--- title: "future-agi vs AssetOpsBench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-future-agi-vs-ibm-assetopsbench" tools: ["future-agi-future-agi", "ibm-assetopsbench"] --- # future-agi vs AssetOpsBench *GraphCanon updated Sep 20, 2026* ## Verdict 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,; pick AssetOpsBench if assetOpsBench is a specialized framework for developing and evaluating AI agents in Industry 4.0 contexts, with an emphasis on operations and maintenance scenarios including HVAC. [future-agi](https://futureagi.com) reports 2.0k GitHub stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. [AssetOpsBench](https://github.com/IBM/AssetOpsBench) has 2.3k stars, 325 forks, and 45 open issues, last pushed Sep 20, 2026. Figures are from public GitHub metadata via [future-agi's repository](https://github.com/future-agi/future-agi) and [AssetOpsBench's repository](https://github.com/IBM/AssetOpsBench). | | [future-agi](/tools/future-agi-future-agi.md) | [AssetOpsBench](/tools/ibm-assetopsbench.md) | | --- | --- | --- | | Tagline | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | Framework for building and evaluating AI agents targeting Industry 4.0 asset operations | | Stars | 2,032 | 2,304 | | Forks | 627 | 325 | | Open issues | 961 | 45 | | Language | Python | Python | | 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, | AssetOpsBench is a specialized framework for developing and evaluating AI agents in Industry 4.0 contexts, with an emphasis on operations and maintenance scenarios including HVAC systems and IoT management. | | Persona | - | - | | Runtime | - | - | | 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. | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability, LLM Frameworks | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [future-agi](/tools/future-agi-future-agi.md) | [AssetOpsBench](/tools/ibm-assetopsbench.md) | | --- | --- | --- | | Open issues (now) | 961 | 45 | | Stars delta | +473 (30d) | +235 (30d) | | Open issues delta | +365 (30d) | 0 (30d) | | Full report | [trust report](/tools/future-agi-future-agi/trust.md) | [trust report](/tools/ibm-assetopsbench/trust.md) | ## 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. ## Decision facts: AssetOpsBench - **Adopt for:** AssetOpsBench is a specialized framework for developing and evaluating AI agents in Industry 4.0 contexts, with an emphasis on operations and maintenance scenarios including HVAC systems and IoT management. ## Choose when ### 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 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. ### Choose AssetOpsBench if… - Tags unique to AssetOpsBench: ai-for-physical-assets, condition-based-maintenance, hvac-maintenance, iot. - When you need detailed evaluation frameworks for multiple types of AI agents operating in industry environments - More GitHub stars (2.3k vs 2.0k) - visibility, not fit. ## 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. ## When NOT to use AssetOpsBench - If your project focus is on general-purpose AI outside the domain-specific context of industrial operations - Do not use if you require real-time agent orchestration without any emphasis on condition-based or predictive maintenance in asset management ## Common questions ### What is the difference between future-agi and AssetOpsBench? future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. AssetOpsBench: Framework for building and evaluating AI agents targeting Industry 4.0 asset operations. See the comparison table for live GitHub stats and shared categories. ### When should I choose future-agi over AssetOpsBench? Choose future-agi over AssetOpsBench 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 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 choose AssetOpsBench over future-agi? Choose AssetOpsBench over future-agi when Tags unique to AssetOpsBench: ai-for-physical-assets, condition-based-maintenance, hvac-maintenance, iot; When you need detailed evaluation frameworks for multiple types of AI agents operating in industry environments; More GitHub stars (2.3k vs 2.0k) - visibility, not fit. ### 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. ### When should I avoid AssetOpsBench? If your project focus is on general-purpose AI outside the domain-specific context of industrial operations Do not use if you require real-time agent orchestration without any emphasis on condition-based or predictive maintenance in asset management ### Is future-agi or AssetOpsBench more popular on GitHub? AssetOpsBench has more GitHub stars (2,304 vs 2,032). Stars measure visibility, not whether either tool fits your constraints. ### Are future-agi and AssetOpsBench open source? Yes - both are open-source projects on GitHub (future-agi: Apache-2.0, AssetOpsBench: Apache-2.0). ### Where can I find alternatives to future-agi or AssetOpsBench? GraphCanon lists graph-backed alternatives at [future-agi alternatives](/tools/future-agi-future-agi/alternatives) and [AssetOpsBench alternatives](/tools/ibm-assetopsbench/alternatives) ([future-agi markdown twin](/tools/future-agi-future-agi/alternatives.md), [AssetOpsBench markdown twin](/tools/ibm-assetopsbench/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/future-agi-future-agi-vs-ibm-assetopsbench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, future-agi or AssetOpsBench? future-agi: Very active. AssetOpsBench: 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 future-agi and AssetOpsBench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [future-agi trust report](/tools/future-agi-future-agi/trust); [AssetOpsBench trust report](/tools/ibm-assetopsbench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-agi-future-agi`](/api/graphcanon/graph?tool=future-agi-future-agi) - 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/_