OpenInference is a set of conventions and plugins that is complimentary to OpenTelemetry to
enable tracing of AI applications. OpenInference is natively supported
by arize-phoenix, but can be used with any OpenTelemetry-compatible backend as
well.
The OpenInference specification is edited in markdown files found in the spec directory. It's designed to
provide insight into the invocation of LLMs and the surrounding application context such as retrieval from vector stores
and the usage of external tools such as search engines or APIs. The specification is transport and file-format agnostic,
and is intended to be used in conjunction with other specifications such as JSON, ProtoBuf, and DataFrames.
OpenInference provides a set of instrumentations for popular machine learning SDKs and frameworks in a variety of
languages.
Normalize and convert data across other instrumentation libraries by adding span processors that unify data.
| Name |
Description |
Complexity Level |
| Agno |
Agno agent examples |
Beginner |
| OpenAI SDK |
OpenAI Python SDK, including chat completions and embeddings |
Beginner |
| MistralAI SDK |
MistralAI Python SDK |
Beginner |
| VertexAI SDK |
VertexAI Python SDK |
Beginner |
| LlamaIndex |
LlamaIndex query engines |
Beginner |
| DSPy |
DSPy primitives and custom RAG modules |
Beginner |
| Boto3 Bedrock Client |
Boto3 Bedrock client |
Beginner |
| LangChain |
LangChain primitives and simple chains |
Beginner |
| LiteLLM |
A lightweight LiteLLM framework |
Beginner |
| LiteLLM Proxy |
LiteLLM Proxy to log OpenAI, Azure, Vertex, Bedrock |
Beginner |
| Groq |
Groq and AsyncGroq chat completions |
Beginner |
| Anthropic |
Anthropic Messages client |
Beginner |
| BeeAI |
Agentic instrumentation in the BeeAI framework |
Beginner |
| LlamaIndex + Next.js Chatbot |
A fully functional chatbot using Next.js and a LlamaIndex FastAPI backend |
Intermediate |
| LangServe |
A LangChain application deployed with LangServe using custom metadata on a per-request basis |
Intermediate |
| DSPy |
A DSPy RAG application using FastAPI, Weaviate, and Cohere |
Intermediate |
| Haystack |
A Haystack QA RAG application |
Intermediate |
| OpenAI Agents |
OpenAI Agents with handoffs |
Intermediate |
| Autogen AgentChat |
Microsoft Autogen Assistant Agent and Team Chat |
Intermediate |
| PydanticAI |
PydanticAI agent examples |
Intermediate |
| Name |
Description |
Complexity Level |
| OpenAI SDK |
OpenAI Node.js client |
Beginner |
| BeeAI framework - ReAct agent |
Agentic ReActAgent instrumentation in the BeeAI framework |
Beginner |
| BeeAI framework - ToolCalling agent |
Agentic ToolCallingAgent instrumentation in the BeeAI framework |
Beginner |
| BeeAI framework - LLM |
See how to run instrumentation only for the specific LLM module part in the BeeAI framework |
Beginner |
| LlamaIndex Express App |
A fully functional LlamaIndex chatbot with a Next.js frontend and a LlamaIndex Express backend, instrumented using openinference-instrumentation-openai |
Intermediate |
| LangChain OpenAI |
A simple script to call OpenAI via LangChain, instrumented using openinference-instrumentation-langchain |
Beginner |
| LangChain RAG Express App |
A fully functional LangChain chatbot that uses RAG to answer user questions. It has a Next.js frontend and a LangChain Express backend, instrumented using openinference-instrumentation-langchain |
Intermediate |
| Next.js + OpenAI |
A Next.js 13 project bootstrapped with create-next-app that uses OpenAI to generate text |
Beginner |
| Name |
Description |
Complexity Level |
| LangChain4j Example |
Simple example using LangChain4j with OpenAI |
Beginner |
| Spring AI Example |
Spring AI example with OpenAI and tool calling |
Beginner |
OpenInference supports the following destinations as span collectors.
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