--- title: "future-agi vs WeaveBench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-future-agi-vs-weavebench-weavebench" tools: ["future-agi-future-agi", "weavebench-weavebench"] --- # future-agi vs WeaveBench *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 WeaveBench if weaveBench is designed for evaluating computer-use agents that integrate both GUI and CLI interactions in real-world scenarios across various work domains. [future-agi](https://futureagi.com) reports 2.0k GitHub stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. [WeaveBench](https://weavebench.github.io) has 167 stars, 4 forks, and 5 open issues, last pushed Aug 22, 2026. Figures are from public GitHub metadata via [future-agi's repository](https://github.com/future-agi/future-agi) and [WeaveBench's repository](https://github.com/weavebench/WeaveBench). | | [future-agi](/tools/future-agi-future-agi.md) | [WeaveBench](/tools/weavebench-weavebench.md) | | --- | --- | --- | | Tagline | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | A Long-Horizon Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces | | Stars | 2,032 | 167 | | Forks | 627 | 4 | | Open issues | 961 | 5 | | 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, | WeaveBench is designed for evaluating computer-use agents that integrate both GUI and CLI interactions in real-world scenarios across various work domains. | | 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. | MIT | | 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) | [WeaveBench](/tools/weavebench-weavebench.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 28d | | Open issues (now) | 961 | 5 | | Stars delta | +473 (30d) | +10 (30d) | | Open issues delta | +365 (30d) | +1 (30d) | | Full report | [trust report](/tools/future-agi-future-agi/trust.md) | [trust report](/tools/weavebench-weavebench/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: WeaveBench - **Adopt for:** WeaveBench is designed for evaluating computer-use agents that integrate both GUI and CLI interactions in real-world scenarios across various work domains. ## Choose when ### Choose future-agi if… - License: future-agi is Apache-2.0, WeaveBench is MIT. - 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 WeaveBench if… - License: WeaveBench is MIT, future-agi is Apache-2.0. - Tags unique to WeaveBench: agent-as-judge, benchmark, computer-use-agent, gui-agent. - Use WeaveBench if you need to assess agents capable of handling tasks that require intermingling graphical user interface operations with command-line or code-based actions. ## 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 WeaveBench - Avoid WeaveBench if your testing needs do not involve scenarios that require the integration of both GUI and CLI operations. - Do not use it when you are specifically interested only in benchmarking agents designed for single-channel tasks, either strictly CLI-based or purely graphical interface-driven. ## Common questions ### What is the difference between future-agi and WeaveBench? future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. WeaveBench: A Long-Horizon Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces. See the comparison table for live GitHub stats and shared categories. ### When should I choose future-agi over WeaveBench? Choose future-agi over WeaveBench when License: future-agi is Apache-2.0, WeaveBench is MIT; 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 WeaveBench over future-agi? Choose WeaveBench over future-agi when License: WeaveBench is MIT, future-agi is Apache-2.0; Tags unique to WeaveBench: agent-as-judge, benchmark, computer-use-agent, gui-agent; Use WeaveBench if you need to assess agents capable of handling tasks that require intermingling graphical user interface operations with command-line or code-based actions. ### 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 WeaveBench? Avoid WeaveBench if your testing needs do not involve scenarios that require the integration of both GUI and CLI operations. Do not use it when you are specifically interested only in benchmarking agents designed for single-channel tasks, either strictly CLI-based or purely graphical interface-driven. ### Is future-agi or WeaveBench more popular on GitHub? future-agi has more GitHub stars (2,032 vs 167). Stars measure visibility, not whether either tool fits your constraints. ### Are future-agi and WeaveBench open source? Yes - both are open-source projects on GitHub (future-agi: Apache-2.0, WeaveBench: MIT). ### Where can I find alternatives to future-agi or WeaveBench? GraphCanon lists graph-backed alternatives at [future-agi alternatives](/tools/future-agi-future-agi/alternatives) and [WeaveBench alternatives](/tools/weavebench-weavebench/alternatives) ([future-agi markdown twin](/tools/future-agi-future-agi/alternatives.md), [WeaveBench markdown twin](/tools/weavebench-weavebench/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-weavebench-weavebench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, future-agi or WeaveBench? future-agi: Very active. WeaveBench: 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 WeaveBench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [future-agi trust report](/tools/future-agi-future-agi/trust); [WeaveBench trust report](/tools/weavebench-weavebench/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/_