--- title: "future-agi vs awesome-ai-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-future-agi-vs-rohitg00-awesome-ai-apps" tools: ["future-agi-future-agi", "rohitg00-awesome-ai-apps"] --- # future-agi vs awesome-ai-apps *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 awesome-ai-apps if awesome-ai-apps offers curated AI application examples with diverse tech stacks including OpenAI, Gemini, and local models. [future-agi](https://futureagi.com) reports 2.0k GitHub stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. [awesome-ai-apps](https://agenstskills.com) has 828 stars, 177 forks, and 33 open issues, last pushed Feb 10, 2026. Figures are from public GitHub metadata via [future-agi's repository](https://github.com/future-agi/future-agi) and [awesome-ai-apps's repository](https://github.com/rohitg00/awesome-ai-apps). | | [future-agi](/tools/future-agi-future-agi.md) | [awesome-ai-apps](/tools/rohitg00-awesome-ai-apps.md) | | --- | --- | --- | | Tagline | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | A curated collection of AI Agents and LLM Apps with various tech stacks | | Stars | 2,032 | 828 | | Forks | 627 | 177 | | Open issues | 961 | 33 | | Language | Python | HTML | | 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, | awesome-ai-apps offers curated AI application examples with diverse tech stacks including OpenAI, Gemini, and local models. | | 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, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [future-agi](/tools/future-agi-future-agi.md) | [awesome-ai-apps](/tools/rohitg00-awesome-ai-apps.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 221d | | Open issues (now) | 961 | 33 | | Stars delta | +473 (30d) | +11 (30d) | | Open issues delta | +365 (30d) | +6 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/future-agi-future-agi/trust.md) | [trust report](/tools/rohitg00-awesome-ai-apps/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: awesome-ai-apps - **Adopt for:** awesome-ai-apps offers curated AI application examples with diverse tech stacks including OpenAI, Gemini, and local models. ## Choose when ### Choose future-agi if… - future-agi is primarily Python; awesome-ai-apps is HTML. - 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 Evaluation & Observability. - 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 awesome-ai-apps if… - awesome-ai-apps is primarily HTML; future-agi is Python. - Tags unique to awesome-ai-apps: agents, ai, apps, automation. - For exploring real-world implementations of AI agents across different technologies ## 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 awesome-ai-apps - When seeking detailed implementation steps specific to one technology stack - In scenarios demanding a deep dive into proprietary or less publicly-known application codes ## Common questions ### What is the difference between future-agi and awesome-ai-apps? future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. awesome-ai-apps: A curated collection of AI Agents and LLM Apps with various tech stacks. See the comparison table for live GitHub stats and shared categories. ### When should I choose future-agi over awesome-ai-apps? Choose future-agi over awesome-ai-apps when future-agi is primarily Python; awesome-ai-apps is HTML; 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 Evaluation & Observability; 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 awesome-ai-apps over future-agi? Choose awesome-ai-apps over future-agi when awesome-ai-apps is primarily HTML; future-agi is Python; Tags unique to awesome-ai-apps: agents, ai, apps, automation; For exploring real-world implementations of AI agents across different technologies. ### 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 awesome-ai-apps? When seeking detailed implementation steps specific to one technology stack In scenarios demanding a deep dive into proprietary or less publicly-known application codes ### Is future-agi or awesome-ai-apps more popular on GitHub? future-agi has more GitHub stars (2,032 vs 828). Stars measure visibility, not whether either tool fits your constraints. ### Are future-agi and awesome-ai-apps open source? Yes - both are open-source projects on GitHub (future-agi: Apache-2.0, awesome-ai-apps: Apache-2.0). ### Where can I find alternatives to future-agi or awesome-ai-apps? GraphCanon lists graph-backed alternatives at [future-agi alternatives](/tools/future-agi-future-agi/alternatives) and [awesome-ai-apps alternatives](/tools/rohitg00-awesome-ai-apps/alternatives) ([future-agi markdown twin](/tools/future-agi-future-agi/alternatives.md), [awesome-ai-apps markdown twin](/tools/rohitg00-awesome-ai-apps/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-rohitg00-awesome-ai-apps.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, future-agi or awesome-ai-apps? future-agi: Very active. awesome-ai-apps: Slowing. 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 awesome-ai-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [future-agi trust report](/tools/future-agi-future-agi/trust); [awesome-ai-apps trust report](/tools/rohitg00-awesome-ai-apps/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/_