--- title: "future-agi vs invariant-gateway" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-future-agi-vs-invariantlabs-ai-invariant-gateway" tools: ["future-agi-future-agi", "invariantlabs-ai-invariant-gateway"] --- # future-agi vs invariant-gateway *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 invariant-gateway if the invariant-gateway is an observability proxy crafted for Python-based AI agents running under LLMs, offering robust debugging and guardrail implementations. [future-agi](https://futureagi.com) reports 2.0k GitHub stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. [invariant-gateway](https://github.com/invariantlabs-ai/invariant-gateway) has 80 stars, 9 forks, and 1 open issues, last pushed Nov 6, 2025. Figures are from public GitHub metadata via [future-agi's repository](https://github.com/future-agi/future-agi) and [invariant-gateway's repository](https://github.com/invariantlabs-ai/invariant-gateway). | | [future-agi](/tools/future-agi-future-agi.md) | [invariant-gateway](/tools/invariantlabs-ai-invariant-gateway.md) | | --- | --- | --- | | Tagline | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | LLM proxy for observing and debugging AI agent activities | | Stars | 2,032 | 80 | | Forks | 627 | 9 | | Open issues | 961 | 1 | | 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, | The invariant-gateway is an observability proxy crafted for Python-based AI agents running under LLMs, offering robust debugging and guardrail implementations. | | 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, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [future-agi](/tools/future-agi-future-agi.md) | [invariant-gateway](/tools/invariantlabs-ai-invariant-gateway.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 310d | | Open issues (now) | 961 | 1 | | Stars delta | +473 (30d) | +2 (30d) | | Open issues delta | +365 (30d) | 0 (30d) | | Full report | [trust report](/tools/future-agi-future-agi/trust.md) | [trust report](/tools/invariantlabs-ai-invariant-gateway/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: invariant-gateway - **Adopt for:** The invariant-gateway is an observability proxy crafted for Python-based AI agents running under LLMs, offering robust debugging and guardrail implementations. ## Choose when ### Choose future-agi if… - 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. ### Choose invariant-gateway if… - Tags unique to invariant-gateway: debugging, llm, observability, proxy. - If you are working on Python-based projects and need detailed monitoring of AI agent activities to enhance security or compliance; - Leaner open-issue backlog (1). ## 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 invariant-gateway - In scenarios where the programming language used is not Python, given that invariant-gateway is specifically designed for Python-based applications; - For organizations that do not require sophisticated guardrails or observability features when dealing with their AI agents' operations; ## Common questions ### What is the difference between future-agi and invariant-gateway? future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. invariant-gateway: LLM proxy for observing and debugging AI agent activities. See the comparison table for live GitHub stats and shared categories. ### When should I choose future-agi over invariant-gateway? Choose future-agi over invariant-gateway when 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 choose invariant-gateway over future-agi? Choose invariant-gateway over future-agi when Tags unique to invariant-gateway: debugging, llm, observability, proxy; If you are working on Python-based projects and need detailed monitoring of AI agent activities to enhance security or compliance;; Leaner open-issue backlog (1). ### 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 invariant-gateway? In scenarios where the programming language used is not Python, given that invariant-gateway is specifically designed for Python-based applications; For organizations that do not require sophisticated guardrails or observability features when dealing with their AI agents' operations; ### Is future-agi or invariant-gateway more popular on GitHub? future-agi has more GitHub stars (2,032 vs 80). Stars measure visibility, not whether either tool fits your constraints. ### Are future-agi and invariant-gateway open source? Yes - both are open-source projects on GitHub (future-agi: Apache-2.0, invariant-gateway: Apache-2.0). ### Where can I find alternatives to future-agi or invariant-gateway? GraphCanon lists graph-backed alternatives at [future-agi alternatives](/tools/future-agi-future-agi/alternatives) and [invariant-gateway alternatives](/tools/invariantlabs-ai-invariant-gateway/alternatives) ([future-agi markdown twin](/tools/future-agi-future-agi/alternatives.md), [invariant-gateway markdown twin](/tools/invariantlabs-ai-invariant-gateway/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-invariantlabs-ai-invariant-gateway.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, future-agi or invariant-gateway? future-agi: Very active. invariant-gateway: 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 invariant-gateway? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [future-agi trust report](/tools/future-agi-future-agi/trust); [invariant-gateway trust report](/tools/invariantlabs-ai-invariant-gateway/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/_