# 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
```
