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@@ -22,6 +22,7 @@ Set up LangSmith tracing to get visibility into your production applications.
 - [Vercel AI SDK (JS only)](./how_to_guides/trace_with_vercel_ai_sdk)
 - [OpenTelemetry](./how_to_guides/trace_with_opentelemetry)
 - [OpenAI Agent SDK (Python only)](./how_to_guides/trace_with_openai_agents_sdk)
+- [PromptFlow](./how_to_guides/promptflow_tracing)
 
 ### Advanced configuration
 
diff --git a/docs/observability/how_to_guides/promptflow_tracing.mdx b/docs/observability/how_to_guides/promptflow_tracing.mdx
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@@ -0,0 +1,173 @@
+---
+sidebar_position: 17
+---
+
+# Tracing PromptFlow applications in LangSmith
+
+## Introduction
+
+This tutorial shows how to correctly trace a [PromptFlow](https://github.com/microsoft/promptflow) application in LangSmith. It covers initial setup, creating nested traces between nodes, and integrating LangGraph within a PromptFlow tool.
+
+## Prerequisites
+
+Before getting started, make sure you have:
+
+- PromptFlow installed and configured
+- LangSmith API key set up in your environment
+- Basic understanding of PromptFlow's DAG structure
+
+## Setting up LangSmith tracing in PromptFlow
+
+### Basic node setup
+
+When creating a PromptFlow application, each node can be traced as a separate trace in LangSmith. To achieve this, you use the `@traceable` decorator from LangSmith along with the `@tool` decorator from PromptFlow.
+
+Here's the basic structure for a node:
+
+```python
+from promptflow.core import tool
+import langsmith as ls
+
+@ls.traceable(run_type="chain")
+@tool
+def my_tool_function(input_parameter: str) -> str:
+    # Your logic here
+    processed = f"Processed: {input_parameter}"
+    return processed
+```
+
+## Creating nested traces
+
+To create nested traces (where one trace is a child of another), you need to:
+
+1. Get the current run tree headers in the parent node
+2. Pass those headers to the child node via the `langsmith_extra` parameter
+3. Use those headers in the child node
+
+### Parent node example
+
+```python
+from promptflow.core import tool
+import langsmith as ls
+from langsmith.run_helpers import get_current_run_tree
+
+@ls.traceable(run_type="chain")
+@tool
+def parent_node(input_text: str) -> dict:
+    # Your custom logic here
+    processed = f"Processed: {input_text}"
+
+    # Get current run tree headers to pass downstream
+    run_tree = get_current_run_tree()
+    headers = run_tree.to_headers() if run_tree else {}
+
+    return {
+        "processed": processed,
+        "langsmith_extra": {
+            "parent": headers
+        }
+    }
+```
+
+### Child node example
+
+```python
+from promptflow.core import tool
+import langsmith as ls
+
+@ls.traceable(run_type="chain")
+@tool
+def child_node(processed_text: str, langsmith_extra: dict = None) -> str:
+    # Your logic here
+    enhanced_text = f"Enhanced: {processed_text}"
+    return enhanced_text
+```
+
+### Flow YAML configuration
+
+Here's how to configure the flow.dag.yaml to connect these nodes with proper trace propagation:
+
+```yaml
+inputs:
+  user_input:
+    type: string
+
+outputs:
+  final_output:
+    type: string
+    reference: ${child.output}
+
+nodes:
+  - name: parent
+    type: python
+    source:
+      type: code
+      path: parent_node.py
+    function: parent_node
+    inputs:
+      input_text: ${inputs.user_input}
+
+  - name: child
+    type: python
+    source:
+      type: code
+      path: child_node.py
+    function: child_node
+    inputs:
+      processed_text: ${parent.output.processed}
+      langsmith_extra: ${parent.output.langsmith_extra}
+```
+
+## Important considerations
+
+1. **Return type**: Make sure to correctly specify the return type of your function in the function signature to match what you're actually returning.
+
+2. **Headers propagation**: Always check if the run_tree exists before calling to_headers() to avoid errors.
+
+## Using LangGraph with PromptFlow and LangSmith
+
+You can run a LangGraph graph inside a PromptFlow tool, and it will be automatically traced within LangSmith. Here's an example:
+
+```python
+from promptflow.core import tool
+import langsmith as ls
+from langgraph.prebuilt import create_react_agent
+from langchain_openai import ChatOpenAI
+from langchain_core.tools import tool as langchain_tool
+
+# Define a tool for the LangGraph agent
+@langchain_tool
+def get_weather(city):
+    """Get weather information for a city."""
+    if city == "nyc":
+        return "Cloudy in NYC"
+    elif city == "sf":
+        return "Sunny in SF"
+    else:
+        return "Unknown city"
+
+# Create the agent
+model = ChatOpenAI(model="gpt-4o-mini")
+tools = [get_weather]
+agent = create_react_agent(model, tools=tools)
+
+@ls.traceable(run_type="chain")
+@tool
+def graph_node(input_text: str, langsmith_extra: dict = None) -> str:
+    # Process the input
+    processed = f"Processing query: {input_text}"
+
+    # Invoke the LangGraph agent
+    response = agent.invoke({
+        "messages": [{"role": "user", "content": input_text}]
+    })
+
+    return f"Agent response: {response}"
+```
+
+This will produce the following trace tree in LangSmith:
+![](./static/promptflow_langsmith.png)
+
+:::tip Note
+Find out more about nested traces in LangSmith [here](/observability/how_to_guides/nest_traces).
+:::
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