[ai.agentserver] add azure-ai-agentserver-agentframework and azure-ai-azureagentserver-langgraph package

eng/tools/azure-sdk-tools/ci_tools/functions.py

@@ -55,7 +55,12 @@
     "sdk/textanalytics/azure-ai-textanalytics",
 ]
 
-TEST_COMPATIBILITY_MAP = {"azure-ai-ml": ">=3.7"}
+TEST_COMPATIBILITY_MAP = {
+    "azure-ai-ml": ">=3.7",
+    "azure-ai-agentserver-core": ">=3.9",  # override to allow build with python 3.9
+    "azure-ai-agentserver-langgraph": ">=3.9",  # override to allow build with python 3.9
+    "azure-ai-agentserver-agentframework": ">=3.9",  # override to allow build with python 3.9
+}
 TEST_PYTHON_DISTRO_INCOMPATIBILITY_MAP = {
     "azure-storage-blob": "pypy",
     "azure-storage-queue": "pypy",

sdk/ai/azure-ai-agentserver-agentframework/CHANGELOG.md

@@ -0,0 +1,7 @@
+# Release History
+
+## 1.0.0
+
+### Features Added
+
+First version

sdk/ai/azure-ai-agentserver-agentframework/MANIFEST.in

@@ -0,0 +1,8 @@
+include *.md
+include LICENSE
+recursive-include tests *.py
+recursive-include samples *.py *.md
+recursive-include doc *.rst *.md
+include azure/__init__.py
+include azure/ai/__init__.py
+include azure/ai/agentserver/__init__.py

sdk/ai/azure-ai-agentserver-agentframework/README.md

@@ -0,0 +1,23 @@
+# Azure AI Agent Server adapter for agent-framework
+
+## Install
+
+In current folder, run:
+```bash
+pip install -e .
+```
+
+## Usage
+
+```python
+# your existing agent
+from my_framework_agent import my_awesome_agent
+
+# agent framework utils
+from azure.ai.agentserver.agentframework import from_agent_framework
+
+if __name__ == "__main__":
+    # with this simple line, your agent will be hosted on http://localhost:8088
+    from_agent_framework(my_awesome_agent).run()
+
+```

sdk/ai/azure-ai-agentserver-agentframework/azure/__init__.py

@@ -0,0 +1 @@
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)  # type: ignore

sdk/ai/azure-ai-agentserver-agentframework/azure/ai/__init__.py

@@ -0,0 +1 @@
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)  # type: ignore

sdk/ai/azure-ai-agentserver-agentframework/azure/ai/agentserver/__init__.py

@@ -0,0 +1 @@
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)  # type: ignore

sdk/ai/azure-ai-agentserver-agentframework/azure/ai/agentserver/agentframework/__init__.py

@@ -0,0 +1,16 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
+
+from ._version import VERSION
+
+
+def from_agent_framework(agent):
+    from .agent_framework import AgentFrameworkCBAgent
+
+    return AgentFrameworkCBAgent(agent)
+
+
+__all__ = ["from_agent_framework"]
+__version__ = VERSION

sdk/ai/azure-ai-agentserver-agentframework/azure/ai/agentserver/agentframework/_version.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) Python Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+VERSION = "1.0.0a1"

sdk/ai/azure-ai-agentserver-agentframework/azure/ai/agentserver/agentframework/agent_framework.py

@@ -0,0 +1,146 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+# pylint: disable=logging-fstring-interpolation
+from __future__ import annotations
+
+import asyncio
+import os
+from typing import Any, AsyncGenerator, Union
+
+from agent_framework import AgentProtocol
+from agent_framework.azure import AzureAIAgentClient
+from azure.ai.agentserver.core import AgentRunContext, FoundryCBAgent
+from azure.ai.agentserver.core.constants import Constants as AdapterConstants
+from azure.ai.agentserver.core.logger import get_logger
+from azure.ai.agentserver.core.models import (
+    CreateResponse,
+    Response as OpenAIResponse,
+    ResponseStreamEvent,
+)
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+from opentelemetry import trace
+
+from .models.agent_framework_input_converters import AgentFrameworkInputConverter
+from .models.agent_framework_output_non_streaming_converter import (
+    AgentFrameworkOutputNonStreamingConverter,
+)
+from .models.agent_framework_output_streaming_converter import AgentFrameworkOutputStreamingConverter
+from .models.constants import Constants
+
+logger = get_logger()
+
+
+class AgentFrameworkCBAgent(FoundryCBAgent):
+    """
+    Adapter class for integrating Agent Framework agents with the FoundryCB agent interface.
+
+    This class wraps an Agent Framework `AgentProtocol` instance and provides a unified interface
+    for running agents in both streaming and non-streaming modes. It handles input and output
+    conversion between the Agent Framework and the expected formats for FoundryCB agents.
+
+    Parameters:
+        agent (AgentProtocol): An instance of an Agent Framework agent to be adapted.
+
+    Usage:
+        - Instantiate with an Agent Framework agent.
+        - Call `agent_run` with a `CreateResponse` request body to execute the agent.
+        - Supports both streaming and non-streaming responses based on the `stream` flag.
+    """
+
+    def __init__(self, agent: AgentProtocol):
+        super().__init__()
+        self.agent = agent
+        logger.info(f"Initialized AgentFrameworkCBAgent with agent: {type(agent).__name__}")
+
+    def _resolve_stream_timeout(self, request_body: CreateResponse) -> float:
+        """Resolve idle timeout for streaming updates.
+
+        Order of precedence:
+        1) request_body.stream_timeout_s (if provided)
+        2) env var Constants.AGENTS_ADAPTER_STREAM_TIMEOUT_S
+        3) Constants.DEFAULT_STREAM_TIMEOUT_S
+        """
+        override = request_body.get("stream_timeout_s", None)
+        if override is not None:
+            return float(override)
+        env_val = os.getenv(Constants.AGENTS_ADAPTER_STREAM_TIMEOUT_S)
+        return float(env_val) if env_val is not None else float(Constants.DEFAULT_STREAM_TIMEOUT_S)
+
+    def init_tracing(self):
+        exporter = os.environ.get(AdapterConstants.OTEL_EXPORTER_ENDPOINT)
+        app_insights_conn_str = os.environ.get(AdapterConstants.APPLICATION_INSIGHTS_CONNECTION_STRING)
+        project_endpoint = os.environ.get(AdapterConstants.AZURE_AI_PROJECT_ENDPOINT)
+
+        if project_endpoint:
+            project_client = AIProjectClient(endpoint=project_endpoint, credential=DefaultAzureCredential())
+            agent_client = AzureAIAgentClient(project_client=project_client)
+            agent_client.setup_azure_ai_observability()
+        elif exporter or app_insights_conn_str:
+            os.environ["WORKFLOW_ENABLE_OTEL"] = "true"
+            from agent_framework.observability import setup_observability
+
+            setup_observability(
+                enable_sensitive_data=True,
+                otlp_endpoint=exporter,
+                applicationinsights_connection_string=app_insights_conn_str,
+            )
+        self.tracer = trace.get_tracer(__name__)
+
+    async def agent_run(
+        self, context: AgentRunContext
+    ) -> Union[
+        OpenAIResponse,
+        AsyncGenerator[ResponseStreamEvent, Any],
+    ]:
+        logger.info(f"Starting agent_run with stream={context.stream}")
+        request_input = context.request.get("input")
+
+        input_converter = AgentFrameworkInputConverter()
+        message = input_converter.transform_input(request_input)
+        logger.debug(f"Transformed input message type: {type(message)}")
+
+        # Use split converters
+        if context.stream:
+            logger.info("Running agent in streaming mode")
+            streaming_converter = AgentFrameworkOutputStreamingConverter(context)
+
+            async def stream_updates():
+                update_count = 0
+                timeout_s = self._resolve_stream_timeout(context.request)
+                logger.info("Starting streaming with idle-timeout=%.2fs", timeout_s)
+                for ev in streaming_converter.initial_events():
+                    yield ev
+
+                # Iterate with per-update timeout; terminate if idle too long
+                aiter = self.agent.run_stream(message).__aiter__()
+                while True:
+                    try:
+                        update = await asyncio.wait_for(aiter.__anext__(), timeout=timeout_s)
+                    except StopAsyncIteration:
+                        logger.debug("Agent streaming iterator finished (StopAsyncIteration)")
+                        break
+                    except asyncio.TimeoutError:
+                        logger.warning("Streaming idle timeout reached (%.1fs); terminating stream.", timeout_s)
+                        for ev in streaming_converter.completion_events():
+                            yield ev
+                        return
+                    update_count += 1
+                    transformed = streaming_converter.transform_output_for_streaming(update)
+                    for event in transformed:
+                        yield event
+                for ev in streaming_converter.completion_events():
+                    yield ev
+                logger.info("Streaming completed with %d updates", update_count)
+
+            return stream_updates()
+
+        # Non-streaming path
+        logger.info("Running agent in non-streaming mode")
+        non_streaming_converter = AgentFrameworkOutputNonStreamingConverter(context)
+        result = await self.agent.run(message)
+        logger.debug(f"Agent run completed, result type: {type(result)}")
+        transformed_result = non_streaming_converter.transform_output_for_response(result)
+        logger.info("Agent run and transformation completed successfully")
+        return transformed_result

sdk/ai/azure-ai-agentserver-agentframework/azure/ai/agentserver/agentframework/models/__init__.py

@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)

sdk/ai/azure-ai-agentserver-agentframework/azure/ai/agentserver/agentframework/models/agent_framework_input_converters.py

@@ -0,0 +1,118 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+# pylint: disable=too-many-nested-blocks,too-many-return-statements,too-many-branches
+from __future__ import annotations
+
+from typing import Dict, List
+
+from agent_framework import ChatMessage, Role as ChatRole
+from agent_framework._types import TextContent
+from azure.ai.agentserver.core.logger import get_logger
+
+logger = get_logger()
+
+
+class AgentFrameworkInputConverter:
+    """Normalize inputs for agent.run.
+
+    Accepts: str | List | None
+    Returns: None | str | ChatMessage | list[str] | list[ChatMessage]
+    """
+
+    def transform_input(
+        self,
+        input: str | List[Dict] | None,
+    ) -> str | ChatMessage | list[str] | list[ChatMessage] | None:
+        logger.debug("Transforming input of type: %s", type(input))
+
+        if input is None:
+            return None
+
+        if isinstance(input, str):
+            return input
+
+        try:
+            if isinstance(input, list):
+                messages: list[str | ChatMessage] = []
+
+                for item in input:
+                    # Case 1: ImplicitUserMessage with content as str or list of ItemContentInputText
+                    if self._is_implicit_user_message(item):
+                        content = item.get("content", None)
+                        if isinstance(content, str):
+                            messages.append(content)
+                        elif isinstance(content, list):
+                            text_parts: list[str] = []
+                            for content_item in content:
+                                text_content = self._extract_input_text(content_item)
+                                if text_content:
+                                    text_parts.append(text_content)
+                            if text_parts:
+                                messages.append(" ".join(text_parts))
+
+                    # Case 2: Explicit message params (user/assistant/system)
+                    elif (
+                        item.get("type") == "message"
+                        and item.get("role") is not None
+                        and item.get("content") is not None
+                    ):
+                        role_map = {
+                            "user": ChatRole.USER,
+                            "assistant": ChatRole.ASSISTANT,
+                            "system": ChatRole.SYSTEM,
+                        }
+                        role = role_map.get(item.get("role", "user"), ChatRole.USER)
+
+                        content_text = ""
+                        item_content = item.get("content", None)
+                        if item_content and isinstance(item_content, list):
+                            text_parts: list[str] = []
+                            for content_item in item_content:
+                                item_text = self._extract_input_text(content_item)
+                                if item_text:
+                                    text_parts.append(item_text)
+                            content_text = " ".join(text_parts) if text_parts else ""
+                        elif item_content and isinstance(item_content, str):
+                            content_text = str(item_content)
+
+                        if content_text:
+                            messages.append(ChatMessage(role=role, text=content_text))
+
+                # Determine the most natural return type
+                if not messages:
+                    return None
+                if len(messages) == 1:
+                    return messages[0]
+                if all(isinstance(m, str) for m in messages):
+                    return [m for m in messages if isinstance(m, str)]
+                if all(isinstance(m, ChatMessage) for m in messages):
+                    return [m for m in messages if isinstance(m, ChatMessage)]
+
+                # Mixed content: coerce ChatMessage to str by extracting TextContent parts
+                result: list[str] = []
+                for msg in messages:
+                    if isinstance(msg, ChatMessage):
+                        text_parts: list[str] = []
+                        for c in getattr(msg, "contents", []) or []:
+                            if isinstance(c, TextContent):
+                                text_parts.append(c.text)
+                        result.append(" ".join(text_parts) if text_parts else str(msg))
+                    else:
+                        result.append(str(msg))
+                return result
+
+            raise TypeError(f"Unsupported input type: {type(input)}")
+        except Exception as e:
+            logger.error("Error processing messages: %s", e, exc_info=True)
+            raise Exception(f"Error processing messages: {e}") from e   # pylint: disable=broad-exception-raised
+
+    def _is_implicit_user_message(self, item: Dict) -> bool:
+        return "content" in item and "role" not in item and "type" not in item
+
+    def _extract_input_text(self, content_item: Dict) -> str:
+        if content_item.get("type") == "input_text" and "text" in content_item:
+            text_content = content_item.get("text")
+            if isinstance(text_content, str):
+                return text_content
+        return None # type: ignore