Comparison
gorilla vs awesome-LLM-resources
Verdict
Pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a.
Markdown twin · gorilla alternatives · awesome-LLM-resources alternatives
GraphCanon updated 6d
vs
Trust & integrity
| Signal | gorilla | awesome-LLM-resources |
|---|---|---|
| Maintenance | Slowing (117d since push) As of 2w · github_public_v1 | Very active (2d since push) As of 6d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 6d · 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-LLM-resources
- Summary of the world's best LLM resources.
Stars
- gorilla
- 13k
- awesome-LLM-resources
- 8.8k
Forks
- gorilla
- 1.4k
- awesome-LLM-resources
- 950
Open issues
- gorilla
- 272
- awesome-LLM-resources
- 23
Language
- gorilla
- Python
- awesome-LLM-resources
- -
Adopt for
- gorilla
- Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages.
- awesome-LLM-resources
- awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a
Persona
- gorilla
- -
- awesome-LLM-resources
- -
Runtime
- gorilla
- -
- awesome-LLM-resources
- -
License
- gorilla
- Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes.
- awesome-LLM-resources
- Apache-2.0
Last pushed
- gorilla
- Apr 13, 2026
- awesome-LLM-resources
- Aug 14, 2026
Categories
- gorilla
- Evaluation & Observability, Model Training
- awesome-LLM-resources
- AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- gorilla
- Slowing (36%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- gorilla
- 117d
- awesome-LLM-resources
- 2d
Open issues (now)
- gorilla
- 272
- awesome-LLM-resources
- 23
Stars delta
- gorilla
- Unknown
- awesome-LLM-resources
- +142 (30d)
Open issues delta
- gorilla
- Unknown
- awesome-LLM-resources
- -13 (30d)
Full report
- gorilla
- Trust report
- awesome-LLM-resources
- Trust report
Choose gorilla if…
- 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-LLM-resources if…
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models.
- Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks.
- - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
When NOT to use awesome-LLM-resources
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
- - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
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 (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Aug 14, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 10, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gorilla 13k · awesome-LLM-resources 8.8k (synced Aug 8, 2026).
Common questions
- What is the difference between gorilla and awesome-LLM-resources?
- gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose gorilla over awesome-LLM-resources?
- Choose gorilla over awesome-LLM-resources when 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-LLM-resources over gorilla?
- Choose awesome-LLM-resources over gorilla when Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
- 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-LLM-resources?
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
- Is gorilla or awesome-LLM-resources more popular on GitHub?
- gorilla has more GitHub stars (12,988 vs 8,845). Stars measure visibility, not whether either tool fits your constraints.
- Are gorilla and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (gorilla: Apache-2.0, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to gorilla or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at gorilla alternatives and awesome-LLM-resources alternatives (gorilla markdown twin, awesome-LLM-resources 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-LLM-resources?
- gorilla: Slowing. awesome-LLM-resources: Very active. 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-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gorilla trust report; awesome-LLM-resources trust report.