fix(instrumentation): all 4 usage sources are 'if' not 'elif'

src/nullrun/__version__.py

@@ -276,7 +276,31 @@
 SDK_MIN_VERSION_FOR_V3 = "0.12.0". Recommended upgrade path:
 0.13.1 -> 0.13.2 (typing-only change for end users; visible
 delta is the per-file mypy table in pyproject.toml).
+
+v3.16 / 0.13.4 (2026-07-08) -- bug-fix: complete the LangChain
+usage-extraction elif-chain.
+
+Pre-fix extract_usage_from_response walked the 4 source branches
+if-hasattr-usage_metadata ... elif-hasattr-generations ...
+elif-hasattr-usage ... elif-hasattr-response_metadata. A LangChain
+AIMessage can carry token info on multiple attributes at once.
+When the first branch's hasattr returned True but the value was
+empty or 0/0/0 (streaming init state, some provider wrappers),
+every subsequent elif was skipped and the SDK shipped tokens=0
+to the backend -- making the LLM call invisible on the dashboard.
+
+Switched all 4 source branches to plain if so each one attempts
+its read; later branches naturally overwrite the zero default when
+the earlier branch value is empty. New regression test
+test_extract_usage_metadata_zero_response_metadata_real.
+
+39 tests in test_langgraph_callback.py still pass; no
+regression in test_extractors.py or
+test_instrumentation_phase41.py. Wire format is unchanged.
+
+Recommended upgrade path: 0.13.3 -> 0.13.4. No SDK_MIN_VERSION
+bump; backends on 1.0.0 keep working unchanged.
 """
 
-__version__ = "0.13.3"
+__version__ = "0.13.4"
 __platform_version__ = "1.0.0"

src/nullrun/instrumentation/langgraph.py

@@ -211,7 +211,7 @@ def extract_usage_from_response(response: Any, provider: str, model: str) -> dic
             }
 
     # For callback-based LLMResult, check generations[0][0].message.usage_metadata
-    elif hasattr(response, 'generations') and response.generations:
+    if hasattr(response, 'generations') and response.generations:
         first_gen = response.generations[0][0] if response.generations else None
         if first_gen and hasattr(first_gen, 'message'):
             msg = first_gen.message
@@ -233,7 +233,7 @@ def extract_usage_from_response(response: Any, provider: str, model: str) -> dic
                     }
 
     # Try response.usage (Anthropic, standard OpenAI format)
-    elif hasattr(response, 'usage') and response.usage:
+    if hasattr(response, 'usage') and response.usage:
         usage_raw = response.usage
         if isinstance(usage_raw, dict):
             usage["input_tokens"] = usage_raw.get('input_tokens', 0) or 0
@@ -251,8 +251,20 @@ def extract_usage_from_response(response: Any, provider: str, model: str) -> dic
                 'total_tokens': usage["total_tokens"],
             }
 
+    # All 4 sources above are `if` (not `elif`) because the same
+    # response can carry token info on multiple attributes (e.g.
+    # `usage_metadata = {}` plus `response_metadata.token_usage =
+    # {real tokens}`). `elif` would silently drop the
+    # `response_metadata` branch whenever the previous branch's
+    # hasattr() returned True with an empty value. The first
+    # non-empty source wins; later branches may overwrite (LangChain
+    # providers in practice never put conflicting numbers on two
+    # attributes of the same response, so a "last-wins" is safe
+    # in practice; see the `_extract_usage` docstring for the
+    # priority order rationale).
+    #
     # Try response_metadata (some providers) - also check llm_output for LLMResult
-    elif hasattr(response, 'response_metadata'):
+    if hasattr(response, 'response_metadata'):
         resp_meta = response.response_metadata
         if isinstance(resp_meta, dict):
             # Some providers put token info here
@@ -269,7 +281,7 @@ def extract_usage_from_response(response: Any, provider: str, model: str) -> dic
                 usage["total_tokens"] = token_usage.get('total_tokens', 0) or 0
                 usage["raw_usage"] = dict(token_usage)
     # Check llm_output for LLMResult (callback case)
-    elif hasattr(response, 'llm_output') and response.llm_output:
+    if hasattr(response, 'llm_output') and response.llm_output:
         token_usage = response.llm_output.get('token_usage', {})
         if isinstance(token_usage, dict):
             usage["input_tokens"] = (

tests/test_langgraph_callback.py

@@ -46,6 +46,37 @@ def test_extract_usage_metadata_dict_form():
     assert usage["has_usage"] is True
 
 
+def test_extract_usage_metadata_zero_response_metadata_real() -> None:
+    """2026-07-08: regression for the `elif`-chain token-extraction bug.
+
+    The pre-fix extractor walked `if hasattr(... usage_metadata): ...
+    elif hasattr(... response_metadata): ...`. If a LangChain AIMessage
+    carried an empty `usage_metadata` (0/0/0) but a populated
+    `response_metadata.token_usage` (the real numbers), the elif skipped
+    `response_metadata` and the SDK shipped `tokens=0` to the backend —
+    making the LLM call invisible on the dashboard.
+
+    After the fix, all 4 source branches are `if` (not `elif`) so the
+    populated `response_metadata` overwrites the empty `usage_metadata`.
+    The test asserts the non-zero numbers come through to the wire shape.
+    """
+    response = SimpleNamespace(
+        usage_metadata={"input_tokens": 0, "output_tokens": 0, "total_tokens": 0},
+        response_metadata={
+            "token_usage": {
+                "prompt_tokens": 26,
+                "completion_tokens": 48,
+                "total_tokens": 74,
+            }
+        },
+    )
+    usage = extract_usage_from_response(response, provider="openai", model="gpt-4.1-mini")
+    assert usage["input_tokens"] == 26, f"expected 26, got {usage['input_tokens']}"
+    assert usage["output_tokens"] == 48, f"expected 48, got {usage['output_tokens']}"
+    assert usage["total_tokens"] == 74, f"expected 74, got {usage['total_tokens']}"
+    assert usage["has_usage"] is True
+
+
 def test_extract_usage_metadata_object_form():
     """Object with .input_tokens / .output_tokens / .total_tokens attrs."""
     response = SimpleNamespace(