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Echobox — One box → all the boxes. Multimodal annotation agent with S…

About Echobox

Echobox is an MCP server in the Agents category: one box → all the boxes. Multimodal annotation agent with SAM2-backed exemplar detection.

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From source

git clone https://github.com/AntColony10086/echobox

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README

What it does · 核心能力

EN. Echobox is a multimodal annotation agent with two workspaces in one app: the Workbench for full agent-driven annotation, and the Viewer for a zero-dependency offline preview of YOLO / point annotations. In the Workbench, you draw one bounding box and an LLM-supervised exemplar detector (GECO2, backed by Meta's SAM2) returns every similar object in the image. You adjust, accept, save. A LangGraph agent handles the boring parts — folder scanning, train/val/test split, label suggestions, and dataset export to COCO / YOLO / Pascal VOC / Label Studio JSON. The same tools are exposed as MCP, so other agents (Claude Code, Cursor, …) can drive annotation programmatically.

中文. Echobox 是一个多模态智能标注 Agent，一个 app 里两套工作区： 工作台 走 Agent 全流程标注，预览是零依赖的离线 YOLO / 点标注预览器。工作台里你只需画一个 bbox，LLM 监督下的 exemplar 检测器（GECO2，底层用 Meta 的 SAM2）就把图里所有相似目标都框出来。你确认、调整、保存。LangGraph agent 负责扫描文件夹、切分 train/val/test、推荐标签、导出 COCO / YOLO / Pascal VOC / Label Studio JSON。同一套工具暴露为 MCP，其它 agent （Claude Code、Cursor 等）也可以编程方式调用。

Architecture · 架构

4 processes, all run locally — no Docker required. · 4 个进程纯本地运行，不需要 Docker。

Browser ──▶ frontend (Vite, port 5173)
                │
                ▼
          app (FastAPI, port 8000) ──▶ ml_backend (FastAPI + GPU, port 9090)
                │                              │
                │                              └─▶ GECO2 / SAM2 inference
                │
                ├─▶ OpenAI-compatible LLM (DashScope / MiniMax / OpenAI / …)
                └─▶ SQLite + filesystem workspace

mcp_server (stdio) ──▶ app HTTP — for Claude Code / Cursor consumers

Quick start · 快速开始

# 1. Clone (with GECO2 submodule)
git clone --recurse-submodules https://github.com/AntColony10086/echobox
cd echobox

# 2. Configure your LLM (any OpenAI-compatible)
cp .env.example .env
# Edit .env — set ECHOBOX_APP_LLM_API_KEY, _BASE_URL, _MODEL

# 3. Install (uv handles Python; npm handles JS)
make setup

# 4. Get GECO2 weights
mkdir -p .data/weights
curl -L https://github.com/jerpelhan/GECO2/releases/download/v1.0/CNTQG_multitrain_ca44.pth \
  -o .data/weights/CNTQG_multitrain_ca44.pth

# 5. Initialize the database
make db-upgrade

# 6. Start everything (4 processes via honcho) and open the browser
make dev
# open http://localhost:5173

EN. Need help on step 4? See GECO2 releases for the latest weight URL and SHA256.

中文. 第 4 步遇到问题，去 GECO2 releases 查最新权重链接和 SHA256。

Screenshots · 界面预览

MCP integration · MCP 集成

EN. Add this to your Claude Code / Cursor MCP config to call echobox tools from another agent:

{
  "mcpServers": {
    "echobox": {
      "command": "uv",
      "args": ["run", "--package", "echobox-mcp", "echobox-mcp"],
      "cwd": "/path/to/echobox",
      "env": {
        "ECHOBOX_MCP_APP_URL": "http://localhost:8000"
      }
    }
  }
}

Available tools: start_annotation_project, search_annotations, export_dataset, plus all setup tools.

中文. 添加上面 JSON 到你的 Claude Code / Cursor MCP 配置即可让其它 agent 调用 echobox 工具。工具清单：start_annotation_project、search_annotations、export_dataset，以及全部 setup 工具。

How GECO2 works · GECO2 原理

EN. GECO2 (Generalist Exemplar-based COunting and detection) takes one user-drawn exemplar and uses SAM2's image embedding to find every similar object. We use it as a detector (not a counter): the predicted heatmap is binarised and converted to bboxes. Paper · jerpelhan/GECO2

中文. GECO2 用一个用户画的 exemplar，借助 SAM2 的 image embedding，找出所有相似目标。我们把它当作检测器用（不是计数器）：把预测的热力图二值化转成 bbox。论文 · jerpelhan/GECO2

Project layout · 工程结构

echobox/
├─ packages/
│  ├─ app/          (FastAPI + LangGraph agent + REST API)
│  ├─ ml_backend/   (FastAPI + GECO2/SAM2 inference)
│  └─ mcp_server/   (MCP stdio server, calls app HTTP)
├─ frontend/        (React + Vite + react-konva)
├─ docs/            (architecture, API, dev, extending)
├─ assets/          (logo, screenshots, social card)
├─ Procfile         (honcho config: 4 processes)
├─ Makefile         (dev shortcuts)
└─ .env.example

Acknowledgements · 致谢

Echobox stands on the shoulders of these projects — credit and copyright belong to their authors. · Echobox 完全基于以下项目，版权与归属归原作者所有：

GECO2 — jerpelhan/GECO2 — exemplar-based detector
SAM2 — facebookresearch/sam2 — Meta AI
Deformable-DETR — fundamentalvision/Deformable-DETR
LangGraph — langchain-ai/langgraph
FastAPI — tiangolo/fastapi

See NOTICE for the full attribution list.

License · 许可

Apache-2.0 — see LICENSE and NOTICE.

Contributing · 参与开发

PRs welcome — see CONTRIBUTING.md. · PR 欢迎，详见 CONTRIBUTING.md。

Citation

If echobox helps your research, a citation is appreciated:

@software{echobox2026,
  title  = {echobox: Multimodal annotation agent with exemplar-based detection},
  author = {The echobox contributors},
  year   = {2026},
  url    = {https://github.com/AntColony10086/echobox}
}

视频简介 · Video Pitch

echobox 是一个开源（MIT）的 AI 图像标注工作台，把"画一个框 → AI 自动框出图里所有相似目标"做成完整工作流。

echobox is an open-source (MIT) AI image-labeling workspace that turns "draw one box → auto-detect every similar object" into an end-to-end flow.

50 秒视频 · assets/promo/echobox-promo.mp4 带你看完核心能力：

🎯 一框出全图 — 你画一个 bbox，GECO2 + Meta SAM2 把图里所有相似目标自动框出来
🖼️ 标注工作台 — 画布 / 图像列表 / 类别面板一站式
💬 Agent 对话 — 自然语言驱动整个流程（扫描文件夹、切分 train/val/test、推荐标签）
📤 多格式导出 — YOLO / COCO / Pascal VOC / Label Studio JSON
👀 离线预览 — 拖入图片 + YOLO 标注立即可视化，零依赖
🔌 MCP 集成 — Claude Code / Cursor 等 Agent 可以直接调用同一套工具

⚠️ 项目目前刚刚起步 · This is an early-stage project

这是 v0.1.0 的早期版本，意味着：

一部分功能还在打磨（分割任务、视频帧标注、多人协作等）
文档、示例、测试都还不够丰富
一些边界场景可能会有 bug
部分 UI 细节、键盘快捷键还没补全

我相信"一个人能做出原型，但好工具一定是大家一起做出来的"。 One person can ship a prototype; great tools take a community.

如果你也在做图像标注相关的工作，或者对 AI Agent + 数据工程感兴趣，非常欢迎你参与进来：

🐛 遇到 bug — 直接提 Issue，附上复现步骤即可
💡 觉得哪里可以更好用 — 提 Feature Request 或者直接发 PR
📝 用过其它标注平台 — 把对比体验告诉我，让我们少走弯路
🌱 新人想练手 — 文档完善、翻译、写测试、出教程都是非常好的切入点
⭐ 暂时没时间贡献代码 — 给个 Star 也是很大的鼓励，能让更多人看到这个项目

让我们一起把它做成中文社区里最好用的开源标注工具。 Let's make this the friendliest open-source image-labeling tool out there — together.

一句话版本 · TL;DR

刚开源了一个 AI 图像标注工具：画一个框 → AI 自动框出图里所有相似目标。支持 YOLO / COCO / VOC 多格式导出，可以用自然语言和 Agent 对话来管理整个流程。

项目刚起步，欢迎大家一起来完善——提 Issue、发 PR、给个 Star 都是巨大的鼓励 🙏

#开源 #图像标注 #AI #ComputerVision #YOLO #SAM2 #LangGraph #FastAPI #React #LLM #Agent

Read full README

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About Echobox

Install

Ask AI

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README

What it does · 核心能力

Architecture · 架构

Quick start · 快速开始

Screenshots · 界面预览

MCP integration · MCP 集成

How GECO2 works · GECO2 原理

Project layout · 工程结构

Acknowledgements · 致谢

License · 许可

Contributing · 参与开发

Citation

视频简介 · Video Pitch

⚠️ 项目目前刚刚起步 · This is an early-stage project

一句话版本 · TL;DR

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