12 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.
ADR 通过可观测性、安全基准测试与威胁检测，为企业级 AI Agent 提供安全保障。已部署于 Uber。ADR secures enterprise AI agents through observability, security benchmarking, and threat detection. Deployed at Uber.
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.
🦀️ CRAB: 面向多模态语言模型 Agent 的跨环境 Agent 基准。https://crab.camel-ai.org/🦀️ CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents. https://crab.camel-ai.org/
面向 coding agent 的开源机器人仿真器：HTTP/JSON + MCP 控制、Newton 物理、wgpu 渲染、ROS 2，以及可复现的 benchmark。Open-source robotics simulator for coding agents: HTTP/JSON + MCP control, Newton physics, wgpu rendering, ROS 2, and reproducible benchmarks.
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.
查找你的 AI coding Agent 泄露到磁盘的敏感信息。免费、MIT 许可、零依赖、零网络调用。在我们公开发布的基准测试中击败 TruffleHog 和 GitGuardian 的引擎。Find secrets your AI coding agent leaked to disk. Free, MIT, zero deps, zero network calls. Beat TruffleHog and GitGuardian's engine on our own published benchmark.
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