> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/Arize-ai/openinference/llms.txt > Use this file to discover all available pages before exploring further. # Python Instrumentations > Auto-instrumentation packages for popular AI/ML frameworks OpenInference provides auto-instrumentation packages for popular AI/ML frameworks and LLM providers. ## Available Instrumentations | Package | Framework | Description | | ------------------------------------------------- | ----------------- | ------------------------------------------------------------- | | `openinference-instrumentation-openai` | OpenAI | OpenAI Python SDK (chat, completions, embeddings, assistants) | | `openinference-instrumentation-langchain` | LangChain | LangChain framework for LLM applications | | `openinference-instrumentation-llama-index` | LlamaIndex | LlamaIndex data framework (formerly GPT Index) | | `openinference-instrumentation-anthropic` | Anthropic | Anthropic Claude API | | `openinference-instrumentation-bedrock` | AWS Bedrock | Amazon Bedrock LLM service | | `openinference-instrumentation-vertexai` | Vertex AI | Google Cloud Vertex AI | | `openinference-instrumentation-mistralai` | Mistral AI | Mistral AI models | | `openinference-instrumentation-groq` | Groq | Groq LLM API | | `openinference-instrumentation-litellm` | LiteLLM | LiteLLM unified LLM interface | | `openinference-instrumentation-dspy` | DSPy | DSPy framework for LM programs | | `openinference-instrumentation-haystack` | Haystack | Haystack NLP framework | | `openinference-instrumentation-crewai` | CrewAI | CrewAI multi-agent framework | | `openinference-instrumentation-autogen` | AutoGen | Microsoft AutoGen framework | | `openinference-instrumentation-autogen-agentchat` | AutoGen AgentChat | AutoGen AgentChat API | | `openinference-instrumentation-instructor` | Instructor | Instructor structured outputs library | | `openinference-instrumentation-guardrails` | Guardrails AI | Guardrails AI validation framework | | `openinference-instrumentation-pydantic-ai` | Pydantic AI | Pydantic AI agent framework | | `openinference-instrumentation-smolagents` | smol-agents | Hugging Face smol-agents | | `openinference-instrumentation-google-genai` | Google GenAI | Google Generative AI SDK | | `openinference-instrumentation-google-adk` | Google ADK | Google Agent Developer Kit | | `openinference-instrumentation-openai-agents` | OpenAI Agents | OpenAI Agents API | | `openinference-instrumentation-mcp` | MCP | Model Context Protocol servers | | `openinference-instrumentation-beeai` | BeeAI | BeeAI agent framework | | `openinference-instrumentation-agno` | Agno | Agno agent framework | | `openinference-instrumentation-strands-agents` | Strands Agents | Strands agent framework | | `openinference-instrumentation-portkey` | Portkey | Portkey AI gateway | | `openinference-instrumentation-openllmetry` | OpenLLMetry | OpenLLMetry observability | | `openinference-instrumentation-openlit` | OpenLIT | OpenLIT observability | | `openinference-instrumentation-pipecat` | Pipecat | Pipecat voice agents | | `openinference-instrumentation-agentspec` | AgentSpec | AgentSpec protocol | | `openinference-instrumentation-agent-framework` | Agent Framework | Generic agent framework support | ## Installation Install any instrumentation package using pip: ```bash theme={null} pip install openinference-instrumentation-{name} ``` For example: ```bash theme={null} pip install openinference-instrumentation-openai pip install openinference-instrumentation-langchain pip install openinference-instrumentation-llama-index ``` ## Basic Usage Pattern All instrumentations follow a similar pattern: ```python Setup theme={null} from openinference.instrumentation.{name} import {Name}Instrumentor from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import SimpleSpanProcessor # Configure tracer provider tracer_provider = TracerProvider() tracer_provider.add_span_processor( SimpleSpanProcessor(OTLPSpanExporter("http://localhost:6006/v1/traces")) ) # Instrument {Name}Instrumentor().instrument(tracer_provider=tracer_provider) ``` ```python With TraceConfig theme={null} from openinference.instrumentation import TraceConfig from openinference.instrumentation.{name} import {Name}Instrumentor config = TraceConfig( hide_inputs=False, hide_outputs=False, base64_image_max_length=1000 ) {Name}Instrumentor().instrument( tracer_provider=tracer_provider, config=config ) ``` ```python Uninstrument theme={null} # Remove instrumentation {Name}Instrumentor().uninstrument() ``` ## Example: OpenAI ```python theme={null} import openai from openinference.instrumentation.openai import OpenAIInstrumentor from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import SimpleSpanProcessor # Setup tracing tracer_provider = TracerProvider() tracer_provider.add_span_processor( SimpleSpanProcessor(OTLPSpanExporter("http://localhost:6006/v1/traces")) ) # Instrument OpenAI OpenAIInstrumentor().instrument(tracer_provider=tracer_provider) # Use OpenAI as normal - automatically traced client = openai.OpenAI() response = client.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": "Hello!"}] ) ``` ## Example: LangChain ```python theme={null} from langchain.chains import LLMChain from langchain.llms import OpenAI from langchain.prompts import PromptTemplate from openinference.instrumentation.langchain import LangChainInstrumentor # Instrument LangChain LangChainInstrumentor().instrument(tracer_provider=tracer_provider) # Use LangChain - automatically traced llm = OpenAI(temperature=0.9) prompt = PromptTemplate( input_variables=["product"], template="What is a good name for a company that makes {product}?", ) chain = LLMChain(llm=llm, prompt=prompt) response = chain.run("eco-friendly water bottles") ``` ## Example: LlamaIndex ```python theme={null} from llama_index import VectorStoreIndex, SimpleDirectoryReader from openinference.instrumentation.llama_index import LlamaIndexInstrumentor # Instrument LlamaIndex LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider) # Use LlamaIndex - automatically traced documents = SimpleDirectoryReader("data").load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() response = query_engine.query("What is the main topic?") ``` ## Features All instrumentations provide: * **Automatic span creation** - Traces are created without code changes * **OpenInference semantic conventions** - Standardized attributes * **Context propagation** - Session, user, metadata, tags * **TraceConfig support** - Privacy and payload controls * **Suppression support** - Use `suppress_tracing()` to pause tracing * **OpenTelemetry compatible** - Works with any OTel collector ## Testing Instrumentations use pytest with VCR cassettes for testing: ```bash theme={null} # Run tests for a specific instrumentation cd python/instrumentation/openinference-instrumentation-openai pytest tests/ # Record new cassettes (requires API key) pytest tests/ -k test_name --vcr-record=once ``` ## Contributing See the [development guide](https://github.com/Arize-ai/openinference/blob/main/python/DEVELOPMENT.md) for creating new instrumentations. ## Resources * [GitHub Repository](https://github.com/Arize-ai/openinference) * [OpenInference Specification](https://github.com/Arize-ai/openinference/tree/main/spec) * [Phoenix Documentation](https://docs.arize.com/phoenix)