Ruff format

.github/workflows/examples-integration-test.yml

@@ -77,6 +77,7 @@ jobs:
 
           # Google ADK examples
           - { path: 'examples/google_adk/human_approval.py', name: 'Google ADK Human Approval' }
+          - { path: 'examples/google_adk/sse_function_call_example.py', name: 'Google ADK SSE Function Call' }
 
           # LlamaIndex examples
           # - { path: 'examples/llamaindex/llamaindex_example.py', name: 'LlamaIndex' }

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.2.1"
+    rev: "v0.12.9"
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]

agentops/client/api/base.py

@@ -15,8 +15,7 @@
 class TokenFetcher(Protocol):
     """Protocol for token fetching functions"""
 
-    def __call__(self, api_key: str) -> str:
-        ...
+    def __call__(self, api_key: str) -> str: ...
 
 
 class BaseApiClient:

agentops/client/client.py

@@ -41,9 +41,9 @@ class Client:
     config: Config
     _initialized: bool
     _init_trace_context: Optional[TraceContext] = None  # Stores the context of the auto-started trace
-    _legacy_session_for_init_trace: Optional[
-        Session
-    ] = None  # Stores the legacy Session wrapper for the auto-started trace
+    _legacy_session_for_init_trace: Optional[Session] = (
+        None  # Stores the legacy Session wrapper for the auto-started trace
+    )
 
     __instance = None  # Class variable for singleton pattern
 

agentops/instrumentation/agentic/google_adk/patch.py

@@ -304,16 +304,16 @@ def _extract_llm_attributes(llm_request_dict: dict, llm_response: Any) -> dict:
                     elif "function_call" in part:
                         # This is a function call in the response
                         func_call = part["function_call"]
-                        attributes[
-                            MessageAttributes.COMPLETION_TOOL_CALL_NAME.format(i=0, j=tool_call_index)
-                        ] = func_call.get("name", "")
-                        attributes[
-                            MessageAttributes.COMPLETION_TOOL_CALL_ARGUMENTS.format(i=0, j=tool_call_index)
-                        ] = json.dumps(func_call.get("args", {}))
+                        attributes[MessageAttributes.COMPLETION_TOOL_CALL_NAME.format(i=0, j=tool_call_index)] = (
+                            func_call.get("name", "")
+                        )
+                        attributes[MessageAttributes.COMPLETION_TOOL_CALL_ARGUMENTS.format(i=0, j=tool_call_index)] = (
+                            json.dumps(func_call.get("args", {}))
+                        )
                         if "id" in func_call:
-                            attributes[
-                                MessageAttributes.COMPLETION_TOOL_CALL_ID.format(i=0, j=tool_call_index)
-                            ] = func_call["id"]
+                            attributes[MessageAttributes.COMPLETION_TOOL_CALL_ID.format(i=0, j=tool_call_index)] = (
+                                func_call["id"]
+                            )
                         tool_call_index += 1
 
                 if text_parts:

agentops/instrumentation/agentic/openai_agents/attributes/completion.py

@@ -115,9 +115,9 @@ def get_raw_response_attributes(response: Dict[str, Any]) -> Dict[str, Any]:
                                 result[MessageAttributes.COMPLETION_TOOL_CALL_NAME.format(i=j, j=k)] = function.get(
                                     "name", ""
                                 )
-                                result[
-                                    MessageAttributes.COMPLETION_TOOL_CALL_ARGUMENTS.format(i=j, j=k)
-                                ] = function.get("arguments", "")
+                                result[MessageAttributes.COMPLETION_TOOL_CALL_ARGUMENTS.format(i=j, j=k)] = (
+                                    function.get("arguments", "")
+                                )
 
     return result
 

agentops/instrumentation/agentic/smolagents/attributes/model.py

@@ -102,13 +102,13 @@ def get_model_attributes(
                 if hasattr(return_value, "tool_calls") and return_value.tool_calls:
                     for j, tool_call in enumerate(return_value.tool_calls):
                         if hasattr(tool_call, "function"):
-                            attributes[
-                                MessageAttributes.COMPLETION_TOOL_CALL_NAME.format(i=0, j=j)
-                            ] = tool_call.function.name
+                            attributes[MessageAttributes.COMPLETION_TOOL_CALL_NAME.format(i=0, j=j)] = (
+                                tool_call.function.name
+                            )
                             if hasattr(tool_call.function, "arguments"):
-                                attributes[
-                                    MessageAttributes.COMPLETION_TOOL_CALL_ARGUMENTS.format(i=0, j=j)
-                                ] = tool_call.function.arguments
+                                attributes[MessageAttributes.COMPLETION_TOOL_CALL_ARGUMENTS.format(i=0, j=j)] = (
+                                    tool_call.function.arguments
+                                )
                             if hasattr(tool_call, "id"):
                                 attributes[MessageAttributes.COMPLETION_TOOL_CALL_ID.format(i=0, j=j)] = tool_call.id
 

agentops/instrumentation/agentic/xpander/instrumentor.py

@@ -11,7 +11,7 @@
 ✅ Span creation: Using tracer.make_span() instead of manual span creation
 ✅ Error handling: Using _finish_span_success/_finish_span_error utilities
 ✅ Attribute management: Using existing SpanAttributeManager
-✅ Serialization: Using safe_serialize and model_to_dict utilities  
+✅ Serialization: Using safe_serialize and model_to_dict utilities
 ✅ Attribute setting: Using _update_span utility
 
 RUNTIME-SPECIFIC LOGIC KEPT (Cannot be replaced):

agentops/instrumentation/common/attributes.py

@@ -98,8 +98,7 @@ class IndexedAttribute(Protocol):
     formatting of attribute keys based on the indices.
     """
 
-    def format(self, *, i: int, j: Optional[int] = None) -> str:
-        ...
+    def format(self, *, i: int, j: Optional[int] = None) -> str: ...
 
 
 IndexedAttributeMap = Dict[IndexedAttribute, str]  # target_attribute_key: source_attribute

agentops/logging/instrument_logging.py

@@ -11,6 +11,7 @@
 
 print_logger = None
 
+
 def setup_print_logger() -> None:
     """
     Instruments the built-in print function and configures logging to use a memory buffer.
@@ -29,8 +30,9 @@ def setup_print_logger() -> None:
 
         # Ensure the new logger doesn't propagate to root
         buffer_logger.propagate = False
-    
+
     global print_logger
+
     def print_logger(*args: Any, **kwargs: Any) -> None:
         """
         Custom print function that logs to buffer and console.

agentops/sdk/exporters.py

@@ -177,7 +177,7 @@ def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult:
                 self._last_auth_failure = time.time()
 
             logger.warning(
-                f"JWT token expired during span export: {e}. " f"Will retry in {self._auth_failure_threshold} seconds."
+                f"JWT token expired during span export: {e}. Will retry in {self._auth_failure_threshold} seconds."
             )
             return SpanExportResult.FAILURE
 

examples/ag2/ag2_async_agent.ipynb

@@ -81,7 +81,7 @@
    },
    "outputs": [],
    "source": [
-    "# Define an asynchronous function that simulates async processing \n",
+    "# Define an asynchronous function that simulates async processing\n",
     "async def simulate_async_processing(task_name: str, delay: float = 1.0) -> str:\n",
     "    \"\"\"\n",
     "    Simulate some asynchronous processing (e.g., API calls, file operations, etc.)\n",

examples/ag2/ag2_async_agent.py

@@ -1,7 +1,7 @@
 # AG2 Async Agent Chat with Automated Responses
 #
-# This notebook demonstrates how to leverage asynchronous programming with AG2 agents 
-# to create automated conversations between AI agents, eliminating the need for human 
+# This notebook demonstrates how to leverage asynchronous programming with AG2 agents
+# to create automated conversations between AI agents, eliminating the need for human
 # input while maintaining full traceability.
 #
 # Overview
@@ -12,9 +12,9 @@
 # 3. Automate the entire conversation flow without requiring manual intervention
 # 4. Track all interactions using AgentOps for monitoring and analysis
 #
-# By using async operations and automated responses, you can create fully autonomous 
-# agent conversations that simulate real-world scenarios. This is particularly useful 
-# for testing, prototyping, and creating demos where you want to showcase agent 
+# By using async operations and automated responses, you can create fully autonomous
+# agent conversations that simulate real-world scenarios. This is particularly useful
+# for testing, prototyping, and creating demos where you want to showcase agent
 # capabilities without manual input.
 
 # %pip install agentops
@@ -38,7 +38,8 @@
 agentops.init(auto_start_session=False, trace_name="AG2 Async Demo")
 tracer = agentops.start_trace(trace_name="AG2 Async Agent Demo", tags=["ag2-async-demo", "agentops-example"])
 
-# Define an asynchronous function that simulates async processing 
+
+# Define an asynchronous function that simulates async processing
 async def simulate_async_processing(task_name: str, delay: float = 1.0) -> str:
     """
     Simulate some asynchronous processing (e.g., API calls, file operations, etc.)
@@ -48,6 +49,7 @@ async def simulate_async_processing(task_name: str, delay: float = 1.0) -> str:
     print(f"✅ Completed async task: {task_name}")
     return f"Processed: {task_name}"
 
+
 # Define a custom UserProxyAgent that simulates automated user responses
 class AutomatedUserProxyAgent(UserProxyAgent):
     def __init__(self, name: str, **kwargs):
@@ -81,6 +83,7 @@ async def a_receive(
     ):
         await super().a_receive(message, sender, request_reply, silent)
 
+
 # Define an AssistantAgent that simulates async processing before responding
 class AsyncAssistantAgent(AssistantAgent):
     async def a_receive(
@@ -149,7 +152,7 @@ async def main():
 
     print("\n🎉 Demo completed successfully!")
 
+
 # Run the main async demo
 nest_asyncio.apply()
 asyncio.run(main())
-

examples/ag2/groupchat.ipynb

@@ -87,17 +87,13 @@
     "researcher = autogen.AssistantAgent(\n",
     "    name=\"researcher\",\n",
     "    llm_config=llm_config,\n",
-    "    system_message=\"You are a researcher who specializes in finding accurate information.\"\n",
+    "    system_message=\"You are a researcher who specializes in finding accurate information.\",\n",
     ")\n",
     "coder = autogen.AssistantAgent(\n",
-    "    name=\"coder\",\n",
-    "    llm_config=llm_config,\n",
-    "    system_message=\"You are an expert programmer who writes clean, efficient code.\"\n",
+    "    name=\"coder\", llm_config=llm_config, system_message=\"You are an expert programmer who writes clean, efficient code.\"\n",
     ")\n",
     "critic = autogen.AssistantAgent(\n",
-    "    name=\"critic\",\n",
-    "    llm_config=llm_config,\n",
-    "    system_message=\"You review solutions and provide constructive feedback.\"\n",
+    "    name=\"critic\", llm_config=llm_config, system_message=\"You review solutions and provide constructive feedback.\"\n",
     ")"
    ]
   },
@@ -120,13 +116,10 @@
     "groupchat = autogen.GroupChat(\n",
     "    agents=[user_proxy, researcher, coder, critic],\n",
     "    messages=[],\n",
-    "    max_round=4  # Limits the total number of chat rounds\n",
+    "    max_round=4,  # Limits the total number of chat rounds\n",
     ")\n",
     "# The manager coordinates the group chat and LLM configuration\n",
-    "manager = autogen.GroupChatManager(\n",
-    "    groupchat=groupchat,\n",
-    "    llm_config=llm_config\n",
-    ")\n",
+    "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
     "# Start the group chat with an initial task and a maximum number of user turns\n",
     "user_proxy.initiate_chat(\n",
     "    manager,\n",

examples/ag2/groupchat.py

@@ -40,17 +40,13 @@
 researcher = autogen.AssistantAgent(
     name="researcher",
     llm_config=llm_config,
-    system_message="You are a researcher who specializes in finding accurate information."
+    system_message="You are a researcher who specializes in finding accurate information.",
 )
 coder = autogen.AssistantAgent(
-    name="coder",
-    llm_config=llm_config,
-    system_message="You are an expert programmer who writes clean, efficient code."
+    name="coder", llm_config=llm_config, system_message="You are an expert programmer who writes clean, efficient code."
 )
 critic = autogen.AssistantAgent(
-    name="critic",
-    llm_config=llm_config,
-    system_message="You review solutions and provide constructive feedback."
+    name="critic", llm_config=llm_config, system_message="You review solutions and provide constructive feedback."
 )
 
 # The user proxy agent simulates a human participant in the chat
@@ -65,13 +61,10 @@
 groupchat = autogen.GroupChat(
     agents=[user_proxy, researcher, coder, critic],
     messages=[],
-    max_round=4  # Limits the total number of chat rounds
+    max_round=4,  # Limits the total number of chat rounds
 )
 # The manager coordinates the group chat and LLM configuration
-manager = autogen.GroupChatManager(
-    groupchat=groupchat,
-    llm_config=llm_config
-)
+manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
 # Start the group chat with an initial task and a maximum number of user turns
 user_proxy.initiate_chat(
     manager,
@@ -89,4 +82,3 @@
 except agentops.ValidationError as e:
     print(f"\n❌ Error validating spans: {e}")
     raise
-

examples/dspy/dspy_calculator.py

@@ -1,5 +1,4 @@
 import os
-import time
 
 import dspy
 from dspy import Tool
@@ -16,9 +15,11 @@
 lm = dspy.LM("openai/gpt-4o-mini", temperature=0.5)
 dspy.configure(lm=lm, callbacks=[handler])
 
+
 def multiplier(*, a: int, b: int) -> int:
     return a * b
 
+
 multiplier = Tool(multiplier)
 
 agent = dspy.ProgramOfThought("question -> answer: int")

examples/google_adk/requirements.txt

@@ -1,4 +1,4 @@
-google-adk==1.0.0
+google-adk
 google-genai==1.16.1
 nest-asyncio==1.6.0
 agentops

examples/google_adk/sse_function_call_example.py

@@ -15,11 +15,13 @@
 StreamingMode: Optional[object] = None
 try:
     from google.adk.runners import RunConfig as _RunConfig, StreamingMode as _StreamingMode  # type: ignore
+
     RunConfig = _RunConfig
     StreamingMode = _StreamingMode
 except Exception:
     try:
         from google.adk.types import RunConfig as _RunConfig2, StreamingMode as _StreamingMode2  # type: ignore
+
         RunConfig = _RunConfig2
         StreamingMode = _StreamingMode2
     except Exception:
@@ -73,7 +75,9 @@ async def main():
         run_config_kw["run_config"] = RunConfig(streaming_mode=StreamingMode.SSE, response_modalities=["TEXT"])  # type: ignore
 
     final_text = None
-    async for event in runner.run_async(user_id=USER_ID, session_id=SESSION_ID, new_message=user_message, **run_config_kw):
+    async for event in runner.run_async(
+        user_id=USER_ID, session_id=SESSION_ID, new_message=user_message, **run_config_kw
+    ):
         # Print out any parts safely; this will include function_call parts when they occur
         if hasattr(event, "content") and event.content and getattr(event.content, "parts", None):
             for part in event.content.parts:
@@ -91,4 +95,4 @@ async def main():
 
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main())

examples/langgraph/langgraph_example.ipynb

@@ -169,9 +169,9 @@
     "        tool_args = tool_call[\"args\"]\n",
     "\n",
     "        # Find and execute the requested tool\n",
-    "        for tool in tools:\n",
-    "            if tool.name == tool_name:\n",
-    "                result = tool.invoke(tool_args)\n",
+    "        for available_tool in tools:\n",
+    "            if available_tool.name == tool_name:\n",
+    "                result = available_tool.invoke(tool_args)\n",
     "                tool_messages.append(ToolMessage(content=str(result), tool_call_id=tool_call[\"id\"]))\n",
     "                break\n",
     "\n",

examples/smolagents/multi_smolagents_system.ipynb

@@ -96,7 +96,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "agentops.init(auto_start_session=False)\n",
     "tracer = agentops.start_trace(\n",
     "    trace_name=\"Orchestrate a Multi-Agent System\", tags=[\"smolagents\", \"example\", \"multi-agent\", \"agentops-example\"]\n",

examples/xpander/coding_agent.py

@@ -129,7 +129,7 @@ async def on_execution_request(execution_task: AgentExecution) -> AgentExecution
         email = getattr(user, "email", "")
         user_info = f"👤 From user: {name}\n📧 Email: {email}"
 
-    IncomingEvent = f"\n📨 Incoming message: {execution_task.input.text}\n" f"{user_info}"
+    IncomingEvent = f"\n📨 Incoming message: {execution_task.input.text}\n{user_info}"
 
     logger.info(f"[on_execution_request] IncomingEvent: {IncomingEvent}")
     logger.info(f"[on_execution_request] Calling agent_backend.init_task with execution={execution_task.model_dump()}")

tests/unit/instrumentation/openai_core/test_instrumentor.py

@@ -107,9 +107,9 @@ def test_instrument_method_wraps_response_api(self, instrumentor):
             instrumentor_obj._custom_wrap()
 
             # Verify wrap_function_wrapper was called for Response API methods
-            assert (
-                mock_wfw.call_count >= 2
-            ), f"Expected at least 2 calls to wrap_function_wrapper, got {mock_wfw.call_count}"
+            assert mock_wfw.call_count >= 2, (
+                f"Expected at least 2 calls to wrap_function_wrapper, got {mock_wfw.call_count}"
+            )
 
             # Find Response API calls
             response_api_calls = []

tests/unit/sdk/instrumentation_tester.py

@@ -45,8 +45,7 @@ def reset_trace_globals():
 
 class HasAttributesViaProperty(Protocol):
     @property
-    def attributes(self) -> Attributes:
-        ...
+    def attributes(self) -> Attributes: ...
 
 
 class HasAttributesViaAttr(Protocol):