--- title: "instruct-eval vs future-agi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/declare-lab-instruct-eval-vs-future-agi-future-agi" tools: ["declare-lab-instruct-eval", "future-agi-future-agi"] --- # instruct-eval vs future-agi *GraphCanon updated Sep 20, 2026* ## Verdict Pick instruct-eval if key facts about instruct-eval; pick future-agi if future-AGI is an open-source platform for evaluating and observing LLM and AI agent applications, offering features like tracing, evaluations, simulations, and guardrails. It is self-hostable and supports deployment via,. [instruct-eval](https://declare-lab.github.io/instruct-eval/) reports 553 GitHub stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. [future-agi](https://futureagi.com) has 2.0k stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [instruct-eval's repository](https://github.com/declare-lab/instruct-eval) and [future-agi's repository](https://github.com/future-agi/future-agi). | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Tagline | Quantitative evaluation for instruction-tuned language models | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | | Stars | 553 | 2,032 | | Forks | 45 | 627 | | Open issues | 24 | 961 | | Language | Python | Python | | Adopt for | Key facts about instruct-eval | Future-AGI is an open-source platform for evaluating and observing LLM and AI agent applications, offering features like tracing, evaluations, simulations, and guardrails. It is self-hostable and supports deployment via, | | Persona | - | - | | Runtime | - | - | | License | The tool is distributed under Apache-2.0 license | Apache License 2.0, allowing for free use, modification, and distribution of the software, with the condition that any derivative works also be licensed under the same terms. | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 910d | 0d | | Open issues (now) | 24 | 961 | | Stars delta | +1 (30d) | +473 (30d) | | Open issues delta | 0 (30d) | +365 (30d) | | Full report | [trust report](/tools/declare-lab-instruct-eval/trust.md) | [trust report](/tools/future-agi-future-agi/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: future-agi - **Pricing:** freemium - The core platform is free and open-source under the Apache License 2.0. However, for managed services or additional support, contact sales for pricing. - **Requirements:** Min 4 GB RAM; Requires Docker; Future-AGI requires Docker for deployment, with support for Docker Compose and upcoming Kubernetes and Helm support.; For production environments, a setup script is provided to generate secrets and pin image tags, ensuring a secure and reproducible deployment. - **Adopt for:** Future-AGI is an open-source platform for evaluating and observing LLM and AI agent applications, offering features like tracing, evaluations, simulations, and guardrails. It is self-hostable and supports deployment via, - **License detail:** Apache License 2.0, allowing for free use, modification, and distribution of the software, with the condition that any derivative works also be licensed under the same terms. ## 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 future-agi if… - Pricing: The core platform is free and open-source under the Apache License 2.0. However, for managed services or additional support, contact sales for pricing.. - Requirements: Min 4 GB RAM; Requires Docker; Future-AGI requires Docker for deployment, with support for Docker Compose and upcoming Kubernetes and Helm support.; For production environments, a setup script is provided to generate secrets and pin image tags, ensuring a secure and reproducible deployment.. - Tags unique to future-agi: ai-agents, ai-evals, ai-gateway, ai-optimization. - Also covers AI Agents, LLM Frameworks. - future-agi ships Docker support for self-hosted deployment. - You need a self-hostable solution that does not phone home, ensuring full control over your data and evaluation logic. ## 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 future-agi - You require immediate Kubernetes or Helm support, as these are not yet available, though they are in development. - You are seeking a managed service or a solution available through AWS Marketplace, as these options are not yet available, though they are planned for the future. - Your project is in a highly dynamic environment where frequent updates and vendor support are critical, as Future-AGI is a community-driven project with a focus on self-hosting and open-source. ## Common questions ### What is the difference between instruct-eval and future-agi? instruct-eval: Quantitative evaluation for instruction-tuned language models. future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose instruct-eval over future-agi? Choose instruct-eval over future-agi 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 future-agi over instruct-eval? Choose future-agi over instruct-eval when Pricing: The core platform is free and open-source under the Apache License 2.0. However, for managed services or additional support, contact sales for pricing.; Requirements: Min 4 GB RAM; Requires Docker; Future-AGI requires Docker for deployment, with support for Docker Compose and upcoming Kubernetes and Helm support.; For production environments, a setup script is provided to generate secrets and pin image tags, ensuring a secure and reproducible deployment.; Tags unique to future-agi: ai-agents, ai-evals, ai-gateway, ai-optimization; Also covers AI Agents, LLM Frameworks; future-agi ships Docker support for self-hosted deployment; You need a self-hostable solution that does not phone home, ensuring full control over your data and evaluation logic. ### 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 future-agi? You require immediate Kubernetes or Helm support, as these are not yet available, though they are in development. You are seeking a managed service or a solution available through AWS Marketplace, as these options are not yet available, though they are planned for the future. Your project is in a highly dynamic environment where frequent updates and vendor support are critical, as Future-AGI is a community-driven project with a focus on self-hosting and open-source. ### Is instruct-eval or future-agi more popular on GitHub? future-agi has more GitHub stars (2,032 vs 553). Stars measure visibility, not whether either tool fits your constraints. ### Are instruct-eval and future-agi open source? Yes - both are open-source projects on GitHub (instruct-eval: Apache-2.0, future-agi: Apache-2.0). ### Where can I find alternatives to instruct-eval or future-agi? GraphCanon lists graph-backed alternatives at [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) and [future-agi alternatives](/tools/future-agi-future-agi/alternatives) ([instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md), [future-agi markdown twin](/tools/future-agi-future-agi/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-future-agi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, instruct-eval or future-agi? instruct-eval: Dormant. future-agi: Very 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 future-agi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust); [future-agi trust report](/tools/future-agi-future-agi/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/_