--- title: "contextcheck vs future-agi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/addepto-contextcheck-vs-future-agi-future-agi" tools: ["addepto-contextcheck", "future-agi-future-agi"] --- # contextcheck vs future-agi *GraphCanon updated Sep 20, 2026* ## Verdict Pick contextcheck if contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines; 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,. [contextcheck](https://addepto.com/) reports 97 GitHub stars, 11 forks, and 1 open issues, last pushed Dec 11, 2024. [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 [contextcheck's repository](https://github.com/Addepto/contextcheck) and [future-agi's repository](https://github.com/future-agi/future-agi). | | [contextcheck](/tools/addepto-contextcheck.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Tagline | Framework for LLMs and RAGs testing in Python | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | | Stars | 97 | 2,032 | | Forks | 11 | 627 | | Open issues | 1 | 961 | | Language | Python | Python | | Adopt for | Contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines. | 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 | MIT | 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 | Evaluation & Observability, Model Training | AI Agents, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [contextcheck](/tools/addepto-contextcheck.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 635d | 0d | | Open issues (now) | 1 | 961 | | Stars delta | +1 (30d) | +473 (30d) | | Open issues delta | 0 (30d) | +365 (30d) | | Full report | [trust report](/tools/addepto-contextcheck/trust.md) | [trust report](/tools/future-agi-future-agi/trust.md) | ## Decision facts: contextcheck - **Adopt for:** Contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines. ## 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 contextcheck if… - License: contextcheck is MIT, future-agi is Apache-2.0. - Tags unique to contextcheck: ai-chat, ai-testing, chatbot-framework, ci-integration. - Also covers Model Training. - When you require a framework specifically designed to test the robustness of both LLMs and Retrieval-Augmented Generation systems within Python projects. ### Choose future-agi if… - License: future-agi is Apache-2.0, contextcheck 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 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 NOT to use contextcheck - Avoid if you aim for a framework without configuration flexibility through YAML as contextcheck strictly relies on this format for setting up test environments. - Skip contextcheck if your project environment or requirements do not align with the MIT license terms, especially in contexts where licensing compatibility must be strictly observed. ## 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 contextcheck and future-agi? contextcheck: Framework for LLMs and RAGs testing in Python. 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 contextcheck over future-agi? Choose contextcheck over future-agi when License: contextcheck is MIT, future-agi is Apache-2.0; Tags unique to contextcheck: ai-chat, ai-testing, chatbot-framework, ci-integration; Also covers Model Training; When you require a framework specifically designed to test the robustness of both LLMs and Retrieval-Augmented Generation systems within Python projects. ### When should I choose future-agi over contextcheck? Choose future-agi over contextcheck when License: future-agi is Apache-2.0, contextcheck 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 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 avoid contextcheck? Avoid if you aim for a framework without configuration flexibility through YAML as contextcheck strictly relies on this format for setting up test environments. Skip contextcheck if your project environment or requirements do not align with the MIT license terms, especially in contexts where licensing compatibility must be strictly observed. ### 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 contextcheck or future-agi more popular on GitHub? future-agi has more GitHub stars (2,032 vs 97). Stars measure visibility, not whether either tool fits your constraints. ### Are contextcheck and future-agi open source? Yes - both are open-source projects on GitHub (contextcheck: MIT, future-agi: Apache-2.0). ### Where can I find alternatives to contextcheck or future-agi? GraphCanon lists graph-backed alternatives at [contextcheck alternatives](/tools/addepto-contextcheck/alternatives) and [future-agi alternatives](/tools/future-agi-future-agi/alternatives) ([contextcheck markdown twin](/tools/addepto-contextcheck/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/addepto-contextcheck-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, contextcheck or future-agi? contextcheck: Dormant. 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 contextcheck and future-agi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [contextcheck trust report](/tools/addepto-contextcheck/trust); [future-agi trust report](/tools/future-agi-future-agi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=addepto-contextcheck`](/api/graphcanon/graph?tool=addepto-contextcheck) - 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/_