{"data":{"slug":"databufflabs-databuff","name":"databuff","tagline":"AI-native OpenTelemetry APM with multi-agent root-cause analysis across traces, metrics, and service topology","github_url":"https://github.com/databufflabs/databuff","owner":"databufflabs","repo":"databuff","owner_avatar_url":"https://avatars.githubusercontent.com/u/184463508?v=4","primary_language":"Java","stars":309,"forks":60,"topics":["ai","ai-native","aiops","apm","application-monitoring","devops","distributed-tracing","java","llm-observability","microservices","monitoring","multi-agent","observability","open-source","opentelemetry","root-cause-analysis","sreagent","tracing"],"archived":false,"github_pushed_at":"2026-07-15T10:12:36+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/databufflabs-databuff","markdown_url":"https://www.graphcanon.com/tools/databufflabs-databuff.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/databufflabs-databuff","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=databufflabs-databuff","description":"AI-native OpenTelemetry APM with multi-agent root-cause analysis across traces, metrics, and service topology","homepage_url":"https://databuff.ai","license":"AGPL-3.0","open_issues":12,"watchers":12,"ai_summary":null,"readme_excerpt":"<div align=\"center\">\n\n<p align=\"center\">\n  <img src=\"ai-apm-frontend/public/img/logo_login.png\" alt=\"\" height=\"56\" align=\"middle\" />\n  &nbsp;&nbsp;\n  <img src=\"ai-apm-frontend/public/img/logo_wordmark.svg\" alt=\"Databuff\" height=\"32\" align=\"middle\" />\n</p>\n\n<h3>AI Native OpenTelemetry APM</h3>\n\n<p align=\"center\">\n  <a href=\"https://demo.databuff.ai\">在线演示</a>\n  &nbsp;|&nbsp;\n  <a href=\"docs/README.md\">文档</a>\n  &nbsp;|&nbsp;\n  <a href=\"README_en.md\">English</a>\n  &nbsp;|&nbsp;\n  <a href=\"#交流群\">交流群</a>\n</p>\n<p align=\"center\">\n  在线演示 Demo，需要加入下方交流群获取账号密码\n</p>\n\n</div>\n\n<br/>\n\n<p align=\"center\">\n  <img src=\"docs/images/feature-pillars.png\" alt=\"OpenTelemetry APM 与 AI Native 能力概览\" width=\"880\" />\n</p>\n\n<br/>\n\n---\nDataBuff 是一款面向 AI 智能体、微服务、云原生场景的 **AI 原生开源 APM 软件**，以 OpenTelemetry 标准接入，提供**全链路监控**、服务拓扑、RED 指标、智能体监控与 AI 工作台。\n\n## 功能特性\n\n- 🤖 **AI 原生，不是外挂聊天框** — LLM 直接查询 Trace、指标、拓扑、告警，回答基于真实数据\n- 🧠 **多智能体协同** — AI 大脑统一编排，智能问数 / 巡检专家各司其职，复杂问题并行协作\n- 🎯 **AI 应用监控**（Roadmap）— LLM 调用链 · Token 分析 · Agent 拓扑 · 技能/工具/模型调用追踪\n- ⚡ **eBPF APM**（Roadmap）— 内核级无侵入采集，零修改代码获取调用链与性能数据\n- 📊 **OpenTelemetry APM 底座** — OTLP 标准接入，覆盖故障排查、链路追踪、服务指标、服务拓扑\n- 🚨 **告警闭环** — 阈值与突变检测、定时评估、告警事件记录\n- 🔧 **Skill + Tool 可扩展** — 内置 Skill 可覆盖，支持自定义数字专家，无需改核心代码\n- 🔌 **MCP 双向开放** — 平台暴露 MCP 供 Cursor / Claude 等调用；也可接入外部 MCP（Prometheus、SkyWalking 等）\n- 🐳 **极简三组件架构** — Ingest + Doris + Web，Docker / K8s 一条命令跑起来\n- 🌐 **自带模型** — OpenAI 兼容 + Anthropic；支持 Kimi、DeepSeek、GLM、Ollama 等\n---\n\n<h2 align=\"center\" id=\"效果展示\">效果展示</h2>\n\n<p align=\"center\"><strong>AI 分析</strong></p>\n\n<table border=\"0\" cellspacing=\"12\" cellpadding=\"0\" align=\"center\">\n<tr>\n<td align=\"center\" width=\"450\">\n  <img src=\"docs/images/screenshots/ai-interaction-1.jpg\" alt=\"AI 智能问数\" width=\"450\" />\n  <br/><sub>智能问数 · 自然语言查指标与 Trace</sub>\n</td>\n<td align=\"center\" width=\"450\">\n  <img src=\"docs/images/screenshots/ai-interaction-2.jpg\" alt=\"AI 多 Agent 协同\" width=\"450\" />\n  <br/><sub>多 Agent 协同 · 汇总证据给出结论</sub>\n</td>\n</tr>\n</table>\n\n<p align=\"center\"><strong>APM 可观测</strong></p>\n\n<table border=\"0\" cellspacing=\"12\" cellpadding=\"0\" align=\"center\">\n<tr>\n<td align=\"center\" width=\"450\">\n  <img src=\"docs/images/screenshots/service-list.jpg\" alt=\"服务列表\" width=\"450\" />\n  <br/><sub>服务列表 · 红绿灯锁定异常</sub>\n</td>\n<td align=\"center\" width=\"450\">\n  <img src=\"docs/images/screenshots/global-topology.jpg\" alt=\"全局拓扑\" width=\"450\" />\n  <br/><sub>全局拓扑 · 自动绘制调用关系</sub>\n</td>\n</tr>\n<tr>\n<td align=\"center\" width=\"450\">\n  <img src=\"docs/images/screenshots/service-detail.jpg\" alt=\"服务详情\" width=\"450\" />\n  <br/><sub>服务详情 · 指标趋势与实例</sub>\n</td>\n<td align=\"center\" width=\"450\">\n  <img src=\"docs/images/screenshots/service-flow.jpg\" alt=\"服务流\" width=\"450\" />\n  <br/><sub>服务流 · 上下游依赖</sub>\n</td>\n</tr>\n</table>\n\n---\n\n<h2 align=\"center\">极简架构</h2>\n\n<p align=\"center\">\n  <img src=\"docs/images/screenshots/simple-architecture.jpg\" alt=\"极简架构\" width=\"920\" />\n</p>\n\n---\n\n<h2 align=\"center\" id=\"安装\">快速安装</h2>\n\n> ⚡ 从执行安装命令到 Demo 应用上报数据、看到链路追踪与拓扑，约 **5 分钟** 即可出效果。\n\n<p align=\"center\">\n  <img src=\"https://img.shields.io/badge/Docker-docker_+_compose-2496ED?style=for-the-badge&logo=docker&logoColor=white\" height=\"28\" />\n</p>\n\n依赖 **docker**、**docker-compose**；安装脚本自动识别 amd64/arm64，下载对应镜像包。\n\n**1. 安装平台**\n\n```bash\ncurl -fsSL https://databuff.ai/databuff/ai-apm-install.sh | bash\n```\n\n**2. 安装 Demo 应用**（可选）\n\n```bash\ncurl -fsSL https://databuff.ai/databuff/ai-apm-demo-install.sh | bash\n```\n\n<details>\n<summary><b>离线安装</b></summary>\n\n无法访问镜像仓库时，按架构下载离线包后在目标机器安装。版本与下载链接见 [官网安装页](https://databuff.ai/#install) **Docker → 离线安装**，或：\n\n`https://openocta.com/pkg/databuff/<version>/offline/databuff-ai-apm-offline-<version>-<arch>.tar.gz`\n\n```bash\ntar -zxvf databuff-ai-apm-offline-<version>-<arch>.tar.gz\ncd databuff-ai-apm-offline-<version>-<arch>\n\n# 安装平台\nsudo ./install.sh\n```\n\n</details>\n\n<details>\n<summary><b>Kubernetes 安装</b></summary>\n\n依赖 **kubectl** 与可用 K8s 集群；脚本通过 K8s manifest 直装平台。\n\n**1. 安装平台**\n\n```bash\ncurl -fsSL https://databuff.ai/databuff/ai-apm-k8s-install.sh | bash\n```\n\n**2. 安装 Demo 应用**（可选）\n\n```bas","github_created_at":"2026-06-18T02:06:27+00:00","created_at":"2026-07-15T10:41:15.643636+00:00","updated_at":"2026-07-15T10:41:18.820382+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"llm-frameworks","name":"LLM 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