chore: publish from staged

plugins/phoenix/skills/phoenix-tracing/references/instrumentation-auto-python.md

@@ -0,0 +1,85 @@
+# Phoenix Tracing: Auto-Instrumentation (Python)
+
+**Automatically create spans for LLM calls without code changes.**
+
+## Overview
+
+Auto-instrumentation patches supported libraries at runtime to create spans automatically. Use for supported frameworks (LangChain, LlamaIndex, OpenAI SDK, etc.). For custom logic, manual-instrumentation-python.md.
+
+## Supported Frameworks
+
+**Python:**
+
+- LLM SDKs: OpenAI, Anthropic, Bedrock, Mistral, Vertex AI, Groq, Ollama
+- Frameworks: LangChain, LlamaIndex, DSPy, CrewAI, Instructor, Haystack
+- Install: `pip install openinference-instrumentation-{name}`
+
+## Setup
+
+**Install and enable:**
+
+```bash
+pip install arize-phoenix-otel
+pip install openinference-instrumentation-openai  # Add others as needed
+```
+
+```python
+from phoenix.otel import register
+
+register(project_name="my-app", auto_instrument=True)  # Discovers all installed instrumentors
+```
+
+**Example:**
+
+```python
+from phoenix.otel import register
+from openai import OpenAI
+
+register(project_name="my-app", auto_instrument=True)
+
+client = OpenAI()
+response = client.chat.completions.create(
+    model="gpt-4",
+    messages=[{"role": "user", "content": "Hello!"}]
+)
+```
+
+Traces appear in Phoenix UI with model, input/output, tokens, timing automatically captured. See span kind files for full attribute schemas.
+
+**Selective instrumentation** (explicit control):
+
+```python
+from phoenix.otel import register
+from openinference.instrumentation.openai import OpenAIInstrumentor
+
+tracer_provider = register(project_name="my-app")  # No auto_instrument
+OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
+```
+
+## Limitations
+
+Auto-instrumentation does NOT capture:
+
+- Custom business logic
+- Internal function calls
+
+**Example:**
+
+```python
+def my_custom_workflow(query: str) -> str:
+    preprocessed = preprocess(query)  # Not traced
+    response = client.chat.completions.create(...)  # Traced (auto)
+    postprocessed = postprocess(response)  # Not traced
+    return postprocessed
+```
+
+**Solution:** Add manual instrumentation:
+
+```python
+@tracer.chain
+def my_custom_workflow(query: str) -> str:
+    preprocessed = preprocess(query)
+    response = client.chat.completions.create(...)
+    postprocessed = postprocess(response)
+    return postprocessed
+```