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.

Continue reading at the original source →