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Phoenix is built on OpenTelemetry and provides auto-instrumentation for popular LLM frameworks, SDKs, and providers. Tracing integrations allow you to observe your LLM application’s runtime behavior with minimal code changes.

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Supported Frameworks

Phoenix provides first-class support for the most popular LLM application frameworks:

LangChain

Auto-instrument LangChain applications in Python and JavaScript

LlamaIndex

Trace LlamaIndex queries, retrievals, and agent workflows

OpenAI

Monitor OpenAI API calls including GPT-4, embeddings, and agents

Anthropic

Instrument Claude API calls with streaming support

OpenTelemetry

Custom instrumentation using OpenTelemetry primitives

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LLM Providers

Phoenix supports instrumentation for all major LLM providers:

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Python Integrations

ProviderPackageDescription
OpenAIopeninference-instrumentation-openaiGPT models, embeddings, and function calling
Anthropicopeninference-instrumentation-anthropicClaude models with streaming support
Google GenAIopeninference-instrumentation-google-genaiGemini and PaLM models
AWS Bedrockopeninference-instrumentation-bedrockAmazon Bedrock models
MistralAIopeninference-instrumentation-mistralaiMistral models
VertexAIopeninference-instrumentation-vertexaiGoogle Vertex AI models
Groqopeninference-instrumentation-groqGroq inference engine
LiteLLMopeninference-instrumentation-litellmUnified interface for 100+ LLMs

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JavaScript/TypeScript Integrations

ProviderPackageDescription
OpenAI@arizeai/openinference-instrumentation-openaiOpenAI Node.js SDK
LangChain.js@arizeai/openinference-instrumentation-langchainLangChain JavaScript framework
Vercel AI SDK@arizeai/openinference-vercelVercel AI SDK streaming
BeeAI@arizeai/openinference-instrumentation-beeaiBeeAI agent framework

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Agent Frameworks

Phoenix supports popular agent and workflow frameworks:
  • DSPy - openinference-instrumentation-dspy
  • CrewAI - openinference-instrumentation-crewai
  • Haystack - openinference-instrumentation-haystack
  • Guardrails - openinference-instrumentation-guardrails
  • Instructor - openinference-instrumentation-instructor
  • Agno - openinference-instrumentation-agno
  • Pydantic AI - openinference-instrumentation-pydantic-ai

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Getting Started

Most integrations follow a simple three-step pattern:
1

Install the instrumentation package

pip install openinference-instrumentation-{provider}
2

Register Phoenix tracer

from phoenix.otel import register

tracer_provider = register(
  project_name="my-llm-app",
  auto_instrument=True  # Auto-instrument based on installed packages
)
3

Run your application

Your LLM calls will now be automatically traced and sent to Phoenix.

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How It Works

Phoenix integrations use OpenTelemetry to capture traces from your LLM applications:
  1. Auto-instrumentation: Instrumentation packages automatically patch SDK methods to capture trace data
  2. OpenInference conventions: Traces follow standardized semantic conventions for LLM observability
  3. OTLP export: Trace data is exported to Phoenix using the OpenTelemetry Protocol (OTLP)
  4. Zero-code changes: Most integrations require no changes to your application code
All Phoenix integrations are open source and part of the OpenInference project.

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Next Steps

OpenAI Integration

Get started with OpenAI tracing

LangChain Integration

Instrument LangChain applications

Custom Instrumentation

Build custom traces with OpenTelemetry

All Integrations

Browse all available integrations

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