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Thursday, May 7, 2026

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[Release] openai/openai-agents-python v0.16.0: v0.16.0

seratchMay 7, 2026

OpenAI Agents Python SDK v0.16.0 introduces significant updates including a default model change from gpt-4.1 to gpt-5.4-mini, new configuration options for disabling max_turns limits, SDK-side tool execution concurrency control, and server-prefixed MCP tool naming to prevent conflicts. The release also includes stability fixes for tool call output indexes and security improvements for symlink handling. These changes enhance performance and provide greater flexibility for agent configuration while maintaining backward compatibility through explicit model specification.

  • Default model upgraded to gpt-5.4-mini with GPT-5 defaults (reasoning.effort='none', verbosity='low') — explicitly set model if you need gpt-4.1 behavior
  • New max_turns=None option disables run turn limits while preserving DEFAULT_MAX_TURNS (10) when omitted
  • ToolExecutionConfig(max_function_tool_concurrency=...) added to RunConfig for SDK-side local function tool execution scheduling
  • +7 more key points...
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[Release] langchain-ai/langchain langchain==1.3.0a2: langchain==1.3.0a2

LangChain v1.3.0a2 is an alpha release featuring improvements to stream events protocol (v3), ordered schema resolution, and agent middleware enhancements. The release includes performance optimizations, bug fixes across multiple integrations (OpenAI, Anthropic, Fireworks), and new features like HITL middleware respond decision and dynamic tool registration. Key updates involve dependency bumps, security patches, and refinements to the agent creation and model initialization systems.

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CO

Outcomes: agents that verify their own work May 2026 • Agent Patterns Evals Build a grade-and-revise loop with Outcomes: a writer drafts a cited research brief, a stateless grader fetches every URL and checks every quote against a rubric, and feedback drives revisions until the brief passes. Covers user.define_outcome, the span.outcome_evaluation_* events, and how to write a rubric the grader can act on.

This guide teaches how to build a grade-and-revise loop using Outcomes in Claude Managed Agents, where a writer agent drafts a cited research brief and a stateless grader independently verifies every URL, quote, and claim against a detailed rubric. The grader provides structured feedback that drives revisions until the brief passes, eliminating manual review cycles. Key techniques include writing specific, actionable rubrics that force concrete evidence, using span.outcome_evaluation_* events to track the loop, and understanding when Outcomes is the right tool for quality assurance.

54%
CO

Multiagent: coordinate a specialist team May 2026 • Agent Patterns Tools Heterogeneous team via the multiagent coordinator config — a coordinator runs three specialists (web-search researcher, file-reading librarian, rules-based pricer) with scoped toolsets to assemble a sales proposal. Covers the multiagent field, the thread_created / thread_message_received event types, and per-role tool scoping.

This cookbook demonstrates building a multiagent coordinator system using Claude Managed Agents to automate sales-proposal generation. A coordinator agent orchestrates three specialist subagents—a web-search researcher, a case-study librarian, and a rules-based pricing modeler—each with scoped toolsets and specific responsibilities. The pattern shows how to structure heterogeneous teams, use per-role tool scoping, and leverage thread_created/thread_message_received events for agent coordination to assemble tailored sales proposals.

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video (9)release (7)tutorial (2)

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beginner
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