449 个
BISHENG 是面向下一代企业 AI 应用的开放 LLM devops 平台。强大且全面的功能包括：GenAI workflow、RAG、Agent、统一模型管理、评估、SFT、数据集管理、企业级系统管理、可观测性等。BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include: GenAI workflow, RAG, Agent, Unified model management, Evaluation, SFT, Dataset Management, Enterprise-level System Management, Observability and more.
Pocket Flow：一个仅 100 行代码的 LLM 框架。让 Agent 构建 Agent！Pocket Flow: 100-line LLM framework. Let Agents build Agents!
Deeplake 是面向 Agent 的 AI 数据运行时，提供无服务器 postgres 与多模态 datalake，支持可扩展的检索与训练Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.
22 种 prompt engineering 技术，附带 Jupyter Notebook 实战教程，覆盖从基础概念到利用 LLM 的高级策略。22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
网页解析的终结，可扩展像素原生搜索的开端。链接：https://pixelrag.ai/The end of web parsing. The beginning of scalable pixel-native search. link: https://pixelrag.ai/
用于构建 agentic apps 的开源框架，支持 JavaScript、Go、Dart 与 Python，由 Google 在生产环境中构建并使用。Open-source framework for building agentic apps in JavaScript, Go, Dart, and Python, built and used in production by Google
三语（繁中 / English / 简中）Agentic AI 学习路线图：从 LLM 基础到多 Agent 系统，收录 240+ 精选资源与动手示例。中文 AI agent 學習地圖。A trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples. 中文 AI agent 學習地圖。
为 Agent 打造的快速精准代码搜索，相比 grep+read 减少约 98% 的 token 用量。Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read
🐢 面向 LLM Agent 的开源评估与测试库🐢 Open-Source Evaluation & Testing library for LLM Agents
用于构建复杂创新 RAG 流水线的低代码 MCP 框架A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines
用于构建 AI agent 的企业级 AI 开发框架。它提供多 provider LLM 的统一管理、具备高精度检索的安全企业知识库、可视化工作流编排与多 agent 协作。兼容主流 Agent Skill 标准，使开发者能够高效构建生产级 [原文未完整]。An enterprise AI development framework for building AI agents. It provides unified management of multi-provider LLMs, secure enterprise knowledge bases with high-precision retrieval, visual workflow orchestration and multi-agent coordination. Compatible with mainstream Agent Skill standards, it enables developers to efficiently build production-gra
⚡️ 下一代个人 AI 助手，由 LLM、RAG 和 Agent 循环驱动，支持 computer-use、browser-use 和 coding agent，演示地址：https://demo.openagentai.org⚡️next-generation personal AI assistant powered by LLM, RAG and agent loops, supporting computer-use, browser-use and coding agent, demo: https://demo.openagentai.org
LLM 实战指南：从基础到使用 LLMOps 最佳实践将 LLM 和 RAG 应用部署到 AWSThe LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices
面向 monorepo 的终极 RAG。借助 AI 与知识图谱的能力，对多语言代码库进行查询、理解与编辑。The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs
面向 AI 编码 agent 的首选本地优先网页工具——基于 MCP 提供搜索、抓取、爬取与研究能力。无需 API key，无需云端，$0/query。现已开放公开测试。The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.
终端中的 Agent，配备本地工具：编写代码、使用终端、浏览网页。在其之上构建你自己的持久化自主 Agent。Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web. Make your own persistent autonomous agent on top!
免费学习如何使用 LLMOps 最佳实践构建端到端生产级 LLM & RAG 系统：源码 + 12 个实操课程。🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴
开源 LLMOps 平台：集成 prompt playground、prompt 管理、LLM 评估和 LLM 可观测性。The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.
针对加速基础设施和微服务架构优化的生成式 AI 参考工作流。Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
⚡FlashRAG：面向高效 RAG 研究的 Python 工具包（WWW2025 Resource）⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)
ReMe：面向 Agent 的记忆管理工具包——Remember Me, Refine Me.ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
一个精简且可定制的高效大模型（LLM、VLM、AIGC）评估与性能基准测试框架。A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
学习使用 LLM、agent、RAG、微调、LLMOps 和 AI 系统技术构建你的 Second Brain AI 助手。Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.
几乎可视为 DSPy 在 TypeScript 上的"官方"框架。The pretty much "official" DSPy framework for Typescript
开源推理服务器与生产级集群，支持 Agent 所需的全部模型Open-source inference server and production cluster for all the models your agent needs.
深度对接你的 Zotero 库的研究 Agent 系统。An open-sourced research agent system deeply rooted in your Zotero library.
生成式AI综合资源，包含详细路线图、项目、用例、面试准备与编码练习Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.
Apache Hamilton 帮助数据科学家和工程师定义可测试、模块化、自文档化的数据流，内置 lineage/tracing 与 metadata，可在任何支持 Python 的环境中运行和扩展。Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
AI 工程面试速查表——问题与答案Your Cheat Sheet for AI Engineering Interview – Questions and Answers.
代码探索场景下削减AI token成本95%以上。基于tree-sitter AST实现精准符号级GitHub代码检索的主流MCP服务器，兼容Claude Code、Cursor及任何MCP客户端，已节省313B+ tokensCut AI token costs 95%+ on code exploration. The leading MCP server for precise, symbol-level GitHub code retrieval via tree-sitter AST. Works with Claude Code, Cursor & any MCP client. 313B+ tokens saved.
国家法令信息MCP v4.4 | 法制处42个API → 9个MCP工具。法令·判例·条例·条约 + 多阶段研究(legal_research) + 精准分析(legal_analysis: 引文验证·判例存废·行为时法·影响图谱) | 42个韩国法律API → 9个MCP工具법제처 국가법령정보를 LLM에서 바로 조회하는 MCP 서버. 법령·판례·조례 검색과 인용 검증 | MCP server for Korean law — search statutes, precedents, and ordinances, and verify citations
MCP服务器，使AI助手能够与Google Gemini CLI交互，借助Gemini的大token窗口进行大文件分析与代码库理解MCP server that enables AI assistants to interact with Google Gemini CLI, leveraging Gemini's massive token window for large file analysis and codebase understanding
NVIDIA AI Blueprint for video search and summarization（VSS）是一个 GPU 加速参考架构，用于构建具备实时验证告警、视觉问答与自动报告能力的视频分析 Agent。VSS Blueprint 采用 NVIDIA Cosmos 等视觉语言模型（VLM）、NVIDIA Nemotron 等 LLM，并结合 RAG 与 NVIDIA NIM。NVIDIA AI Blueprint for video search and summarization (VSS) is a GPU-accelerated reference architecture for building video analytics agents with real-time verified alerts, visual Q&A, and automated reporting. The VSS Blueprint uses vision language models (VLMs) such as NVIDIA Cosmos, LLMs such as NVIDIA Nemotron, RAG, and NVIDIA NIMs.