Add enhanced Traces/Spans and PydanticAI Instrumentor

.github/workflows/test.yml

@@ -27,7 +27,7 @@ jobs:
       - name: Install package and test deps
         run: |
           pip install -e .
-          pip install pytest pytest-cov
+          pip install pytest pytest-cov pydantic-ai
 
       - name: Run unit tests with coverage
         run: |

gradient_adk/decorator.py

@@ -18,9 +18,12 @@
 
 logger = get_logger(__name__)
 
-from gradient_adk.runtime.langgraph.helpers import capture_graph, get_tracker
+# Initialize framework instrumentation using the centralized registry
+# This is idempotent and will only install instrumentation once
+# Each instrumentor checks for its own environment variable to allow disabling
+from gradient_adk.runtime.helpers import capture_all, get_tracker
 
-capture_graph()
+capture_all()
 
 
 class _StreamingIteratorWithTracking:

gradient_adk/digital_ocean_api/__init__.py

@@ -1,5 +1,10 @@
 from .models import (
     TraceSpanType,
+    SpanCommon,
+    LLMSpanDetails,
+    ToolSpanDetails,
+    RetrieverSpanDetails,
+    WorkflowSpanDetails,
     Span,
     Trace,
     CreateTracesInput,
@@ -38,6 +43,11 @@
 
 __all__ = [
     "TraceSpanType",
+    "SpanCommon",
+    "LLMSpanDetails",
+    "ToolSpanDetails",
+    "RetrieverSpanDetails",
+    "WorkflowSpanDetails",
     "Span",
     "Trace",
     "CreateTracesInput",
@@ -70,4 +80,4 @@
     "DOAPINetworkError",
     "DOAPIValidationError",
     "AsyncDigitalOceanGenAI",
-]
+]

gradient_adk/digital_ocean_api/models.py

@@ -12,13 +12,68 @@ class TraceSpanType(str, Enum):
     TRACE_SPAN_TYPE_LLM = "TRACE_SPAN_TYPE_LLM"
     TRACE_SPAN_TYPE_RETRIEVER = "TRACE_SPAN_TYPE_RETRIEVER"
     TRACE_SPAN_TYPE_TOOL = "TRACE_SPAN_TYPE_TOOL"
+    TRACE_SPAN_TYPE_WORKFLOW = "TRACE_SPAN_TYPE_WORKFLOW"
+    TRACE_SPAN_TYPE_AGENT = "TRACE_SPAN_TYPE_AGENT"
+
+
+class SpanCommon(BaseModel):
+    """Common fields for all span types."""
+
+    model_config = ConfigDict(populate_by_name=True, extra="allow")
+
+    duration_ns: Optional[int] = Field(None, description="Duration in nanoseconds")
+    metadata: Optional[Dict[str, Any]] = Field(None, description="Additional metadata")
+    tags: Optional[List[str]] = Field(None, description="Tags for the span")
+    status_code: Optional[int] = Field(None, description="HTTP status code if applicable")
+
+
+class LLMSpanDetails(BaseModel):
+    """LLM-specific span details."""
+
+    model_config = ConfigDict(populate_by_name=True, extra="allow")
+
+    common: Optional[SpanCommon] = None
+    model: Optional[str] = Field(None, description="Model name")
+    num_input_tokens: Optional[int] = Field(None, description="Number of input tokens")
+    num_output_tokens: Optional[int] = Field(None, description="Number of output tokens")
+    total_tokens: Optional[int] = Field(None, description="Total tokens")
+    temperature: Optional[float] = Field(None, description="Temperature setting")
+    time_to_first_token_ns: Optional[int] = Field(
+        None, description="Time to first token in nanoseconds"
+    )
+
+
+class ToolSpanDetails(BaseModel):
+    """Tool-specific span details."""
+
+    model_config = ConfigDict(populate_by_name=True, extra="allow")
+
+    common: Optional[SpanCommon] = None
+    tool_call_id: Optional[str] = Field(None, description="Tool call identifier")
+
+
+class RetrieverSpanDetails(BaseModel):
+    """Retriever-specific span details."""
+
+    model_config = ConfigDict(populate_by_name=True, extra="allow")
+
+    common: Optional[SpanCommon] = None
+
+
+class WorkflowSpanDetails(BaseModel):
+    """Workflow span containing nested sub-spans."""
+
+    model_config = ConfigDict(populate_by_name=True, extra="allow")
+
+    spans: List["Span"] = Field(default_factory=list, description="Nested sub-spans")
 
 
 class Span(BaseModel):
     """
-    Represents a span within a trace (e.g., LLM call, retriever, tool).
+    Represents a span within a trace (e.g., LLM call, retriever, tool, workflow).
     - created_at: RFC3339 timestamp (protobuf Timestamp)
     - input/output: json
+    - For workflow spans, contains nested sub-spans in the 'workflow' field
     """
 
     model_config = ConfigDict(populate_by_name=True, extra="allow")
@@ -29,6 +84,23 @@ class Span(BaseModel):
     output: Dict[str, Any]
     type: TraceSpanType = Field(default=TraceSpanType.TRACE_SPAN_TYPE_UNKNOWN)
 
+    # Common fields for all span types
+    common: Optional[SpanCommon] = Field(None, description="Common span metadata")
+
+    # Type-specific fields
+    llm: Optional[LLMSpanDetails] = Field(None, description="LLM-specific details")
+    tool: Optional[ToolSpanDetails] = Field(None, description="Tool-specific details")
+    retriever: Optional[RetrieverSpanDetails] = Field(
+        None, description="Retriever-specific details"
+    )
+    workflow: Optional[WorkflowSpanDetails] = Field(
+        None, description="Workflow span with nested sub-spans"
+    )
+
+
+# Update forward reference for WorkflowSpanDetails
+WorkflowSpanDetails.model_rebuild()
+
 
 class Trace(BaseModel):
     """
@@ -919,4 +991,4 @@ class ListEvaluationMetricsOutput(BaseModel):
 
     metrics: List[EvaluationMetric] = Field(
         default_factory=list, description="List of evaluation metrics"
-    )
+    )

gradient_adk/runtime/digitalocean_tracker.py

@@ -12,6 +12,11 @@
     Trace,
     Span,
     TraceSpanType,
+    SpanCommon,
+    LLMSpanDetails,
+    ToolSpanDetails,
+    RetrieverSpanDetails,
+    WorkflowSpanDetails,
 )
 from .interfaces import NodeExecution
 
@@ -324,22 +329,126 @@ def _to_span(self, ex: NodeExecution) -> Span:
             out = dict(out)
             out["_llm_endpoints"] = list(ex.metadata["llm_endpoints"])
 
-        # classify LLM/tool/retriever via metadata set by the instrumentor
+        # classify span type via metadata set by the instrumentor
         metadata = ex.metadata or {}
-        if metadata.get("is_llm_call"):
+
+        # Check if this is a workflow span
+        if metadata.get("is_workflow"):
+            span_type = TraceSpanType.TRACE_SPAN_TYPE_WORKFLOW
+
+            # Build sub-spans from the workflow's collected spans
+            sub_spans_list = metadata.get("sub_spans", [])
+            sub_spans = [self._to_span(sub) for sub in sub_spans_list]
+
+            # Calculate duration from start to end
+            duration_ns = None
+            if ex.start_time and ex.end_time:
+                duration_ns = int(
+                    (ex.end_time - ex.start_time).total_seconds() * 1_000_000_000
+                )
+
+            # Build common fields
+            common = SpanCommon(
+                duration_ns=duration_ns,
+                metadata={"agent_name": metadata.get("agent_name")},
+                status_code=200 if ex.error is None else 500,
+            )
+
+            # Build workflow details with nested sub-spans
+            workflow_details = WorkflowSpanDetails(spans=sub_spans)
+
+            return Span(
+                created_at=_utc(ex.start_time),
+                name=ex.node_name,
+                input=inp,
+                output=out,
+                type=span_type,
+                common=common,
+                workflow=workflow_details,
+            )
+        elif metadata.get("is_llm_call"):
             span_type = TraceSpanType.TRACE_SPAN_TYPE_LLM
+
+            # Calculate duration
+            duration_ns = None
+            if ex.start_time and ex.end_time:
+                duration_ns = int(
+                    (ex.end_time - ex.start_time).total_seconds() * 1_000_000_000
+                )
+
+            # Build LLM-specific details
+            llm_common = SpanCommon(
+                duration_ns=duration_ns,
+                status_code=200 if ex.error is None else 500,
+            )
+
+            # Extract LLM-specific fields from output if available
+            llm_details = LLMSpanDetails(
+                common=llm_common,
+                model=metadata.get("model_name") or ex.node_name.replace("llm:", ""),
+            )
+
+            return Span(
+                created_at=_utc(ex.start_time),
+                name=ex.node_name,
+                input=inp,
+                output=out,
+                type=span_type,
+                llm=llm_details,
+            )
         elif metadata.get("is_retriever_call"):
             span_type = TraceSpanType.TRACE_SPAN_TYPE_RETRIEVER
+
+            # Calculate duration
+            duration_ns = None
+            if ex.start_time and ex.end_time:
+                duration_ns = int(
+                    (ex.end_time - ex.start_time).total_seconds() * 1_000_000_000
+                )
+
+            # Build retriever-specific details
+            retriever_common = SpanCommon(
+                duration_ns=duration_ns,
+                status_code=200 if ex.error is None else 500,
+            )
+
+            retriever_details = RetrieverSpanDetails(common=retriever_common)
+
+            return Span(
+                created_at=_utc(ex.start_time),
+                name=ex.node_name,
+                input=inp,
+                output=out,
+                type=span_type,
+                retriever=retriever_details,
+            )
         else:
+            # Default to tool span
             span_type = TraceSpanType.TRACE_SPAN_TYPE_TOOL
 
-        return Span(
-            created_at=_utc(ex.start_time),
-            name=ex.node_name,
-            input=inp,
-            output=out,
-            type=span_type,
-        )
+            # Calculate duration
+            duration_ns = None
+            if ex.start_time and ex.end_time:
+                duration_ns = int(
+                    (ex.end_time - ex.start_time).total_seconds() * 1_000_000_000
+                )
+
+            # Build tool-specific details
+            tool_common = SpanCommon(
+                duration_ns=duration_ns,
+                status_code=200 if ex.error is None else 500,
+            )
+
+            tool_details = ToolSpanDetails(common=tool_common)
+
+            return Span(
+                created_at=_utc(ex.start_time),
+                name=ex.node_name,
+                input=inp,
+                output=out,
+                type=span_type,
+                tool=tool_details,
+            )
 
     def _coerce_top(self, val: Any, kind: str) -> Dict[str, Any]:
         """