--- title: "future-agi vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-future-agi-vs-hidai25-eval-view" tools: ["future-agi-future-agi", "hidai25-eval-view"] --- # future-agi vs eval-view *GraphCanon updated Aug 2, 2026* ## Verdict Pick future-agi if future-AGI is an open-source toolkit for evaluating and improving LLMs and AI agents. It includes features like tracing, evaluations, simulations, datasets, gateway operations, and guardrails; pick eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time. [future-agi](https://futureagi.com) reports 1.6k GitHub stars, 449 forks, and 596 open issues, last pushed Aug 1, 2026. [eval-view](https://evalview.com) has 126 stars, 21 forks, and 3 open issues, last pushed Jul 26, 2026. Figures are from public GitHub metadata via [future-agi's repository](https://github.com/future-agi/future-agi) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [future-agi](/tools/future-agi-future-agi.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | End-to-end platform for evaluating, observing, and improving LLM and AI agent applications | Regression testing for AI agents | | Stars | 1,559 | 126 | | Forks | 449 | 21 | | Open issues | 596 | 3 | | Language | Python | Python | | Adopt for | Future-AGI is an open-source toolkit for evaluating and improving LLMs and AI agents. It includes features like tracing, evaluations, simulations, datasets, gateway operations, and guardrails. | Regression testing for AI agents to detect behavioral changes and output quality regressions over time. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The software uses the Apache-2.0 license, offering permissive terms for use and distribution. | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [future-agi](/tools/future-agi-future-agi.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Days since push | 1d | 6d | | Open issues (now) | 596 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/future-agi-future-agi/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Decision facts: future-agi - **Pricing:** freemium - Future-AGI is open-source under the Apache 2.0 license, allowing for free use but with potential paid services through deployment and support channels. - **Requirements:** Min 4 GB RAM; Requires Docker - **Adopt for:** Future-AGI is an open-source toolkit for evaluating and improving LLMs and AI agents. It includes features like tracing, evaluations, simulations, datasets, gateway operations, and guardrails. ## Decision facts: eval-view - **Pricing:** freemium - Free to use under the terms of the Apache License, Version 2.0. - **Requirements:** Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality. - **Adopt for:** Regression testing for AI agents to detect behavioral changes and output quality regressions over time. - **License detail:** The software uses the Apache-2.0 license, offering permissive terms for use and distribution. ## Choose when ### Choose future-agi if… - Pricing: Future-AGI is open-source under the Apache 2.0 license, allowing for free use but with potential paid services through deployment and support channels.. - Requirements: Min 4 GB RAM; Requires Docker. - Tags unique to future-agi: ai-gateway, docker-compose, evals, llm. - - Use Future-AGI when you require an end-to-end evaluation platform that supports self-hosting through Docker Compose or VM-based services on public clouds. ### Choose eval-view if… - Pricing: Free to use under the terms of the Apache License, Version 2.0.. - Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality.. - Tags unique to eval-view: agent-benchmark, agent-evaluation, ai-agents, regression-testing. - When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. ## When NOT to use future-agi - - Avoid using Future-AGI if you require Kubernetes or Helm support as of the current state; though these are planned for future release, they are not yet available. - - If your deployment strategy relies on a managed service like AWS Marketplace, consider other options since it is currently 'Coming Soon'. ## When NOT to use eval-view - If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. - When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge. ## Common questions ### What is the difference between future-agi and eval-view? future-agi: End-to-end platform for evaluating, observing, and improving LLM and AI agent applications. eval-view: Regression testing for AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose future-agi over eval-view? Choose future-agi over eval-view when Pricing: Future-AGI is open-source under the Apache 2.0 license, allowing for free use but with potential paid services through deployment and support channels.; Requirements: Min 4 GB RAM; Requires Docker; Tags unique to future-agi: ai-gateway, docker-compose, evals, llm; - Use Future-AGI when you require an end-to-end evaluation platform that supports self-hosting through Docker Compose or VM-based services on public clouds. ### When should I choose eval-view over future-agi? Choose eval-view over future-agi when Pricing: Free to use under the terms of the Apache License, Version 2.0.; Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality.; Tags unique to eval-view: agent-benchmark, agent-evaluation, ai-agents, regression-testing; When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. ### When should I avoid future-agi? - Avoid using Future-AGI if you require Kubernetes or Helm support as of the current state; though these are planned for future release, they are not yet available. - If your deployment strategy relies on a managed service like AWS Marketplace, consider other options since it is currently 'Coming Soon'. ### When should I avoid eval-view? If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge. ### Is future-agi or eval-view more popular on GitHub? future-agi has more GitHub stars (1,559 vs 126). Stars measure visibility, not whether either tool fits your constraints. ### Are future-agi and eval-view open source? Yes - both are open-source projects on GitHub (future-agi: Apache-2.0, eval-view: Apache-2.0). ### Where can I find alternatives to future-agi or eval-view? GraphCanon lists graph-backed alternatives at [future-agi alternatives](/tools/future-agi-future-agi/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([future-agi markdown twin](/tools/future-agi-future-agi/alternatives.md), [eval-view markdown twin](/tools/hidai25-eval-view/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-hidai25-eval-view.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, future-agi or eval-view? future-agi: Very active. eval-view: 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 eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [future-agi trust report](/tools/future-agi-future-agi/trust); [eval-view trust report](/tools/hidai25-eval-view/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/_