Comparison
openinference vs Awesome-LLMOps
Verdict
Pick openinference if 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; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training.
Markdown twin · openinference alternatives · Awesome-LLMOps alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | openinference | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Slowing (91d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- openinference
- OpenTelemetry Instrumentation for AI Observability
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- openinference
- 1.2k
- Awesome-LLMOps
- 5.9k
Forks
- openinference
- 299
- Awesome-LLMOps
- 993
Open issues
- openinference
- 238
- Awesome-LLMOps
- 247
Language
- openinference
- Python
- Awesome-LLMOps
- Shell
Adopt for
- openinference
- 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
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- openinference
- -
- Awesome-LLMOps
- -
Runtime
- openinference
- -
- Awesome-LLMOps
- -
License
- openinference
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- openinference
- Aug 20, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- openinference
- Evaluation & Observability
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- openinference
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- openinference
- 0d
- Awesome-LLMOps
- 91d
Open issues (now)
- openinference
- 238
- Awesome-LLMOps
- 247
Stars delta
- openinference
- +55 (30d)
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- openinference
- +5 (30d)
- Awesome-LLMOps
- +66 (30d)
Full report
- openinference
- Trust report
- Awesome-LLMOps
- Trust report
Choose openinference if…
- openinference is primarily Python; Awesome-LLMOps is Shell.
- License: openinference is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to openinference: aiops, openinference, telemetry, tracing.
- When you are working with AI applications that need detailed tracing capabilities alongside observability features provided by OpenTelemetry.
When NOT to use openinference
- 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.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; openinference is Python.
- License: Awesome-LLMOps is CC0-1.0, openinference is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, mlops.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Arize-ai/openinference) · observed Aug 21, 2026
- GitHub forks (Arize-ai/openinference) · observed Aug 21, 2026
- Last push (Arize-ai/openinference) · observed Aug 20, 2026
- License file (Apache-2.0) · observed Aug 21, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: openinference 1.2k · Awesome-LLMOps 5.9k (synced Aug 21, 2026).
Common questions
- What is the difference between openinference and Awesome-LLMOps?
- openinference: OpenTelemetry Instrumentation for AI Observability. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose openinference over Awesome-LLMOps?
- Choose openinference over Awesome-LLMOps when openinference is primarily Python; Awesome-LLMOps is Shell; License: openinference is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to openinference: aiops, openinference, telemetry, tracing; When you are working with AI applications that need detailed tracing capabilities alongside observability features provided by OpenTelemetry.
- When should I choose Awesome-LLMOps over openinference?
- Choose Awesome-LLMOps over openinference when Awesome-LLMOps is primarily Shell; openinference is Python; License: Awesome-LLMOps is CC0-1.0, openinference is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid openinference?
- 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.
- When should I avoid Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is openinference or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 1,159). Stars measure visibility, not whether either tool fits your constraints.
- Are openinference and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (openinference: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to openinference or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at openinference alternatives and Awesome-LLMOps alternatives (openinference markdown twin, Awesome-LLMOps markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, openinference or Awesome-LLMOps?
- openinference: Very active. Awesome-LLMOps: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for openinference and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: openinference trust report; Awesome-LLMOps trust report.