210 个 · Agent 智能体 · 框架
由 LLM 编排的自主 EDA 与数据预处理框架 — 自动生成面向特定数据集的研究问题，编写并自我修正分析代码，自动选择可视化。基于 Claude/GPT 构建。LLM-orchestrated framework for autonomous EDA and data pre-processing — generates dataset-specific research questions, writes and self-corrects analysis code, and auto-selects visualizations. Built with Claude/GPT.
受治理的、与 provider 无关的运行时，面向证据驱动的自主软件工程、研究和数据分析工作流。Governed, provider-neutral runtime for evidence-driven autonomous software engineering, research, and data-analysis workflows.
面向研究的 Skill 定义精选集合，覆盖文献综述、证据综合与研究空白分析；汇总多个社区驱动的 Skill，便于在 Codex 或 Copilot 工作流中对比、审阅与按需安装A curated collection of research-focused skill definitions for literature review, evidence synthesis, and research-gap analysis. It brings together multiple community-driven skills so they can be compared, reviewed, and selectively installed for use in Codex or Copilot workflows.
由 AI 驱动的交易研究与市场情报平台，使用智能 AI agent、SQL、RAG、市场数据以及外部工具来分析研究问题并生成结构化的市场洞察。An AI-powered trading research and market intelligence platform that uses an intelligent AI agent, SQL, RAG, market data, and external tools to analyze research questions and generate structured market insights.
企业级 Java AI Agent 任务编排、安全审批、RAG 与全链路审计平台
本地、开源的推理框架，支持 Claude 和 Codex，包含 48 个编写好的系统，仅在需要更深层推理时选用。Local, open-source reasoning framework for Claude and Codex, with 48 authored systems selected only when deeper reasoning helps.
Autonomous AI Algorithm Discovery Laboratory：一个有边界、可复现的研究平台，在固定算力预算下提出、实现、测试、拒绝、改进并报告 AI 系统的候选改进方案。Autonomous AI Algorithm Discovery Laboratory: a bounded, reproducible research platform that proposes, implements, tests, rejects, refines, and reports candidate improvements to AI systems under a fixed compute budget.
ResearchOS 是一款 MVP 级自主研究平台：输入研究问题，AI Agent 自动发现来源、验证证据、提取关键信息、交叉核对结论，并产出结构化的最终报告 — 全程实时可视化ResearchOS is an MVP autonomous research platform. Enter a research question; AI agents discover sources, verify evidence, extract key information, cross-check findings, and produce a structured final report — all visualized in real-time.
可验证混合 AI 执行的合约层 —— 研究预标准 0.36.0。A contract layer for verifiable hybrid AI execution — research pre-standard 0.36.0
面向真实工程师的图工程（Graph-Engineering）：图定义工作流，工作流构建图。基于 mattpocock/skills。Graph-Engineering for Real Engineers: Graphs define workflows; workflows build graphs. Based on mattpocock/skills.
AI 时代的工具联盟：可追溯的知识（Nexus·Archon）、可问责的审查与治理（Arbiter·Observer）、可理解的变更质量（Adept·Probe）。AI 构建，你来理解。An alliance of tools for the AI era: grounded knowledge (Nexus·Archon), accountable review & governance (Arbiter·Observer), and comprehension & mutation quality (Adept·Probe). AI builds it — you understand it.
2026 自主 AI Agent 平台 🚀——基于 MCP 与 DAG 的多 Agent SaaSAutonomous AI Agent Platform 2026 🚀 - Multi-Agent SaaS with MCP & DAG
一个工作流框架，适配所有编码 agent。通过与工具无关的核心，将你的 AI 编码规范安装到 Claude Code、Cursor、GitHub Copilot、Gemini CLI、Codex 和 Windsurf。Apache 2.0。One workflow framework, every coding agent. Install your AI-coding discipline into Claude Code, Cursor, GitHub Copilot, Gemini CLI, Codex, and Windsurf from one tool-agnostic core. Apache 2.0.
模块化的学术写作 skills 套件，面向 AI 辅助研究，覆盖摘要、引言、相关工作、方法、实验与结论，具备证据感知推理、引用核查、术语一致性以及 clean/audit 模式。A modular suite of academic writing skills for AI-assisted research, covering Abstract, Introduction, Related Work, Method, Experiments, and Conclusion with evidence-aware reasoning, citation checks, terminology consistency, and clean/audit modes.
证据合成自动化框架——PRISMA 流水线：检索、去重、AI 筛选、Meta 分析（D-L 随机效应）、PRISMA 流程图与森林图。Evidence Synthesis Automation Framework — PRISMA pipeline: search, dedup, AI screening, meta-analysis (D-L random effects), PRISMA/forest plots.
Mielepiù 工程案例研究——一款 B2B 销售平台，其 AI Agent 基于真实数据作答并给出引用来源。Engineering case study of Mielepiù, a B2B sales platform whose AI agent answers on real data and cites its sources.
一个端到端的研究智能系统：摄取学术论文并进行语义理解，映射研究领域的关系与演进，探测尚未探索的研究空白，并使用多 Agent AI 流水线生成文献综述与回答研究问题——全部以纯 Python 构建。An end-to-end research intelligence system that ingests academic papers, understands them semantically, maps how research areas relate and evolve, detects unexplored research gaps, and uses a multi-agent AI pipeline to generate literature reviews and answer research questions — all built in pure Python.
面向 Text-to-Motion、Physics RL 与 Pose Estimation 文献综述的自动化多 Agent 研究框架，抓取 ArXiv 论文并去重，通过 LLM 子 Agent 抽取结构化指标，自动下载 PDF 以供 RAG/NotebookLM 摄取。Automated multi-agent research framework for academic literature reviews on Text-to-Motion, Physics RL, and Pose Estimation. Fetches ArXiv papers, deduplicates, extracts structured metrics via LLM sub-agents, and auto-downloads PDFs for RAG/NotebookLM ingestion.
支持 PRISMA、Kitchenham 指南等标准系统综述框架的循证综合方法Evidence synthesis that will support standard systematic review frameworks like PRISMA, Kitchenham guidelines, etc.
基于 AgentTeams 构建的、有证据支撑的 AI 软件工程团队。An evidence-backed AI software engineering team built on AgentTeams.
基于 git 构建的可治理 multi-agent 框架。A governed multi-agent framework built on git
使用简洁的 markdown 工作空间（基于可检视的文件）管理持久化 agent 记忆，无需复杂框架。Manage durable agent memory using a simple markdown workspace with inspectable files instead of complex frameworks.
企业级 Agent SaaS 平台 — 通过本体引导的 AI 流水线将非结构化数据转化为结构化知识。Enterprise agentic SaaS platform — transform unstructured data into structured knowledge through ontology-guided AI pipelines
从研究问题到论文——全自动化。基于多框架的机器学习研究流水线（CrewAI · AutoGen · LangGraph），支持流式 UI 与人在环控制。From a research question to a paper — automatically. Multi-framework ML research pipeline (CrewAI · AutoGen · LangGraph) with streaming UI and human-in-the-loop control.
Claude Code 抵押贷款插件 2026 - AI 驱动的再融资定价工具Claude Code Mortgage Plugin 2026 - AI-Powered Refinancing Pricing Tool
多 Agent 研究编排框架，覆盖文献检索、论文分析、证据抽取、跨论文对比、知识综合与经过验证的研究空白发现。A multi-agent research orchestration framework for literature retrieval, paper analysis, evidence extraction, cross-paper comparison, knowledge synthesis, and validated research gap discovery.
Agentic AI 平台，用于仓库分析、迁移规划、上下文驱动的代码转换及迁移后验证。Agentic AI platform for repository analysis, migration planning, context-grounded code transformation, and post-migration validation.
系统综述与验证的质量模板——扩展 Consultant 角色原型。Quality template for systematic review and validation - Extends Consultant archetype
后端实现 Agent Framework，用于编排 agent 工作流和消息交换。作为基于 Vercel 的客户端与 Microsoft 系统之间的内容和响应包装器，提供结构化的请求/响应处理、路由和生命周期管理，但不嵌入 AI 逻辑本身。Backend implementing the Agent Framework for orchestrating agent workflows and message exchange. Acts as a content and response wrapper between Vercel-based clients and Microsoft systems, providing structured request/response handling, routing, and lifecycle management without embedding AI logic itself.
面向 agentic AI 创业领域的战略情报平台——FastAPI + SQLite + 原生 JS SPA，内置专业 LLM agent 集群、咨询级行业档案、业务线领域矩阵以及 RAG copilot。Strategic intelligence platform for the agentic-AI startup landscape — FastAPI + SQLite + vanilla-JS SPA with a specialist LLM agent fleet, consulting-grade industry dossiers, a line-of-business domain matrix, and a RAG copilot.