--- title: "future-agi vs autoarena" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-future-agi-vs-kolenaio-autoarena" tools: ["future-agi-future-agi", "kolenaio-autoarena"] --- # future-agi vs autoarena *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 autoarena if autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users. [future-agi](https://futureagi.com) reports 2.0k GitHub stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. [autoarena](https://www.kolena.com/autoarena/) has 108 stars, 9 forks, and 4 open issues, last pushed Dec 16, 2024. Figures are from public GitHub metadata via [future-agi's repository](https://github.com/future-agi/future-agi) and [autoarena's repository](https://github.com/kolenaIO/autoarena). | | [future-agi](/tools/future-agi-future-agi.md) | [autoarena](/tools/kolenaio-autoarena.md) | | --- | --- | --- | | Tagline | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | Automated evaluation of LLMs and RAG systems | | Stars | 2,032 | 108 | | Forks | 627 | 9 | | Open issues | 961 | 4 | | Language | Python | TypeScript | | 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, | autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users. | | 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 license | | Categories | AI Agents, Evaluation & Observability, LLM Frameworks | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [future-agi](/tools/future-agi-future-agi.md) | [autoarena](/tools/kolenaio-autoarena.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 642d | | Open issues (now) | 961 | 4 | | Stars delta | +473 (30d) | 0 (30d) | | Open issues delta | +365 (30d) | 0 (30d) | | Full report | [trust report](/tools/future-agi-future-agi/trust.md) | [trust report](/tools/kolenaio-autoarena/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: autoarena - **Hosting:** self hosted - **Requirements:** Python environment and internet access are needed for PyPI installation via pip. - **Adopt for:** autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users. - **License detail:** Apache-2.0 license ## Choose when ### Choose future-agi if… - future-agi is primarily Python; autoarena is TypeScript. - 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. ### Choose autoarena if… - autoarena is primarily TypeScript; future-agi is Python. - Requirements: Python environment and internet access are needed for PyPI installation via pip.. - Tags unique to autoarena: ai, evaluation, llm-evaluation, rag. - When you need a TypeScript-based tool to rank LLMs and RAG systems via automated head-to-head comparisons, and a web UI is preferable. ## 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 autoarena - If your environment lacks the necessary Python packages or you cannot install from PyPI due to restrictions. - When real-time evaluation needs surpass capabilities, such as requiring immediate feedback beyond autoarena's batch-processing approach. ## Common questions ### What is the difference between future-agi and autoarena? future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. autoarena: Automated evaluation of LLMs and RAG systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose future-agi over autoarena? Choose future-agi over autoarena when future-agi is primarily Python; autoarena is TypeScript; 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 choose autoarena over future-agi? Choose autoarena over future-agi when autoarena is primarily TypeScript; future-agi is Python; Requirements: Python environment and internet access are needed for PyPI installation via pip.; Tags unique to autoarena: ai, evaluation, llm-evaluation, rag; When you need a TypeScript-based tool to rank LLMs and RAG systems via automated head-to-head comparisons, and a web UI is preferable. ### 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 autoarena? If your environment lacks the necessary Python packages or you cannot install from PyPI due to restrictions. When real-time evaluation needs surpass capabilities, such as requiring immediate feedback beyond autoarena's batch-processing approach. ### Is future-agi or autoarena more popular on GitHub? future-agi has more GitHub stars (2,032 vs 108). Stars measure visibility, not whether either tool fits your constraints. ### Are future-agi and autoarena open source? Yes - both are open-source projects on GitHub (future-agi: Apache-2.0, autoarena: Apache-2.0). ### Where can I find alternatives to future-agi or autoarena? GraphCanon lists graph-backed alternatives at [future-agi alternatives](/tools/future-agi-future-agi/alternatives) and [autoarena alternatives](/tools/kolenaio-autoarena/alternatives) ([future-agi markdown twin](/tools/future-agi-future-agi/alternatives.md), [autoarena markdown twin](/tools/kolenaio-autoarena/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-kolenaio-autoarena.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, future-agi or autoarena? future-agi: Very active. autoarena: 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 future-agi and autoarena? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [future-agi trust report](/tools/future-agi-future-agi/trust); [autoarena trust report](/tools/kolenaio-autoarena/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/_