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.Play Next step Reset Adapter version V0

Built-in Evolvers: GEPA, SkillOpt, MetaWorld Model (MWM), Harness², Continual Harness. ACE and ReHarness are listed as coming soon. Self-reported results, all with GLM 5.3 Flash as the acting model. Pick a benchmark.

OfficeQA CL-Bench database ServeLearnBench (H) Train once, then deploy the adapter into an agent workspace or export it as a native plugin.

$ pip install hermit-harness $ hermit train --config run.yaml $ hermit eval --config run.yaml --adapter final $ hermit deploy --adapter DIR --workspace WS --host codex $ hermit convert --adapter DIR --output OUT --target claude-code # other convert targets: codex, pi, hermes

Codex Claude Code Pi Hermes

Backends by reference: Fireworks, OpenAI, Anthropic, DeepSeek, vLLM, SGLang, Ollama.The offline Quick Start needs no GPU, model account or dataset.Source: github.com/Infini-AI-Lab/hermit · self-reported© Marktechpost (function(){ var R=document.getElementById('mtp-hermit-x');if(!

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