--- title: "agent-learning-kit vs future-agi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-learning-kit-vs-future-agi-future-agi" tools: ["future-agi-agent-learning-kit", "future-agi-future-agi"] --- # agent-learning-kit vs future-agi *GraphCanon updated Sep 20, 2026* ## Verdict Pick agent-learning-kit if agent-learning-kit is a Python-based toolkit for evaluating and simulating AI workflows, particularly suited for AI agents. It offers optional extras for specific functionalities and a TypeScript SDK for broader language; 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. [agent-learning-kit](https://futureagi.com) reports 119 GitHub stars, 44 forks, and 19 open issues, last pushed Sep 18, 2026. [future-agi](https://futureagi.com) has 2.0k stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [agent-learning-kit's repository](https://github.com/future-agi/agent-learning-kit) and [future-agi's repository](https://github.com/future-agi/future-agi). | | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Tagline | General Purpose Evaluation and Simulation Environment for all your AI related Workflows | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | | Stars | 119 | 2,032 | | Forks | 44 | 627 | | Open issues | 19 | 961 | | Language | Python | Python | | Adopt for | agent-learning-kit is a Python-based toolkit for evaluating and simulating AI workflows, particularly suited for AI agents. It offers optional extras for specific functionalities and a TypeScript SDK for broader language | 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, | | Persona | - | - | | Runtime | - | - | | License | Other | 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. | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Open issues (now) | 19 | 961 | | Stars delta | +1 (30d) | +473 (30d) | | Open issues delta | +13 (30d) | +365 (30d) | | Full report | [trust report](/tools/future-agi-agent-learning-kit/trust.md) | [trust report](/tools/future-agi-future-agi/trust.md) | ## Decision facts: agent-learning-kit - **Adopt for:** agent-learning-kit is a Python-based toolkit for evaluating and simulating AI workflows, particularly suited for AI agents. It offers optional extras for specific functionalities and a TypeScript SDK for broader language ## 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. ## Choose when ### Choose agent-learning-kit if… - License: agent-learning-kit is Other, future-agi is Apache-2.0. - Tags unique to agent-learning-kit: agentic-ai, cicd, evaluation, ml. - When you need a comprehensive environment for evaluating and simulating AI workflows, especially for AI agents ### Choose future-agi if… - License: future-agi is Apache-2.0, agent-learning-kit is Other. - 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-evals, ai-gateway, ai-optimization, ai-simulations. - 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 NOT to use agent-learning-kit - If your project strictly requires a different programming language other than Python or TypeScript - When you need a tool that is already at a mature v1 release, as agent-learning-kit is still developing its TypeScript SDK and extras ## 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. ## Common questions ### What is the difference between agent-learning-kit and future-agi? agent-learning-kit: General Purpose Evaluation and Simulation Environment for all your AI related Workflows. future-agi: Open-source, 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 agent-learning-kit over future-agi? Choose agent-learning-kit over future-agi when License: agent-learning-kit is Other, future-agi is Apache-2.0; Tags unique to agent-learning-kit: agentic-ai, cicd, evaluation, ml; When you need a comprehensive environment for evaluating and simulating AI workflows, especially for AI agents. ### When should I choose future-agi over agent-learning-kit? Choose future-agi over agent-learning-kit when License: future-agi is Apache-2.0, agent-learning-kit is Other; 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-evals, ai-gateway, ai-optimization, ai-simulations; 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 avoid agent-learning-kit? If your project strictly requires a different programming language other than Python or TypeScript When you need a tool that is already at a mature v1 release, as agent-learning-kit is still developing its TypeScript SDK and extras ### 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. ### Is agent-learning-kit or future-agi more popular on GitHub? future-agi has more GitHub stars (2,032 vs 119). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-learning-kit and future-agi open source? Yes - both are open-source projects on GitHub (agent-learning-kit: Other, future-agi: Apache-2.0). ### Where can I find alternatives to agent-learning-kit or future-agi? GraphCanon lists graph-backed alternatives at [agent-learning-kit alternatives](/tools/future-agi-agent-learning-kit/alternatives) and [future-agi alternatives](/tools/future-agi-future-agi/alternatives) ([agent-learning-kit markdown twin](/tools/future-agi-agent-learning-kit/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/future-agi-agent-learning-kit-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, agent-learning-kit or future-agi? agent-learning-kit: Very 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 agent-learning-kit and future-agi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-learning-kit trust report](/tools/future-agi-agent-learning-kit/trust); [future-agi trust report](/tools/future-agi-future-agi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-agi-agent-learning-kit`](/api/graphcanon/graph?tool=future-agi-agent-learning-kit) - 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/_