Infini-AI-Lab拥有发布 Hermit，一个用于AI agents的开源机能适配器框架。 agent的模型和操纵被冻结。 2026年10月25日发售的首发版本为v0.1.0. 10月27日CMU邮编Haizhong Zheng 宣布于X. 对于开发者来说，投出过程很简单：使Codex或Claude Code agents适应新任务而无需微调或forking接引。
TL; DR 数据
- 脚印：Python框架。 没有自己的参数。 Apache -2.0, pip安装隐士机能。
- 运行在：离线快速启动不需要GPU，模型账户或数据集。 Real runs使用任意API后端或本地的vLLM,SGLang或Ollama服务器。
- 性能：报告在OfficeQA、CL-Bench和ServielLearn Bench的不适应基线上的增益，均带有GLM 5.3 Flash。
- 最佳：MWM适配器达到 0.795精度在CL-Bench数据库勘探上，与0.580相比为内文本基线。
- 最差：同一地点的快速适配器得分为0.470分，低于0.580的基准。
- 底线：最佳：5个取用进化方法的培训循环。 最糟糕的是：大多数结果都是单行，自报。
什么样的吊带适配器？
接合器是一种可训练的外接器。 它的工作就像一个插件，里面有训练支持。 一个模型适配器，如LoRA，可以改造一个模型。 取而代之的是，一个被操纵的适配器可以适应现有的agent.
适配器可以持有指令，技能，内存，工具，可执行代码和agent协调逻辑。 它甚至可以是一个完整的仓库。 其规格称哪些部件是可学习的以及如何检查更新。 基础模型和取用永远不会改变。
团队为此设计给出了3个理由。 许多吊带是闭源的。 编辑打开的可以打破仔细调制的组件交互。 而有学识的适配器可以在相容的模型和牵引器之间移动。
隐士如何训练适配器？
隐士采用RL型训练回路。 一个训练员通过有当前适配器的代理agent运行一批任务。 法官对产出进行评分。 一个Evolver读取了轨迹和反馈，然后提出一个新的适配器。 训练员对照模板进行检查，并将其承诺作为下个版本。
每个适配器版本都生活在一棵 gi树上。 你可以检查，滚回或运送任何版本。 学习者程序可以运行沙盒，这需要用气泡包装的Linux.
现今的五种进化方法飞船：GEPA, Skillopt, MetaWorld Model (MWM), Harness2和Continual Harness. ACE和ReHarness很快被列出。 也可以写出自己的"Evolver".
基准测试结果如何？.
所有下行都使用GLM 5.3 Flash作为动作模型。 这些数字来自Repo的例子READMEs，并且是自报的。
OfficeQA(recipe)提出了172个测试问题
GEPA和SkillOpt列车50个问题并被冷冻测试。 连续的Harness和Harness2转而学习，同时流出测试集一次。 球队报告哈内斯2在其参考赛跑中获得+7.6分的得分增益，95%的间隔为+1.7至+13.4.
CL-Bench(数据库，扑克)测试来自"持续学习教席"论文的状态环境。 在数据库探索中，MWM达到了0.795的精度。 基准测试内文基线得分0.580分。 在可被利用的扑克上，MWM每只手赢得了3.08个大百叶窗，对阵2.51个内文学习。
ServiLearn Bench (recipe) 流 624 零售支持请求在一个隐藏的，不断变化的政策下。 MWM在隐藏政策测试中得分为45.3分。 领导板上最好的学习方法CH，得分28.7. oracle给政策评分89.2.
隐士与其他agent优化器相比如何？.
你怎么安装和部署它？
从 PyPI 安装 pip 安装隐士- harness，或复制 repo 以作为示例。 CLI涵盖检查，火车，eval，部署和转换。 模型被引用为"后端"/"模式id". 所支持的后端包括烟花，OpenAI,Anthropic,DeepSeek,vLLM,SGLang和Ollama.
隐士部署为 Codex, Claude Code 或 Pi 写一个工作空间。 隐士转换导出一个训练有素的适配器作为 Codex, Claude Code, Pi 或 Hermes 的本地插件。 一个完成的Lean合作适配器坐标为5 Codex agents，共同证明一个定理。
关键外卖
- 隐士训练一个外部适配器；模型和操纵保持被冻结。
- 5个方法船：GEPA、Skillopt、MWM、Harness2、Continual Harness。
- 在CL-Bench数据库中，MWM达到0.795，对0.580的文本。
- 将适配器部署到Codex、Claude Code和Pi，或导出为插件。
——结果自报，多为单行，有GLM 5.3 Flash.
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Infini-AI-Lab has released Hermit, an open-source harness adapter framework for AI agents. Hermit is a framework from Infini-AI-Lab that trains an external ‘harness adapter’ while the agent’s model and harness stay frozen. The first release, v0.1.0, shipped on October 25, 2026. CMU postdoc Haizhong Zheng announced it on X on October 27. For developers, the pitch is simple: adapt Codex or Claude Code agents to new tasks without fine-tuning or forking the harness.
TL;DR
- Footprint: A Python framework. No parameters of its own. Apache-2.0, pip install hermit-harness.
- Runs on: The offline Quick Start needs no GPU, model account or dataset. Real runs use any API backend or a local vLLM, SGLang or Ollama server.
- Performance: Reported gains over no-adapter baselines on OfficeQA, CL-Bench and ServeLearnBench, all with GLM 5.3 Flash.
- Best: MWM adapter hits 0.795 accuracy on CL-Bench database exploration, vs 0.580 for the in-context baseline.
- Worst: The prompt-only adapter scores 0.470 on the same venue, below that 0.580 baseline.
- Bottom line: Best: one training loop for 5 harness evolution methods. Worst: most results are single runs, self-reported.
What is a harness adapter?
A harness adapter is a trainable external harness. It works like a plugin with training support built in. A model adapter, such as LoRA, adapts a model. A harness adapter adapts an existing agent instead.
The adapter can hold instructions, skills, memory, tools, executable code and agent coordination logic. It can even be a whole repository. Its specification says which parts are learnable and how updates get checked. The base model and harness never change.
The team gives 3 reasons for this design. Many harnesses are closed-source. Editing open ones can break carefully tuned component interactions. And a learned adapter can move between compatible models and harnesses.
How does Hermit train an adapter?
Hermit uses an RL-style training loop. A Trainer runs a batch of tasks through the acting agent with the current adapter. A judge scores the outputs. An Evolver reads the trajectories and feedback, then proposes a new adapter. The Trainer checks it against the template and commits it as the next version.
Every adapter version lives in a git tree. You can inspect, roll back or ship any version. Learner programs can run sandboxed, which requires Linux with bubblewrap.
Five evolution methods ship today: GEPA, SkillOpt, MetaWorld Model (MWM), Harness² and Continual Harness. ACE and ReHarness are listed as coming soon. You can also write your own Evolver.
What are the benchmark results?
All runs below use GLM 5.3 Flash as the acting model. The numbers come from the repo’s example READMEs and are self-reported.
OfficeQA (recipe) asks 172 test questions about U.S. Treasury Bulletins, scored by exact match:
GEPA and SkillOpt train on 50 questions and are tested frozen. Continual Harness and Harness² instead learn while streaming the test set once. The team reports a +7.6 point gain for Harness² over its reference run, with a 95% interval of +1.7 to +13.4.
CL-Bench (database, poker) tests stateful environments from the Continual Learning Bench paper. On database exploration, MWM reaches 0.795 accuracy. The benchmark’s in-context baseline scores 0.580. On exploitable poker, MWM wins 3.08 big blinds per hand, vs 2.51 for in-context learning.
ServeLearnBench (recipe) streams 624 retail support requests under a hidden, shifting policy. MWM scores 45.3 on hidden-policy tests. The best learning method on the leaderboard, CH, scores 28.7. An oracle given the policy scores 89.2.
How does Hermit compare with other agent optimizers?
How do you install and deploy it?
Install from PyPI with pip install hermit-harness, or clone the repo for examples. The CLI covers check, train, eval, deploy and convert. Models are referenced as <backend>/<model id>. Supported backends include Fireworks, OpenAI, Anthropic, DeepSeek, vLLM, SGLang and Ollama.
hermit deploy writes a workspace for Codex, Claude Code or Pi. hermit convert exports a trained adapter as a native plugin for Codex, Claude Code, Pi or Hermes. A finished Lean collaboration adapter coordinates 5 Codex agents proving one theorem together.
Key Takeaways
- Hermit trains an external adapter; model and harness stay frozen.
- 5 methods ship: GEPA, SkillOpt, MWM, Harness², Continual Harness.
- MWM reaches 0.795 on CL-Bench database vs 0.580 in-context.
- Adapters deploy to Codex, Claude Code and Pi, or export as plugins.
- Results are self-reported, mostly single runs, with GLM 5.3 Flash.
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