--- title: "deepeval vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-lm-sys-fastchat" tools: ["confident-ai-deepeval", "lm-sys-fastchat"] --- # deepeval vs FastChat *GraphCanon updated Jul 11, 2026* ## Verdict Pick deepeval when tags unique to deepeval: python, llm-evaluation-framework, evaluation-metrics, llm-evaluation-metrics; pick FastChat when tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving. [deepeval](https://deepeval.com) reports 17k GitHub stars, 1.6k forks, and 334 open issues, last pushed Jul 10, 2026. [FastChat](https://github.com/lm-sys/FastChat) has 39k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [deepeval](/tools/confident-ai-deepeval.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | The LLM Evaluation Framework | An open platform for training, serving, and evaluating large language models | | Stars | 16,767 | 39,490 | | Forks | 1,641 | 4,788 | | Open issues | 334 | 1,027 | | Language | Python | Python | | Adopt for | - | FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Evaluation & Observability | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 71d | | Open issues (now) | 334 | 1.0k | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## Decision facts: FastChat - **Adopt for:** FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB ## Choose when ### Choose deepeval if… - Tags unique to deepeval: python, llm-evaluation-framework, evaluation-metrics, llm-evaluation-metrics. - More recently updated (last pushed Jul 10, 2026). ### Choose FastChat if… - Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving. - Also covers Model Training, Inference & Serving. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ## When NOT to use deepeval - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use FastChat - - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on - + Mac. ## Common questions ### What is the difference between deepeval and FastChat? deepeval: The LLM Evaluation Framework. FastChat: An open platform for training, serving, and evaluating large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over FastChat? Choose deepeval over FastChat when Tags unique to deepeval: python, llm-evaluation-framework, evaluation-metrics, llm-evaluation-metrics; More recently updated (last pushed Jul 10, 2026). ### When should I choose FastChat over deepeval? Choose FastChat over deepeval when Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; Also covers Model Training, Inference & Serving; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I avoid deepeval? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid FastChat? - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac. ### Is deepeval or FastChat more popular on GitHub? FastChat has more GitHub stars (39,490 vs 16,767). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and FastChat open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to deepeval or FastChat? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [FastChat markdown twin](/tools/lm-sys-fastchat/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-lm-sys-fastchat.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or FastChat? deepeval: Very active. FastChat: Steady. 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 FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [FastChat trust report](/tools/lm-sys-fastchat/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/_