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an open-source alternative to the dots, bots and muses of the world, run an office of claude code/codex like agents on your laptop, sandboxes or anywhere, uses your existing subscriptions
Built on Agent OS, we will be launching out-of-box QwenPaw applications — such as QwenPaw Creator and QwenPaw Insight — stay tuned. 0 Release Notes →
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1
0 edition of the unified interface for agent harnesses. Run Codex, Claude Code, Hermes, PI, DSH, and more through one API, with sessions, streaming, files, cancellation, and failure handling. Implements the Unified Harness Protocol (UHP), an open standard.
Deep Agents is an open source agent harness — an opinionated agent that runs out of the box. Extend, override, or replace any piece.
Pydantic AI is the Python AI SDK: a typed, extensible agent loop with every model a string swap away. The same agent runs everywhere you need it: behind a web frontend, in the terminal, on a voice call, on a durable background queue, or as a plain object you call run() on.
The open-source agent harness - the runtime layer that turns an LLM into a working agent.
Prefer to manage the project files manually? gz from the release, and copy runtime/ / into your project.
Harness engineering is the discipline of designing the scaffolding — context delivery, tool interfaces, planning artifacts, verification loops, memory systems, and sandboxes — that surrounds an AI agent and determines whether it succeeds or fails on real tasks.
> Harness is a team-architecture factory for Claude Code.
Harness Engineering 学习指南 — 从概念理解到独立实践的深度学习档案
简体中文 · Configuration · Troubleshooting · Security · 🔍 ModSearch (the best free web search plugin for DSH)
Symphony turns project work into isolated, autonomous implementation runs, allowing teams to manage work instead of supervising coding agents.
Work with DeepCode in a visual workspace for Sessions, goals, tool activity, code changes, and verification.
This repo is not maintained and is not accepting contributions.
> A curated list of articles, playbooks, benchmarks, specifications, and open-source projects for harness engineering: the practice of shaping the environment around AI agents so they can work reliably.
Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 14 runtimes: Claude Code, Codex CLI, Gemini CLI, OpenCode, Copilot, Kiro and more.
The agent harness for production workloads — state management, failure recovery, observability, and human oversight so your agents actually run.
js 22 或更高版本：先运行 npm ci，再运行 npm run check:diagrams。
A hands-on guide to Hermes Agent, the open-source AI Agent framework by Nous Research — the first agent that ships with its "reins" built in, and the reins grow themselves.
JIT-Agent is a compact meta-agent that writes your agent harness on the fly.
GoClaw - GoClaw is OpenClaw rebuilt in Go — with multi-tenant isolation, 5-layer security, and native concurrency. Deploy AI agent teams at scale without compromising on safety.
J-Space Cognition Suite is a model-agnostic inference-time control system for deep reasoning, long-horizon work, tool use, verification, and recovery.
Hands-on, framework-free Colab notebooks for the AI Engineer / Forward Deployed Engineer (FDE) skill set — model APIs, structured output, tool calling, RAG, evals-as-the-spine, agents (loop from scratch, tool design, guardrails, MCP, Skills), fine-tuning vs LoRA…
A curated collection of research papers, benchmarks, and open-source projects on Graph Engineering in the era of LLM Agents.
A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score.
The System One harness for System One models. ai.
Most coding agents grep and glob raw source into LLM's context window and hope the model is smart. Benzi compiles your codebase into a queryable map of calls, data flow, control flow and class hierarchy first; then it reads, explains, runs, edits and verifies your code using it.