feat: add Microsoft Agent Framework integration

python/frameworks/agent-framework/CHANGELOG.md

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+## [0.1.0] - 2026-05-25
+### Feature
+- Added support for Microsoft Agent Framework instrumentation.

python/frameworks/agent-framework/README.md

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+# Microsoft Agent Framework OpenTelemetry Integration
+
+## Overview
+This integration provides support for using OpenTelemetry with Microsoft Agent Framework. It enables tracing and monitoring of applications built with Agent Framework.
+
+## Installation
+
+1. **Install traceAI Agent Framework**
+
+```bash
+pip install traceAI-agent-framework
+```
+
+2. **Install Microsoft Agent Framework**
+
+```bash
+pip install agent-framework
+```
+
+
+### Set Environment Variables
+Set up your environment variables to authenticate with FutureAGI
+
+```python
+import os
+
+os.environ["FI_API_KEY"] = FI_API_KEY
+os.environ["FI_SECRET_KEY"] = FI_SECRET_KEY
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+```
+
+## Quickstart
+
+### Register Tracer Provider
+Set up the trace provider to establish the observability pipeline. The trace provider:
+
+```python
+from fi_instrumentation import register
+from fi_instrumentation.fi_types import ProjectType
+
+trace_provider = register(
+    project_type=ProjectType.OBSERVE,
+    project_name="agent_framework_app",
+    set_global_tracer_provider=True,
+)
+```
+
+### Configure Agent Framework Instrumentation
+Turn on Agent Framework's native OpenTelemetry emission and install the FI attribute mapping.
+
+```python
+from agent_framework.observability import enable_instrumentation
+from traceai_agent_framework import enable_fi_attribute_mapping
+
+enable_instrumentation(enable_sensitive_data=True)
+enable_fi_attribute_mapping()
+```
+
+### Create Agent Framework Components
+Set up your Agent Framework client with built-in observability.
+
+```python
+import asyncio
+from agent_framework import Agent
+from agent_framework.openai import OpenAIChatClient
+
+agent = Agent(
+    OpenAIChatClient(model="gpt-4o-mini"),
+    name="weather_agent",
+    instructions="You are a concise weather assistant.",
+)
+
+async def main():
+    response = await agent.run("What's the weather in Paris?")
+    print(response)
+
+asyncio.run(main())
+```

python/frameworks/agent-framework/examples/basic_agent.py

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+"""Minimal Microsoft Agent Framework example traced into Future AGI.
+
+Calls a real LLM (OpenAI) and a real tool (wttr.in — no API key needed).
+You should see invoke_agent / chat / execute_tool spans in the FI dashboard.
+
+Run with:
+    export FI_API_KEY=...
+    export FI_SECRET_KEY=...
+    export OPENAI_API_KEY=...
+    python examples/basic_agent.py
+"""
+
+import asyncio
+import os
+import urllib.parse
+import urllib.request
+
+from agent_framework import Agent
+from agent_framework.observability import enable_instrumentation
+from agent_framework.openai import OpenAIChatClient
+from fi_instrumentation import register
+from fi_instrumentation.fi_types import ProjectType
+
+from traceai_agent_framework import enable_fi_attribute_mapping
+
+
+def get_weather(city: str) -> str:
+    """Get the current weather for a city.
+
+    Args:
+        city: City name, e.g. "Paris", "Tokyo", "New York".
+    """
+    url = f"https://wttr.in/{urllib.parse.quote(city)}?format=3"
+    with urllib.request.urlopen(url, timeout=10) as resp:
+        return resp.read().decode("utf-8").strip()
+
+
+def main() -> None:
+    if not os.getenv("OPENAI_API_KEY"):
+        raise SystemExit("Set OPENAI_API_KEY before running this example.")
+
+    # 1) FI tracer provider on the global slot so agent_framework reads from there.
+    register(
+        project_type=ProjectType.OBSERVE,
+        project_name="agent-framework-basic-example",
+        set_global_tracer_provider=True,
+    )
+
+    # 2) Agent Framework's own observability + sensitive-data opt-in so messages
+    #    are captured in spans (required to see prompts/completions in FI).
+    enable_instrumentation(enable_sensitive_data=True)
+
+    # 3) Install our SpanProcessor on the FI tracer provider; it re-keys
+    #    Agent Framework's gen_ai.* spans into FI conventions as they end.
+    enable_fi_attribute_mapping()
+
+    # 4) Build an agent with a real weather tool.
+    agent = Agent(
+        OpenAIChatClient(model="gpt-4o-mini"),
+        name="weather_agent",
+        description="Answers questions about weather using the get_weather tool.",
+        instructions="You are a concise weather assistant. Always use get_weather.",
+        tools=[get_weather],
+    )
+
+    async def run() -> None:
+        response = await agent.run("What's the weather in Paris right now?")
+        print("Agent response:\n", response)
+
+    asyncio.run(run())
+
+
+if __name__ == "__main__":
+    main()

python/frameworks/agent-framework/examples/requirements.txt

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+traceAI-agent-framework>=0.1.0
+agent-framework>=1.0.0
+fi-instrumentation-otel>=0.1.14
+openai>=1.0.0

python/frameworks/agent-framework/examples/workflow_multi_agent.py

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+"""Multi-agent workflow example traced into Future AGI.
+
+Two agents handing off through a WorkflowBuilder graph:
+  researcher → summarizer → output
+
+Run with:
+    export FI_API_KEY=...
+    export FI_SECRET_KEY=...
+    export OPENAI_API_KEY=...
+    python examples/workflow_multi_agent.py
+
+In the FI dashboard you should see a ``workflow.run`` (CHAIN) span containing
+nested ``executor.process`` (CHAIN) spans and ``invoke_agent`` (AGENT) spans
+for each agent invocation.
+"""
+
+import asyncio
+import os
+
+from agent_framework import Agent, WorkflowBuilder, WorkflowContext
+from agent_framework._workflows._function_executor import executor
+from agent_framework.observability import enable_instrumentation
+from agent_framework.openai import OpenAIChatClient
+from fi_instrumentation import register
+from fi_instrumentation.fi_types import ProjectType
+
+from traceai_agent_framework import enable_fi_attribute_mapping
+
+
+# --- Workflow nodes ---------------------------------------------------------
+
+
+@executor(id="researcher")
+async def research(topic: str, ctx: WorkflowContext[str]) -> None:
+    """Step 1: ask the research agent for a few bullet points."""
+    agent = Agent(
+        OpenAIChatClient(),
+        name="researcher",
+        instructions="You are a research assistant. Return 3 short bullet points.",
+    )
+    response = await agent.run(f"Research this topic: {topic}")
+    await ctx.send_message(str(response))
+
+
+@executor(id="summarizer")
+async def summarize(notes: str, ctx: WorkflowContext[None, str]) -> None:
+    """Step 2: ask the summarizer agent to compress to one sentence."""
+    agent = Agent(
+        OpenAIChatClient(),
+        name="summarizer",
+        instructions="Compress the input into a single concise sentence.",
+    )
+    response = await agent.run(notes)
+    await ctx.yield_output(str(response))
+
+
+# --- Main -------------------------------------------------------------------
+
+
+def main() -> None:
+    if not os.getenv("OPENAI_API_KEY"):
+        raise SystemExit("Set OPENAI_API_KEY before running this example.")
+
+    register(
+        project_type=ProjectType.OBSERVE,
+        project_name="agent-framework-workflow-example",
+        set_global_tracer_provider=True,
+    )
+    enable_instrumentation(enable_sensitive_data=True)
+    enable_fi_attribute_mapping()
+
+    workflow = (
+        WorkflowBuilder(start_executor=research)
+        .add_edge(research, summarize)
+        .build()
+    )
+
+    async def run() -> None:
+        result = await workflow.run("the discovery of penicillin")
+        print("Workflow result:\n", result)
+
+    asyncio.run(run())
+
+
+if __name__ == "__main__":
+    main()

python/frameworks/agent-framework/pyproject.toml

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+[tool.poetry]
+name = "traceAI-agent-framework"
+version = "0.1.0"
+description = "OpenTelemetry instrumentation for Microsoft Agent Framework"
+authors = ["Future AGI <no-reply@futureagi.com>"]
+readme = "README.md"
+packages = [
+    { include = "traceai_agent_framework" }
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.15"
+agent-framework = ">=1.0.0"
+fi-instrumentation-otel = ">=0.1.14"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^8.0.0"
+pytest-asyncio = "^0.23.0"
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

python/frameworks/agent-framework/tests/_fixtures/sample_spans.json

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+[
+  {
+    "name": "execute_tool get_weather",
+    "scope": "agent_framework",
+    "kind": "INTERNAL",
+    "status": "UNSET",
+    "attributes": {
+      "gen_ai.operation.name": "execute_tool",
+      "gen_ai.tool.name": "get_weather",
+      "gen_ai.tool.call.id": "call-123",
+      "gen_ai.tool.type": "function",
+      "gen_ai.tool.description": "Get the current weather for a city.",
+      "gen_ai.tool.call.result": "The weather in Paris is sunny, 22 degrees.",
+      "gen_ai.tool.call.arguments": "{\"city\": \"Paris\"}"
+    }
+  },
+  {
+    "name": "invoke_agent weather_agent",
+    "scope": "agent_framework",
+    "kind": "INTERNAL",
+    "status": "UNSET",
+    "attributes": {
+      "gen_ai.input.messages": "[{\"role\": \"user\", \"parts\": [{\"type\": \"text\", \"content\": \"What's the weather in Paris?\"}]}]",
+      "gen_ai.system_instructions": "[{\"type\": \"text\", \"content\": \"You are a helpful weather assistant.\"}]",
+      "gen_ai.request.choice.count": 1,
+      "gen_ai.operation.name": "invoke_agent",
+      "gen_ai.provider.name": "microsoft.agent_framework",
+      "gen_ai.agent.id": "396693c8-ed0d-44b1-9ad9-ae5b0f9b4ad5",
+      "gen_ai.agent.name": "weather_agent",
+      "gen_ai.agent.description": "Tells you the weather",
+      "gen_ai.tool.definitions": "[{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get the current weather for a city.\", \"parameters\": {\"properties\": {\"city\": {\"title\": \"City\", \"type\": \"string\"}}, \"required\": [\"city\"], \"title\": \"get_weather_input\", \"type\": \"object\"}}}]",
+      "gen_ai.response.id": "resp-abc",
+      "gen_ai.output.messages": "[{\"role\": \"assistant\", \"parts\": [{\"type\": \"text\", \"content\": \"The weather in Paris is sunny.\"}]}]"
+    }
+  },
+  {
+    "name": "workflow.build",
+    "scope": "agent_framework",
+    "kind": "INTERNAL",
+    "status": "UNSET",
+    "attributes": {
+      "workflow_builder.name": "WorkflowBuilder-d9fed0bb-2d5e-4a93-a5c3-e116d5ed366f",
+      "workflow.id": "a86ba8e4-0a04-49fd-915a-902f18b83605",
+      "workflow.definition": "{\"name\": \"WorkflowBuilder-d9fed0bb-2d5e-4a93-a5c3-e116d5ed366f\", \"id\": \"a86ba8e4-0a04-49fd-915a-902f18b83605\", \"start_executor_id\": \"upper\", \"max_iterations\": 100, \"edge_groups\": [{\"id\": \"InternalEdgeGroup/1d68d9bb-9f75-496e-ad64-a1215e304d96\", \"type\": \"InternalEdgeGroup\", \"edges\": [{\"source_id\": \"internal:upper\", \"target_id\": \"upper\"}]}, {\"id\": \"InternalEdgeGroup/481bb9d6-475e-444f-ac05-a0a0739dcc90\", \"type\": \"InternalEdgeGroup\", \"edges\": [{\"source_id\": \"internal:exclaim\", \"target_id\": \"exclaim\"}]}, {\"id\": \"SingleEdgeGroup/9d7bd9c3-be19-4bf8-9589-4e331c7c02ce\", \"type\": \"SingleEdgeGroup\", \"edges\": [{\"source_id\": \"upper\", \"target_id\": \"exclaim\"}]}], \"executors\": {\"upper\": {\"id\": \"upper\", \"type\": \"FunctionExecutor\"}, \"exclaim\": {\"id\": \"exclaim\", \"type\": \"FunctionExecutor\"}}, \"output_executors\": null, \"intermediate_executors\": null}"
+    }
+  },
+  {
+    "name": "message.send",
+    "scope": "agent_framework",
+    "kind": "PRODUCER",
+    "status": "UNSET",
+    "attributes": {
+      "message.type": "str"
+    }
+  },
+  {
+    "name": "executor.process upper",
+    "scope": "agent_framework",
+    "kind": "INTERNAL",
+    "status": "UNSET",
+    "attributes": {
+      "executor.id": "upper",
+      "executor.type": "FunctionExecutor",
+      "message.type": "MessageType.STANDARD",
+      "message.payload_type": "str"
+    }
+  },
+  {
+    "name": "edge_group.process SingleEdgeGroup",
+    "scope": "agent_framework",
+    "kind": "INTERNAL",
+    "status": "UNSET",
+    "attributes": {
+      "edge_group.type": "SingleEdgeGroup",
+      "edge_group.id": "SingleEdgeGroup/9d7bd9c3-be19-4bf8-9589-4e331c7c02ce",
+      "message.source_id": "upper",
+      "edge_group.delivered": true,
+      "edge_group.delivery_status": "delivered"
+    }
+  },
+  {
+    "name": "executor.process exclaim",
+    "scope": "agent_framework",
+    "kind": "INTERNAL",
+    "status": "UNSET",
+    "attributes": {
+      "executor.id": "exclaim",
+      "executor.type": "FunctionExecutor",
+      "message.type": "MessageType.STANDARD",
+      "message.payload_type": "WorkflowMessage"
+    }
+  },
+  {
+    "name": "workflow.run",
+    "scope": "agent_framework",
+    "kind": "INTERNAL",
+    "status": "UNSET",
+    "attributes": {
+      "workflow.id": "a86ba8e4-0a04-49fd-915a-902f18b83605",
+      "workflow.name": "WorkflowBuilder-d9fed0bb-2d5e-4a93-a5c3-e116d5ed366f"
+    }
+  }
+]