Update API Client

examples/agent/google-adk/README.md

@@ -26,7 +26,7 @@ SPLUNK_AO_PROJECT=
 SPLUNK_AO_LOG_STREAM=
 ```
 
-For the `SPLUNK_AO_API_ENDPOINT`, this is different to the console URL that you would normally use. If you are using `app.galileo.ai` for example, the endpoint is `https://api.galileo.ai/otel/traces`.
+For the `SPLUNK_AO_API_ENDPOINT`, this is different to the console URL that you would normally use. If you are using `app.galileo.ai` for example, the endpoint is `https://api.galileo.ai/otel/v1/traces`.
 
 See the [Splunk AO OTel and OpenInference documentation](https://docs.galileo.ai/sdk-api/third-party-integrations/opentelemetry-and-openinference) for more details.
 

examples/agent/langgraph-open-telemetry/pyproject.toml

@@ -9,7 +9,7 @@ dependencies = [
     "langgraph-prebuilt>=0.6.0",
     "openai",
     "python-dotenv>=1.1.0",
-    "splunk-ao[otel]",
+    "splunk-ao",
     "opentelemetry-instrumentation-langchain>=0.48.1",
     "opentelemetry-instrumentation-openai-v2>=2.1b0",
 ]

examples/agent/langgraph-otel/main.py

@@ -1,9 +1,18 @@
 import os
 from typing import TypedDict
 
-# Load environment variables first (contains API keys and project settings)
 import dotenv
+import openai
+from langgraph.graph import END, StateGraph
+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.resources import Resource
+from opentelemetry.sdk.trace.export import BatchSpanProcessor  # Efficiently batches spans before export
 
+# Load environment variables first (contains API keys and project settings)
 dotenv.load_dotenv()
 
 # ============================================================================
@@ -13,27 +22,6 @@
 # traces, metrics, and logs from your applications. Think of it as a way to
 # "instrument" your code so you can see exactly what's happening during execution.
 
-# Core OpenTelemetry imports
-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
-
-# 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
 # ============================================================================
@@ -75,16 +63,14 @@
 # Splunk AO's OTel ingest lives on the `api.` subdomain (not the `console.`/`app.`
 # one you log into). We derive it from SPLUNK_AO_CONSOLE_URL so custom deployments
 # (e.g. https://console.demo-v2.galileocloud.io/) route to their own ingest
-# (https://api.demo-v2.galileocloud.io/otel/traces) instead of app.galileo.ai.
+# (https://api.demo-v2.galileocloud.io/otel/v1/traces) instead of app.galileo.ai.
 console_url = os.environ.get("SPLUNK_AO_CONSOLE_URL", "https://app.galileo.ai").rstrip("/")
 api_url = console_url.replace("://console.", "://api.").replace("://app.", "://api.")
-endpoint = f"{api_url}/otel/traces"
+endpoint = f"{api_url}/otel/v1/traces"
 print(f"OTEL endpoint: {endpoint}")
 
 # Create a TracerProvider with descriptive resource information
 # This helps identify these traces as coming from OpenTelemetry in Splunk AO
-from opentelemetry.sdk.resources import Resource
-
 resource = Resource.create(
     {
         "service.name": "LangGraph-OpenTelemetry-Demo",
@@ -143,7 +129,7 @@ class AgentState(TypedDict, total=False):
 
 # Node 1: Input Validation
 # Validates and prepares the user input for processing
-def validate_input(state: AgentState):
+def validate_input(state: AgentState) -> AgentState:
     user_input = state.get("user_input", "")
     print(f"📥 Validating input: '{user_input}'")
 
@@ -160,18 +146,15 @@ def validate_input(state: AgentState):
 # Node 2: Generate Response
 # Calls OpenAI to generate a response to the user's question
 # OpenAI instrumentation will automatically create detailed spans
-def generate_response(state: AgentState):
+def generate_response(state: AgentState) -> AgentState:
     user_input = state["user_input"]
 
     try:
         print(f"⚙️ Calling OpenAI with: '{user_input}'")
 
         # 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
@@ -183,12 +166,12 @@ def generate_response(state: AgentState):
 
     except Exception as e:
         print(f"❌ Error calling OpenAI: {e}")
-        return {"llm_response": f"Error: {str(e)}"}
+        return {"llm_response": f"Error: {e!s}"}
 
 
 # Node 3: Format Answer
 # Extracts and formats a clean answer from the raw LLM response
-def format_answer(state: AgentState):
+def format_answer(state: AgentState) -> AgentState:
     llm_response = state.get("llm_response", "")
 
     # Simple parsing - extract first sentence for a concise answer

examples/agent/microsoft-agent-framework/requirements.txt

@@ -1,3 +1,3 @@
 agent-framework>=1.0.0b251120
-splunk-ao[otel]
+splunk-ao
 pydantic

examples/agent/pydantic-ai-support-agent/pyproject.toml

@@ -5,6 +5,6 @@ description = "An example project for using Splunk AO with PydanticAI."
 readme = "README.md"
 requires-python = ">=3.11, <3.14"
 dependencies = [
-    "splunk-ao[otel]",
+    "splunk-ao",
     "pydantic-ai>=1.0.0",
 ]

examples/agent/startup-simulator-3000/test_setup.py

@@ -6,7 +6,7 @@
 
 import os
 import sys
-from importlib.metadata import version, PackageNotFoundError
+from importlib.metadata import PackageNotFoundError, version
 
 from dotenv import load_dotenv
 

examples/agent/strands-agents/agent.py

@@ -1,16 +1,17 @@
 import os
 
+# Load environment variables from the .env file
+from dotenv import load_dotenv
 from strands import Agent, tool
 from strands.telemetry import StrandsTelemetry
 from strands_tools import calculator, current_time
 
-# Load environment variables from the .env file
-from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 # Export the Splunk AO OTel API endpoint for OTel
-os.environ["OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"] = os.environ.get("SPLUNK_AO_API_ENDPOINT", "https://api.galileo.ai/otel/traces")
+os.environ["OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"] = os.environ.get(
+    "SPLUNK_AO_API_ENDPOINT", "https://api.galileo.ai/otel/v1/traces"
+)
 
 # Export the Splunk AO OTel headers pointing to the correct API key, project, and log stream
 headers = {

examples/logging-samples/distributed-tracing-otel-python-java/python-service/requirements.txt

@@ -3,7 +3,7 @@ uvicorn==0.34.0
 openai==1.82.0
 langgraph==0.4.1
 chromadb-client==0.6.3
-splunk-ao[otel]
+splunk-ao
 opentelemetry-api
 opentelemetry-sdk
 opentelemetry-exporter-otlp-proto-http