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 is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
Markdown twin · gorilla alternatives · awesome-LLM-resources alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | gorilla | awesome-LLM-resources |
|---|---|---|
| Maintenance | Slowing (147d since push) As of Sep 8, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 8, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 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
- 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
- 9.0k
Forks
- gorilla
- 1.4k
- awesome-LLM-resources
- 993
Open issues
- gorilla
- 278
- awesome-LLM-resources
- 40
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 is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
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
- The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.
Last pushed
- gorilla
- Apr 13, 2026
- awesome-LLM-resources
- Sep 14, 2026
Categories
- gorilla
- Evaluation & Observability, Model Training
- awesome-LLM-resources
- AI Agents, Computer Vision, Data & Retrieval, 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
- 147d
- awesome-LLM-resources
- 3d
Open issues (now)
- gorilla
- 278
- awesome-LLM-resources
- 40
Stars delta
- gorilla
- +29 (30d)
- awesome-LLM-resources
- +123 (30d)
Open issues delta
- gorilla
- +6 (30d)
- awesome-LLM-resources
- +17 (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…
- Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs..
- Requirements: The repository does not specify any technical requirements for accessing its content..
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models.
- Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Inference & Serving, LLM Frameworks.
- When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
When NOT to use awesome-LLM-resources
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider.
- When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
Explore
gorilla trust report →awesome-LLM-resources trust report →Evaluation & Observability category →Model Training category →AI Agents category →Computer Vision category →Data & Retrieval category →Developer Tools category →Inference & Serving category →LLM Frameworks category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ShishirPatil/gorilla) · observed Sep 20, 2026
- GitHub forks (ShishirPatil/gorilla) · observed Sep 20, 2026
- Last push (ShishirPatil/gorilla) · observed Apr 13, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Sep 14, 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 on cards: gorilla 13k · awesome-LLM-resources 9.0k (synced Sep 20, 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 Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Inference & Serving, LLM Frameworks; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
- 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?
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
- Is gorilla or awesome-LLM-resources more popular on GitHub?
- gorilla has more GitHub stars (13,017 vs 8,968). 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.