaging/dependency-management setup alongside the long-lived codebase.

Code Quality: The tests/ directory covers the core surfaces directly (test_basic.py, test_s3.py, test_dynamo.py, test_s3_cse.py, test_patches.py, and an experimental Chalice integration test), and CI runs via GitHub Actions per the README badge. With 47 contributors but a heavily maintainer-driven commit history and low current commit velocity (0 commits/month per the latest snapshot, though still receiving periodic releases), this reads as a mature, stable wrapper library rather than one under active feature development — consistent with a project that has already covered its target AWS resource surface.

API Design: The library deliberately mirrors boto3’s own resource API shape (session.resource('dynamodb'), table.query(), bucket.objects.all()) so existing boto3 knowledge transfers almost directly, with the main added ceremony being the async context-manager requirement for session/client/resource creation — a real but well-documented boilerplate cost given the breaking change history called out prominently in the README.