--- title: "awesome-evals vs instruct-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-declare-lab-instruct-eval" tools: ["benchflow-ai-awesome-evals", "declare-lab-instruct-eval"] --- # awesome-evals vs instruct-eval *GraphCanon updated Aug 7, 2026* ## Verdict Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick instruct-eval if key facts about instruct-eval. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 761 GitHub stars, 71 forks, and 21 open issues, last pushed Jul 1, 2026. [instruct-eval](https://declare-lab.github.io/instruct-eval/) has 552 stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. Figures are from public GitHub metadata via [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [instruct-eval's repository](https://github.com/declare-lab/instruct-eval). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | Quantitative evaluation for instruction-tuned language models | | Stars | 761 | 552 | | Forks | 71 | 45 | | Open issues | 21 | 24 | | Language | - | Python | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | Key facts about instruct-eval | | Persona | - | - | | Runtime | - | - | | License | Other | The tool is distributed under Apache-2.0 license | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 26d | 879d | | Open issues (now) | 21 | 24 | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/declare-lab-instruct-eval/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## 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 ## Choose when ### Choose awesome-evals if… - License: awesome-evals is Other, instruct-eval is Apache-2.0. - Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks. - Also covers AI Agents. - Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation ### Choose instruct-eval if… - License: instruct-eval is Apache-2.0, awesome-evals is Other. - 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 NOT to use awesome-evals - Require real-time interactive support or direct tool integrations not covered by a static resource list - Seeking proprietary tools from specific vendors rather than open resources and community content ## 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. ## Common questions ### What is the difference between awesome-evals and instruct-eval? awesome-evals: A curated library of resources for building and evaluating AI agents. instruct-eval: Quantitative evaluation for instruction-tuned language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-evals over instruct-eval? Choose awesome-evals over instruct-eval when License: awesome-evals is Other, instruct-eval is Apache-2.0; Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Also covers AI Agents; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation. ### When should I choose instruct-eval over awesome-evals? Choose instruct-eval over awesome-evals when License: instruct-eval is Apache-2.0, awesome-evals is Other; 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 avoid awesome-evals? Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content ### 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. ### Is awesome-evals or instruct-eval more popular on GitHub? awesome-evals has more GitHub stars (761 vs 552). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and instruct-eval open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, instruct-eval: Apache-2.0). ### Where can I find alternatives to awesome-evals or instruct-eval? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/alternatives.md), [instruct-eval markdown twin](/tools/declare-lab-instruct-eval/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/benchflow-ai-awesome-evals-vs-declare-lab-instruct-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-evals or instruct-eval? awesome-evals: Active. instruct-eval: 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-evals and instruct-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-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/_