--- title: "AstrBot vs Awesome-LLM-Eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/astrbotdevs-astrbot-vs-onejune2018-awesome-llm-eval" tools: ["astrbotdevs-astrbot", "onejune2018-awesome-llm-eval"] --- # AstrBot vs Awesome-LLM-Eval *GraphCanon updated Jul 11, 2026* ## Verdict Pick AstrBot when license: AstrBot is AGPL-3.0, Awesome-LLM-Eval is MIT; pick Awesome-LLM-Eval when license: Awesome-LLM-Eval is MIT, AstrBot is AGPL-3.0. [AstrBot](https://astrbot.app) reports 36k GitHub stars, 2.5k forks, and 1.3k open issues, last pushed Jul 9, 2026. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 648 stars, 78 forks, and 38 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [AstrBot's repository](https://github.com/AstrBotDevs/AstrBot) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval). | | [AstrBot](/tools/astrbotdevs-astrbot.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Tagline | AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature | Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界. | | Stars | 36,186 | 648 | | Forks | 2,513 | 78 | | Open issues | 1,315 | 38 | | Language | Python | - | | Adopt for | AstrBot is an AI agent assistant and development framework that integrates multiple instant messaging platforms with various language models and plugins under the AGPL-3.0 license. | - | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | MIT | | Categories | LLM Frameworks, AI Agents | LLM Frameworks, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AstrBot](/tools/astrbotdevs-astrbot.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 229d | | Open issues (now) | 1.3k | 38 | | Owner type | Organization | User | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/astrbotdevs-astrbot/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | ## Decision facts: AstrBot - **Adopt for:** AstrBot is an AI agent assistant and development framework that integrates multiple instant messaging platforms with various language models and plugins under the AGPL-3.0 license. ## Choose when ### Choose AstrBot if… - License: AstrBot is AGPL-3.0, Awesome-LLM-Eval is MIT. - Tags unique to AstrBot: ai, gemini, docker, discord. - Also covers AI Agents. - AstrBot ships Docker support for self-hosted deployment. - When you require a highly integrative environment where your AI agents can communicate across different instant messaging platforms like Discord, Telegram, QQ, and others. ### Choose Awesome-LLM-Eval if… - License: Awesome-LLM-Eval is MIT, AstrBot is AGPL-3.0. - Tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark. - Also covers Evaluation & Observability. ## When NOT to use AstrBot - If you are looking for proprietary solutions or do not want to comply with the AGPL-3.0 open-source license requirements, as AstrBot might not suit businesses preferring closed development. - In scenarios where a very streamlined or simple AI agent without extensive plugin and platform customizability is required due to time constraints or a specific project scope that does not need broad, - +adaptability. ## When NOT to use Awesome-LLM-Eval - Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on Awesome-LLM-Eval. - 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. ## Common questions ### What is the difference between AstrBot and Awesome-LLM-Eval? AstrBot: AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature. Awesome-LLM-Eval: Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界.. See the comparison table for live GitHub stats and shared categories. ### When should I choose AstrBot over Awesome-LLM-Eval? Choose AstrBot over Awesome-LLM-Eval when License: AstrBot is AGPL-3.0, Awesome-LLM-Eval is MIT; Tags unique to AstrBot: ai, gemini, docker, discord; Also covers AI Agents; AstrBot ships Docker support for self-hosted deployment; When you require a highly integrative environment where your AI agents can communicate across different instant messaging platforms like Discord, Telegram, QQ, and others. ### When should I choose Awesome-LLM-Eval over AstrBot? Choose Awesome-LLM-Eval over AstrBot when License: Awesome-LLM-Eval is MIT, AstrBot is AGPL-3.0; Tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark; Also covers Evaluation & Observability. ### When should I avoid AstrBot? If you are looking for proprietary solutions or do not want to comply with the AGPL-3.0 open-source license requirements, as AstrBot might not suit businesses preferring closed development. In scenarios where a very streamlined or simple AI agent without extensive plugin and platform customizability is required due to time constraints or a specific project scope that does not need broad, +adaptability. ### When should I avoid Awesome-LLM-Eval? Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on Awesome-LLM-Eval. 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. ### Is AstrBot or Awesome-LLM-Eval more popular on GitHub? AstrBot has more GitHub stars (36,186 vs 648). Stars measure visibility, not whether either tool fits your constraints. ### Are AstrBot and Awesome-LLM-Eval open source? Yes - both are open-source projects on GitHub (AstrBot: AGPL-3.0, Awesome-LLM-Eval: MIT). ### Where can I find alternatives to AstrBot or Awesome-LLM-Eval? GraphCanon lists graph-backed alternatives at [AstrBot alternatives](/tools/astrbotdevs-astrbot/alternatives) and [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) ([AstrBot markdown twin](/tools/astrbotdevs-astrbot/alternatives.md), [Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/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/astrbotdevs-astrbot-vs-onejune2018-awesome-llm-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AstrBot or Awesome-LLM-Eval? AstrBot: Very active. Awesome-LLM-Eval: Slowing. 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 AstrBot and Awesome-LLM-Eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AstrBot trust report](/tools/astrbotdevs-astrbot/trust); [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=astrbotdevs-astrbot`](/api/graphcanon/graph?tool=astrbotdevs-astrbot) - 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/_