arize-phoenix-otel 0.14.0
LLM Observability
License: Apache-2.0
Requires Python: <3.14,>=3.8
Keywords: Explainability, Monitoring, Observability

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        arize-phoenix-otel
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Provides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aware defaults. Phoenix OTEL also gives you access to tracing decorators for common GenAI patterns.

## Features

`arize-phoenix-otel` simplifies OpenTelemetry configuration for Phoenix users by providing:

- **Phoenix-aware defaults** for common OpenTelemetry primitives
- **Automatic configuration** from environment variables
- **Drop-in replacements** for OTel classes with enhanced functionality
- **Simplified tracing setup** with the `register()` function
- **Tracing decorators** for GenAI patterns

## Key Benefits

- **Zero Code Changes**: Enable `auto_instrument=True` to automatically instrument AI libraries
- **Production Ready**: Built-in batching and authentication
- **Phoenix Integration**: Seamless integration with Phoenix Cloud and self-hosted instances
- **OpenTelemetry Compatible**: Works with existing OpenTelemetry infrastructure

These defaults are aware of environment variables you may have set to configure Phoenix:

- `PHOENIX_COLLECTOR_ENDPOINT`
- `PHOENIX_PROJECT_NAME`
- `PHOENIX_CLIENT_HEADERS`
- `PHOENIX_API_KEY`
- `PHOENIX_GRPC_PORT`

## Installation

Install via `pip`:

```shell
pip install arize-phoenix-otel
```

## Quick Start

**Recommended**: Enable automatic instrumentation to trace your AI libraries with zero code changes:

```python
from phoenix.otel import register

# Recommended: Automatic instrumentation + production settings
tracer_provider = register(
    auto_instrument=True,  # Auto-trace OpenAI, LangChain, LlamaIndex, etc.
    batch=True,           # Production-ready batching
    project_name="my-app" # Organize your traces
)
```

That's it! All `openinference-*` AI libraries are now automatically traced and sent to Phoenix.

**Note**: `auto_instrument=True` only works if the corresponding OpenInference instrumentation libraries are installed. For example, to automatically trace OpenAI calls, you need `openinference-instrumentation-openai` installed:

```bash
pip install openinference-instrumentation-openai
pip install openinference-instrumentation-langchain  # For LangChain
pip install openinference-instrumentation-llama-index  # For LlamaIndex
```

See the [OpenInference repository](https://github.com/Arize-ai/openinference) for the complete list of available instrumentation packages.

### Authentication

```bash
export PHOENIX_API_KEY="your-api-key"
```

```python
# Or pass directly to register()
tracer_provider = register(api_key="your-api-key")
```

### Endpoint Configuration

Configure where to send your traces:

**Environment Variables** (Recommended):

```bash
export PHOENIX_COLLECTOR_ENDPOINT="https://app.phoenix.arize.com/s/your-space"
export PHOENIX_PROJECT_NAME="my-project"
```

**Direct Configuration**:

```python
tracer_provider = register(
    endpoint="http://localhost:6006/v1/traces",  # HTTP endpoint
    protocol="grpc"  # Or force gRPC protocol
)
```

## Usage Examples

### Simple Setup

```python
from phoenix.otel import register

# Basic setup - sends to localhost
tracer_provider = register(auto_instrument=True)
```

### Production Configuration

```python
tracer_provider = register(
    project_name="my-production-app",
    auto_instrument=True,      # Auto-trace AI/ML libraries
    batch=True,               # Background batching for performance
    api_key="your-api-key",   # Authentication
    endpoint="https://app.phoenix.arize.com/s/your-space"
)
```

### Manual Configuration

For advanced use cases, use Phoenix OTEL components directly:

```python
from phoenix.otel import TracerProvider, BatchSpanProcessor, HTTPSpanExporter

tracer_provider = TracerProvider()
exporter = HTTPSpanExporter(endpoint="http://localhost:6006/v1/traces")
processor = BatchSpanProcessor(span_exporter=exporter)
tracer_provider.add_span_processor(processor)
```

### Using Decorators

```python
from phoenix.otel import register

tracer_provider = register()

# Get a tracer for manual instrumentation
tracer = tracer_provider.get_tracer(__name__)

@tracer.chain
def process_data(data):
    return data + " processed"

@tracer.tool
def weather(location):
    return "sunny"
```

## Environment Variables

| Variable                     | Description          | Example                                      |
| ---------------------------- | -------------------- | -------------------------------------------- |
| `PHOENIX_COLLECTOR_ENDPOINT` | Where to send traces | `https://app.phoenix.arize.com/s/your-space` |
| `PHOENIX_PROJECT_NAME`       | Project name         | `my-llm-app`                                 |
| `PHOENIX_API_KEY`            | Authentication key   | `your-api-key`                               |
| `PHOENIX_CLIENT_HEADERS`     | Custom headers       | `Authorization=Bearer token`                 |
| `PHOENIX_GRPC_PORT`          | gRPC port override   | `4317`                                       |

## Documentation

- **[Full Documentation](https://arize-phoenix.readthedocs.io/projects/otel/en/latest/index.html)** - Complete API reference and guides
- **[Phoenix Docs](https://arize.com/docs/phoenix)** - Detailed tracing examples and patterns
- **[OpenInference](https://github.com/Arize-ai/openinference)** - Auto-instrumentation libraries for frameworks

## Community

Join our community to connect with thousands of AI builders:

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