--- title: "instruct-eval vs tree-of-thoughts" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/declare-lab-instruct-eval-vs-kyegomez-tree-of-thoughts" tools: ["declare-lab-instruct-eval", "kyegomez-tree-of-thoughts"] --- # instruct-eval vs tree-of-thoughts *GraphCanon updated Aug 7, 2026* ## Verdict Pick instruct-eval if key facts about instruct-eval; pick tree-of-thoughts if (Tree-of-Thoughts) Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning. [instruct-eval](https://declare-lab.github.io/instruct-eval/) reports 552 GitHub stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. [tree-of-thoughts](https://discord.gg/qUtxnK2NMf) has 4.6k stars, 374 forks, and 21 open issues, last pushed Jul 29, 2025. Figures are from public GitHub metadata via [instruct-eval's repository](https://github.com/declare-lab/instruct-eval) and [tree-of-thoughts's repository](https://github.com/kyegomez/tree-of-thoughts). | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [tree-of-thoughts](/tools/kyegomez-tree-of-thoughts.md) | | --- | --- | --- | | Tagline | Quantitative evaluation for instruction-tuned language models | Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning | | Stars | 552 | 4,590 | | Forks | 45 | 374 | | Open issues | 24 | 21 | | Language | Python | Python | | Adopt for | Key facts about instruct-eval | (Tree-of-Thoughts) Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning | | Persona | - | - | | Runtime | - | - | | License | The tool is distributed under Apache-2.0 license | Licensed under Apache-2.0, allowing for wide usage but requires preservation of copyright and license notices | | Categories | Evaluation & Observability | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [tree-of-thoughts](/tools/kyegomez-tree-of-thoughts.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 879d | 364d | | Open issues (now) | 24 | 21 | | Owner type | Organization | User | | Full report | [trust report](/tools/declare-lab-instruct-eval/trust.md) | [trust report](/tools/kyegomez-tree-of-thoughts/trust.md) | ## Decision facts: instruct-eval - **Requirements:** Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation. - **Adopt for:** Key facts about instruct-eval - **License detail:** The tool is distributed under Apache-2.0 license ## Decision facts: tree-of-thoughts - **Pricing:** freemium - Free to use due to open-source nature; potential costs associated with hosting and any paid models it interfaces with - **Requirements:** Min 4 GB RAM - **Adopt for:** (Tree-of-Thoughts) Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning - **License detail:** Licensed under Apache-2.0, allowing for wide usage but requires preservation of copyright and license notices ## Choose when ### Choose instruct-eval if… - Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.. - Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm. - When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ### Choose tree-of-thoughts if… - Pricing: Free to use due to open-source nature; potential costs associated with hosting and any paid models it interfaces with. - Requirements: Min 4 GB RAM. - Tags unique to tree-of-thoughts: artificial-intelligence, chatgpt, deep-learning, gpt4. - Also covers Model Training. - - When you require enhanced reasoning capabilities from large language models through structured problem-solving techniques ## When NOT to use instruct-eval - When primarily interested in general model evaluation without a focus on instruction-tuned LMs. - If your primary interest lies in qualitative assessment rather than quantitative metrics. - If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ## When NOT to use tree-of-thoughts - - Avoid if you need solutions that are heavily customizable beyond what is provided, as it may not offer deep configuration options - - Should be avoided in scenarios where minimal dependency installations are critical, as this tool might come with broader package dependencies that could complicate setup ## Common questions ### What is the difference between instruct-eval and tree-of-thoughts? instruct-eval: Quantitative evaluation for instruction-tuned language models. tree-of-thoughts: Plug in and Play Implementation of Tree of Thoughts for Elevated Model Reasoning. See the comparison table for live GitHub stats and shared categories. ### When should I choose instruct-eval over tree-of-thoughts? Choose instruct-eval over tree-of-thoughts when Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ### When should I choose tree-of-thoughts over instruct-eval? Choose tree-of-thoughts over instruct-eval when Pricing: Free to use due to open-source nature; potential costs associated with hosting and any paid models it interfaces with; Requirements: Min 4 GB RAM; Tags unique to tree-of-thoughts: artificial-intelligence, chatgpt, deep-learning, gpt4; Also covers Model Training; - When you require enhanced reasoning capabilities from large language models through structured problem-solving techniques. ### When should I avoid instruct-eval? When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ### When should I avoid tree-of-thoughts? - Avoid if you need solutions that are heavily customizable beyond what is provided, as it may not offer deep configuration options - Should be avoided in scenarios where minimal dependency installations are critical, as this tool might come with broader package dependencies that could complicate setup ### Is instruct-eval or tree-of-thoughts more popular on GitHub? tree-of-thoughts has more GitHub stars (4,590 vs 552). Stars measure visibility, not whether either tool fits your constraints. ### Are instruct-eval and tree-of-thoughts open source? Yes - both are open-source projects on GitHub (instruct-eval: Apache-2.0, tree-of-thoughts: Apache-2.0). ### Where can I find alternatives to instruct-eval or tree-of-thoughts? GraphCanon lists graph-backed alternatives at [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) and [tree-of-thoughts alternatives](/tools/kyegomez-tree-of-thoughts/alternatives) ([instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md), [tree-of-thoughts markdown twin](/tools/kyegomez-tree-of-thoughts/alternatives.md)), 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](/compare/declare-lab-instruct-eval-vs-kyegomez-tree-of-thoughts.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, instruct-eval or tree-of-thoughts? instruct-eval: Dormant. tree-of-thoughts: 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 instruct-eval and tree-of-thoughts? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust); [tree-of-thoughts trust report](/tools/kyegomez-tree-of-thoughts/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=declare-lab-instruct-eval`](/api/graphcanon/graph?tool=declare-lab-instruct-eval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_