Agent Harness

An open source agent harness is the runtime layer that turns a raw LLM into a working agent, handling the execution loop, tool calls, sandboxing, and context management so runs are reproducible. TrueForge is a TypeScript, MIT-licensed harness that delivers this in one process with SQLite locally or Postgres and Redis when hosted, claiming the same accuracy as Claude Managed Agents and deepagents at lower cost. What Is an Agent Harness and Why Does It Matter? Building an agent is easy; running one well is hard. The gap between a prompt that calls a model and a system that reliably completes multi-step tasks is exactly what an agent harness fills. A harness is the software layer that owns the agent execution loop: it decides when to call the model, how to route tool invocations, how to manage the growing context window, and how to keep the whole run safe and auditable. ...

ZhiShi is an open-source agent harness purpose-built for security research — a human-in-the-loop workbench for binary exploitation, penetration testing, whitebox code audit, and AI security — that layers a domain-specific research loop on top of the MIT-licensed @earendil-works/pi-agent-core engine. It uses a Tauri (Rust) desktop shell wrapping a TypeScript/Node core, and publishes unusual evidence-first proof of capability: 100% Pwnable.kr CTF completion, 98% Hacker101, and a fully reproduced exploit chain for CVE-2026-34621 (Adobe Acrobat Reader, CVSS 8.6). ...

OpenHarness is an open-source, CLI-first agent runtime that lets you run autonomous AI agents against any LLM — Claude, GPT-5, Gemini, Ollama, or any OpenAI-compatible endpoint — without rewriting your harness each time you switch providers. As of April 2026, the HKUDS/OpenHarness project has 9,100 GitHub stars and ships 43+ built-in tools out of the box. What Is OpenHarness? (The Name Collision Problem Explained) OpenHarness refers to at least three distinct open-source projects that share the same name but solve the same fundamental problem: building a reusable execution layer that wraps an LLM and gives it tools, memory, permissions, and a structured agentic loop. The most prominent is HKUDS/OpenHarness (Hong Kong University of Data Science), a CLI-first runtime with 9,100 GitHub stars as of April 2026 and 43 built-in tools. A second project, AgentBoardTT/openharness, focuses on multi-provider SDK integration with explicit support for Claude, GPT, Gemini, and Ollama under a unified auth model. A third lives at OpenHarness.ai and emphasizes harness interoperability. Despite the naming confusion, all three projects share the same philosophical root: Agent = Model + Harness. The model provides intelligence; the harness provides everything else — tools, memory, lifecycle hooks, permissions, and observability. In a market projected to grow from $8.29 billion in 2025 to $12.06 billion in 2026 at a CAGR of 45.5%, building vendor-agnostic harnesses is becoming the defining engineering challenge of the AI era. Understanding which “OpenHarness” you’re working with is the first step. ...