4.9.0rc2

Estimated end-of-life date, accurate to within three months: 10-2027
See the support level definitions for more information.

New Features

• LLM Observability: Adds an optional cost_tags argument to LLMObs.annotate() and LLMObs.annotation_context(). Pass a list of tag keys (already set via tags or annotated previously on the same span) to have them attached to the cost and token metrics generated from LLM and embedding spans, which can help breaking down spend by team, project, org, or any custom dimension.

• profiling: Add DD_PROFILING_LOCK_EXCLUDE_MODULES config to skip lock profiling for framework-internal locks. Excluded locks remain native with zero profiling overhead. Set it to a comma-separated list of module prefixes (e.g., django.db,sqlalchemy.pool,urllib3).

Bug Fixes

• azure_cosmos: This change removes the http.status_code tags from Azure CosmosDB spans and replaces them with the use of the db.response.status_code metric. For customers using ddtrace v4.8.0 and relying on the http.status_code tag of cosmosdb.query spans, this is a breaking change.

• profiling: Fixes an issue where the lock profiler silently stopped capturing lock events when running under ddtrace-run with gevent installed.

• Fixed a startup deadlock when using snowflake-connector-python >= 4.4.0 with DD_TRACE_SNOWFLAKE_ENABLED=true.

4.7.2

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

Bug Fixes

• CI Visibility: 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.

• ai_guard: This fix resolves a conflict between ddtrace.auto and strands when imported in the same file, which left Strands hooks silently disabled. The Strands integration now loads lazily on first attribute access so its event class identities match those the agent dispatches.

• AAP: This fix resolves an issue where Application and API Protection (AAP) was incorrectly reported as an enabled product in internal telemetry for all services by default. Previously, registering remote configuration listeners caused AAP to be reported as activated even when it was not actually enabled. This had no impact on customers as it only affected internal telemetry data. AAP is now only reported as activated when it is explicitly enabled or enabled through remote configuration.

• iast: A crash has been fixed.

• profiling: A crash that could happen in child processes after fork has been fixed.

• tracing: This fix resolves a memory leak where reference cycles through a span's properties were invisible to Python's cyclic garbage collector and accumulated proportionally to traced call volume.

• internal: 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.

• profiling: A rare crash occurring when profiling asyncio code with many tasks or deep call stacks has been fixed.

• profiling: 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.

• profiling: A race condition which could make asyncio code raise exceptions at exit has been fixed.

• 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.

4.8.2

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

Bug Fixes

• ai_guard
  • This fix resolves a conflict between ddtrace.auto and strands when imported in the same file, which left Strands hooks silently disabled. The Strands integration now loads lazily on first attribute access so its event class identities match those the agent dispatches.
• tracing
  • Limits parsing of the W3C tracestate header during tracecontext extraction to 32 list-members and 512 UTF-8 bytes, consistent with the W3C Trace Context specification (https://www.w3.org/TR/trace-context/). Extra list-members and trailing whole entries that would exceed the byte budget are ignored, so unusually large headers no longer expand unbounded work during extraction. The Datadog dd= list-member is preferred: it is kept when present (including when it appears late in the header or alone exceeds the byte cap), and other vendors are dropped first. List-members longer than DD_TRACE_TRACESTATE_ITEM_MAX_CHARS (128) characters are removed first when trimming by list-member count or byte budget, so shorter vendor entries are kept when possible.
  • tracing: Parsing incoming baggage HTTP headers now respects DD_TRACE_BAGGAGE_MAX_ITEMS [default 64] and DD_TRACE_BAGGAGE_MAX_BYTES [default 8192], consistent with baggage injection. Previously, extraction could retain every comma-separated entry regardless of those limits. The tracer drops excess pairs and records truncation telemetry when limits apply.

4.8.1

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

Bug Fixes

• django: Stop tagging async view and middleware spans as errored on routine ASGI cancellations (e.g. client disconnects on streaming responses), a regression introduced in 4.8.0rc4. Cancellation still propagates; the span just finishes without error.type='asyncio.exceptions.CancelledError'.

• tracing: This fix resolves a memory leak where reference cycles through a span's properties were invisible to Python's cyclic garbage collector and accumulated proportionally to traced call volume.

4.8.0

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

Upgrade Notes

• claude_agent_sdk: Tool span resource names have changed from the tool name (e.g. Read, Bash) to claude_agent_sdk.tool. The specific tool name is still available in the span name (e.g. claude_agent_sdk.tool.Read). Users relying on tool resource names should update them accordingly.

• ray: Adds DD_TRACE_RAY_SUBMISSION_SPANS_ENABLED (default: False) configuration to control Ray submission tracing. Set DD_TRACE_RAY_SUBMISSION_SPANS_ENABLED=true to trace task.submit and actor_method.submit spans. Leave it unset to trace only execution spans. See Ray integration documentation for more details.

• 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

• Tracing: 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.

• 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.

• 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.

New Features

• AI Guard: Add DD_AI_GUARD_BLOCK environment variable. Defaults to True, which means the blocking behavior configured in the Datadog AI Guard UI (in-app) will be honored. Set to False to force monitor-only mode locally: evaluations are still performed but AIGuardAbortError is never raised, regardless of the in-app blocking setting.

• AI Guard response objects now include a dict field tag_probs with the probabilities for each tag.

• CI Visibility: Adds Bazel offline execution support with two modes: manifest mode (DD_TEST_OPTIMIZATION_MANIFEST_FILE), which reads settings and test data from pre-fetched cache files without network access; and payload-files mode (DD_TEST_OPTIMIZATION_PAYLOADS_IN_FILES), which writes test event, coverage, and telemetry payloads as JSON files instead of sending HTTP requests. Both modes can be used independently or together.

• LLM Observability: Captures individual LLM spans for each Claude model turn within a Claude Agent SDK session. Each LLM span captures the input messages, output messages, model name, and token usage metrics (for claude_agent_sdk >= 0.1.49).

• AAP: This adds Application Security support for FastAPI and Starlette applications using mounted sub-applications (via app.mount()). WAF evaluation, path parameter extraction, API endpoint discovery, and http.route reporting now correctly account for mount prefixes in sub-application routing.

• google_cloud_pubsub: This adds tracing for Google Cloud Pub/Sub admin operations on topic, subscription, snapshot, and schema management methods.

• google_cloud_pubsub: Adds support for Google Cloud Pub/Sub push subscriptions. When a push subscription delivers a message via HTTP, the integration now creates an inferred gcp.pubsub.receive span that captures subscription and message metadata. Use DD_GOOGLE_CLOUD_PUBSUB_PROPAGATION_AS_SPAN_LINKS to control whether the inferred span becomes a child of the producer trace or starts a new trace with the producer context attached as a span link (default: False).

• LLM Observability: Add ExperimentRun.as_dataframe() to convert experiment run results into a pandas.DataFrame with a two-level MultiIndex on columns. Each top-level group (input, output, expected_output, evaluations, metadata, error, span_id, trace_id) maps to the first index level. Dict-valued fields are flattened one level deep; scalar fields use an empty string as the sub-column name. Each evaluator gets its own column containing the full evaluation result dict. Requires pandas to be installed (pip install pandas).

• LLM Observability: Adds an eval_scope parameter to LLMObs.submit_evaluation() (one of "span" (default) or "trace"). Use eval_scope="trace" to associate an evaluation with an entire trace by passing the root span context.

• LLM Observability: Adds LLMObs.get_spans() to retrieve span events from the Datadog platform API (GET /api/v2/llm-obs/v1/spans/events). Supports filtering by trace ID, span ID, span kind, span name, ML app, tags, and time range. Results are auto-paginated. Requires DD_API_KEY and DD_APP_KEY.

• profiling: Profiles generated from fork-based servers now include a process_type tag with the value main or worker.

• tracing: Support for making the default span name for @tracer.wrap include the class name has been added. For now, this is opt-in and can be enabled by setting DD_TRACE_WRAP_SPAN_NAME_INCLUDE_CLASS=true. The new naming will become the default in the next major release.

• llmobs: Adds support for enabling and disabling LLMObs via Remote Configuration.

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

• 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.

• 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.

• 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.

• LLM Observability: Introduces a decorator tag to LLM Observability spans that are traced by a function decorator.

• 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.

• 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: 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

• CI visibility: This fix resolves issues where CI provider metadata could omit pull request base branch and head commit details or report incorrect pull request values for some providers.

• AAP: This fix resolves an issue where Application and API Protection (AAP) was incorrectly reported as an enabled product in internal telemetry for all services by default. Previously, registering remote configuration listeners caused AAP to be reported as activated even when it was not actually enabled. This had no impact on customers as it only affected internal telemetry data. AAP is now only reported as activated when it is explicitly enabled or enabled through remote configuration.

• asgi: Fixed an issue caused network.client.ip and ht...

4.9.0rc1

Estimated end-of-life date, accurate to within three months: 10-2027
See the support level definitions for more information.

Upgrade Notes

• ray: Adds DD_TRACE_RAY_IGNORED_ACTORS configuration to exclude specific Ray actor methods from instrumentation. Set DD_TRACE_RAY_IGNORED_ACTORS='{"ActorA": ["method1"], "ActorB": "*"}' to leave matching methods or actors uninstrumented while continuing to trace other Ray actor methods. Matching is based on actor class name only.

New Features

• AI Guard: When DD_AI_GUARD_ENABLED=true is set and an ai_guard span is created during a request, the tracer now populates http.client_ip and network.client.ip on the service-entry (local root) span, mirroring the behavior used for Application Security. If AI Guard does not run during the request, no client IP tags are added. DD_TRACE_CLIENT_IP_ENABLED is ignored once AI Guard reports, and DD_TRACE_CLIENT_IP_HEADER continues to override header resolution.

Bug Fixes

• CI Visibility: Fixes code coverage instrumentation on Python 3.13, 3.14, and 3.15. Resolves lost per-test line data caused by: sys.monitoring callbacks running in a snapshot context where ContextVar changes are not visible (Python 3.14+); empty modules emitting no LINE events (Python 3.13+); and ProcessPoolExecutor child coverage not being propagated to the parent context. Also fixes a stale-data bug where child process executable lines could inflate coverage denominators after stop_coverage() was called before join().

• django: API endpoint discovery now supports Django sub-applications mounted with django.urls.include(...). Endpoints are reported with their full URL path including the parent prefix — for example, a view served at /api/users/ is now reported as /api/users/ instead of losing the /api/ prefix.

• django: API endpoint discovery now reports the correct HTTP methods for views decorated with @require_http_methods combined with another decorator such as @csrf_exempt; the declared methods are reported instead of a generic wildcard entry.

• LLM Observability: This fix resolves an issue where running an experiment with a dataset whose records had null metadata caused the summary evaluator to crash with a TypeError while preparing evaluator inputs.

• LLM Observability: Changes the default model_name and model_provider of LLM and embedding spans from custom to unknown if not provided or empty. This applies to both auto-instrumented spans and manual instrumentation via LLMObs.llm() / LLMObs.embedding() and the @llm / @embedding decorators.

• profiling: A crash that could happen in child processes after fork has been fixed.

• profiling: A rare crash caused by the memory allocation profiler has been fixed.

• RemoteConfig: Fixed an issue where deleted remote configurations were not applied, causing stale settings to persist.

• wsgi: This fix resolves an issue where the http.url tag on inbound request spans contained the WSGI mount prefix twice (for example /admin/admin/users instead of /admin/users) when the application was served behind werkzeug.middleware.dispatcher.DispatcherMiddleware or any other in-process mount that preserves the original request URI in RAW_URI / REQUEST_URI while also setting SCRIPT_NAME.

• profiling: A rare crash happening on systems with small stack sizes when profiling asyncio code has been fixed.

4.8.0rc5

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

Upgrade Notes

• claude_agent_sdk: Tool span resource names have changed from the tool name (e.g. Read, Bash) to claude_agent_sdk.tool. The specific tool name is still available in the span name (e.g. claude_agent_sdk.tool.Read). Users relying on tool resource names should update them accordingly.

• ray: Adds DD_TRACE_RAY_SUBMISSION_SPANS_ENABLED (default: False) configuration to control Ray submission tracing. Set DD_TRACE_RAY_SUBMISSION_SPANS_ENABLED=true to trace task.submit and actor_method.submit spans. Leave it unset to trace only execution spans. See Ray integration documentation for more details.

• 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

• Tracing: 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.

• 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.

• 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.

New Features

• AI Guard: Add DD_AI_GUARD_BLOCK environment variable. Defaults to True, which means the blocking behavior configured in the Datadog AI Guard UI (in-app) will be honored. Set to False to force monitor-only mode locally: evaluations are still performed but AIGuardAbortError is never raised, regardless of the in-app blocking setting.

• AI Guard response objects now include a dict field tag_probs with the probabilities for each tag.

• CI Visibility: Adds Bazel offline execution support with two modes: manifest mode (DD_TEST_OPTIMIZATION_MANIFEST_FILE), which reads settings and test data from pre-fetched cache files without network access; and payload-files mode (DD_TEST_OPTIMIZATION_PAYLOADS_IN_FILES), which writes test event, coverage, and telemetry payloads as JSON files instead of sending HTTP requests. Both modes can be used independently or together.

• LLM Observability: Captures individual LLM spans for each Claude model turn within a Claude Agent SDK session. Each LLM span captures the input messages, output messages, model name, and token usage metrics (for claude_agent_sdk >= 0.1.49).

• AAP: This adds Application Security support for FastAPI and Starlette applications using mounted sub-applications (via app.mount()). WAF evaluation, path parameter extraction, API endpoint discovery, and http.route reporting now correctly account for mount prefixes in sub-application routing.

• google_cloud_pubsub: This adds tracing for Google Cloud Pub/Sub admin operations on topic, subscription, snapshot, and schema management methods.

• google_cloud_pubsub: Adds support for Google Cloud Pub/Sub push subscriptions. When a push subscription delivers a message via HTTP, the integration now creates an inferred gcp.pubsub.receive span that captures subscription and message metadata. Use DD_GOOGLE_CLOUD_PUBSUB_PROPAGATION_AS_SPAN_LINKS to control whether the inferred span becomes a child of the producer trace or starts a new trace with the producer context attached as a span link (default: False).

• LLM Observability: Add ExperimentRun.as_dataframe() to convert experiment run results into a pandas.DataFrame with a two-level MultiIndex on columns. Each top-level group (input, output, expected_output, evaluations, metadata, error, span_id, trace_id) maps to the first index level. Dict-valued fields are flattened one level deep; scalar fields use an empty string as the sub-column name. Each evaluator gets its own column containing the full evaluation result dict. Requires pandas to be installed (pip install pandas).

• LLM Observability: Adds an eval_scope parameter to LLMObs.submit_evaluation() (one of "span" (default) or "trace"). Use eval_scope="trace" to associate an evaluation with an entire trace by passing the root span context.

• LLM Observability: Adds LLMObs.get_spans() to retrieve span events from the Datadog platform API (GET /api/v2/llm-obs/v1/spans/events). Supports filtering by trace ID, span ID, span kind, span name, ML app, tags, and time range. Results are auto-paginated. Requires DD_API_KEY and DD_APP_KEY.

• profiling: Profiles generated from fork-based servers now include a process_type tag with the value main or worker.

• tracing: Support for making the default span name for @tracer.wrap include the class name has been added. For now, this is opt-in and can be enabled by setting DD_TRACE_WRAP_SPAN_NAME_INCLUDE_CLASS=true. The new naming will become the default in the next major release.

• llmobs: Adds support for enabling and disabling LLMObs via Remote Configuration.

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

• 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.

• 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.

• 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.

• LLM Observability: Introduces a decorator tag to LLM Observability spans that are traced by a function decorator.

• 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.

• 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: 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

• CI visibility: This fix resolves issues where CI provider metadata could omit pull request base branch and head commit details or report incorrect pull request values for some providers.

• AAP: This fix resolves an issue where Application and API Protection (AAP) was incorrectly reported as an enabled product in internal telemetry for all services by default. Previously, registering remote configuration listeners caused AAP to be reported as activated even when it was not actually enabled. This had no impact on customers as it only affected internal telemetry data. AAP is now only reported as activated when it is explicitly enabled or enabled through remote configuration.

• asgi: Fixed an issue caused network.client.ip and ht...

4.7.1

Estimated end-of-life date, accurate to within three months: 06-2027
See the support level definitions for more information.

Bug Fixes

• CI Visibility: 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.

• 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.

• 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.

4.6.8

Estimated end-of-life date, accurate to within three months: 06-2027
See the support level definitions for more information.

Bug Fixes

• CI Visibility: 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.

• 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.

4.5.10

Estimated end-of-life date, accurate to within three months: 06-2027
See the support level definitions for more information.

Bug Fixes

• CI Visibility: 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.

• Fixed a race condition with internal periodic threads that could have caused a rare crash when forking.