--- title: "future-agi vs manifest" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-future-agi-vs-mnfst-manifest" tools: ["future-agi-future-agi", "mnfst-manifest"] --- # future-agi vs manifest *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 manifest if manifest is a TypeScript framework under MIT license that enables integration of AI agents across various providers, with an emphasis on observability and tracking mechanisms. [future-agi](https://futureagi.com) reports 2.0k GitHub stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. [manifest](https://manifest.build) has 7.5k stars, 508 forks, and 110 open issues, last pushed Sep 10, 2026. Figures are from public GitHub metadata via [future-agi's repository](https://github.com/future-agi/future-agi) and [manifest's repository](https://github.com/mnfst/manifest). | | [future-agi](/tools/future-agi-future-agi.md) | [manifest](/tools/mnfst-manifest.md) | | --- | --- | --- | | Tagline | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | Connect Your Agents And Harnesses With Any Provider | | Stars | 2,032 | 7,514 | | Forks | 627 | 508 | | Open issues | 961 | 110 | | Language | Python | TypeScript | | 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, | Manifest is a TypeScript framework under MIT license that enables integration of AI agents across various providers, with an emphasis on observability and tracking mechanisms such as token and cost-tracking. | | 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. | MIT | | 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) | [manifest](/tools/mnfst-manifest.md) | | --- | --- | --- | | Open issues (now) | 961 | 110 | | Stars delta | +473 (30d) | +100 (30d) | | Open issues delta | +365 (30d) | +1 (30d) | | Full report | [trust report](/tools/future-agi-future-agi/trust.md) | [trust report](/tools/mnfst-manifest/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: manifest - **Adopt for:** Manifest is a TypeScript framework under MIT license that enables integration of AI agents across various providers, with an emphasis on observability and tracking mechanisms such as token and cost-tracking. ## Choose when ### Choose future-agi if… - future-agi is primarily Python; manifest is TypeScript. - License: future-agi is Apache-2.0, manifest 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-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 manifest if… - manifest is primarily TypeScript; future-agi is Python. - License: manifest is MIT, future-agi is Apache-2.0. - Tags unique to manifest: llm-observability. - When you need to establish connections between multiple AI providers without altering the core functionality or architecture of your existing systems. ## 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 manifest - If your project strictly requires use cases with non-TypeScript languages, as this would necessitate additional integration work. - When you require a mature framework; given Manifest's beta status, it might not be the best choice for production environments sensitive to potential bugs or incomplete features. ## Common questions ### What is the difference between future-agi and manifest? future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. manifest: Connect Your Agents And Harnesses With Any Provider. See the comparison table for live GitHub stats and shared categories. ### When should I choose future-agi over manifest? Choose future-agi over manifest when future-agi is primarily Python; manifest is TypeScript; License: future-agi is Apache-2.0, manifest 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-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 manifest over future-agi? Choose manifest over future-agi when manifest is primarily TypeScript; future-agi is Python; License: manifest is MIT, future-agi is Apache-2.0; Tags unique to manifest: llm-observability; When you need to establish connections between multiple AI providers without altering the core functionality or architecture of your existing systems. ### 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 manifest? If your project strictly requires use cases with non-TypeScript languages, as this would necessitate additional integration work. When you require a mature framework; given Manifest's beta status, it might not be the best choice for production environments sensitive to potential bugs or incomplete features. ### Is future-agi or manifest more popular on GitHub? manifest has more GitHub stars (7,514 vs 2,032). Stars measure visibility, not whether either tool fits your constraints. ### Are future-agi and manifest open source? Yes - both are open-source projects on GitHub (future-agi: Apache-2.0, manifest: MIT). ### Where can I find alternatives to future-agi or manifest? GraphCanon lists graph-backed alternatives at [future-agi alternatives](/tools/future-agi-future-agi/alternatives) and [manifest alternatives](/tools/mnfst-manifest/alternatives) ([future-agi markdown twin](/tools/future-agi-future-agi/alternatives.md), [manifest markdown twin](/tools/mnfst-manifest/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-mnfst-manifest.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, future-agi or manifest? future-agi: Very active. manifest: 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 future-agi and manifest? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [future-agi trust report](/tools/future-agi-future-agi/trust); [manifest trust report](/tools/mnfst-manifest/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/_