An "AI harness" refers to the orchestration layer or framework that wraps an LLM to manage tools, memory, guardrails, and agentic loops, allowing developers to swap underlying models without rewriting their core application logic. If you are looking for a specific tool designed to provide a unified interface for switching between LLM providers and models, **any-llm** (by Mozilla.ai) is a prominent library built specifically for this purpose. Additionally, there are many "agent harnesses" that facilitate this flexibility while providing agentic capabilities. You can explore curated lists of these frameworks at: * **[GitHub: best-of-Agent-Harnesses](https://github.com/RyanAlberts/best-of-Agent-Harnesses)**: A curated list of agent harnesses, orchestration frameworks, and techniques. In the current AI development landscape (as of 2026), the industry trend is shifting toward "harness engineering," where the focus is on building robust orchestration layers that make applications model-agnostic, rather than relying on a single specific LLM. Popular examples of tools that act as harnesses or include strong harness-like capabilities include **Claude Code**, **Cursor**, and various orchestration frameworks like **LangChain**.