ddtrace 4.8.0rc2

on Python PyPI

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

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 <https://docs.datadoghq.com/llm_observability/evaluations/external_evaluations\> for more information.

New Features

• 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.

• LLM Observability

  • 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.
• 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.
• 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.
• 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.