# Phoenix Tracing: Custom Metadata (Python)
Add custom attributes to spans for richer observability.
## Install
```bash
pip install openinference-instrumentation
```
## Session
```python
from openinference.instrumentation import using_session
with using_session(session_id="my-session-id"):
# Spans get: "session.id" = "my-session-id"
...
```
## User
```python
from openinference.instrumentation import using_user
with using_user("my-user-id"):
# Spans get: "user.id" = "my-user-id"
...
```
## Metadata
```python
from openinference.instrumentation import using_metadata
with using_metadata({"key": "value", "experiment_id": "exp_123"}):
# Spans get: "metadata" = '{"key": "value", "experiment_id": "exp_123"}'
...
```
## Tags
```python
from openinference.instrumentation import using_tags
with using_tags(["tag_1", "tag_2"]):
# Spans get: "tag.tags" = '["tag_1", "tag_2"]'
...
```
## Combined (using_attributes)
```python
from openinference.instrumentation import using_attributes
with using_attributes(
session_id="my-session-id",
user_id="my-user-id",
metadata={"environment": "production"},
tags=["prod", "v2"],
prompt_template="Answer: {question}",
prompt_template_version="v1.0",
prompt_template_variables={"question": "What is Phoenix?"},
):
# All attributes applied to spans in this context
...
```
## On a Single Span
```python
span.set_attribute("metadata", json.dumps({"key": "value"}))
span.set_attribute("user.id", "user_123")
span.set_attribute("session.id", "session_456")
```
## As Decorators
All context managers can be used as decorators:
```python
@using_session(session_id="my-session-id")
@using_user("my-user-id")
@using_metadata({"env": "prod"})
def my_function():
...
```
