chore(langchain): auto-instrument with langgraph

ddtrace/contrib/internal/langchain/patch.py

@@ -180,6 +180,9 @@ def traced_llm_generate(langchain, pin, func, instance, args, kwargs):
         api_key=_extract_api_key(instance),
     )
     completions = None
+
+    integration.record_instance(instance, span)
+
     try:
         if integration.is_pc_sampled_span(span):
             for idx, prompt in enumerate(prompts):
@@ -231,6 +234,9 @@ async def traced_llm_agenerate(langchain, pin, func, instance, args, kwargs):
         model=model,
         api_key=_extract_api_key(instance),
     )
+
+    integration.record_instance(instance, span)
+
     completions = None
     try:
         if integration.is_pc_sampled_span(span):
@@ -282,6 +288,9 @@ def traced_chat_model_generate(langchain, pin, func, instance, args, kwargs):
         model=_extract_model_name(instance),
         api_key=_extract_api_key(instance),
     )
+
+    integration.record_instance(instance, span)
+
     chat_completions = None
     try:
         for message_set_idx, message_set in enumerate(chat_messages):
@@ -372,6 +381,9 @@ async def traced_chat_model_agenerate(langchain, pin, func, instance, args, kwar
         model=_extract_model_name(instance),
         api_key=_extract_api_key(instance),
     )
+
+    integration.record_instance(instance, span)
+
     chat_completions = None
     try:
         for message_set_idx, message_set in enumerate(chat_messages):
@@ -469,6 +481,9 @@ def traced_embedding(langchain, pin, func, instance, args, kwargs):
         model=_extract_model_name(instance),
         api_key=_extract_api_key(instance),
     )
+
+    integration.record_instance(instance, span)
+
     embeddings = None
     try:
         if isinstance(input_texts, str):
@@ -520,6 +535,9 @@ def traced_lcel_runnable_sequence(langchain, pin, func, instance, args, kwargs):
     )
     inputs = None
     final_output = None
+
+    integration.record_steps(instance, span)
+
     try:
         try:
             inputs = get_argument_value(args, kwargs, 0, "input")
@@ -564,6 +582,9 @@ async def traced_lcel_runnable_sequence_async(langchain, pin, func, instance, ar
     )
     inputs = None
     final_output = None
+
+    integration.record_steps(instance, span)
+
     try:
         try:
             inputs = get_argument_value(args, kwargs, 0, "input")
@@ -608,6 +629,9 @@ def traced_similarity_search(langchain, pin, func, instance, args, kwargs):
         provider=provider,
         api_key=_extract_api_key(instance),
     )
+
+    integration.record_instance(instance, span)
+
     documents = []
     try:
         if integration.is_pc_sampled_span(span):
@@ -655,6 +679,7 @@ def traced_chain_stream(langchain, pin, func, instance, args, kwargs):
     integration: LangChainIntegration = langchain._datadog_integration
 
     def _on_span_started(span: Span):
+        integration.record_instance(instance, span)
         inputs = get_argument_value(args, kwargs, 0, "input")
         if not integration.is_pc_sampled_span(span):
             return
@@ -712,6 +737,7 @@ def traced_chat_stream(langchain, pin, func, instance, args, kwargs):
     model = _extract_model_name(instance)
 
     def _on_span_started(span: Span):
+        integration.record_instance(instance, span)
         if not integration.is_pc_sampled_span(span):
             return
         chat_messages = get_argument_value(args, kwargs, 0, "input")
@@ -771,6 +797,7 @@ def traced_llm_stream(langchain, pin, func, instance, args, kwargs):
     model = _extract_model_name(instance)
 
     def _on_span_start(span: Span):
+        integration.record_instance(instance, span)
         if not integration.is_pc_sampled_span(span):
             return
         inp = get_argument_value(args, kwargs, 0, "input")
@@ -818,6 +845,8 @@ def traced_base_tool_invoke(langchain, pin, func, instance, args, kwargs):
         submit_to_llmobs=True,
     )
 
+    integration.record_instance(instance, span)
+
     tool_output = None
     tool_info = {}
     try:
@@ -869,6 +898,8 @@ async def traced_base_tool_ainvoke(langchain, pin, func, instance, args, kwargs)
         submit_to_llmobs=True,
     )
 
+    integration.record_instance(instance, span)
+
     tool_output = None
     tool_info = {}
     try:

ddtrace/contrib/internal/langgraph/patch.py

@@ -121,6 +121,7 @@ def traced_pregel_stream(langgraph, pin, func, instance, args, kwargs):
         result = func(*args, **kwargs)
     except Exception:
         span.set_exc_info(*sys.exc_info())
+        integration.llmobs_set_tags(span, args=args, kwargs={**kwargs, "name": name}, response=None, operation="graph")
         span.finish()
         raise
 
@@ -139,6 +140,9 @@ def _stream():
                 break
             except Exception:
                 span.set_exc_info(*sys.exc_info())
+                integration.llmobs_set_tags(
+                    span, args=args, kwargs={**kwargs, "name": name}, response=None, operation="graph"
+                )
                 span.finish()
                 raise
 
@@ -160,6 +164,7 @@ def traced_pregel_astream(langgraph, pin, func, instance, args, kwargs):
         result = func(*args, **kwargs)
     except Exception:
         span.set_exc_info(*sys.exc_info())
+        integration.llmobs_set_tags(span, args=args, kwargs={**kwargs, "name": name}, response=None, operation="graph")
         span.finish()
         raise
 
@@ -178,6 +183,9 @@ async def _astream():
                 break
             except Exception:
                 span.set_exc_info(*sys.exc_info())
+                integration.llmobs_set_tags(
+                    span, args=args, kwargs={**kwargs, "name": name}, response=None, operation="graph"
+                )
                 span.finish()
                 raise
 
@@ -204,6 +212,8 @@ def patch():
     if not should_patch or getattr(langgraph, "_datadog_patch", False):
         return
 
+    os.environ["_DD_LLMOBS_AUTO_SPAN_LINKING_ENABLED"] = "true"
+
     langgraph._datadog_patch = True
 
     Pin().onto(langgraph)

ddtrace/llmobs/_integrations/base.py

@@ -65,6 +65,10 @@ def __init__(self, integration_config: IntegrationConfig) -> None:
             self.start_log_writer()
         self._llmobs_pc_sampler = RateSampler(sample_rate=config._llmobs_sample_rate)
 
+    @property
+    def span_linking_enabled(self) -> bool:
+        return asbool(os.getenv("_DD_LLMOBS_AUTO_SPAN_LINKING_ENABLED", "false"))
+
     @property
     def metrics_enabled(self) -> bool:
         """