Fix(llm):When LLM calls are retried, it can lead to inaccurate records.

openhands-sdk/openhands/sdk/llm/llm.py

@@ -459,7 +459,6 @@ def completion(
             }
             if tools and not use_native_fc:
                 log_ctx["raw_messages"] = original_fncall_msgs
-        self._telemetry.on_request(log_ctx=log_ctx)
 
         # 5) do the call with retries
         @self.retry_decorator(
@@ -472,6 +471,7 @@ def completion(
         )
         def _one_attempt(**retry_kwargs) -> ModelResponse:
             assert self._telemetry is not None
+            self._telemetry.on_request(log_ctx=log_ctx)
             # Merge retry-modified kwargs (like temperature) with call_kwargs
             final_kwargs = {**call_kwargs, **retry_kwargs}
             resp = self._transport_call(messages=formatted_messages, **final_kwargs)

tests/sdk/llm/test_llm_retry_telemetry.py

@@ -0,0 +1,306 @@
+"""
+Test that telemetry records are accurate when LLM calls are retried.
+
+This test ensures that when an LLM call is retried, the telemetry only
+records the latency and metrics for the successful attempt, not the
+combined time of all failed attempts plus the successful one.
+"""
+
+import time
+from unittest.mock import patch
+
+from litellm.exceptions import APIConnectionError
+from litellm.types.utils import Choices, Message as LiteLLMMessage, ModelResponse, Usage
+from pydantic import SecretStr
+
+from openhands.sdk.llm import LLM, Message, TextContent
+
+
+def create_mock_response(
+    content: str = "Test response",
+    response_id: str = "test-id",
+    prompt_tokens: int = 10,
+    completion_tokens: int = 5,
+):
+    """Helper function to create properly structured mock responses."""
+    return ModelResponse(
+        id=response_id,
+        choices=[
+            Choices(
+                finish_reason="stop",
+                index=0,
+                message=LiteLLMMessage(content=content, role="assistant"),
+            )
+        ],
+        created=1234567890,
+        model="gpt-4o",
+        object="chat.completion",
+        system_fingerprint="test",
+        usage=Usage(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens,
+        ),
+    )
+
+
+@patch("openhands.sdk.llm.llm.litellm_completion")
+def test_telemetry_records_only_successful_attempt_latency(mock_litellm_completion):
+    """
+    Test that when LLM calls are retried, telemetry only records the latency
+    of the successful attempt, not the cumulative time of all attempts.
+
+    Before the fix, on_request was called once before retry logic, causing
+    the latency to include all failed attempts + wait times. After the fix,
+    on_request is called for each retry attempt, so only the successful
+    attempt's latency is recorded.
+    """
+    # Create mock responses for failed and successful attempts
+    mock_response = create_mock_response("Success after retry")
+
+    # Simulate 2 failures followed by success
+    mock_litellm_completion.side_effect = [
+        APIConnectionError(
+            message="Connection failed 1",
+            llm_provider="test_provider",
+            model="test_model",
+        ),
+        APIConnectionError(
+            message="Connection failed 2",
+            llm_provider="test_provider",
+            model="test_model",
+        ),
+        mock_response,  # Third attempt succeeds
+    ]
+
+    # Create LLM with retry configuration and minimal wait times for faster test
+    llm = LLM(
+        model="gpt-4o",
+        api_key=SecretStr("test_key"),
+        num_retries=3,
+        retry_min_wait=1,  # 1 second minimum wait
+        retry_max_wait=1,  # 1 second maximum wait (same as min for consistent timing)
+        usage_id="test-service",
+    )
+
+    # Record the start time of the entire operation
+    operation_start = time.time()
+
+    # Make the completion call (will retry twice, then succeed)
+    response = llm.completion(
+        messages=[Message(role="user", content=[TextContent(text="Hello!")])],
+    )
+
+    # Record the total operation time
+    total_operation_time = time.time() - operation_start
+
+    # Verify the call succeeded
+    assert response.raw_response == mock_response
+    assert mock_litellm_completion.call_count == 3
+
+    # Get the metrics to check recorded latency
+    metrics = llm.metrics
+
+    # The recorded latency should be much less than the total operation time
+    # because it should only include the successful attempt, not the failed ones
+    recorded_latencies = [latency.latency for latency in metrics.response_latencies]
+
+    # There should be exactly one latency record (for the successful attempt)
+    assert len(recorded_latencies) == 1
+
+    recorded_latency = recorded_latencies[0]
+
+    # The recorded latency should be significantly less than total operation time
+    # Total operation time includes:
+    # - First attempt (failed) + wait time
+    # - Second attempt (failed) + wait time
+    # - Third attempt (successful)
+    #
+    # The recorded latency should only include the third attempt
+    assert recorded_latency < total_operation_time * 0.5, (
+        f"Recorded latency ({recorded_latency:.3f}s) should be much less "
+        f"than total operation time ({total_operation_time:.3f}s)"
+    )
+
+    # The recorded latency should be relatively small (just the mock call time)
+    # Since we're mocking, it should be very quick (< 100ms typically)
+    assert recorded_latency < 0.5, (
+        f"Recorded latency ({recorded_latency:.3f}s) should be < 0.5s for a mocked call"
+    )
+
+
+@patch("openhands.sdk.llm.llm.litellm_completion")
+def test_telemetry_on_request_called_per_retry(mock_litellm_completion):
+    """
+    Test that telemetry.on_request() is called for each retry attempt.
+
+    This ensures that each retry resets the request timer, so only the
+    successful attempt's latency is recorded.
+
+    We verify this by checking the _req_start timestamps which are set
+    by on_request(). With the fix, _req_start should be reset for each retry.
+    """
+    # Track _req_start values to see when on_request is called
+    req_start_values = []
+
+    mock_response = create_mock_response("Success after one retry")
+
+    # Create a side effect function that captures _req_start after each attempt
+    def mock_transport_call_side_effect(*args, **kwargs):
+        # Capture the current _req_start value (set by on_request)
+        # This runs inside _one_attempt, after on_request is called
+        nonlocal req_start_values
+        req_start_values.append(time.time())
+
+        # First call fails, second succeeds
+        if len(req_start_values) == 1:
+            raise APIConnectionError(
+                message="Connection failed",
+                llm_provider="test_provider",
+                model="test_model",
+            )
+        return mock_response
+
+    mock_litellm_completion.side_effect = mock_transport_call_side_effect
+
+    # Create LLM instance
+    llm = LLM(
+        model="gpt-4o",
+        api_key=SecretStr("test_key"),
+        num_retries=2,
+        retry_min_wait=1,
+        retry_max_wait=1,
+        usage_id="test-service",
+    )
+
+    # Make the completion call
+    response = llm.completion(
+        messages=[Message(role="user", content=[TextContent(text="Test")])],
+    )
+
+    # Verify the call succeeded
+    assert response.raw_response == mock_response
+
+    # Should have attempted twice (one failure, one success)
+    assert len(req_start_values) == 2, (
+        f"Expected 2 attempts, got {len(req_start_values)}"
+    )
+
+    # Verify there was a time gap between the attempts (retry wait time)
+    # This proves on_request was called for each attempt
+    time_gap = req_start_values[1] - req_start_values[0]
+    assert time_gap >= 0.5, (
+        "There should be a wait time between retry attempts "
+        f"(gap: {time_gap:.3f}s, expected >= 0.5s due to 1 second retry wait)"
+    )
+
+
+@patch("openhands.sdk.llm.llm.litellm_completion")
+def test_telemetry_metrics_accurate_with_retries(mock_litellm_completion):
+    """
+    Test that all telemetry metrics (tokens, cost, latency) are accurate
+    when retries occur.
+    """
+    # Create a response with specific token counts
+    mock_response = create_mock_response(
+        "Success", prompt_tokens=100, completion_tokens=50
+    )
+
+    # Simulate one failure then success
+    mock_litellm_completion.side_effect = [
+        APIConnectionError(
+            message="Connection failed",
+            llm_provider="test_provider",
+            model="test_model",
+        ),
+        mock_response,
+    ]
+
+    # Create LLM with cost tracking
+    llm = LLM(
+        model="gpt-4o",
+        api_key=SecretStr("test_key"),
+        num_retries=2,
+        retry_min_wait=1,
+        retry_max_wait=1,
+        usage_id="test-service",
+        input_cost_per_token=0.001,
+        output_cost_per_token=0.002,
+    )
+
+    # Make the completion call
+    response = llm.completion(
+        messages=[Message(role="user", content=[TextContent(text="Test")])],
+    )
+
+    # Verify the call succeeded
+    assert response.raw_response == mock_response
+
+    # Get metrics
+    metrics = llm.metrics
+
+    # Token usage should only reflect the successful attempt
+    assert len(metrics.token_usages) == 1
+    token_usage = metrics.token_usages[0]
+    assert token_usage.prompt_tokens == 100
+    assert token_usage.completion_tokens == 50
+
+    # Cost should only reflect the successful attempt
+    # Note: Cost calculation depends on litellm, so we just verify it's positive
+    assert metrics.accumulated_cost > 0
+
+    # Latency should only reflect the successful attempt (should be small)
+    assert len(metrics.response_latencies) == 1
+    assert metrics.response_latencies[0].latency < 0.5
+
+
+@patch("openhands.sdk.llm.llm.litellm_completion")
+def test_telemetry_no_multiple_records_on_retry(mock_litellm_completion):
+    """
+    Test that telemetry doesn't create multiple records for failed attempts.
+
+    Only the successful attempt should result in telemetry records.
+    """
+    mock_response = create_mock_response("Success")
+
+    # Simulate multiple failures then success
+    mock_litellm_completion.side_effect = [
+        APIConnectionError(
+            message="Fail 1", llm_provider="test_provider", model="test_model"
+        ),
+        APIConnectionError(
+            message="Fail 2", llm_provider="test_provider", model="test_model"
+        ),
+        APIConnectionError(
+            message="Fail 3", llm_provider="test_provider", model="test_model"
+        ),
+        mock_response,
+    ]
+
+    llm = LLM(
+        model="gpt-4o",
+        api_key=SecretStr("test_key"),
+        num_retries=5,
+        retry_min_wait=1,
+        retry_max_wait=1,
+        usage_id="test-service",
+    )
+
+    # Make the completion call
+    response = llm.completion(
+        messages=[Message(role="user", content=[TextContent(text="Test")])],
+    )
+
+    assert response.raw_response == mock_response
+
+    metrics = llm.metrics
+
+    # Should only have ONE latency record (for the successful attempt)
+    assert len(metrics.response_latencies) == 1
+
+    # Should only have ONE token usage record (for the successful attempt)
+    assert len(metrics.token_usages) == 1
+
+    # Should only have ONE cost record (for the successful attempt)
+    # Cost is accumulated, so we just check it's positive
+    assert metrics.accumulated_cost > 0