Nikhilc/message structure for auto

libraries/microsoft-agents-a365-observability-core/microsoft_agents_a365/observability/core/models/messages.py

@@ -66,7 +66,7 @@ class ToolCallRequestPart:
 
     name: str
     id: str | None = None
-    arguments: dict[str, object] | list[object] | None = None
+    arguments: dict[str, object] | list[object] | str | None = None
     type: str = field(default="tool_call", init=False)
 
 

libraries/microsoft-agents-a365-observability-extensions-agentframework/docs/design.md

@@ -55,5 +55,5 @@ microsoft_agents_a365/observability/extensions/agentframework/
 
 ## Dependencies
 
-- `agent-framework-azure-ai` - Microsoft Agents SDK
+- `agent-framework` - Microsoft Agents SDK
 - `microsoft-agents-a365-observability-core` - Core observability

libraries/microsoft-agents-a365-observability-extensions-agentframework/microsoft_agents_a365/observability/extensions/agentframework/message_mapper.py

@@ -0,0 +1,188 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Maps Agent Framework span tag messages to A365 versioned message format.
+
+Agent Framework sets ``gen_ai.input.messages`` / ``gen_ai.output.messages`` as span
+tags containing JSON arrays of ``{role, parts[{type, content}], finish_reason?}``.
+This mapper converts them to :class:`InputMessages` / :class:`OutputMessages`.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+from microsoft_agents_a365.observability.core.message_utils import serialize_messages
+from microsoft_agents_a365.observability.core.models.messages import (
+    BlobPart,
+    ChatMessage,
+    FilePart,
+    GenericPart,
+    InputMessages,
+    MessagePart,
+    MessageRole,
+    OutputMessage,
+    OutputMessages,
+    ReasoningPart,
+    TextPart,
+    ToolCallRequestPart,
+    ToolCallResponsePart,
+    UriPart,
+)
+
+logger = logging.getLogger(__name__)
+
+_ROLE_MAP: dict[str, MessageRole] = {
+    "system": MessageRole.SYSTEM,
+    "user": MessageRole.USER,
+    "assistant": MessageRole.ASSISTANT,
+    "tool": MessageRole.TOOL,
+}
+
+
+def map_input_messages(messages_json: str) -> str | None:
+    """Map a ``gen_ai.input.messages`` tag value to a serialized A365 JSON string.
+
+    Args:
+        messages_json: The raw JSON string from the span attribute.
+
+    Returns:
+        Serialized :class:`InputMessages` JSON string, or ``None`` if the
+        input is empty or cannot be parsed.
+    """
+    try:
+        raw = json.loads(messages_json)
+    except (json.JSONDecodeError, TypeError):
+        logger.debug("Failed to parse input messages JSON: %s", messages_json[:200])
+        return None
+
+    if not isinstance(raw, list):
+        return None
+
+    chat_messages: list[ChatMessage] = []
+    for msg in raw:
+        if not isinstance(msg, dict):
+            continue
+        role = _map_role(msg.get("role"), MessageRole.USER)
+        parts = _map_parts(msg)
+        if parts:
+            chat_messages.append(ChatMessage(role=role, parts=parts, name=msg.get("name")))
+
+    if not chat_messages:
+        return None
+
+    return serialize_messages(InputMessages(messages=chat_messages))
+
+
+def map_output_messages(messages_json: str) -> str | None:
+    """Map a ``gen_ai.output.messages`` tag value to a serialized A365 JSON string.
+
+    Args:
+        messages_json: The raw JSON string from the span attribute.
+
+    Returns:
+        Serialized :class:`OutputMessages` JSON string, or ``None`` if the
+        input is empty or cannot be parsed.
+    """
+    try:
+        raw = json.loads(messages_json)
+    except (json.JSONDecodeError, TypeError):
+        logger.debug("Failed to parse output messages JSON: %s", messages_json[:200])
+        return None
+
+    if not isinstance(raw, list):
+        return None
+
+    output_messages: list[OutputMessage] = []
+    for msg in raw:
+        if not isinstance(msg, dict):
+            continue
+        role = _map_role(msg.get("role"), MessageRole.ASSISTANT)
+        parts = _map_parts(msg)
+        finish_reason = msg.get("finish_reason")
+        if parts:
+            output_messages.append(
+                OutputMessage(role=role, parts=parts, finish_reason=finish_reason)
+            )
+
+    if not output_messages:
+        return None
+
+    return serialize_messages(OutputMessages(messages=output_messages))
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+
+def _map_role(role: str | None, default: MessageRole) -> MessageRole:
+    """Map a raw role string to a :class:`MessageRole` enum."""
+    if not role:
+        return default
+    return _ROLE_MAP.get(role.lower(), default)
+
+
+def _map_parts(msg: dict[str, Any]) -> list[MessagePart]:
+    """Map all parts in a raw message dict."""
+    parts_data = msg.get("parts", [])
+    if not isinstance(parts_data, list):
+        return []
+    mapped = [_map_single_part(p) for p in parts_data if isinstance(p, dict)]
+    return [p for p in mapped if p is not None]
+
+
+def _map_single_part(part: dict[str, Any]) -> MessagePart | None:
+    """Map a single raw part dict to the appropriate A365 message part."""
+    part_type = part.get("type", "")
+
+    if part_type == "text":
+        content = part.get("content", "")
+        return TextPart(content=content) if content else None
+
+    if part_type == "reasoning":
+        content = part.get("content", "")
+        return ReasoningPart(content=content) if content else None
+
+    if part_type == "tool_call":
+        name = part.get("name")
+        if not name:
+            return None
+        return ToolCallRequestPart(
+            name=name,
+            id=part.get("id"),
+            arguments=part.get("arguments"),
+        )
+
+    if part_type == "tool_call_response":
+        return ToolCallResponsePart(
+            id=part.get("id"),
+            response=part.get("response"),
+        )
+
+    if part_type == "blob":
+        modality = part.get("modality", "")
+        content = part.get("content", "")
+        if not modality or not content:
+            return None
+        return BlobPart(modality=modality, content=content, mime_type=part.get("mime_type"))
+
+    if part_type == "file":
+        modality = part.get("modality", "")
+        file_id = part.get("file_id", "")
+        if not modality or not file_id:
+            return None
+        return FilePart(modality=modality, file_id=file_id, mime_type=part.get("mime_type"))
+
+    if part_type == "uri":
+        modality = part.get("modality", "")
+        uri = part.get("uri", "")
+        if not modality or not uri:
+            return None
+        return UriPart(modality=modality, uri=uri, mime_type=part.get("mime_type"))
+
+    # Fallback: GenericPart for unknown/future part types
+    data = {k: v for k, v in part.items() if k != "type"}
+    return GenericPart(type=part_type, data=data) if part_type else None

libraries/microsoft-agents-a365-observability-extensions-agentframework/microsoft_agents_a365/observability/extensions/agentframework/span_enricher.py

@@ -2,8 +2,10 @@
 # Licensed under the MIT License.
 
 from microsoft_agents_a365.observability.core.constants import (
+    CHAT_OPERATION_NAME,
     EXECUTE_TOOL_OPERATION_NAME,
     GEN_AI_INPUT_MESSAGES_KEY,
+    GEN_AI_OPERATION_NAME_KEY,
     GEN_AI_OUTPUT_MESSAGES_KEY,
     GEN_AI_TOOL_ARGS_KEY,
     GEN_AI_TOOL_CALL_RESULT_KEY,
@@ -12,33 +14,50 @@
 from microsoft_agents_a365.observability.core.exporters.enriched_span import EnrichedReadableSpan
 from opentelemetry.sdk.trace import ReadableSpan
 
-from .utils import extract_input_content, extract_output_content
+from .message_mapper import map_input_messages, map_output_messages
 
 # Agent Framework specific attribute keys
 AF_TOOL_CALL_ARGUMENTS_KEY = "gen_ai.tool.call.arguments"
 AF_TOOL_CALL_RESULT_KEY = "gen_ai.tool.call.result"
 
+_MESSAGE_OPERATIONS = {INVOKE_AGENT_OPERATION_NAME, CHAT_OPERATION_NAME}
+
 
 def enrich_agent_framework_span(span: ReadableSpan) -> ReadableSpan:
+    """Enricher function for Agent Framework spans.
+
+    For ``invoke_agent`` and ``chat`` operations, maps the raw
+    ``gen_ai.input.messages`` / ``gen_ai.output.messages`` JSON arrays
+    to the A365 versioned format.
+
+    For ``execute_tool`` operations, maps Agent Framework tool attribute
+    keys to the A365 standard keys.
     """
-    Enricher function for Agent Framework spans.
-    """
-    extra_attributes = {}
+    extra_attributes: dict[str, str] = {}
     attributes = span.attributes or {}
+    operation = attributes.get(GEN_AI_OPERATION_NAME_KEY, "")
+
+    is_message_span = operation in _MESSAGE_OPERATIONS or span.name.startswith(
+        INVOKE_AGENT_OPERATION_NAME
+    )
+    is_tool_span = operation == EXECUTE_TOOL_OPERATION_NAME or span.name.startswith(
+        EXECUTE_TOOL_OPERATION_NAME
+    )
 
-    # Only extract content for invoke_agent spans
-    if span.name.startswith(INVOKE_AGENT_OPERATION_NAME):
-        # Extract all text content from input messages
+    if is_message_span:
         input_messages = attributes.get(GEN_AI_INPUT_MESSAGES_KEY)
         if input_messages:
-            extra_attributes[GEN_AI_INPUT_MESSAGES_KEY] = extract_input_content(input_messages)
+            mapped = map_input_messages(input_messages)
+            if mapped is not None:
+                extra_attributes[GEN_AI_INPUT_MESSAGES_KEY] = mapped
 
         output_messages = attributes.get(GEN_AI_OUTPUT_MESSAGES_KEY)
         if output_messages:
-            extra_attributes[GEN_AI_OUTPUT_MESSAGES_KEY] = extract_output_content(output_messages)
+            mapped = map_output_messages(output_messages)
+            if mapped is not None:
+                extra_attributes[GEN_AI_OUTPUT_MESSAGES_KEY] = mapped
 
-    # Map tool attributes for execute_tool spans
-    elif span.name.startswith(EXECUTE_TOOL_OPERATION_NAME):
+    elif is_tool_span:
         if AF_TOOL_CALL_ARGUMENTS_KEY in attributes:
             extra_attributes[GEN_AI_TOOL_ARGS_KEY] = attributes[AF_TOOL_CALL_ARGUMENTS_KEY]
 

libraries/microsoft-agents-a365-observability-extensions-agentframework/microsoft_agents_a365/observability/extensions/agentframework/span_processor.py

@@ -7,12 +7,11 @@
 class AgentFrameworkSpanProcessor(SpanProcessor):
     """SpanProcessor for Agent Framework.
 
-    Note: The span processing logic was removed as GEN_AI_EVENT_CONTENT is no longer used.
-    This processor is kept for interface compatibility.
+    Attribute mutation happens in the enricher (via :class:`EnrichedReadableSpan`)
+    because OTel Python ``ReadableSpan`` is immutable after ``on_end``.
+    The enricher is invoked at export time by the ``EnrichingSpanProcessor``.
     """
 
-    TOOL_CALL_RESULT_TAG = "gen_ai.tool.call.result"
-
     def __init__(self, service_name: str | None = None):
         self.service_name = service_name
         super().__init__()
@@ -22,5 +21,9 @@ def on_start(self, span, parent_context):
         pass
 
     def on_end(self, span):
-        """Called when a span ends. Intentionally a no-op."""
+        """Called when a span ends. Intentionally a no-op.
+
+        Message mapping is handled by the span enricher at export time
+        since ReadableSpan is immutable in the Python OTel SDK.
+        """
         pass

libraries/microsoft-agents-a365-observability-extensions-agentframework/microsoft_agents_a365/observability/extensions/agentframework/trace_instrumentor.py

@@ -23,7 +23,7 @@
 # -----------------------------
 # 3) The Instrumentor class
 # -----------------------------
-_instruments = ("agent-framework-azure-ai >= 1.0.0",)
+_instruments = ("agent-framework >= 1.0.0",)
 
 
 class AgentFrameworkInstrumentor(BaseInstrumentor):