8 个 · Agent 智能体 · 评测集
Local-first 代码智能图谱，面向 MCP 与 CLI。为代码库构建持久化映射，使 AI 编程工具只读取关键内容，在代码评审与大仓库工作流中实现可基准测试的上下文缩减。Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.
基于真实场景基准测试的、面向 AI 编程 Agent 的 #1 持久化记忆方案#1 Persistent memory for AI coding agents based on real-world benchmarks
AssetOpsBench - Industry 4.0：面向工业4.0资产运维的领域AI Agent构建、编排与评估统一基准与框架，包含460+场景、5类专业Agent（IoT、FMSR、TSFM、工单等），以及基于MCP的多Agent编排蓝图（MetaAgent、AgentHive）AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scenarios, 5 specialist agents (IoT, FMSR, TSFM, Work Order,...), and multi-agent orchestration blueprints (MetaAgent, AgentHive) over MCP.
基于 Google PaperOrchestra 论文实现的全自动 AI 研究论文写作器，通过技能-基准测试 + 自动评分器，配合任意编码 Agent（Claude Code、Cursor、Antigravity、Cline、Aider）。无需 API Key，无需 LLM SDK。An automated AI research-paper writer based off Google's PaperOrchestra paper's implementation through a skills - benchmark + autoraters using any coding agent (Claude Code, Cursor, Antigravity, Cline, Aider). No API keys, no LLM SDKs.
面向 Godot 的开源游戏编程 Agent 环境与基准An open-source game coding agent environment and benchmark for Godot.
AI agent memory 与基础设施全景——912 个系统 × 68 列的对比目录，覆盖记忆层、agent 框架、运行时、vector store、知识图谱、MCP server、benchmark。支持按类型化边、谱系、引用进行检索。AI agent memory & infrastructure landscape — comparative catalog of 912 systems × 68 columns covering memory layers, agent frameworks, runtimes, vector stores, knowledge graphs, MCP servers, benchmarks. Searchable with typed edges, lineages, citations.
面向文档溯源 QA Agent 的生产级 Harness：检索、起草、自评、重写、升级循环，配套 LLM-as-judge 评测套件、分级权限工具、对破坏性操作的人工审批，以及一键容器化部署。A production-grade harness for a document-grounded QA agent: a retrieve, draft, self-score, re-draft, escalate loop with an LLM-as-judge eval suite, permission-tiered tools, human-in-the-loop approval for destructive actions, and a one-command container deploy.
面向 AI coding agents 的 token 预算 context pack，基于编译器解析的 Kotlin 结构（Analysis API/PSI）构建，并附带衡量其是否优于 chunk RAG 的 benchmark。Token-budgeted context packs for AI coding agents, built from compiler-resolved Kotlin structure (Analysis API/PSI) — with the benchmark that measures whether it beats chunk RAG