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This pull request adds a dropped_count property to BatchTraceProcessor to provide visibility into trace data loss.

Previously, when the internal queue of BatchTraceProcessor became full (which can happen under heavy load or if the background export thread falls behind), traces and spans were dropped with only a logger.warning. There was no programmatic way to observe or alert on this data loss.

This PR:

  • Adds a _dropped_count instance variable to track the number of dropped items.
  • Increments _dropped_count whenever queue.Full is caught during on_trace_start and on_span_end.
  • Exposes this metric via a public dropped_count property.

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Comment threadsrc/agents/tracing/processors.py
try:
self._queue.put_nowait(trace)
except queue.Full:
self._dropped_count += 1

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P2 Badge Serialize concurrent dropped-count updates

When multiple application threads finish spans or start traces while the queue is full, both callbacks can read the same _dropped_count value before either stores its increment, causing the public metric to undercount dropped telemetry; this is especially reachable on free-threaded Python builds and other interpreters because += is not an atomic counter operation. Protect both increments and reads with a shared lock or another thread-safe counter, and cover the interleaving with a controlled concurrency test rather than only the sequential test.

AGENTS.md reference: AGENTS.md:L149-L149

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feat: add dropped_count property to BatchTraceProcessor by Showmick119 · Pull Request #4792 · openai/openai-agents-python · GitHub
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This pull request adds a dropped_count property to BatchTraceProcessor to provide visibility into trace data loss.
Previously, when the internal queue of BatchTraceProcessor became full (which can happen under heavy load or if the background export thread falls behind), traces and spans were dropped with only a logger.warning. There was no programmatic way to observe or alert on this data loss.
This PR:
  • Adds a _dropped_count instance variable to track the number of dropped items.
  • Increments _dropped_count whenever queue.Full is caught during on_trace_start and on_span_end.
  • Exposes this metric via a public dropped_count property.
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Comment threadsrc/agents/tracing/processors.py
 try:
 self._queue.put_nowait(trace)
 except queue.Full:
 self._dropped_count += 1
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P2 Badge Serialize concurrent dropped-count updates
When multiple application threads finish spans or start traces while the queue is full, both callbacks can read the same _dropped_count value before either stores its increment, causing the public metric to undercount dropped telemetry; this is especially reachable on free-threaded Python builds and other interpreters because += is not an atomic counter operation. Protect both increments and reads with a shared lock or another thread-safe counter, and cover the interleaving with a controlled concurrency test rather than only the sequential test.
AGENTS.md reference: AGENTS.md:L149-L149
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feat: add dropped_count property to BatchTraceProcessor by Showmick119 · Pull Request #4792 · openai/openai-agents-python · GitHub
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This pull request adds a dropped_count property to BatchTraceProcessor to provide visibility into trace data loss.
Previously, when the internal queue of BatchTraceProcessor became full (which can happen under heavy load or if the background export thread falls behind), traces and spans were dropped with only a logger.warning. There was no programmatic way to observe or alert on this data loss.
This PR:
  • Adds a _dropped_count instance variable to track the number of dropped items.
  • Increments _dropped_count whenever queue.Full is caught during on_trace_start and on_span_end.
  • Exposes this metric via a public dropped_count property.
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Comment threadsrc/agents/tracing/processors.py
 try:
 self._queue.put_nowait(trace)
 except queue.Full:
 self._dropped_count += 1
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P2 Badge Serialize concurrent dropped-count updates
When multiple application threads finish spans or start traces while the queue is full, both callbacks can read the same _dropped_count value before either stores its increment, causing the public metric to undercount dropped telemetry; this is especially reachable on free-threaded Python builds and other interpreters because += is not an atomic counter operation. Protect both increments and reads with a shared lock or another thread-safe counter, and cover the interleaving with a controlled concurrency test rather than only the sequential test.
AGENTS.md reference: AGENTS.md:L149-L149
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feat: add dropped_count property to BatchTraceProcessor by Showmick119 · Pull Request #4792 · openai/openai-agents-python · GitHub
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This pull request adds a dropped_count property to BatchTraceProcessor to provide visibility into trace data loss.
Previously, when the internal queue of BatchTraceProcessor became full (which can happen under heavy load or if the background export thread falls behind), traces and spans were dropped with only a logger.warning. There was no programmatic way to observe or alert on this data loss.
This PR:
  • Adds a _dropped_count instance variable to track the number of dropped items.
  • Increments _dropped_count whenever queue.Full is caught during on_trace_start and on_span_end.
  • Exposes this metric via a public dropped_count property.
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Comment threadsrc/agents/tracing/processors.py
 try:
 self._queue.put_nowait(trace)
 except queue.Full:
 self._dropped_count += 1
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P2 Badge Serialize concurrent dropped-count updates
When multiple application threads finish spans or start traces while the queue is full, both callbacks can read the same _dropped_count value before either stores its increment, causing the public metric to undercount dropped telemetry; this is especially reachable on free-threaded Python builds and other interpreters because += is not an atomic counter operation. Protect both increments and reads with a shared lock or another thread-safe counter, and cover the interleaving with a controlled concurrency test rather than only the sequential test.
AGENTS.md reference: AGENTS.md:L149-L149
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feat: add dropped_count property to BatchTraceProcessor by Showmick119 · Pull Request #4792 · openai/openai-agents-python · GitHub
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This pull request adds a dropped_count property to BatchTraceProcessor to provide visibility into trace data loss.
Previously, when the internal queue of BatchTraceProcessor became full (which can happen under heavy load or if the background export thread falls behind), traces and spans were dropped with only a logger.warning. There was no programmatic way to observe or alert on this data loss.
This PR:
  • Adds a _dropped_count instance variable to track the number of dropped items.
  • Increments _dropped_count whenever queue.Full is caught during on_trace_start and on_span_end.
  • Exposes this metric via a public dropped_count property.
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Comment threadsrc/agents/tracing/processors.py
 try:
 self._queue.put_nowait(trace)
 except queue.Full:
 self._dropped_count += 1
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P2 Badge Serialize concurrent dropped-count updates
When multiple application threads finish spans or start traces while the queue is full, both callbacks can read the same _dropped_count value before either stores its increment, causing the public metric to undercount dropped telemetry; this is especially reachable on free-threaded Python builds and other interpreters because += is not an atomic counter operation. Protect both increments and reads with a shared lock or another thread-safe counter, and cover the interleaving with a controlled concurrency test rather than only the sequential test.
AGENTS.md reference: AGENTS.md:L149-L149
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This pull request adds a dropped_count property to BatchTraceProcessor to provide visibility into trace data loss.
Previously, when the internal queue of BatchTraceProcessor became full (which can happen under heavy load or if the background export thread falls behind), traces and spans were dropped with only a logger.warning. There was no programmatic way to observe or alert on this data loss.
This PR:
  • Adds a _dropped_count instance variable to track the number of dropped items.
  • Increments _dropped_count whenever queue.Full is caught during on_trace_start and on_span_end.
  • Exposes this metric via a public dropped_count property.
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Comment threadsrc/agents/tracing/processors.py
 try:
 self._queue.put_nowait(trace)
 except queue.Full:
 self._dropped_count += 1
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P2 Badge Serialize concurrent dropped-count updates
When multiple application threads finish spans or start traces while the queue is full, both callbacks can read the same _dropped_count value before either stores its increment, causing the public metric to undercount dropped telemetry; this is especially reachable on free-threaded Python builds and other interpreters because += is not an atomic counter operation. Protect both increments and reads with a shared lock or another thread-safe counter, and cover the interleaving with a controlled concurrency test rather than only the sequential test.
AGENTS.md reference: AGENTS.md:L149-L149
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feat: add dropped_count property to BatchTraceProcessor by Showmick119 · Pull Request #4792 · openai/openai-agents-python · GitHub
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feat: add dropped_count property to BatchTraceProcessor - #4792
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Showmick119:fix/batch-trace-queue-drop
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This pull request adds a dropped_count property to BatchTraceProcessor to provide visibility into trace data loss.
Previously, when the internal queue of BatchTraceProcessor became full (which can happen under heavy load or if the background export thread falls behind), traces and spans were dropped with only a logger.warning. There was no programmatic way to observe or alert on this data loss.
This PR:
  • Adds a _dropped_count instance variable to track the number of dropped items.
  • Increments _dropped_count whenever queue.Full is caught during on_trace_start and on_span_end.
  • Exposes this metric via a public dropped_count property.
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Reviewed commit:09797974d6
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Comment threadsrc/agents/tracing/processors.py
 try:
 self._queue.put_nowait(trace)
 except queue.Full:
 self._dropped_count += 1
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P2 Badge Serialize concurrent dropped-count updates
When multiple application threads finish spans or start traces while the queue is full, both callbacks can read the same _dropped_count value before either stores its increment, causing the public metric to undercount dropped telemetry; this is especially reachable on free-threaded Python builds and other interpreters because += is not an atomic counter operation. Protect both increments and reads with a shared lock or another thread-safe counter, and cover the interleaving with a controlled concurrency test rather than only the sequential test.
AGENTS.md reference: AGENTS.md:L149-L149
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feat: add dropped_count property to BatchTraceProcessor by Showmick119 · Pull Request #4792 · openai/openai-agents-python · GitHub
Skip to content
feat: add dropped_count property to BatchTraceProcessor - #4792
Open
Showmick119 wants to merge 1 commit into
openai:mainfrom 
Showmick119:fix/batch-trace-queue-drop
Open
feat: add dropped_count property to BatchTraceProcessor#4792
Showmick119 wants to merge 1 commit into
openai:mainfrom 
Showmick119:fix/batch-trace-queue-drop
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@Showmick119
@Showmick119Showmick119
commented
Aug 31, 2026

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This pull request adds a dropped_count property to BatchTraceProcessor to provide visibility into trace data loss.
Previously, when the internal queue of BatchTraceProcessor became full (which can happen under heavy load or if the background export thread falls behind), traces and spans were dropped with only a logger.warning. There was no programmatic way to observe or alert on this data loss.
This PR:
  • Adds a _dropped_count instance variable to track the number of dropped items.
  • Increments _dropped_count whenever queue.Full is caught during on_trace_start and on_span_end.
  • Exposes this metric via a public dropped_count property.
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Reviewed commit:09797974d6
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Comment threadsrc/agents/tracing/processors.py
 try:
 self._queue.put_nowait(trace)
 except queue.Full:
 self._dropped_count += 1
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P2 Badge Serialize concurrent dropped-count updates
When multiple application threads finish spans or start traces while the queue is full, both callbacks can read the same _dropped_count value before either stores its increment, causing the public metric to undercount dropped telemetry; this is especially reachable on free-threaded Python builds and other interpreters because += is not an atomic counter operation. Protect both increments and reads with a shared lock or another thread-safe counter, and cover the interleaving with a controlled concurrency test rather than only the sequential test.
AGENTS.md reference: AGENTS.md:L149-L149
Useful? React with 👍 / 👎.
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