chore(deps): bump ddtrace from 4.0.1 to 4.1.0

[dependabot]

Bumps ddtrace from 4.0.1 to 4.1.0.

Release notes

Sourced from ddtrace's releases.

4.1.0

Estimated end-of-life date, accurate to within three months: 05-2027 See the support level definitions for more information.

Upgrade Notes

• LLM Observability
  • Experiments spans now contain metadata from the dataset record.
  • Experiments spans' input, output, expected_output fields are now emitted as is so that if data in any of the columns are objects, they can be searchable in Datadog.
  • Experiments spans and children spans are now tagged with human readable names to allow better analysis of experiments data. New tags added are: dataset_name, project_name, project_id, experiment_name.
• tornado
  • Updated minimum supported version to v6.1+.

Deprecation Notes

• tornado
  • Deprecated support for Tornado versions older than v6.1. Use Tornado v6.1 or later.
• LLM Observability
  • The ExperimentResult class' rows and summary_evaluations attributes are deprecated and will be removed in the next major release. ExperimentResult.rows/summary_evaluations attributes will only store the results of the first run iteration for multi-run experiments. Use the ExperimentResult.runs attribute instead to access experiment results and summary evaluations.

New Features

• profiling
  • Add support for threading.BoundedSemaphore locking type profiling in Python. The implementation follows the same approach as threading.Semaphore, properly handling internal lock detection to prevent double-counting of the underlying threading.Lock object.
  • Add support for threading.Semaphore locking type profiling in Python. The Lock profiler now detects and marks "internal" Lock objects, i.e. those that are part of implementation of higher-level locking types. One example of such higher-level primitive is threading.Semaphore, which is implemented with threading.Condition, which itself uses threading.Lock internally. Marking internal lock as "internal" will prevent it from being sampled, ensuring that the high-level (e.g. Semaphore) sample is processed.
  • This adds support for Python 3.14 in the Continuous Profiler.
  • This adds the process_id tag to profiles. The value of this tag is the current process ID (PID).
  • The stack sampler supports async generators and asyncio.wait.
  • Shows fully qualified name of functions using codeobject.co_qualname in memory profiler and lock profiler flamegraphs for Python 3.11+. Stack profiler has already been using this. This aligns the user experience across different profile types.
  • This introduces tracking for the asyncio.as_completed util in the Profiler.
  • This introduces tracking for asyncio.wait in the Profiler. This makes it possible to track dependencies between Tasks/Coroutines that await/are awaited through asyncio.wait.
• AAP
  • attach Application and API Protection findings on API Gateway inferred spans to enable AppSec API Catalog coverage of lambda functions
  • This introduces proper support for API10 for redirected requests on urllib3
• anthropic
  • Adds support for the Anthropic Beta client API (client.beta.messages.create() and client.beta.messages.stream()). This feature requires Anthropic client version 0.37.0 or higher.
• aiokafka
  • Adds DSM instrumentation support.
  • Adds instrumentation support for aiokafka>=0.9.0. See the aiokafka<https://ddtrace.readthedocs.io/en/stable/integrations.html#aiokafka> documentation for more information.
• Added support for uWSGI with gevent when threads are also patched. The use of the keyword argument thread=False is no longer required when performing monkey-patching with gevent via gevent.monkey.patch_all.
• LLM Observability
  • Reasoning token counts are now captured from Google GenAI responses.
  • The OpenAI integration now captures prompt metadata (id, version, variables, and chat template) for reusable prompts when using the responses endpoint (available in OpenAI SDK >= 1.87.0).
  • Experiments can now be run multiple times by using the optional runs argument, to assess the true performance of an experiment in the face of the non determinism of LLMs. Use the new ExperimentResult class' runs attribute to access the results and summary evaluations by run iteration.
  • Non-root experiment spans are now tagged with experiment ID, run ID, and run iteration tags.
  • Adds additional tags to MCP client session and tool call spans to power LLM Observability MCP tool call features.
  • Reasoning token counts are now captured from OpenAI and OpenAI Agents responses.
  • openai
    • This introduces support for capturing server-side MCP tool calls invoked via the OpenAI Responses API as a separate span.
• langchain

... (truncated)

Commits

• 2a38b75 chore(llmobs): make tests checking api key more flexible when checking error ...
• 3e147cb update version string
• c4ddcba update version string
• 9b0404f chore(ci_visibility): report flaky tests in new plugin [backport 4.1] (#15676)
• 044e8bf test(llmobs): update expected API error message in agentless writer tests [ba...
• 652ee93 Merge branch 'main' into 4.1
• 126ab54 set proper version string in pyproject
• 62cb64c chore(ci): revert add precomputed pipeline variables (#15582) (#15663)
• 7e53632 chore(profiling): skip test that times out (#15656)
• 671a82b chore(dsm): add data-streams-monitoring to CODEOWNERS (#15537)
• Additional commits viewable in compare view

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