v1.5.0

New Features:

• Azure OpenAI Tools: Added image generation via Dall-E via Azure AI Foundry.
• OpenTelemetry Instrumentation: We have contributed to the OpenInference project and added an auto-instrumentor for Agno agents. This adds tracing instrumentation for Agno Agents for any OpenTelemetry-compatible observability provider. These include Arize, Langfuse and Langsmith. Examples added to illustrate how to use each one (here).
• Evals Updates: Added logic to run accuracy evaluations with pre-generated answers and minor improvements for all evals classes.
• Hybrid Search and Reranker for Milvus Vector DB: Added support for hybrid_search on Milvus.
• MCP with Streamable-HTTP: Now supporting the streamable-HTTP transport for MCP servers.

Improvements:

• Knowledge Filters Cookbook: Instead of storing the sample data locally, we now pull it from s3 at runtime to keep the forking of the repo as light as possible.

Bug Fixes:

• Team Model State: Fixed issues related to state being shared between models on teams.
• Concurrent Agent Runs: Fixed certain race-conditions related to running agents concurrently.

Breaking changes:

• Evals Refactoring:
  • Our performance evaluation class has been renamed from PerfEval to PerformanceEval
  • Our accuracy evaluation class has new required fields: agent, prompt and expected_answer
• Concurrent Agent Runs: We removed duplicate information from some events during streaming (stream=True). Individual events will have more relevant data now.