3GitHub Trending (AI/LLM filtered)·
little-coder: agent harness optimized for smaller LLMs
A TypeScript GitHub project called little-coder provides an agent harness specifically optimized for smaller language models. The repository has accumulated 1,877 stars with 49 added today, suggesting growing community interest. The project addresses the practical challenge of running agentic workflows on smaller, more accessible models rather than frontier-scale ones.
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Introducing Agents.js: Give tools to your LLMs using JavaScript
Hugging Face released Agents.js, a JavaScript library that enables developers to equip large language models with tools and build agent workflows in a JS/TS environment. The library brings tool-use and agent orchestration capabilities—previously more common in Python ecosystems—to the JavaScript developer community. It integrates with Hugging Face's model hub and inference APIs.
wshobson/agents: Multi-harness agentic plugin marketplace for major AI coding tools
A GitHub repository called 'agents' by wshobson provides a multi-harness agentic plugin marketplace targeting Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLI. The project has accumulated 37,134 stars with modest daily momentum (+43 today). It represents a cross-platform approach to agent tooling that spans multiple competing AI coding environments.
Code as Agent Harness: A Survey of Code as Operational Substrate for Agentic AI Systems
This survey paper introduces the concept of 'code as agent harness,' framing code not merely as output but as the operational infrastructure for LLM-based agents—covering reasoning, action, environment modeling, and execution-based verification. The authors organize the analysis across three layers: harness interface, harness mechanisms (planning, memory, tool use, feedback control), and scaling to multi-agent systems. Applications span coding assistants, GUI/OS automation, embodied agents, scientific discovery, and enterprise workflows. Open challenges include evaluation beyond task success, verification under incomplete feedback, and human oversight for safety-critical actions.
Awesome Harness Engineering: Curated List for AI Agent Infrastructure
A GitHub repository aggregating resources on AI agent harness engineering, covering tools, patterns, evaluations, memory systems, MCP (Model Context Protocol), permissions, observability, and orchestration. The list has accumulated 1,318 stars with 39 added today, indicating moderate community traction. It serves as a reference index rather than original research or tooling.
shareAI-lab/learn-claude-code: Minimal Claude Code-style Agent Harness in Python
A GitHub repository implementing a minimal 'nano' version of a Claude Code-style agent harness built from scratch in Python, using Bash as the primary tool interface. The project has accumulated 62,802 stars with 262 added today, indicating significant community interest. It serves as an educational resource for understanding how agentic coding assistants like Claude Code are structured at a low level.
Simon Willison releases llm-coding-agent 0.1a0
Simon Willison published an early alpha release (0.1a0) of llm-coding-agent, a new coding agent tool. The release is part of the broader LLM CLI/plugin ecosystem Willison has been building. As an alpha, it signals early-stage tooling for agentic coding workflows built on top of the LLM framework.
Self-evolving agent harness framework uses quality-diversity archive to generalize harness improvements across model families
A new arXiv preprint introduces a framework for automatically improving LLM agent harnesses (prompts, injected knowledge, runtime control, configuration) without modifying model weights. The system separates change proposal (done by an LLM diagnosing failures) from credit assignment (done by deterministic statistical testing), populating a gated quality-diversity archive keyed on failure pathology rather than task identity to prevent overfitting. Evaluated across seven domains with a frozen open-weight model, the framework achieves +9 to +15.5 percentage point gains on sealed test sets, retaining 86–147% of training gains, with the key finding that pathology-to-patch mappings transfer across model families.
oh-my-openagent: TypeScript Agent Harness (formerly oh-my-opencode)
oh-my-openagent (omo) is a TypeScript-based agent harness project on GitHub, previously known as oh-my-opencode. The repository has accumulated 58,729 stars with 180 new stars today, indicating significant community traction. The rename from 'opencode' to 'openagent' suggests a broadening scope beyond code-focused tasks toward general agent capabilities.