Comparison
traceAI vs Awesome-LLMOps
Verdict
Pick traceAI if traceAI is an open-source observability framework for AI applications, built on OpenTelemetry, that provides structured tracing for LLM calls, prompts, token counts, retrieval steps, and agent decisions; pick Awesome-LLMOps if 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.
Markdown twin · traceAI alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
8views this month
vs
Trust & integrity
| Signal | traceAI | Awesome-LLMOps |
|---|---|---|
| Maintenance | Active (21d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- traceAI
- Open-source observability for AI applications
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- traceAI
- 221
- Awesome-LLMOps
- 5.9k
Forks
- traceAI
- 44
- Awesome-LLMOps
- 1.1k
Open issues
- traceAI
- 15
- Awesome-LLMOps
- 317
Language
- traceAI
- Python
- Awesome-LLMOps
- Shell
Adopt for
- traceAI
- traceAI is an open-source observability framework for AI applications, built on OpenTelemetry, that provides structured tracing for LLM calls, prompts, token counts, retrieval steps, and agent decisions.
- 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
- traceAI
- -
- Awesome-LLMOps
- -
Runtime
- traceAI
- -
- Awesome-LLMOps
- -
License
- traceAI
- traceAI is available under the Apache-2.0 license, allowing for free use, modification, and distribution.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- traceAI
- Aug 27, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- traceAI
- Evaluation & Observability
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- traceAI
- Active (82%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- traceAI
- 21d
- Awesome-LLMOps
- 121d
Open issues (now)
- traceAI
- 15
- Awesome-LLMOps
- 317
Stars delta
- traceAI
- +9 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- traceAI
- +4 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- traceAI
- Trust report
- Awesome-LLMOps
- Trust report
Choose traceAI if…
- traceAI is primarily Python; Awesome-LLMOps is Shell.
- License: traceAI is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Pricing: traceAI is free and open-source, but users may incur costs based on the backend services they choose to integrate with..
- Requirements: Min 2 GB RAM; Python, TypeScript, Java, and C# are supported.; Requires OpenTelemetry-compatible backend for trace storage and visualization..
- Tags unique to traceAI: ai, ai-agents, langchain, large-language-models.
- When you need to trace every LLM call, prompt, token count, retrieval step, and agent decision in your AI application.
When NOT to use traceAI
- If your project does not require detailed tracing of LLM calls, prompts, token counts, retrieval steps, and agent decisions.
- When you are working exclusively with a single AI framework that does not require cross-language consistency.
- If you are looking for a solution that is not open-source or does not support OpenTelemetry.
- When you prefer a vendor-specific solution over a vendor-agnostic approach.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; traceAI is Python.
- License: Awesome-LLMOps is CC0-1.0, traceAI is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, 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 (future-agi/traceAI) · observed Sep 20, 2026
- GitHub forks (future-agi/traceAI) · observed Sep 20, 2026
- Last push (future-agi/traceAI) · observed Aug 27, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: traceAI 221 · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between traceAI and Awesome-LLMOps?
- traceAI: Open-source observability for AI applications. 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 traceAI over Awesome-LLMOps?
- Choose traceAI over Awesome-LLMOps when traceAI is primarily Python; Awesome-LLMOps is Shell; License: traceAI is Apache-2.0, Awesome-LLMOps is CC0-1.0; Pricing: traceAI is free and open-source, but users may incur costs based on the backend services they choose to integrate with.; Requirements: Min 2 GB RAM; Python, TypeScript, Java, and C# are supported.; Requires OpenTelemetry-compatible backend for trace storage and visualization.; Tags unique to traceAI: ai, ai-agents, langchain, large-language-models; When you need to trace every LLM call, prompt, token count, retrieval step, and agent decision in your AI application.
- When should I choose Awesome-LLMOps over traceAI?
- Choose Awesome-LLMOps over traceAI when Awesome-LLMOps is primarily Shell; traceAI is Python; License: Awesome-LLMOps is CC0-1.0, traceAI is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, 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 traceAI?
- If your project does not require detailed tracing of LLM calls, prompts, token counts, retrieval steps, and agent decisions. When you are working exclusively with a single AI framework that does not require cross-language consistency. If you are looking for a solution that is not open-source or does not support OpenTelemetry. When you prefer a vendor-specific solution over a vendor-agnostic approach.
- 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 traceAI or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 221). Stars measure visibility, not whether either tool fits your constraints.
- Are traceAI and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (traceAI: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to traceAI or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at traceAI alternatives and Awesome-LLMOps alternatives (traceAI 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, traceAI or Awesome-LLMOps?
- traceAI: 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 traceAI and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: traceAI trust report; Awesome-LLMOps trust report.