--- title: "simple-evals vs tree-of-thought-llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openai-simple-evals-vs-princeton-nlp-tree-of-thought-llm" tools: ["openai-simple-evals", "princeton-nlp-tree-of-thought-llm"] --- # simple-evals vs tree-of-thought-llm *GraphCanon updated Jul 11, 2026* ## Verdict Pick simple-evals when more recently updated (last pushed Apr 22, 2026); pick tree-of-thought-llm when pricing: Open-source implementation under MIT License with no direct cost. However, dependencies such as GPT-4 backend access might incur costs associated with API usage.. [simple-evals](https://github.com/openai/simple-evals) reports 4.6k GitHub stars, 493 forks, and 56 open issues, last pushed Apr 22, 2026. [tree-of-thought-llm](https://arxiv.org/abs/2305.10601) has 6.0k stars, 620 forks, and 8 open issues, last pushed Jan 16, 2025. Figures are from public GitHub metadata via [simple-evals's repository](https://github.com/openai/simple-evals) and [tree-of-thought-llm's repository](https://github.com/princeton-nlp/tree-of-thought-llm). | | [simple-evals](/tools/openai-simple-evals.md) | [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) | | --- | --- | --- | | Tagline | simple-evals | [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models | | Stars | 4,565 | 6,025 | | Forks | 493 | 620 | | Open issues | 56 | 8 | | Language | Python | Python | | Adopt for | - | Tree-of-thought-llm is a NeurIPS 2023 methodology using large language models for deliberate problem solving, often exemplified through game-solving algorithms. It's implemented with Python and open-sourced under the MIT | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Evaluation & Observability | LLM Frameworks, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [simple-evals](/tools/openai-simple-evals.md) | [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 79d | 540d | | Open issues (now) | 56 | 8 | | Security scan | No lockfile | 90 low (90 low) | | Full report | [trust report](/tools/openai-simple-evals/trust.md) | [trust report](/tools/princeton-nlp-tree-of-thought-llm/trust.md) | ## Decision facts: tree-of-thought-llm - **Pricing:** freemium - Open-source implementation under MIT License with no direct cost. However, dependencies such as GPT-4 backend access might incur costs associated with API usage. - **Requirements:** Min 8 GB RAM - **Adopt for:** Tree-of-thought-llm is a NeurIPS 2023 methodology using large language models for deliberate problem solving, often exemplified through game-solving algorithms. It's implemented with Python and open-sourced under the MIT ## Choose when ### Choose simple-evals if… - More recently updated (last pushed Apr 22, 2026). ### Choose tree-of-thought-llm if… - Pricing: Open-source implementation under MIT License with no direct cost. However, dependencies such as GPT-4 backend access might incur costs associated with API usage.. - Requirements: Min 8 GB RAM. - Tags unique to tree-of-thought-llm: tree-search, llm, large-language-models, prompting. - - This tool should be used when you need to deliberately solve problems with LLMs, especially if your application scenario involves strategic or game-like decision-making processes. ## When NOT to use simple-evals - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use tree-of-thought-llm - - Avoid using this tool if real-time or near-real-time responses are crucial as the methodology can be slow due to its deliberate problem-solving approach (notably with backends like GPT-4). - - Not recommended when the application requires deterministic outcomes. The output of Tree-of-thought-llm, especially in game scenarios, might not always be accurate given it's a probabilistic process - - If your project doesn't have dedicated resources to tune and understand how LLMs reason through problems, this tool may require more setup effort compared to more straightforward application tools. ## Common questions ### What is the difference between simple-evals and tree-of-thought-llm? simple-evals: simple-evals. tree-of-thought-llm: [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose simple-evals over tree-of-thought-llm? Choose simple-evals over tree-of-thought-llm when More recently updated (last pushed Apr 22, 2026). ### When should I choose tree-of-thought-llm over simple-evals? Choose tree-of-thought-llm over simple-evals when Pricing: Open-source implementation under MIT License with no direct cost. However, dependencies such as GPT-4 backend access might incur costs associated with API usage.; Requirements: Min 8 GB RAM; Tags unique to tree-of-thought-llm: tree-search, llm, large-language-models, prompting; - This tool should be used when you need to deliberately solve problems with LLMs, especially if your application scenario involves strategic or game-like decision-making processes. ### When should I avoid simple-evals? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid tree-of-thought-llm? - Avoid using this tool if real-time or near-real-time responses are crucial as the methodology can be slow due to its deliberate problem-solving approach (notably with backends like GPT-4). - Not recommended when the application requires deterministic outcomes. The output of Tree-of-thought-llm, especially in game scenarios, might not always be accurate given it's a probabilistic process - If your project doesn't have dedicated resources to tune and understand how LLMs reason through problems, this tool may require more setup effort compared to more straightforward application tools. ### Is simple-evals or tree-of-thought-llm more popular on GitHub? tree-of-thought-llm has more GitHub stars (6,025 vs 4,565). Stars measure visibility, not whether either tool fits your constraints. ### Are simple-evals and tree-of-thought-llm open source? Yes - both are open-source projects on GitHub (simple-evals: MIT, tree-of-thought-llm: MIT). ### Where can I find alternatives to simple-evals or tree-of-thought-llm? GraphCanon lists graph-backed alternatives at [simple-evals alternatives](/tools/openai-simple-evals/alternatives) and [tree-of-thought-llm alternatives](/tools/princeton-nlp-tree-of-thought-llm/alternatives) ([simple-evals markdown twin](/tools/openai-simple-evals/alternatives.md), [tree-of-thought-llm markdown twin](/tools/princeton-nlp-tree-of-thought-llm/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/openai-simple-evals-vs-princeton-nlp-tree-of-thought-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, simple-evals or tree-of-thought-llm? simple-evals: Steady. tree-of-thought-llm: 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 simple-evals and tree-of-thought-llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [simple-evals trust report](/tools/openai-simple-evals/trust); [tree-of-thought-llm trust report](/tools/princeton-nlp-tree-of-thought-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openai-simple-evals`](/api/graphcanon/graph?tool=openai-simple-evals) - 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/_