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This page provides an overview of how the Avala platform is organized and how its components interact.

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System Overview

┌──────────────────────────────────────────────────────────────────────┐
│                        Mission Control                               │
│                    (Web UI — avala.ai)                               │
│         Annotation Editor · Project Management · Quality Control     │
└──────────────────────────┬───────────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────────┐
│                          REST API                                    │
│               server.avala.ai/api/v1                                 │
│     Datasets · Projects · Tasks · Exports · Organizations           │
├──────────────────────────┬───────────────────────────────────────────┤
│   Direct HTTP / cURL     │  Python & TypeScript SDKs (coming soon)  │
└──────────────────────────┴───────────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────────┐
│                      Backend Services                                │
│   Task Processing · Export Generation · Video Conversion             │
│   MCAP Parsing · Inference Pipeline                                  │
└──────────────────────────┬───────────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────────┐
│                          Storage                                     │
│       Dataset Files · Annotations · Exports · Model Artifacts        │
└──────────────────────────────────────────────────────────────────────┘

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Components

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Mission Control

Mission Control is Avala’s web application at avala.ai. It provides:
  • Annotation Editor — Purpose-built tools for labeling images, video, point clouds, and multi-sensor data with support for bounding boxes, polygons, cuboids, segmentation, polylines, and keypoints.
  • Project Management — Create and configure annotation projects, define label taxonomies, assign work to team members, and monitor progress.
  • Quality Control — Review submitted annotations, flag issues, track acceptance rates, and run consensus workflows.
  • Dataset Management — Upload data, organize items into sequences, browse and search datasets, and manage access permissions.

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REST API

The REST API at server.avala.ai/api/v1 provides programmatic access to every platform capability. All requests authenticate with an API key passed in the X-Avala-Api-Key header. Core resources:
ResourceDescription
DatasetsCreate, list, and manage datasets and their items
ProjectsConfigure annotation workflows, label taxonomies, and task settings
TasksAssign, track, and manage individual annotation work units
ExportsGenerate and download annotated data in various formats
OrganizationsManage teams, members, roles, and permissions
The API follows REST conventions with JSON request and response bodies, cursor-based pagination, and standard HTTP status codes.

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SDKs

You can call the REST API directly from any language using standard HTTP clients (see REST API). Official SDKs are planned to provide typed, idiomatic wrappers:
  • Python SDK — Sync and async clients, Pydantic models, automatic pagination, typed exceptions. (Coming soon)
  • TypeScript SDK — Fully typed client with Promise-based API for Node.js and edge runtimes. (Coming soon)
See the SDKs overview for the current status and available options.

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Backend Services

Backend services handle asynchronous processing that powers the platform:
  • Task Processing — Generates and distributes annotation tasks based on project configuration.
  • Export Generation — Produces downloadable exports in JSON, COCO, KITTI, and other formats.
  • Video Conversion — Splits uploaded video files into frame sequences for annotation.
  • MCAP Parsing — Extracts and synchronizes sensor streams from MCAP recordings.
  • Inference Pipeline — Runs model inference for AI-assisted annotation and pre-labeling.

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Storage

Storage manages all persistent data:
  • Dataset files — Raw images, video frames, point clouds, and MCAP recordings.
  • Annotations — Labeled data produced by annotators (bounding boxes, polygons, cuboids, masks).
  • Exports — Generated export files available for download.
  • Model artifacts — Weights and configurations for inference models used in AI-assisted labeling.

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MCP Server

The MCP (Model Context Protocol) server exposes Avala tools to AI assistants like Claude, Cursor, and VS Code Copilot. It enables natural-language interaction with datasets, projects, and exports without leaving your development environment.
Coming Soon — The standalone MCP server package (@avala/mcp-server) is under active development. An internal MCP endpoint already exists at POST /api/v1/mcp/ for staff use. See the MCP page for details.

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How Components Interact

  1. Users interact with Avala through Mission Control (web UI) or programmatically through the REST API.
  2. All client interfaces communicate with the same REST API, so actions taken in Mission Control are immediately visible via the API and vice versa.
  3. The REST API validates requests, manages resources, and dispatches work to backend services for asynchronous processing.
  4. Backend services process tasks (export generation, video conversion, MCAP parsing) and write results back to storage.

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Next Steps

API Reference

Base URL, authentication, endpoints, and response format.

Use Avala with AI

Connect Avala to Claude, Cursor, and VS Code with MCP.