Comparison
Awesome-LLM-Reasoning vs ThoughtSource
Verdict
Pick Awesome-LLM-Reasoning if awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning; pick ThoughtSource if thoughtSource is a curated, open repository maintained by the Samwald research group for enhancing chain-of-thought reasoning in large language models through data and tools.
Markdown twin · Awesome-LLM-Reasoning alternatives · ThoughtSource alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | Awesome-LLM-Reasoning | ThoughtSource |
|---|---|---|
| Maintenance | Slowing (99d since push) As of 3w · github_public_v1 | Dormant (606d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 1w · 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
- Awesome-LLM-Reasoning
- Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.
- ThoughtSource
- Central resource for data and tools related to chain-of-thought reasoning in LLMs
Stars
- Awesome-LLM-Reasoning
- 3.7k
- ThoughtSource
- 1.0k
Forks
- Awesome-LLM-Reasoning
- 212
- ThoughtSource
- 81
Open issues
- Awesome-LLM-Reasoning
- 26
- ThoughtSource
- 15
Language
- Awesome-LLM-Reasoning
- -
- ThoughtSource
- Jupyter Notebook
Adopt for
- Awesome-LLM-Reasoning
- Awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning.
- ThoughtSource
- ThoughtSource is a curated, open repository maintained by the Samwald research group for enhancing chain-of-thought reasoning in large language models through data and tools.
Persona
- Awesome-LLM-Reasoning
- -
- ThoughtSource
- -
Runtime
- Awesome-LLM-Reasoning
- -
- ThoughtSource
- -
License
- Awesome-LLM-Reasoning
- MIT
- ThoughtSource
- MIT License allows free use, modification, and distribution of the project's source code under its terms and conditions without any cost.
Last pushed
- Awesome-LLM-Reasoning
- Apr 20, 2026
- ThoughtSource
- Dec 16, 2024
Categories
- Awesome-LLM-Reasoning
- LLM Frameworks, Model Training
- ThoughtSource
- Model Training
Trust and health
Maintenance
- Awesome-LLM-Reasoning
- Slowing (36%)
- ThoughtSource
- Dormant (18%)
Days since push
- Awesome-LLM-Reasoning
- 99d
- ThoughtSource
- 606d
Open issues (now)
- Awesome-LLM-Reasoning
- 26
- ThoughtSource
- 15
Stars delta
- Awesome-LLM-Reasoning
- Unknown
- ThoughtSource
- 0 (30d)
Open issues delta
- Awesome-LLM-Reasoning
- Unknown
- ThoughtSource
- 0 (30d)
Owner type
- Awesome-LLM-Reasoning
- User
- ThoughtSource
- Organization
Full report
- Awesome-LLM-Reasoning
- Trust report
- ThoughtSource
- Trust report
Choose Awesome-LLM-Reasoning if…
- Pricing: Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models..
- Tags unique to Awesome-LLM-Reasoning: chain-of-thought, chatgpt, cot, deepseek-r1.
- Also covers LLM Frameworks.
- Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository.
When NOT to use Awesome-LLM-Reasoning
- Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models.
- Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities.
Choose ThoughtSource if…
- Tags unique to ThoughtSource: dataset, machine-learning, natural-language-processing, question-answering.
- You need focused resources on chain-of-thought reasoning techniques.
- Leaner open-issue backlog (15).
When NOT to use ThoughtSource
- Looking for a comprehensive general-purpose AI development environment.
- Prefer tools with multi-language support beyond Jupyter Notebooks.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (atfortes/Awesome-LLM-Reasoning) · observed Jul 28, 2026
- GitHub forks (atfortes/Awesome-LLM-Reasoning) · observed Jul 28, 2026
- Last push (atfortes/Awesome-LLM-Reasoning) · observed Apr 20, 2026
- License file (MIT) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (OpenBioLink/ThoughtSource) · observed Aug 15, 2026
- GitHub forks (OpenBioLink/ThoughtSource) · observed Aug 15, 2026
- Last push (OpenBioLink/ThoughtSource) · observed Dec 16, 2024
- License file (MIT) · observed Aug 15, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLM-Reasoning 3.7k · ThoughtSource 1.0k (synced Jul 28, 2026).
Common questions
- What is the difference between Awesome-LLM-Reasoning and ThoughtSource?
- Awesome-LLM-Reasoning: Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.. ThoughtSource: Central resource for data and tools related to chain-of-thought reasoning in LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLM-Reasoning over ThoughtSource?
- Choose Awesome-LLM-Reasoning over ThoughtSource when Pricing: Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models.; Tags unique to Awesome-LLM-Reasoning: chain-of-thought, chatgpt, cot, deepseek-r1; Also covers LLM Frameworks; Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository.
- When should I choose ThoughtSource over Awesome-LLM-Reasoning?
- Choose ThoughtSource over Awesome-LLM-Reasoning when Tags unique to ThoughtSource: dataset, machine-learning, natural-language-processing, question-answering; You need focused resources on chain-of-thought reasoning techniques; Leaner open-issue backlog (15).
- When should I avoid Awesome-LLM-Reasoning?
- Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models. Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities.
- When should I avoid ThoughtSource?
- Looking for a comprehensive general-purpose AI development environment. Prefer tools with multi-language support beyond Jupyter Notebooks.
- Is Awesome-LLM-Reasoning or ThoughtSource more popular on GitHub?
- Awesome-LLM-Reasoning has more GitHub stars (3,657 vs 1,015). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLM-Reasoning and ThoughtSource open source?
- Yes - both are open-source projects on GitHub (Awesome-LLM-Reasoning: MIT, ThoughtSource: MIT).
- Where can I find alternatives to Awesome-LLM-Reasoning or ThoughtSource?
- GraphCanon lists graph-backed alternatives at Awesome-LLM-Reasoning alternatives and ThoughtSource alternatives (Awesome-LLM-Reasoning markdown twin, ThoughtSource 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, Awesome-LLM-Reasoning or ThoughtSource?
- Awesome-LLM-Reasoning: Slowing. ThoughtSource: Dormant. 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 Awesome-LLM-Reasoning and ThoughtSource?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLM-Reasoning trust report; ThoughtSource trust report.