Agent Frameworks vs. Agent Harness: Understanding the Key Differences

5 The Difference Is What Is Decided Up Front

The distinction lies in how many decisions are pre‑made. A Harness arrives with most choices baked in; a framework exposes options for you to decide. Your problem determines which you need. Often, the simplest path is to bypass frameworks entirely and call a model endpoint directly for a simple Agent.

Some frameworks are adding Harness‑like features. LangChain, for example, introduced Deep Agents, explicitly called an "Agent Harness" that sits atop the framework. It bundles planning tools, file‑system access for context, sub‑Agent launching, and memory persistence. Under the hood it remains LangChain, but Deep Agents provide a "batteries‑included" default configuration.

LangChain itself distinguishes between the core library (the framework) and LangGraph, which they label an "Agent runtime" handling execution, state management, and persistence. Deep Agents act as a Harness on top of both.

This illustrates a company spanning the entire spectrum: the framework for composing Agents, the runtime for reliable execution, and the Harness for plug‑and‑play solutions. While Harnesses are highly opinionated, their source can still be modified—OpenClaw’s code can be changed to alter memory behavior, the Agent loop, or tool handling, though most users stick with the defaults.