--- title: "deepeval vs futureagi-sdk" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-future-agi-futureagi-sdk" tools: ["confident-ai-deepeval", "future-agi-futureagi-sdk"] --- # deepeval vs futureagi-sdk *GraphCanon updated Aug 2, 2026* ## Verdict Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick futureagi-sdk if future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. [deepeval](https://deepeval.com) reports 17k GitHub stars, 1.7k forks, and 404 open issues, last pushed Jul 27, 2026. [futureagi-sdk](https://app.futureagi.com) has 48 stars, 5 forks, and 3 open issues, last pushed Jul 8, 2026. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [futureagi-sdk's repository](https://github.com/future-agi/futureagi-sdk). | | [deepeval](/tools/confident-ai-deepeval.md) | [futureagi-sdk](/tools/future-agi-futureagi-sdk.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | Production-grade AI evaluation, prompt management & observability SDK | | Stars | 17,226 | 48 | | Forks | 1,736 | 5 | | Open issues | 404 | 3 | | Language | Python | Python | | Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | The Future AGI SDK uses the Apache License, Version 2.0 (Apache-2.0). It allows users to freely use, modify, and distribute the software while maintaining copyright notices. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [futureagi-sdk](/tools/future-agi-futureagi-sdk.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 25d | | Open issues (now) | 404 | 3 | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/future-agi-futureagi-sdk/trust.md) | ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Decision facts: futureagi-sdk - **Requirements:** Supports Python and TypeScript languages; Automated evaluations with sub-100ms guardrails - **Adopt for:** Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. - **License detail:** The Future AGI SDK uses the Apache License, Version 2.0 (Apache-2.0). It allows users to freely use, modify, and distribute the software while maintaining copyright notices. ## Choose when ### Choose deepeval if… - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose futureagi-sdk if… - Requirements: Supports Python and TypeScript languages; Automated evaluations with sub-100ms guardrails. - Tags unique to futureagi-sdk: ai-agents, annotations, dataset, development. - Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. ## When NOT to use deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## When NOT to use futureagi-sdk - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between deepeval and futureagi-sdk? deepeval: LLM Evaluation Framework.. futureagi-sdk: Production-grade AI evaluation, prompt management & observability SDK. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over futureagi-sdk? Choose deepeval over futureagi-sdk when Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I choose futureagi-sdk over deepeval? Choose futureagi-sdk over deepeval when Requirements: Supports Python and TypeScript languages; Automated evaluations with sub-100ms guardrails; Tags unique to futureagi-sdk: ai-agents, annotations, dataset, development; Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0. ### When should I avoid deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### When should I avoid futureagi-sdk? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is deepeval or futureagi-sdk more popular on GitHub? deepeval has more GitHub stars (17,226 vs 48). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and futureagi-sdk open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, futureagi-sdk: Apache-2.0). ### Where can I find alternatives to deepeval or futureagi-sdk? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [futureagi-sdk alternatives](/tools/future-agi-futureagi-sdk/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [futureagi-sdk markdown twin](/tools/future-agi-futureagi-sdk/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/confident-ai-deepeval-vs-future-agi-futureagi-sdk.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or futureagi-sdk? deepeval: Very active. futureagi-sdk: 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 deepeval and futureagi-sdk? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [futureagi-sdk trust report](/tools/future-agi-futureagi-sdk/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - 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/_