BerriAI / moyai

# Moyai
Moyai is a shared AI workspace for coding and connected-service tasks. Chat with
an agent, steer its work, answer questions, inspect its computer, and keep the
resulting files and history. Pydantic AI and Harness provide the agent; Temporal
runs durable execution, Modal or AWS Lambda MicroVMs provide its computer, and LiteLLM supplies models.
The application has one Python installation, one lockfile, and one React frontend.
An existing deployment keeps its users, permissions, historical sessions,
credentials and accounting through the shared product services.
*Current frontend with synthetic accounts and session data.*
## Quickstart
Try Moyai on your laptop with Docker, or deploy it on a server so it stays
available when your laptop is off. The local quickstart manages SQLite, encrypted
file storage and a local Temporal development server automatically. You only
need a model gateway, an enabled Responses-capable model, and Modal credentials.
Agents execute in Modal computers; Docker hosts the Moyai application.
For AWS ARM64 computers, follow the native Lambda MicroVM setup.
Modal remains the default for the quickstart below.
### Option 1: Try locally with Docker
**Prerequisites:** Docker Desktop on macOS/Windows, or Docker Engine with Docker
Compose on Linux. The commands below use a macOS/Linux shell or WSL.
Create `moyai.env` in your working directory and replace the placeholders:
 [code] 
Start the image with persistent local data:
 [code] 
The `quickstart` image is built from main for AMD64 and ARM64 after the Docker
startup checks pass. If it is not yet publicly available, use the checkout
fallback below. See image publication
for release status and maintainer setup.
Open and sign in as `admin` with `WORKSPACE_PASSWORD`.
The first start may download Temporal and build the Modal computer image.
Model and computer usage are billed by those providers.
Try: “Create `/workspace/hello.py` that prints Hello from Moyai, run it, and save
it for download.” The chat should show the observed output and a downloadable
file. Follow up in the same conversation to change it.
Use `docker logs -f moyai` to inspect startup and `docker stop moyai` /
`docker start moyai` to stop and start it. Keep the `moyai-data` volume to retain
history, files and encryption keys. This is a local trial; use Option 2 for a
team deployment with managed storage and durable Temporal.
**Checkout fallback:** if you cannot pull the prebuilt image, build it locally
with Git and Docker, keeping `moyai.env` in your current directory:
 [code] 
Then replace `ghcr.io/berriai/moyai:quickstart` with `moyai-local` in the run command
above. From an existing checkout, you can alternatively copy `.env.example` to
`.env`, fill in the same credentials, and run `docker compose up --build`.
### Option 2: Deploy for your team
Use the Docker installation guide for a persistent
server deployment. Bring PostgreSQL, private S3-compatible storage, a reachable
HTTPS origin, and the model/Modal set