OpenInference Microsoft Agent Framework Instrumentation
OpenInference span processor for Microsoft Agent Framework that transforms native OpenTelemetry spans to OpenInference format for compatibility with OpenInference-compliant backends like Arize Phoenix or Arize AX.
Tested with Agent Framework core/OpenAI packages 1.0.0
Installation
pip install openinference-instrumentation-agent-framework
Install with Microsoft Agent Framework core and OpenAI support:
pip install openinference-instrumentation-agent-framework[instruments]
Overview
Microsoft Agent Framework emits telemetry using GenAI semantic conventions (gen_ai.* attributes). This package provides a SpanProcessor that transforms these spans to OpenInference format, enabling compatibility with observability tools that support the OpenInference standard.
Note: This instrumentation targets Agent Framework's stable 1.0.0+ API surface and tracks later stable releases in CI.
Usage
Basic Setup
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
from agent_framework.observability import enable_instrumentation
from openinference.instrumentation.agent_framework import (
AgentFrameworkToOpenInferenceProcessor
)
# Create tracer provider
tracer_provider = TracerProvider()
# Add OpenInference processor to transform spans
tracer_provider.add_span_processor(
AgentFrameworkToOpenInferenceProcessor()
)
# Add exporter (Console for demo, use OTLP for Phoenix)
tracer_provider.add_span_processor(
SimpleSpanProcessor(ConsoleSpanExporter())
)
# Set as global tracer provider
trace.set_tracer_provider(tracer_provider)
# Enable agent-framework instrumentation
enable_instrumentation(enable_sensitive_data=True)
# Use framework normally - spans will be transformed automatically
from agent_framework.openai import OpenAIChatClient
client = OpenAIChatClient(model="gpt-4o-mini", api_key="your-key")
agent = client.as_agent(name="Assistant", instructions="You are helpful.")
response = await agent.run("Hello!")
With Phoenix or Arize AX
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from agent_framework.observability import enable_instrumentation
from openinference.instrumentation.agent_framework import (
AgentFrameworkToOpenInferenceProcessor
)
# Configure Phoenix endpoint
endpoint = "http://localhost:6006/v1/traces"
# Setup tracer with OpenInference processor
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(AgentFrameworkToOpenInferenceProcessor())
tracer_provider.add_span_processor(
SimpleSpanProcessor(OTLPSpanExporter(endpoint=endpoint))
)
trace.set_tracer_provider(tracer_provider)
# Enable instrumentation
enable_instrumentation(enable_sensitive_data=True)
# Your agent code here
Transformation Details
Span Kind Mapping
| MS Agent Framework Operation | OpenInference Span Kind |
|---|---|
chat |
LLM |
execute_tool |
TOOL |
invoke_agent |
AGENT |
workflow.run |
CHAIN |
executor.process |
CHAIN |
Attribute Mapping
| Source (GenAI) | Target (OpenInference) |
|---|---|
gen_ai.request.model |
llm.model_name |
gen_ai.provider.name |
llm.provider |
gen_ai.usage.input_tokens |
llm.token_count.prompt |
gen_ai.usage.output_tokens |
llm.token_count.completion |
gen_ai.input.messages |
llm.input_messages.* (flattened) |
gen_ai.output.messages |
llm.output_messages.* (flattened) |
gen_ai.tool.name |
tool.name |
gen_ai.tool.call.id |
tool.call_id |
gen_ai.tool.call.arguments |
tool.parameters |
gen_ai.conversation.id |
session.id |
Message Format Transformation
MS Agent Framework messages:
{
"role": "user",
"parts": [
{"type": "text", "content": "Hello"}
]
}
Transformed to OpenInference flattened format:
llm.input_messages.0.message.role = "user"
llm.input_messages.0.message.content = "Hello"
Debug Mode
Enable debug mode to log transformation details:
processor = AgentFrameworkToOpenInferenceProcessor(debug=True)
Requirements
- Python >= 3.10, < 3.15
- opentelemetry-api >= 1.39.0
- opentelemetry-sdk >= 1.39.0
- openinference-semantic-conventions >= 0.1.25
- agent-framework-core >= 1.0.0 (optional, install with
[instruments]extra) - agent-framework-openai >= 1.0.0 (optional, install with
[instruments]extra)
Important Notes
Agent Framework API Stability
Microsoft Agent Framework's stable OpenAI client changed meaningfully at 1.0.0:
- This instrumentation is tested against
agent-framework-core==1.0.0andagent-framework-openai==1.0.0 - The
-latesttest variant tracks breaking changes in new releases - If you encounter API compatibility issues, pin to the tested stable version:
pip install agent-framework-core==1.0.0 agent-framework-openai==1.0.0
If you have existing VCR cassettes from the 1.0.0rc1 line, re-record them after upgrading. The stable OpenAIChatClient uses the OpenAI Responses API rather than the older Chat Completions path.
Sensitive Data
Set enable_sensitive_data=True when calling enable_instrumentation() to capture message content in traces. This is required for full observability but may include PII.
Development
Running Tests
Tests use VCR cassettes to replay recorded API interactions:
# Run with recorded cassettes (no API key needed)
pytest tests/test_processor.py -v
# Re-record cassettes (requires OPENAI_API_KEY)
export OPENAI_API_KEY=your_key
rm -rf tests/cassettes/
pytest tests/test_processor.py -v --record-mode=rewrite
More Info
License
Apache-2.0
Metadata
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