Comparison
openlit vs Awesome-LLMOps
Verdict
Pick openlit if openLIT is an open-source observability and evaluation platform for AI and coding agents, leveraging OpenTelemetry for tracking performance and costs; 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 · openlit alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
19views this month
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Trust & integrity
| Signal | openlit | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d 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
- openlit
- Open-source observability & evaluation platform for AI agents and coding agents
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- openlit
- 2.8k
- Awesome-LLMOps
- 5.9k
Forks
- openlit
- 390
- Awesome-LLMOps
- 1.1k
Open issues
- openlit
- 77
- Awesome-LLMOps
- 317
Language
- openlit
- TypeScript
- Awesome-LLMOps
- Shell
Adopt for
- openlit
- OpenLIT is an open-source observability and evaluation platform for AI and coding agents, leveraging OpenTelemetry for tracking performance and costs.
- 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
- openlit
- -
- Awesome-LLMOps
- -
Runtime
- openlit
- -
- Awesome-LLMOps
- -
License
- openlit
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- openlit
- Sep 17, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- openlit
- Evaluation & Observability
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- openlit
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- openlit
- 0d
- Awesome-LLMOps
- 121d
Open issues (now)
- openlit
- 77
- Awesome-LLMOps
- 317
Stars delta
- openlit
- +106 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- openlit
- +29 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- openlit
- Trust report
- Awesome-LLMOps
- Trust report
Typed relationship
openlit Awesome-LLMOpsGiven the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related.
Choose openlit if…
- openlit is primarily TypeScript; Awesome-LLMOps is Shell.
- License: openlit is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Pricing: OpenLIT is free and open-source under the Apache License 2.0..
- Requirements: Installation is available via pip for Python and npm for TypeScript..
- Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related.
- Tags unique to openlit: ai-engineering, ai-evaluation, ai-observability, ai-observability-platform.
- openlit ships Docker support for self-hosted deployment.
- When you need to track and evaluate the performance and costs of AI and coding agents using OpenTelemetry.
When NOT to use openlit
- If your project does not require observability and evaluation features for AI and coding agents.
- When you prefer proprietary tools over open-source solutions.
- If your project does not involve AI applications or does not need detailed cost tracking across multiple dimensions.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; openlit is TypeScript.
- License: Awesome-LLMOps is CC0-1.0, openlit is Apache-2.0.
- Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related.
- 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 (openlit/openlit) · observed Sep 20, 2026
- GitHub forks (openlit/openlit) · observed Sep 20, 2026
- Last push (openlit/openlit) · observed Sep 17, 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: openlit 2.8k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between openlit and Awesome-LLMOps?
- openlit: Open-source observability & evaluation platform for AI agents and coding agents. 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 openlit over Awesome-LLMOps?
- Choose openlit over Awesome-LLMOps when openlit is primarily TypeScript; Awesome-LLMOps is Shell; License: openlit is Apache-2.0, Awesome-LLMOps is CC0-1.0; Pricing: OpenLIT is free and open-source under the Apache License 2.0.; Requirements: Installation is available via pip for Python and npm for TypeScript.; Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related; Tags unique to openlit: ai-engineering, ai-evaluation, ai-observability, ai-observability-platform; openlit ships Docker support for self-hosted deployment; When you need to track and evaluate the performance and costs of AI and coding agents using OpenTelemetry.
- When should I choose Awesome-LLMOps over openlit?
- Choose Awesome-LLMOps over openlit when Awesome-LLMOps is primarily Shell; openlit is TypeScript; License: Awesome-LLMOps is CC0-1.0, openlit is Apache-2.0; Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related; 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 openlit?
- If your project does not require observability and evaluation features for AI and coding agents. When you prefer proprietary tools over open-source solutions. If your project does not involve AI applications or does not need detailed cost tracking across multiple dimensions.
- 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 openlit or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 2,770). Stars measure visibility, not whether either tool fits your constraints.
- Are openlit and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (openlit: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to openlit or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at openlit alternatives and Awesome-LLMOps alternatives (openlit 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, openlit or Awesome-LLMOps?
- openlit: 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 openlit and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: openlit trust report; Awesome-LLMOps trust report.