revert: bump ruff 0.9.7 -> 0.15.20 and openapi-python-client 0.26.1 -> 0.29.0 (HYBIM-777) (#66)

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: "v0.15.20"
+    rev: "v0.9.7"
     hooks:
       - id: ruff
         args: [--fix]

examples/agent/crewAI/research_crew/src/research_crew/crew.py

@@ -1,17 +1,17 @@
 # src/research_crew/crew.py
-
 from crewai import Agent, Crew, Process, Task
-from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.project import CrewBase, agent, crew, task
 from crewai_tools import SerperDevTool
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
 
 @CrewBase
 class ResearchCrew:
     """Research crew for comprehensive topic analysis and reporting"""
 
-    agents: list[BaseAgent]
-    tasks: list[Task]
+    agents: List[BaseAgent]
+    tasks: List[Task]
 
     @agent
     def researcher(self) -> Agent:
@@ -32,4 +32,9 @@ def analysis_task(self) -> Task:
     @crew
     def crew(self) -> Crew:
         """Creates the research crew"""
-        return Crew(agents=self.agents, tasks=self.tasks, process=Process.sequential, verbose=True)
+        return Crew(
+            agents=self.agents,
+            tasks=self.tasks,
+            process=Process.sequential,
+            verbose=True,
+        )

examples/agent/crewAI/research_crew/src/research_crew/main.py

@@ -1,10 +1,8 @@
 #!/usr/bin/env python
 # src/research_crew/main.py
 import os
-
-from dotenv import load_dotenv
-
 from research_crew.crew import ResearchCrew
+from dotenv import load_dotenv
 from splunk_ao.handlers.crewai.handler import CrewAIEventListener
 
 load_dotenv()
@@ -13,7 +11,7 @@
 os.makedirs("output", exist_ok=True)
 
 
-def run() -> None:
+def run():
     # Create the event listener for Splunk AO CrewAI integration
     CrewAIEventListener()
 

examples/agent/crewAI/research_crew/src/research_crew/tools/custom_tool.py

@@ -1,4 +1,5 @@
 from crewai.tools import BaseTool
+from typing import Type
 from pydantic import BaseModel, Field
 
 
@@ -10,10 +11,8 @@ class MyCustomToolInput(BaseModel):
 
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
-    description: str = (
-        "Clear description for what this tool is useful for, your agent will need this information to use it."
-    )
-    args_schema: type[BaseModel] = MyCustomToolInput
+    description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
+    args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
         # Implementation goes here

examples/agent/google-adk/my_agent/agent.py

@@ -1,11 +1,10 @@
 """Agent logic for the Google ADK example."""
 
 from dotenv import load_dotenv
-from google.adk.agents.llm_agent import Agent
-from openinference.instrumentation.google_adk import GoogleADKInstrumentor
 from opentelemetry.sdk import trace as trace_sdk
-
 from splunk_ao import otel
+from openinference.instrumentation.google_adk import GoogleADKInstrumentor
+from google.adk.agents.llm_agent import Agent
 
 load_dotenv()
 
@@ -32,8 +31,6 @@ def get_current_time(city: str) -> dict:
     model="gemini-3-flash-preview",
     name="root_agent",
     description="Tells the current time in a specified city.",
-    instruction=(
-        "You are a helpful assistant that tells the current time in cities.Use the 'get_current_time' tool for this purpose."
-    ),
+    instruction=("You are a helpful assistant that tells the current time in cities." "Use the 'get_current_time' tool for this purpose."),
     tools=[get_current_time],
 )

examples/agent/langchain-agent/main.py

@@ -1,9 +1,8 @@
 from dotenv import load_dotenv
 from langchain.agents import initialize_agent
 from langchain.agents.agent_types import AgentType
-from langchain.tools import tool
 from langchain_openai import ChatOpenAI
-
+from langchain.tools import tool
 from splunk_ao import splunk_ao_context
 from splunk_ao.handlers.langchain import SplunkAOCallback
 

examples/agent/langchain-middleware/main.py

@@ -2,14 +2,13 @@
 import os
 
 from dotenv import load_dotenv
+from splunk_ao import splunk_ao_context
+from splunk_ao.handlers.langchain.middleware import SplunkAOMiddleware
 from langchain.agents.factory import create_agent
 from langchain.tools import tool
 from langchain_core.messages import HumanMessage
 from langchain_openai import ChatOpenAI
 
-from splunk_ao import splunk_ao_context
-from splunk_ao.handlers.langchain.middleware import SplunkAOMiddleware
-
 # Load environment variables (e.g., API keys)
 load_dotenv()
 
@@ -43,10 +42,7 @@ def get_stock_price(symbol: str) -> str:
 def main() -> None:
     # Use the Splunk AO context manager to specify project and log stream
     # All traces created within this context will be associated with this project
-    with splunk_ao_context(
-        project=os.getenv("SPLUNK_AO_PROJECT", "langchain-middleware"),
-        log_stream=os.getenv("SPLUNK_AO_LOG_STREAM", "agent_execution"),
-    ):
+    with splunk_ao_context(project=os.getenv("SPLUNK_AO_PROJECT", "langchain-middleware"), log_stream=os.getenv("SPLUNK_AO_LOG_STREAM", "agent_execution")):
         # Create an agent with SplunkAOMiddleware for automatic logging
         # SplunkAOMiddleware automatically captures:
         # - Agent lifecycle events (start/completion)
@@ -65,15 +61,7 @@ def main() -> None:
         # 3. Understand it needs Apple's stock price
         # 4. Call the get_stock_price tool
         # 5. Synthesize the results into a coherent response
-        result = agent.invoke(
-            {
-                "messages": [
-                    HumanMessage(
-                        content="What's the weather like in San Francisco and what's the current stock price of Apple?"
-                    )
-                ]
-            }
-        )
+        result = agent.invoke({"messages": [HumanMessage(content="What's the weather like in San Francisco and what's the current stock price of Apple?")]})
         print(f"\nAgent Response:\n{result}")
 
 

examples/agent/langgraph-fsi-agent/after/create_sample_logs.py

@@ -1,5 +1,4 @@
 # A script to generate log streams
-# ruff: noqa: E402  -- load_dotenv() must precede all other imports
 
 from dotenv import load_dotenv
 

examples/agent/langgraph-fsi-agent/after/scripts/setup_pinecone.py

@@ -101,9 +101,7 @@ def upload_to_pinecone(chunked_docs, index_name: str, force_upload: bool = False
 
     # Create vector store and upload
     print(f"Uploading {len(chunked_docs)} chunks to Pinecone...")
-    vector_store = PineconeVectorStore.from_documents(
-        documents=chunked_docs, embedding=EMBEDDINGS, index_name=index_name
-    )
+    vector_store = PineconeVectorStore.from_documents(documents=chunked_docs, embedding=EMBEDDINGS, index_name=index_name)
 
     print(f"Successfully uploaded {len(chunked_docs)} document chunks to Pinecone")
     return vector_store

examples/agent/langgraph-fsi-agent/after/test.py

@@ -108,12 +108,7 @@ def test_run_experiment_with_dataset():
         experiment_name="langgraph-fsi-experiment",
         dataset_name=DATASET_NAME,
         function=send_message_to_supervisor_agent,
-        metrics=[
-            SplunkAOMetrics.action_advancement,
-            SplunkAOMetrics.action_completion,
-            SplunkAOMetrics.tool_error_rate,
-            SplunkAOMetrics.tool_selection_quality,
-        ],
+        metrics=[SplunkAOMetrics.action_advancement, SplunkAOMetrics.action_completion, SplunkAOMetrics.tool_error_rate, SplunkAOMetrics.tool_selection_quality],
         project=os.getenv("SPLUNK_AO_PROJECT"),
     )
 

examples/agent/langgraph-fsi-agent/before/scripts/setup_pinecone.py

@@ -101,9 +101,7 @@ def upload_to_pinecone(chunked_docs, index_name: str, force_upload: bool = False
 
     # Create vector store and upload
     print(f"Uploading {len(chunked_docs)} chunks to Pinecone...")
-    vector_store = PineconeVectorStore.from_documents(
-        documents=chunked_docs, embedding=EMBEDDINGS, index_name=index_name
-    )
+    vector_store = PineconeVectorStore.from_documents(documents=chunked_docs, embedding=EMBEDDINGS, index_name=index_name)
 
     print(f"Successfully uploaded {len(chunked_docs)} document chunks to Pinecone")
     return vector_store

examples/agent/langgraph-open-telemetry/main.py

@@ -4,6 +4,7 @@
 
 import dotenv
 import openai
+from splunk_ao import otel
 
 # LangGraph imports - this is what we're actually instrumenting
 from langgraph.graph import END, StateGraph
@@ -13,8 +14,6 @@
 from opentelemetry.sdk import trace as trace_sdk  # SDK for creating traces
 from opentelemetry.sdk.resources import Resource
 
-from splunk_ao import otel
-
 dotenv.load_dotenv()
 logging.basicConfig(level=logging.DEBUG)
 
@@ -85,7 +84,10 @@ def generate_response(state: AgentState):
 
         # Make the OpenAI API call - OpenAI instrumentation handles tracing
         response = client.chat.completions.create(
-            model="gpt-3.5-turbo", messages=[{"role": "user", "content": user_input}], max_tokens=300, temperature=0.7
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": user_input}],
+            max_tokens=300,
+            temperature=0.7,
         )
 
         # Extract the response content
@@ -100,7 +102,7 @@ def generate_response(state: AgentState):
 
     except Exception as e:
         print(f"❌ Error calling OpenAI: {e}")
-        return {"llm_response": f"Error: {e!s}"}
+        return {"llm_response": f"Error: {str(e)}"}
 
 
 # Node 3: Format Answer

examples/agent/langgraph-otel/main.py

@@ -14,20 +14,25 @@
 # "instrument" your code so you can see exactly what's happening during execution.
 
 # Core OpenTelemetry imports
-# OpenAI imports for LLM integration
-import openai
-
-# LangGraph imports - this is what we're actually instrumenting
-from langgraph.graph import END, StateGraph
+from opentelemetry.sdk import trace as trace_sdk  # SDK for creating traces
+from opentelemetry import trace as trace_api  # API for interacting with traces
+from opentelemetry.sdk.trace.export import (
+    BatchSpanProcessor,
+)  # Efficiently batches spans before export
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+    OTLPSpanExporter,
+)  # Sends traces via HTTP
 
 # OpenInference is a specialized instrumentation library that understands AI frameworks
 # It automatically creates meaningful spans for LangChain/LangGraph operations
 from openinference.instrumentation.langchain import LangChainInstrumentor
 from openinference.instrumentation.openai import OpenAIInstrumentor
-from opentelemetry import trace as trace_api  # API for interacting with traces
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter  # Sends traces via HTTP
-from opentelemetry.sdk import trace as trace_sdk  # SDK for creating traces
-from opentelemetry.sdk.trace.export import BatchSpanProcessor  # Efficiently batches spans before export
+
+# LangGraph imports - this is what we're actually instrumenting
+from langgraph.graph import StateGraph, END
+
+# OpenAI imports for LLM integration
+import openai
 
 # ============================================================================
 # STEP 1: CONFIGURE API AUTHENTICATION
@@ -163,7 +168,10 @@ def generate_response(state: AgentState):
 
         # Make the OpenAI API call - OpenAI instrumentation handles tracing
         response = client.chat.completions.create(
-            model="gpt-3.5-turbo", messages=[{"role": "user", "content": user_input}], max_tokens=300, temperature=0.7
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": user_input}],
+            max_tokens=300,
+            temperature=0.7,
         )
 
         # Extract the response content
@@ -175,7 +183,7 @@ def generate_response(state: AgentState):
 
     except Exception as e:
         print(f"❌ Error calling OpenAI: {e}")
-        return {"llm_response": f"Error: {e!s}"}
+        return {"llm_response": f"Error: {str(e)}"}
 
 
 # Node 3: Format Answer

examples/agent/langgraph-telecom-agent/app.py

@@ -102,9 +102,7 @@ async def main(msg: cl.Message) -> None:
         main_step.input = msg.content
 
         # Call the graph with the user's message and stream the response back to the user
-        async for response_msg in supervisor_agent.astream(
-            input=messages, stream_mode="updates", config=runnable_config
-        ):
+        async for response_msg in supervisor_agent.astream(input=messages, stream_mode="updates", config=runnable_config):
             # Debug: Log the response structure
             print(f"Response keys: {response_msg.keys()}")
 

examples/agent/langgraph-telecom-agent/scripts/setup_pinecone.py

@@ -106,9 +106,7 @@ def upload_to_pinecone(chunked_docs, index_name: str, force_upload: bool = False
 
     # Create vector store and upload
     print(f"Uploading {len(chunked_docs)} chunks to Pinecone...")
-    vector_store = PineconeVectorStore.from_documents(
-        documents=chunked_docs, embedding=EMBEDDINGS, index_name=index_name
-    )
+    vector_store = PineconeVectorStore.from_documents(documents=chunked_docs, embedding=EMBEDDINGS, index_name=index_name)
 
     print(f"Successfully uploaded {len(chunked_docs)} document chunks to Pinecone")
     return vector_store

examples/agent/langgraph-telecom-agent/src/tools/billing_tool.py

@@ -36,18 +36,18 @@ def _run(self, customer_id: Optional[str] = None, query_type: str = "summary") -
 
         if query_type == "usage":
             return f"""
-Usage Summary for {customer["name"]}:
-- Data: {customer["data_used"]:.1f} GB used ({customer["data_limit"]})
+Usage Summary for {customer['name']}:
+- Data: {customer['data_used']:.1f} GB used ({customer['data_limit']})
 - Minutes: {random.randint(300, 800)} (Unlimited)
 - Texts: {random.randint(500, 2000)} (Unlimited)
-- Average daily: {customer["data_used"] / 15:.2f} GB
+- Average daily: {customer['data_used'] / 15:.2f} GB
 """
 
         elif query_type == "plan":
             return f"""
-Current Plan: {customer["plan"]}
-- Monthly Cost: ${customer["monthly_charge"]:.2f}
-- Data: {customer["data_limit"]}
+Current Plan: {customer['plan']}
+- Monthly Cost: ${customer['monthly_charge']:.2f}
+- Data: {customer['data_limit']}
 - Talk & Text: Unlimited
 - 5G Access: Included
 
@@ -72,10 +72,10 @@ def _run(self, customer_id: Optional[str] = None, query_type: str = "summary") -
 
         # Default summary
         return f"""
-Account Summary for {customer["name"]}:
-- Account: {customer["account"]}
-- Plan: {customer["plan"]}
-- Amount Due: ${customer["monthly_charge"]:.2f}
-- Due Date: {customer["due_date"]}
-- Data Used: {customer["data_used"]:.1f} GB ({customer["data_limit"]})
+Account Summary for {customer['name']}:
+- Account: {customer['account']}
+- Plan: {customer['plan']}
+- Amount Due: ${customer['monthly_charge']:.2f}
+- Due Date: {customer['due_date']}
+- Data Used: {customer['data_used']:.1f} GB ({customer['data_limit']})
 """

examples/agent/langgraph-telecom-agent/src/tools/technical_support_tool.py

@@ -70,7 +70,7 @@ def _run(self, issue_type: Optional[str] = None, action: str = "help") -> str:
 Support Ticket Created: {ticket_id}
 Priority: High
 Response Time: Within 2 hours
-Callback: {(datetime.now() + timedelta(hours=2)).strftime("%Y-%m-%d %H:%M")}
+Callback: {(datetime.now() + timedelta(hours=2)).strftime('%Y-%m-%d %H:%M')}
 24/7 Support: 1-800-TELECOM
 """