GraphCanon updated 1mo · GitHub synced 1mo
Decision brief
OpenInference is a tool focused on providing observability for AI systems using OpenTelemetry, designed to complement it by enabling tracing of AI applications. It is natively supported by Arize Phoenix and AX but can be
Good fit when
- When you are working with AI applications that need detailed tracing capabilities alongside observability features provided by OpenTelemetry.
- If your project already includes integration or a dependency on Arize tools like Arize Phoenix or Arize AX, making this tool natively compatible for enhanced functionality.
Avoid when
- When your AI applications do not require the specific tracing features supported by OpenInference and can operate effectively with standard OpenTelemetry capabilities alone.
- If you are looking for alternatives that offer more generic observability features without needing to integrate Arize-specific tools, as this might make OpenInference less beneficial.
Observed Jul 9, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 1mo
- Provenance
- Not a fork · Organization account
- As of 1mo
- Security (OSV)
- No MCP manifest
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install openinferenceHow it fits your stack(4)
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Evidence and technical details
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Overview
The OpenInference project provides OpenTelemetry instrumentation tools and specification to improve observability in the context of AI applications, including LLMs and AI agents.
Capability facts
- Languages
- python
Source: github.language · Jul 21, 2026
Categories
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README
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 and Arize AX, but can be used with any OpenTelemetry-compatible backend as well.
Specification
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.
Instrumentation
OpenI
For agents
This page has a .md twin and JSON over the API.