llm-lobbyist vs FastChat

Common questions

What is the difference between llm-lobbyist and FastChat?

llm-lobbyist: Code for the paper: "Large Language Models as Corporate Lobbyists" (2023).. 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 llm-lobbyist over FastChat?

Choose llm-lobbyist over FastChat when llm-lobbyist is primarily Jupyter Notebook; FastChat is Python; Tags unique to llm-lobbyist: jupyter notebook; Also covers Vector Databases.

When should I choose FastChat over llm-lobbyist?

Choose FastChat over llm-lobbyist when FastChat is primarily Python; llm-lobbyist is Jupyter Notebook; 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 llm-lobbyist?

Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. 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 llm-lobbyist or FastChat more popular on GitHub?

FastChat has more GitHub stars (39,490 vs 174). Stars measure visibility, not whether either tool fits your constraints.

Are llm-lobbyist and FastChat open source?

Yes - both are open-source projects on GitHub.

Where can I find alternatives to llm-lobbyist or FastChat?

GraphCanon lists graph-backed alternatives at llm-lobbyist alternatives and FastChat alternatives (llm-lobbyist markdown twin, FastChat markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, llm-lobbyist or FastChat?

llm-lobbyist: Dormant. 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 llm-lobbyist and FastChat?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-lobbyist trust report; FastChat trust report.