ddtrace 4.8.0rc4

on Python PyPI

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

Upgrade Notes

• ray
  • ray.job.submit spans are removed. Ray job submission outcome is now reported on the existing ray.job span through ray.job.submit_status.

Deprecation Notes

• LLM Observability
  • Removes support for the RAGAS integration. As an alternative, if you have RAGAS evaluations, you can manually submit these evaluation results. See LLM Observability external evaluation documentation for more information.
• tracing
  • The pin parameter in ddtrace.contrib.dbapi.TracedConnection, ddtrace.contrib.dbapi.TracedCursor, and ddtrace.contrib.dbapi_async.TracedAsyncConnection is deprecated and will be removed in version 5.0.0. To manage configuration of DB tracing please use integration configuration and environment variables.
  • DD_TRACE_INFERRED_PROXY_SERVICES_ENABLED is deprecated and will be removed in 5.0.0. Use DD_TRACE_INFERRED_SPANS_ENABLED instead. The old environment variable continues to work but emits a DDTraceDeprecationWarning when set.

New Features

• profiling

  • Thread sub-sampling is now supported. This allows to set a maximum number of threads to capture stacks for at each sampling interval. This can be used to reduce the CPU overhead of the Stack Profiler.

• ASM

  • Adds a LiteLLM proxy guardrail integration for Datadog AI Guard. The ddtrace.appsec.ai_guard.integrations.litellm.DatadogAIGuardGuardrail class can be registered as a custom guardrail in the LiteLLM proxy to evaluate requests and responses against AI Guard security policies. Requires the LiteLLM proxy guardrails API v2 available since litellm>=1.46.1.

• azure_cosmos

  • Add tracing support for Azure CosmosDB. This integration traces CRUD operations on CosmosDB databases, containers, and items.

• CI Visibility

  • adds automatic log correlation and submission so that test logs appear alongside their corresponding test run in Datadog. Set DD_AGENTLESS_LOG_SUBMISSION_ENABLED=true for agentless setups, or DD_LOGS_INJECTION=true when using the Datadog Agent.

• llama_index

  • Adds APM tracing and LLM Observability support for llama-index-core>=0.11.0. Traces LLM calls, query engines, retrievers, embeddings, and agents. See the llama_index documentation for more information.

• tracing

  • Adds support for exporting traces in OTLP HTTP/JSON format via libdatadog. Set OTEL_TRACES_EXPORTER=otlp to send spans to an OTLP endpoint instead of the Datadog Agent.

• mysql

  • This introduces tracing support for mysql.connector.aio.connect in the MySQL integration.

• LLM Observability

  • Adds support for enabling and disabling LLMObs via Remote Configuration.
  • Introduces a decorator tag to LLM Observability spans that are traced by a function decorator.
  • Experiments accept a pydantic_evals ReportEvaluator as a summary evaluator when its evaluate return annotation is exactly ScalarResult. The scalar value is recorded as the summary evaluation. Report evaluators that declare a broader analysis return type (for example the full ReportAnalysis union) are not accepted as summary evaluators; use a class-based or function summary evaluator instead. Examples and further documentation can found in our documentation [here](https://docs.datadoghq.com/llm_observability/guide/evaluation_developer_guide).

  Example:

  from pydantic_evals.evaluators import ReportEvaluator
  from pydantic_evals.evaluators import ReportEvaluatorContext
  from pydantic_evals.reporting.analyses import ScalarResult
  
  from ddtrace.llmobs import LLMObs
  
  dataset = LLMObs.create_dataset(
      dataset_name="<DATASET_NAME>",
      description="<DATASET_DESCRIPTION>",
      records=[RECORD_1, RECORD_2, RECORD_3, ...]
  )
  
  class TotalCasesEvaluator(ReportEvaluator):
      def evaluate(self, ctx: ReportEvaluatorContext) -> ScalarResult:
          return ScalarResult(
              title='Total Cases',
              value=len(ctx.report.cases),
              unit='cases',
          )
  
  def my_task(input_data, config):
      return input_data["output"]
  
  equals_expected = EqualsExpected()
  summary_evaluator = TotalCasesEvaluator()
  
  experiment = LLMObs.experiment(
      name="<EXPERIMENT_NAME>",
      task=my_task,
      dataset=dataset,
      evaluators=[equals_expected],
      summary_evaluators=[summary_evaluator],
      description="<EXPERIMENT_DESCRIPTION>."
  )
  
  result = experiment.run()
  

Bug Fixes

• profiling
  • Fixes lock profiling samples not appearing in the Thread Timeline view for events collected on macOS.
  • A rare crash that could occur post-fork in fork-based applications has been fixed.
  • A bug in Lock Profiling that could cause crashes when trying to access attributes of custom Lock subclasses (e.g. in Ray) has been fixed.
  • A rare crash occurring when profiling asyncio code with many tasks or deep call stacks has been fixed.
• internal
  • Fix a potential internal thread leak in fork-heavy applications.
  • This fix resolves an issue where a ModuleNotFoundError could be raised at startup in Python environments without the _ctypes extension module.
  • A crash that could occur post-fork in fork-heavy applications has been fixed.
  • A crash has been fixed.
• LLM Observability
  • Fixes incorrect span hierarchy in LLMObs traces when using the ddtrace SDK alongside OTel-based instrumentation (e.g. Strands Agents). OTel gen_ai spans (e.g. invoke_agent) were incorrectly appearing as siblings of their SDK parent span (e.g. call_agent) rather than being nested under it.
  • Fixes multimodal OpenAI chat completion inputs being rendered as raw iterable objects in LLM Observability traces. Multimodal content parts (text, image, audio) are now properly materialized and formatted as readable text.
  • Fixes model_name and model_provider reported on AWS Bedrock LLM spans as the model_id full model identifier value (e.g., "amazon.nova-lite-v1:0") and "amazon_bedrock" respectively. Bedrock spans' model_name and model_provider now correctly match backend pricing data, which enables features including cost tracking.
  • Fixes an issue where deferred tools (defer_loading=True) in Anthropic and OpenAI integrations caused LLMObs span payloads to include full tool descriptions and JSON schemas for every tool in a large catalog. Deferred tool definitions now have their description and schema stripped from span metadata, with only the tool name preserved.
  • Fixes an issue where deeply nested tool schemas in Anthropic and OpenAI integrations were not yet supported. The Anthropic and OpenAI integrations now check each tool's schema depth at extraction time. If a tool's schema exceeds the maximum allowed depth, the schema is truncated.
• CI Visibility
  • This fix resolves an issue where pytest-xdist worker crashes (os._exit, SIGKILL, segfault) caused buffered test events to be lost. To enable eager flushing, set DD_TRACE_PARTIAL_FLUSH_MIN_SPANS=1.
  • This fix resolves an issue where a failure response from the /search_commits endpoint caused the git metadata upload to fall back to sending the full 30-day commit history instead of aborting. This fallback could trigger cascading write load on the backend. The upload now aborts when search_commits fails, matching the behavior when the /packfile upload itself fails.
• Code Security (IAST)
  • This fix resolves a thread-safety issue in the IAST taint tracking context that could cause vulnerability detection to silently stop working under high concurrency in multi-threaded applications.
  • Fixes a missing return in the IAST taint tracking add_aspect native function that caused redundant work when only the right operand of a string concatenation was tainted.
• celery
  • remove unnecessary warning log about missing span when using Task.replace().
• django
  • Fixes RuntimeError: coroutine ignored GeneratorExit that occurred under ASGI with async views and async middleware hooks on Python 3.13+. Async view methods and middleware hooks are now correctly detected and awaited instead of being wrapped with sync bytecode wrappers.
• ray
  • This fix resolves an issue where Ray integration spans could use an incorrect service name when the Ray job name was set after instrumentation initialization.
• Other
  • Fixed a race condition with internal periodic threads that could have caused a rare crash when forking.
  • Fixes an issue where internal background threads could cause crashes or instability in applications that fork (e.g. Gunicorn, uWSGI) or during Python shutdown. Affected applications could experience intermittent crashes or hangs on exit.
• tracing
  • Fixes the svc.auto process tag attribution logic. The tag now correctly reflects the auto-detected service name derived from the script or module entrypoint, matching the service name the tracer would assign to spans.
  • This fix resolves an issue where applications started with python -m <module> could report entrypoint.name as -m in process tags.
  • Fixed an issue where network.client.ip and http.client_ip span tags were missing when client IP collection was enabled and request had no headers.
• litellm
  • Fix missing LLMObs spans when routing requests through a litellm proxy. Proxy requests were incorrectly suppressed and resulted in empty or missing LLMObs spans. Proxy requests for OpenAI models are now always handled by the litellm integration.
• serverless
  • AWS Lambda functions now appear under their function name as the service when DD_SERVICE is not explicitly configured. Service remapping rules configured in Datadog will now apply correctly to Lambda spans.
• openai
  • Fixes async streaming spans never being finished when using AsyncAPIResponse (e.g. responses.create(stream=True)). The sync handle_request hook called resp.parse() without awaiting the coroutine, preventing the stream from being wrapped in TracedAsyncStream. This caused disconnected LLM Observability traces for streamed sub-agent calls via the OpenAI Agents SDK.