Anthropic Python SDK v0.105.0 Adds Claude Opus 4-8 Support and Mid-Conversation System Blocks

Anthropic released version 0.105.0 of its Python SDK on May 28, 2026, adding support for the Claude Opus 4-8 model alongside two notable API enhancements.

Key Changes

The SDK now supports Claude Opus 4-8, though Anthropic has not yet disclosed specifications or pricing for this model variant. The "4-8" designation suggests a possible April 2024 snapshot or version identifier, following Anthropic's recent naming conventions.

The update introduces mid-conversation system blocks, allowing developers to inject system-level instructions at any point during a conversation rather than only at the start. This capability enables dynamic context adjustments without restarting conversations.

The API now returns usage.output_tokens_details, providing granular breakdowns of token consumption in responses. This data helps developers track costs more precisely when using models with variable pricing for different output types.

Additional Features

The SDK added support for custom file size caps, giving developers control over upload limits when processing documents or images through Claude's multimodal capabilities.

Anthropic also renamed "private-sandbox-worker" to "self-hosted-sandbox-worker" in managed agents examples, clarifying the deployment model for code execution environments.

What This Means

The Claude Opus 4-8 reference represents either a new model variant or a specific snapshot not previously available through the SDK. Anthropic's consistent release pattern suggests Opus 4 is receiving iterative updates, though the company has not confirmed whether 4-8 offers performance improvements over earlier Opus 4 versions.

Mid-conversation system blocks address a significant limitation in conversational AI development. Developers can now modify model behavior mid-session for use cases like progressive assistance or context-sensitive guardrails without conversation history loss.

The enhanced token usage details arrive as pricing transparency becomes critical for production deployments, particularly with models that charge different rates for cached versus generated tokens.