Infini-AI-Lab Releases Hermit, an Open-Source Harness Adapter Framework for AI Agents

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.

<div class="pn on" data-i="0">
<p class="lead">A <b>harness adapter</b> is a trainable external harness, like a plugin with training built in. Click any part to see what it does.</p>
<div class="dg">
<div class="box frz"><h4>Your agent</h4>
<span class="chip" data-d="The base model. Hermit never updates its weights.">Model</span>
<span class="chip" data-d="The existing harness (for example Codex or Claude Code). Hermit never edits it, so closed-source harnesses work too.">Harness</span>
</div>
<div class="plug" aria-hidden="true"></div>
<div class="box ad"><h4>Harness adapter</h4>
<span class="chip" data-d="Instruction files such as ADAPTER.md or AGENTS.md that the agent reads as context.">Instructions</span>
<span class="chip" data-d="Reusable skills, including executable helper scripts.">Skills</span>
<span class="chip" data-d="Notes and stored knowledge the agent carries across tasks.">Memory</span>
<span class="chip" data-d="Extra tools, such as the wm tool MWM exposes for its world model.">Tools</span>
<span class="chip" data-d="Executable code. An adapter can be an entire repository.">Code</span>
<span class="chip" data-d="Coordination logic for several agents, as in the Lean adapter that links 5 Codex agents.">Coordination</span>
</div>
</div>
<div class="info" id="mhx-info">Every adapter version is stored in a git tree, so you can inspect, roll back or ship any one.</div>
</div>

<div class="pn" data-i="1">
<p class="lead">Hermit runs an <b>RL-style loop</b>, but the thing being updated is the adapter, not the weights.</p>
<div class="loop">
<div class="st"><div class="n">STEP 1</div><div class="t">Run tasks</div></div>
<div class="st"><div class="n">STEP 2</div><div class="t">Judge scores</div></div>
<div class="st"><div class="n">STEP 3</div><div class="t">Evolver proposes</div></div>
<div class="st"><div class="n">STEP 4</div><div class="t">Check vs template</div></div>
<div class="st"><div class="n">STEP 5</div><div class="t">Commit version</div></div>
</div>
<div class="info" id="mhx-step">Press Play to step through one training iteration.</div>
<div class="ctl">
<button class="btn" id="mhx-play"> Play</button>
<button class="btn gh" id="mhx-next">Next step</button>
<button class="btn gh" id="mhx-reset">Reset</button>
<span class="ver">Adapter version <b id="mhx-v">V0</b></span>
</div>
<p class="note">Built-in Evolvers: GEPA, SkillOpt, MetaWorld Model (MWM), Harness², Continual Harness. ACE and ReHarness are listed as coming soon.</p>
</div>

<div class="pn" data-i="2">
<p class="lead">Self-reported results, all with <b>GLM 5.3 Flash</b> as the acting model. Pick a benchmark.</p>
<div class="seg">
<button class="tab on" data-b="0">OfficeQA</button>
<button class="tab" data-b="1">CL-Bench database</button>
<button class="tab" data-b="2">ServeLearnBench (H)</button>
</div>
<div class="bars" id="mhx-bars"></div>
<p class="note" id="mhx-bnote"></p>
</div>

<div class="pn" data-i="3">
<p class="lead">Train once, then <b>deploy</b> the adapter into an agent workspace or export it as a native plugin.</p>
<div class="term"><span class="p">$</span> pip install hermit-harness
<span class="p">$</span> hermit train –config run.yaml
<span class="p">$</span> hermit eval –config run.yaml –adapter final
<span class="p">$</span> hermit deploy –adapter DIR –workspace WS –host codex
<span class="p">$</span> hermit convert –adapter DIR –output OUT –target claude-code
<span class="c"># other convert targets: codex, pi, hermes</span></div>
<div class="hosts">
<span class="chip" data-d="Deploy target and plugin export target.">Codex</span>
<span class="chip" data-d="Deploy target and plugin export target.">Claude Code</span>
<span class="chip" data-d="Deploy target and plugin export target.">Pi</span>
<span class="chip" data-d="Plugin export target.">Hermes</span>
</div>
<div class="info" id="mhx-dinfo">Backends by reference: Fireworks, OpenAI, Anthropic, DeepSeek, vLLM, SGLang, Ollama. The offline Quick Start needs no GPU, model account or dataset.</div>
</div>

<div class="ft"><span>Source: github.com/Infini-AI-Lab/hermit · self-reported</span><span class="mtp">© Marktechpost</span></div>
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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:

Method	Accuracy
No adapter	0.541
Initial skill	0.576
GEPA	0.580
SkillOpt	0.600
Continual Harness	0.634
Harness²	0.651

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?

Feature	Hermit	GEPA	ACE	Agent Lightning
What it trains	External harness adapter	Any text parameter (prompts, code)	Context playbook	Prompts and model weights
Changes model weights	No	No	Not disclosed	Yes, via RL or SFT
Methods	5 built in, custom Evolvers	Reflective evolution, Pareto selection	Generator, Reflector, Curator	RL, prompt optimization, SFT
Deploy targets	Codex, Claude Code, Pi; plugin export	DSPy (`dspy.GEPA`)	Not disclosed	LangChain, OpenAI Agents SDK, AutoGen, CrewAI and more
License	Apache-2.0	MIT	Apache-2.0	MIT
Key result	0.795 on CL-Bench database (self-reported)	AIME 2025: 46.6% to 56.6% (GPT-4.1 Mini)	+10.6% on AppWorld	Not disclosed

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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