Pydantic AI v2.60.0 (2026-10-22)
Pydantic AI v2.60.0 adds DockerSandbox and BoxLiteSandbox workspace capabilities, updates ModelSelectionContext with usage and retry limits, strengthens security sanitization, and introduces several compatibility updates including deprecating RetryPromptPart.
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为什么值得关注
Pydantic AI release v2.60.0 introduces several new sandbox harness capabilities, including DockerSandbox (supporting local Docker or Podman containers), BoxLiteSandbox (a local BoxLite micro-VM workspace), and existing VM reuse in SmolSandboxBackend. Key compatibility updates and deprecations include: - Deprecation of RetryPromptPart in favor of ToolReturnPart(outcome='retried') and RetryFeedbackPart. - Embedding models now raise standard model error categories like ModelRateLimitError. - ModelSelectionContext now includes usage limits, deadlines, run ID, conversation ID, output retry counts, and retry budgets. - BedrockConverseModel defaults maxTokens to Claude's maximum output while streaming behind the scenes. - MCP capability default IDs are derived strictly from the server URL hostname, rejecting multiple servers on one host without explicit IDs. - OpenAIDecisionsProvider is deprecated in favor of passing OpenAIProvider to OpenAIDecisionsModel. - Security and sanitization hardening: stripping request-level instructions from untrusted history, dropping client-supplied TextContent.metadata from user prompts, case-insensitive FileSystem denial patterns, and protecting .git files.
可执行摘要
Pydantic AI v2.60.0 introduces DockerSandbox and BoxLiteSandbox workspaces, adds run metadata (usage limits, deadline, run/conversation ID, retry count/budget) to ModelSelectionContext, updates BedrockConverseModel defaults, and replaces RetryPromptPart with ToolReturnPart(outcome='retried') and RetryFeedbackPart.
- Agent 实用度
- 77/100
- 可信度
- 96%
- 机器格式
- JSON + Markdown
开发者应核对什么
- Migrate code using RetryPromptPart to ToolReturnPart(outcome='retried') and RetryFeedbackPart.
- Replace usages of OpenAIDecisionsProvider with OpenAIProvider when configuring OpenAIDecisionsModel.
- Explicitly set IDs when defining multiple MCP servers hosted on the same hostname to avoid initialization errors.
- Verify error handling around embedding calls to support newly raised ModelRateLimitError exceptions.