ddtrace 3.14.0rc1 on Python PyPI

Estimated end-of-life date, accurate to within three months: 08-2026
See the support level definitions for more information.

ai_guard: add AI Guard evaluations support to LangChain instrumentation
LLM Observability
- ml_app is now optional, defaulting to service. while it is still recommended to set ml_app, enabling LLM Observability will no longer throw if one is not provided or propagated from an upstream service.
- This introduces a tool_definitions parameter to the LLMObs.annotate() method for tool calling scenarios. Users can now pass a list of tool definition dictionaries directly to annotate LLM spans with available tools. Each tool definition must include a name (string) field, with optional description (string) and schema (JSON-serializable dictionary) fields.

asyncpg: Fix the error "Error: expected pool connect callback to return an instance of 'asyncpg.connection.Connection', got 'ddtrace.contrib.internal.asyncpg.patch._TracedConnection'" due to using the custom connect option. With this fix, postgres.connect spans will be created when this option is used.
exception replay: fixed an issue that prevented snapshots from retrieving local variables from traceback frames from exception thrown by Celery tasks.
LLM Observability: This fix resolves an issue where decorated functions returning responses with an ambiguous truth value (e.g. pandas dataframes) would raise an error due to the inability to coerce the value into a boolean.
Fixed an issue that could have caused some products to fail to start properly in applications that use pkg_resources, either directly or indirectly.
profiling: Upgrades echion to pick up critical bug fixes and performance improvements.
CI Visibility: This fix resolves an issue where coverage from sessions with pytest-xdist were not submitted with the proper session id, preventing Test Impact Analysis feature from working properly.