Comparison
Hands-On-Large-Language-Models vs ReAct
Verdict
Pick Hands-On-Large-Language-Models if hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵; pick ReAct if reAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3.
Markdown twin · Hands-On-Large-Language-Models alternatives · ReAct alternatives
GraphCanon updated Sep 20, 2026
7views this month
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Trust & integrity
| Signal | Hands-On-Large-Language-Models | ReAct |
|---|---|---|
| Maintenance | Slowing (146d since push) As of Sep 18, 2026 · github_public_v1 | Dormant (923d since push) As of Aug 17, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Aug 17, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings 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
- Hands-On-Large-Language-Models
- Code for the book 'Hands-On Large Language Models'
- ReAct
- ReAct Prompting for decision-making with language models
Stars
- Hands-On-Large-Language-Models
- 29k
- ReAct
- 4.1k
Forks
- Hands-On-Large-Language-Models
- 6.7k
- ReAct
- 396
Open issues
- Hands-On-Large-Language-Models
- 40
- ReAct
- 5
Language
- Hands-On-Large-Language-Models
- Jupyter Notebook
- ReAct
- Jupyter Notebook
Adopt for
- Hands-On-Large-Language-Models
- Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵
- ReAct
- ReAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3.
Persona
- Hands-On-Large-Language-Models
- -
- ReAct
- -
Runtime
- Hands-On-Large-Language-Models
- -
- ReAct
- -
License
- Hands-On-Large-Language-Models
- Apache-2.0
- ReAct
- MIT
Last pushed
- Hands-On-Large-Language-Models
- Apr 24, 2026
- ReAct
- Feb 6, 2024
Categories
- Hands-On-Large-Language-Models
- Developer Tools, LLM Frameworks
- ReAct
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- Hands-On-Large-Language-Models
- Slowing (36%)
- ReAct
- Dormant (18%)
Days since push
- Hands-On-Large-Language-Models
- 146d
- ReAct
- 923d
Open issues (now)
- Hands-On-Large-Language-Models
- 40
- ReAct
- 5
Stars delta
- Hands-On-Large-Language-Models
- +910 (30d)
- ReAct
- +50 (30d)
Open issues delta
- Hands-On-Large-Language-Models
- +2 (30d)
- ReAct
- 0 (30d)
Owner type
- Hands-On-Large-Language-Models
- Organization
- ReAct
- User
OSV dependency advisories
- Hands-On-Large-Language-Models
- Published findings
- ReAct
- No lockfile (source not queried)
Full report
- Hands-On-Large-Language-Models
- Trust report
- ReAct
- Trust report
Choose Hands-On-Large-Language-Models if…
- License: Hands-On-Large-Language-Models is Apache-2.0, ReAct is MIT.
- Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llms, oreilly.
- Also covers Developer Tools.
- Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'.
When NOT to use Hands-On-Large-Language-Models
- Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning.
- Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM.
- If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit.
Choose ReAct if…
- License: ReAct is MIT, Hands-On-Large-Language-Models is Apache-2.0.
- Tags unique to ReAct: decision-making, prompting, reasoning.
- Also covers AI Agents.
- When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3
When NOT to use ReAct
- If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable
- When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (HandsOnLLM/Hands-On-Large-Language-Models) · observed Sep 20, 2026
- GitHub forks (HandsOnLLM/Hands-On-Large-Language-Models) · observed Sep 20, 2026
- Last push (HandsOnLLM/Hands-On-Large-Language-Models) · observed Apr 24, 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 (ysymyth/ReAct) · observed Sep 20, 2026
- GitHub forks (ysymyth/ReAct) · observed Sep 20, 2026
- Last push (ysymyth/ReAct) · observed Feb 6, 2024
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Hands-On-Large-Language-Models 29k · ReAct 4.1k (synced Sep 20, 2026).
Common questions
- What is the difference between Hands-On-Large-Language-Models and ReAct?
- Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. ReAct: ReAct Prompting for decision-making with language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose Hands-On-Large-Language-Models over ReAct?
- Choose Hands-On-Large-Language-Models over ReAct when License: Hands-On-Large-Language-Models is Apache-2.0, ReAct is MIT; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llms, oreilly; Also covers Developer Tools; Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'.
- When should I choose ReAct over Hands-On-Large-Language-Models?
- Choose ReAct over Hands-On-Large-Language-Models when License: ReAct is MIT, Hands-On-Large-Language-Models is Apache-2.0; Tags unique to ReAct: decision-making, prompting, reasoning; Also covers AI Agents; When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3.
- When should I avoid Hands-On-Large-Language-Models?
- Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit.
- When should I avoid ReAct?
- If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred
- Is Hands-On-Large-Language-Models or ReAct more popular on GitHub?
- Hands-On-Large-Language-Models has more GitHub stars (29,162 vs 4,109). Stars measure visibility, not whether either tool fits your constraints.
- Are Hands-On-Large-Language-Models and ReAct open source?
- Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, ReAct: MIT).
- Where can I find alternatives to Hands-On-Large-Language-Models or ReAct?
- GraphCanon lists graph-backed alternatives at Hands-On-Large-Language-Models alternatives and ReAct alternatives (Hands-On-Large-Language-Models markdown twin, ReAct 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, Hands-On-Large-Language-Models or ReAct?
- Hands-On-Large-Language-Models: Slowing. ReAct: 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 Hands-On-Large-Language-Models and ReAct?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Hands-On-Large-Language-Models trust report; ReAct trust report.