Physical AI developer platform

Build on the infrastructure your Physical AI models learn from.

Use typed SDKs, the CLI, REST, or MCP to ingest sensor data, inspect dataset health, govern releases, and carry model feedback into the next training cycle.

Start Building Book a technical walkthrough

First loop

Inspect your data with one authenticated request.

Enter your key through a no-echo prompt and query the public API while the credential stays isolated inside a temporary shell.

Bash or Zsh · curl · an Avala API key

Authenticated public API · Bash or Zsh

(
  printf "Avala API key: " &&
    IFS= read -rs AVALA_API_KEY &&
    printf "\n" &&
    export AVALA_API_KEY &&
    curl \
      --fail \
      --silent \
      --show-error \
      -H "X-Avala-Api-Key: $AVALA_API_KEY" \
      "https://api.avala.ai/api/v1/datasets/?limit=10"
)

Programmable workflow

Program the full Physical AI data loop.

Use stable interfaces for the operational steps between raw sensor data and the next model run.

Ingest sensor data

Bring MCAP, LiDAR, video, ROS bags, or LeRobot data into one dataset model.

CLIPython SDKREST API

Inspect dataset health

Check indexing, sequences, frame counts, and calibration before data enters production.

CLIPython SDKREST API

Run model-assisted labeling

Launch project-scoped auto-label jobs and keep human review inside the same governed workflow.

CLIPython SDKMission Control

Publish training-ready exports

Create a dataset or project export and track it through completion for downstream training.

CLIPython SDKREST API

Hosted MCP

Bring governed dataset context into your AI tools.

Connect one hosted endpoint to inspect the datasets, captures, quality evidence, and operational state your account can access.

Read the MCP guide

Hosted MCP endpoint

https://mcp.avala.ai/mcp

Access follows your Avala permissions. Reviewed staff actions appear only for eligible operators and require explicit confirmation.

Interfaces

Choose the interface that fits your stack.

Python, TypeScript, the public REST API, and hosted MCP share the same Avala data model.

Python SDK

Sync and async clients, cursor pagination, typed models, and explicit errors.

pip install avala

TypeScript SDK

Typed resource clients with zero runtime dependencies and formal support for Node.js 18+.

npm install @avala-ai/sdk

REST API

Use the public api.avala.ai endpoint from any language or authenticated HTTP client.

curl --fail-with-body -H "X-Avala-Api-Key: $AVALA_API_KEY" "https://api.avala.ai/api/v1/datasets/?limit=10"

Hosted MCP

Connect compatible AI tools to permission-scoped dataset and operations context.

https://mcp.avala.ai/mcp

Technical walkthrough

Bring us your hardest data loop.

We will map the interfaces, storage path, quality gates, and release contract around one real workflow.

Book a technical walkthrough Read the API reference