Environment

An environment is everything your agent needs to do its work: GPU compute, models, packages, files, and tools. Seer environments run on Modal, so you get on-demand GPUs without managing infrastructure.

You define what you need declaratively. Seer handles provisioning, model downloads, and caching.

Sandbox

The sandbox is a light abstraction over a modal sandbox. Your agent runs locally and connects to the sandbox to execute code.

config = SandboxConfig(
    gpu="A100",
    models=[ModelConfig(name="google/gemma-2-9b")],
    python_packages=["torch", "transformers"],
)

sandbox = Sandbox(config).start()
# ... agent works ...
sandbox.terminate()

Config options

Field	What it does
`gpu`	GPU type: "A100", "H100", or None for CPU
`gpu_count`	Number of GPUs (default: 1)
`models`	HuggingFace models to download
`python_packages`	pip packages to install
`system_packages`	apt packages to install
`secrets`	Env vars to pass from local .env
`timeout`	Sandbox timeout in seconds (default: 3600)
`local_files`	Files to mount: `[("./local.txt", "/sandbox/path.txt")]`
`local_dirs`	Directories to mount: `[("./data", "/workspace/data")]`
`debug`	Enable VS Code in browser

Models

Models are downloaded to Modal volumes and cached across runs:

models=[
    ModelConfig(name="google/gemma-2-9b"),
    ModelConfig(name="my-org/my-adapter", is_peft=True, base_model="meta-llama/Llama-2-7b"),
]

ModelConfig field	What it does
`name`	HuggingFace model ID
`var_name`	Variable name in model info (default: "model")
`hidden`	Hide model details from agent
`is_peft`	Model is a PEFT/LoRA adapter
`base_model`	Base model ID (required if `is_peft=True`)

Repos

Clone git repos into the sandbox:

repos=[
    RepoConfig(url="https://github.com/org/repo"),
    RepoConfig(url="org/repo", install="pip install -e ."),
]

Working with a running sandbox

Write files:

sandbox.write_file("/workspace/config.json", '{"key": "value"}')
sandbox.ensure_dir("/workspace/outputs")

Run commands:

sandbox.exec("pip install einops")
sandbox.exec_python("print(torch.cuda.is_available())")

Snapshots

Save sandbox state and restore it later:

snapshot = sandbox.snapshot("after setup")

# Later...
new_sandbox = Sandbox.from_snapshot(snapshot, config)

Useful for checkpointing long experiments or sharing reproducible starting points.

Sandbox vs ScopedSandbox

Sandbox — agent has full notebook access, can run arbitrary code

ScopedSandbox — agent can only call functions you expose via an interface file

# Full access
sandbox = Sandbox(config).start()
session = create_notebook_session(sandbox, workspace)

# Scoped access
scoped = ScopedSandbox(config).start()
model_tools = scoped.serve("interface.py", expose_as="library")
session = create_local_session(workspace, workspace_dir)

See Scoped Sandbox for the scoped pattern.

Properties

Property	What it returns
`sandbox.jupyter_url`	Jupyter URL (notebook mode)
`sandbox.code_server_url`	VS Code URL (debug mode)
`sandbox.model_handles`	Prepared model handles
`sandbox.sandbox_id`	Modal sandbox ID