Comparison
gorilla vs Awesome-LLMOps
Verdict
Pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages; 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 · gorilla alternatives · Awesome-LLMOps alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | gorilla | Awesome-LLMOps |
|---|---|---|
| Maintenance | Slowing (117d since push) As of 2w · github_public_v1 | Slowing (91d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 3d · 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
- gorilla
- Training and Evaluating LLMs for Function Calls (Tool Calls)
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- gorilla
- 13k
- Awesome-LLMOps
- 5.9k
Forks
- gorilla
- 1.4k
- Awesome-LLMOps
- 993
Open issues
- gorilla
- 272
- Awesome-LLMOps
- 247
Language
- gorilla
- Python
- Awesome-LLMOps
- Shell
Adopt for
- gorilla
- Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages.
- 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
- gorilla
- -
- Awesome-LLMOps
- -
Runtime
- gorilla
- -
- Awesome-LLMOps
- -
License
- gorilla
- Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- gorilla
- Apr 13, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- gorilla
- Evaluation & Observability, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Days since push
- gorilla
- 117d
- Awesome-LLMOps
- 91d
Open issues (now)
- gorilla
- 272
- Awesome-LLMOps
- 247
Stars delta
- gorilla
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- gorilla
- Unknown
- Awesome-LLMOps
- +66 (30d)
Owner type
- gorilla
- User
- Awesome-LLMOps
- Organization
Full report
- gorilla
- Trust report
- Awesome-LLMOps
- Trust report
Choose gorilla if…
- gorilla is primarily Python; Awesome-LLMOps is Shell.
- License: gorilla is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning..
- Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api.
- You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively.
When NOT to use gorilla
- Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs.
- If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; gorilla is Python.
- License: Awesome-LLMOps is CC0-1.0, gorilla 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, 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 (ShishirPatil/gorilla) · observed Aug 8, 2026
- GitHub forks (ShishirPatil/gorilla) · observed Aug 8, 2026
- Last push (ShishirPatil/gorilla) · observed Apr 13, 2026
- License file (Apache-2.0) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 11, 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: gorilla 13k · Awesome-LLMOps 5.9k (synced Aug 8, 2026).
Common questions
- What is the difference between gorilla and Awesome-LLMOps?
- gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). 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 gorilla over Awesome-LLMOps?
- Choose gorilla over Awesome-LLMOps when gorilla is primarily Python; Awesome-LLMOps is Shell; License: gorilla is Apache-2.0, Awesome-LLMOps is CC0-1.0; Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.; Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api; You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively.
- When should I choose Awesome-LLMOps over gorilla?
- Choose Awesome-LLMOps over gorilla when Awesome-LLMOps is primarily Shell; gorilla is Python; License: Awesome-LLMOps is CC0-1.0, gorilla 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, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid gorilla?
- Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs. If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard.
- 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 gorilla or Awesome-LLMOps more popular on GitHub?
- gorilla has more GitHub stars (12,988 vs 5,915). Stars measure visibility, not whether either tool fits your constraints.
- Are gorilla and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (gorilla: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to gorilla or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at gorilla alternatives and Awesome-LLMOps alternatives (gorilla 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, gorilla or Awesome-LLMOps?
- gorilla: Slowing. 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 gorilla and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gorilla trust report; Awesome-LLMOps trust report.