--- title: "instruct-eval vs futureagi-sdk" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/declare-lab-instruct-eval-vs-future-agi-futureagi-sdk" tools: ["declare-lab-instruct-eval", "future-agi-futureagi-sdk"] --- # instruct-eval vs futureagi-sdk *GraphCanon updated Aug 7, 2026* ## Verdict Pick instruct-eval if key facts about instruct-eval; pick futureagi-sdk if future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. [instruct-eval](https://declare-lab.github.io/instruct-eval/) reports 552 GitHub stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. [futureagi-sdk](https://app.futureagi.com) has 48 stars, 5 forks, and 3 open issues, last pushed Jul 8, 2026. Figures are from public GitHub metadata via [instruct-eval's repository](https://github.com/declare-lab/instruct-eval) and [futureagi-sdk's repository](https://github.com/future-agi/futureagi-sdk). | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [futureagi-sdk](/tools/future-agi-futureagi-sdk.md) | | --- | --- | --- | | Tagline | Quantitative evaluation for instruction-tuned language models | Production-grade AI evaluation, prompt management & observability SDK | | Stars | 552 | 48 | | Forks | 45 | 5 | | Open issues | 24 | 3 | | Language | Python | Python | | Adopt for | Key facts about instruct-eval | Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. | | Persona | - | - | | Runtime | - | - | | License | The tool is distributed under Apache-2.0 license | The Future AGI SDK uses the Apache License, Version 2.0 (Apache-2.0). It allows users to freely use, modify, and distribute the software while maintaining copyright notices. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [futureagi-sdk](/tools/future-agi-futureagi-sdk.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 879d | 25d | | Open issues (now) | 24 | 3 | | Full report | [trust report](/tools/declare-lab-instruct-eval/trust.md) | [trust report](/tools/future-agi-futureagi-sdk/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: futureagi-sdk - **Requirements:** Supports Python and TypeScript languages; Automated evaluations with sub-100ms guardrails - **Adopt for:** Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. - **License detail:** The Future AGI SDK uses the Apache License, Version 2.0 (Apache-2.0). It allows users to freely use, modify, and distribute the software while maintaining copyright 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, instruct-tuning, llm, safety. - 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 futureagi-sdk if… - Requirements: Supports Python and TypeScript languages; Automated evaluations with sub-100ms guardrails. - Tags unique to futureagi-sdk: ai-agents, annotations, dataset, development. - Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. ## 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 futureagi-sdk - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between instruct-eval and futureagi-sdk? instruct-eval: Quantitative evaluation for instruction-tuned language models. futureagi-sdk: Production-grade AI evaluation, prompt management & observability SDK. See the comparison table for live GitHub stats and shared categories. ### When should I choose instruct-eval over futureagi-sdk? Choose instruct-eval over futureagi-sdk 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, instruct-tuning, llm, safety; 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 futureagi-sdk over instruct-eval? Choose futureagi-sdk over instruct-eval when Requirements: Supports Python and TypeScript languages; Automated evaluations with sub-100ms guardrails; Tags unique to futureagi-sdk: ai-agents, annotations, dataset, development; Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. ### 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 futureagi-sdk? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is instruct-eval or futureagi-sdk more popular on GitHub? instruct-eval has more GitHub stars (552 vs 48). Stars measure visibility, not whether either tool fits your constraints. ### Are instruct-eval and futureagi-sdk open source? Yes - both are open-source projects on GitHub (instruct-eval: Apache-2.0, futureagi-sdk: Apache-2.0). ### Where can I find alternatives to instruct-eval or futureagi-sdk? GraphCanon lists graph-backed alternatives at [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) and [futureagi-sdk alternatives](/tools/future-agi-futureagi-sdk/alternatives) ([instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md), [futureagi-sdk markdown twin](/tools/future-agi-futureagi-sdk/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-future-agi-futureagi-sdk.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, instruct-eval or futureagi-sdk? instruct-eval: Dormant. futureagi-sdk: Active. 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 futureagi-sdk? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust); [futureagi-sdk trust report](/tools/future-agi-futureagi-sdk/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/_