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
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Updated
Apr 3, 2026 - Python
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
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
Outcome driven agent development framework and runtime harness
The best agent harness.
Nexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles — unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes.
A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.
A Markdown-first memory system, a standalone library for any AI agent. Inspired by OpenClaw.
The Agent Harness for AI-Human Collaboration, inspired by the AI-DLC (AI-Driven Development Lifecycle)
Evolvable, distributed agent framework & harness for data science.
The production-ready agent harness framework for Python
autonomous systems engineering cli agent for any cloud environment: AWS, GCP, Cloudflare, etc
A ReAct-Based Highly Robust Autonomous Agent Framework.
A lightweight harness designed to be simple to use, efficient, universially compatible with any coding agent to evaluate them over a broad set of coding tasks and languages.
A harness for the Diagnostic Rev. 586220 Test for the Commodore C64
A small test framework to write your own test harness that looks and behaves like the built-in test harness used by `rustc --test`
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