--- title: "awesome-evals vs future-agi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-future-agi-future-agi" tools: ["benchflow-ai-awesome-evals", "future-agi-future-agi"] --- # awesome-evals vs future-agi *GraphCanon updated Aug 2, 2026* ## Verdict Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick future-agi if future-AGI is an open-source toolkit for evaluating and improving LLMs and AI agents. It includes features like tracing, evaluations, simulations, datasets, gateway operations, and guardrails. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 761 GitHub stars, 71 forks, and 21 open issues, last pushed Jul 1, 2026. [future-agi](https://futureagi.com) has 1.6k stars, 449 forks, and 596 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [future-agi's repository](https://github.com/future-agi/future-agi). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | End-to-end platform for evaluating, observing, and improving LLM and AI agent applications | | Stars | 761 | 1,559 | | Forks | 71 | 449 | | Open issues | 21 | 596 | | Language | - | Python | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | Future-AGI is an open-source toolkit for evaluating and improving LLMs and AI agents. It includes features like tracing, evaluations, simulations, datasets, gateway operations, and guardrails. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 26d | 1d | | Open issues (now) | 21 | 596 | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/future-agi-future-agi/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## Decision facts: future-agi - **Pricing:** freemium - Future-AGI is open-source under the Apache 2.0 license, allowing for free use but with potential paid services through deployment and support channels. - **Requirements:** Min 4 GB RAM; Requires Docker - **Adopt for:** Future-AGI is an open-source toolkit for evaluating and improving LLMs and AI agents. It includes features like tracing, evaluations, simulations, datasets, gateway operations, and guardrails. ## Choose when ### Choose awesome-evals if… - License: awesome-evals is Other, future-agi is Apache-2.0. - Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks. - Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation ### Choose future-agi if… - License: future-agi is Apache-2.0, awesome-evals is Other. - Pricing: Future-AGI is open-source under the Apache 2.0 license, allowing for free use but with potential paid services through deployment and support channels.. - Requirements: Min 4 GB RAM; Requires Docker. - Tags unique to future-agi: ai-gateway, docker-compose, evals, llm. - future-agi ships Docker support for self-hosted deployment. - - Use Future-AGI when you require an end-to-end evaluation platform that supports self-hosting through Docker Compose or VM-based services on public clouds. ## When NOT to use awesome-evals - Require real-time interactive support or direct tool integrations not covered by a static resource list - Seeking proprietary tools from specific vendors rather than open resources and community content ## When NOT to use future-agi - - Avoid using Future-AGI if you require Kubernetes or Helm support as of the current state; though these are planned for future release, they are not yet available. - - If your deployment strategy relies on a managed service like AWS Marketplace, consider other options since it is currently 'Coming Soon'. ## Common questions ### What is the difference between awesome-evals and future-agi? awesome-evals: A curated library of resources for building and evaluating AI agents. future-agi: 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 awesome-evals over future-agi? Choose awesome-evals over future-agi when License: awesome-evals is Other, future-agi is Apache-2.0; Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation. ### When should I choose future-agi over awesome-evals? Choose future-agi over awesome-evals when License: future-agi is Apache-2.0, awesome-evals is Other; Pricing: Future-AGI is open-source under the Apache 2.0 license, allowing for free use but with potential paid services through deployment and support channels.; Requirements: Min 4 GB RAM; Requires Docker; Tags unique to future-agi: ai-gateway, docker-compose, evals, llm; future-agi ships Docker support for self-hosted deployment; - Use Future-AGI when you require an end-to-end evaluation platform that supports self-hosting through Docker Compose or VM-based services on public clouds. ### When should I avoid awesome-evals? Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content ### When should I avoid future-agi? - Avoid using Future-AGI if you require Kubernetes or Helm support as of the current state; though these are planned for future release, they are not yet available. - If your deployment strategy relies on a managed service like AWS Marketplace, consider other options since it is currently 'Coming Soon'. ### Is awesome-evals or future-agi more popular on GitHub? future-agi has more GitHub stars (1,559 vs 761). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and future-agi open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, future-agi: Apache-2.0). ### Where can I find alternatives to awesome-evals or future-agi? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [future-agi alternatives](/tools/future-agi-future-agi/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/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/benchflow-ai-awesome-evals-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, awesome-evals or future-agi? awesome-evals: Active. 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 awesome-evals and future-agi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [future-agi trust report](/tools/future-agi-future-agi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-evals) - 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/_