Azure/azure-sdk-for-python azure-ai-projects_2.8.0

on GitHub

2.8.0 (2026-10-02)

Features Added

• Agent Optimization APIs are now GA, including job management, cost estimation, candidate retrieval, and candidate promotion.
• Added typed Agent Optimization models for agent and prompt optimization, configuration, evaluation, candidate search and mutations, cost estimates, latency, token usage, and termination details.
• Data generation job APIs are now GA.
• Added scenario-specific data generation models for evaluation, supervised fine-tuning, reinforcement fine-tuning, and user-conversation simulation.
• Evaluator lifecycle and generation job APIs are now GA.
• Added GA Agent tools BrowserAutomationTool and BrowserAutomationToolboxTool, with ToolboxToolType.BROWSER_AUTOMATION.
• Added ChatCompletionTool, FunctionObject, and FunctionParameters for describing function tools used by chat completions.
• Added optional async_property to CustomToolParam, FunctionTool, and FunctionToolParam.
• Added ConnectionType.OPEN_API and ConnectionType.REMOTE_A2A.
• Added optional ApiError.misalignment with MisalignmentErrorDetailsResource, MisalignmentErrorType, and MisalignmentSteer.
• Added gpt-image-2 and gpt-image-2-2026-04-21 as known ImageGenTool.model values.

Breaking Changes

All breaking changes affect preview APIs.

Breaking changes in preview methods:

• list_optimization_jobs now returns ItemPaged[AgentOptimizationJob] instead of ItemPaged[AgentOptimizationJobListItem].
• .datasets.begin_create_generation_job now accepts DataGenerationJobInputs instead of DataGenerationJob.
• .evaluators.begin_create_generation_job now accepts EvaluatorGenerationInputs directly instead of an EvaluatorGenerationJob wrapping the inputs under an inputs property.

Breaking changes in preview classes:

• Restructured AgentOptimizationCandidate, AgentOptimizationJob, and AgentOptimizationJobResult around typed configuration, candidate output, evaluation, usage, latency, and termination models.
• DataGenerationJob now requires name, sources, generation_configuration, and scenario instead of inputs.
• DataGenerationJobInputs now uses generation_configuration and the scenario-specific input models for output configuration.
• Renamed DataGenerationJobOptions to DataGenerationJobConfiguration, along with its derived classes: SimpleQnADataGenerationJobOptions to SimpleQnADataGenerationJobConfiguration, SimulationSeedDataGenerationJobOptions to SimulationSeedDataGenerationJobConfiguration, ToolUseFineTuningDataGenerationJobOptions to ToolUseFineTuningDataGenerationJobConfiguration, and TracesDataGenerationJobOptions to TracesDataGenerationJobConfiguration.
• Replaced DataGenerationJobOutputOptions with scenario-specific EvaluationDataGenerationJobOutputConfiguration, SupervisedFineTuningDataGenerationJobOutputConfiguration, and ReinforcementFineTuningDataGenerationJobOutputConfiguration models.
• Renamed the supervised and reinforcement fine-tuning DataGenerationJobScenario values with a _PREVIEW suffix. Their wire values now also end in _preview.
• EvaluatorGenerationJob now exposes sources, model, evaluator_name, evaluator_display_name, and evaluator_description as top-level properties and no longer has an inputs property.

Sample updates

• Added sample_dataset_generation_job_management.py demonstrating begin_create_generation_job without SDK polling, list_generation_jobs, get_generation_job and cancel_generation_job on .datasets.
• Added sample_dataset_generation_job_traces_for_evaluation_merge.py demonstrating growing a traces-based evaluation dataset with DataGenerationJobOutputWriteMode.MERGE, which creates the next dataset version with merged, de-duplicated rows.
• Added sample_dataset_generation_job_simulation_seed_for_evaluation.py demonstrating SimulationSeedDataGenerationJobConfiguration to generate multi-turn evaluation seeds from a prompt source.
• Replaced sample_quality_grader.py with sample_output_quality.py for the builtin.output_quality composite evaluator, and added sample_tool_use_quality.py for the builtin.tool_use_quality composite evaluator.
• Updated sample_multiturn_conversation_simulation.py to use the GA azure_ai_user_conversation_simulation data source.
• Updated sample_synthetic_multiturn_evaluation.py to use the GA azure_ai_synthetic_data_generation_with_simulation data source, generating scenarios and simulating conversations in a single eval run.
• Updated the trace-based evaluation samples (sample_multiturn_trace_evaluation_by_id.py, sample_multiturn_trace_evaluation_agent_filter.py, sample_agent_trace_evaluation_smart_filter.py, sample_scheduled_agent_traces_evaluation_smart_filter.py) to use the GA azure_ai_trace_data_source type.