feat(computer): computer envs deploy on local Docker

AGENTS.md

@@ -106,9 +106,9 @@ and deserialized through a registry.
 
 | Package | Contents |
 |---|---|
-| `env/` | `Env` base class: a subclass declares `type`, implements `from_dict` and `async deploy(...) -> DeployedEnv`, and may override `reset` and `load_file_artifact_universe`. Built-ins in `env/envs/`: `MCPServerEnv` (`mcp_server`), `MultiEnv` (`multi`, MCP servers plus websites), `ServiceDBEnv` (`service_db`, PostgreSQL), `WebsiteEnv` (`website`), `GatewayEnv` (`gateway_server`), `ComputerDriverEnv` (`computer_driver`, a computer driver's files and what its class declares for each platform it supports, read at put in a process of its own, `agentenv_protocol.computer._declare`, where the driver's own dependencies needn't be installed, with the `port` it serves on and its install by OS, `[install.<os>]`: files, a `python` requirements file for a venv, and the services the base's init runs; its deploy is refused until agent-env creates computers), `ComputerEnv` (`computer`, in `env/envs/computer/`: a base computer and a driver, matched at put by gate 1 before anything is stored, then composed by `composer.py` into an image for each form and arch with this machine's Docker, the other arch through its emulation, tagged `agentenv-computers:<key>` in the image store by a key of the image's Dockerfile and build context so any env with the same inputs reuses it; each pair's status goes into its metadata, `composed`, the arches built are written as its computer, a `computer` artifact over one index, and `agent-env env build` resumes what a put left; builds end `unverified`, and its deploy is refused until a computer can be booted). `env/gateway/gateway.py` is the env-side gateway server, container-only code. While its clock is armed it serves agents `get_time` (`env/gateway/get_time.py`), which answers in UTC and, given an IANA `time_zone`, also in that zone; executors' working `hours` (`env/gateway/triggers.py`) take a fixed `utc_offset` or an IANA `time_zone`. |
+| `env/` | `Env` base class: a subclass declares `type`, implements `from_dict` and `async deploy(...) -> DeployedEnv`, and may override `reset`, `close` and `load_file_artifact_universe`; teardown closes each env a run deployed before terminating its sandboxes by id. Built-ins in `env/envs/`: `MCPServerEnv` (`mcp_server`), `MultiEnv` (`multi`, MCP servers plus websites), `ServiceDBEnv` (`service_db`, PostgreSQL), `WebsiteEnv` (`website`), `GatewayEnv` (`gateway_server`), `ComputerDriverEnv` (`computer_driver`, a computer driver's files and what its class declares for each platform it supports, read at put in a process of its own, `agentenv_protocol.computer._declare`, where the driver's own dependencies needn't be installed, with the `port` it serves on and its install by OS, `[install.<os>]`: files, a `python` requirements file for a venv, and the services the base's init runs; it deploys only as a computer env's driver), `ComputerEnv` (`computer`, in `env/envs/computer/`: a base computer and a driver, matched at put by gate 1 before anything is stored, then composed by `composer.py` into an image for each form and arch with this machine's Docker, the other arch through its emulation, tagged `agentenv-computers:<key>` in the image store by a key of the image's Dockerfile and build context so any env with the same inputs reuses it; each pair's status goes into its metadata, `composed`, the arches built are written as its computer, a `computer` artifact over one index, and `agent-env env build` resumes what a put left; gate 3 then boots each native build on a configured sandbox provider and probes it at `full`, making it `ready` or `failed`, and a build no provider runs natively stays `unverified`. Its deploy, through the `computer` env provider (`providers/env_providers/env_computer_provider.py`), boots its first `ready` build on a sandbox provider that runs its arch natively (`native_platforms()`), with no gateway in front and the driver's card as the env card, then checks its `display`; `deploy_env` `env_params` take a `form`, `computer_settings` (deploy-phase values of the base's settings; a secret one, here or on the env, only as a bare `secret:`/`env:` reference) and `driver_settings` (the driver's `ComputerSettings`, such as its default coordinate space); a `network` that allows nothing runs it on local Docker's internal network behind a relay (`providers/sandbox_providers/local_docker/relay/`); `reset` and `close` have the driver end its recordings first (`computer-recording/v1` `end_all`, each uploading what's left and its manifest under the grant it started with), and `reset` then boots a fresh container in its place (`reset_container`), keeping the computer's address but not an agent's MCP session to the old driver). An env built on the protocol SDK can serve agents a skill (`@agent_skill`, `skill/v1`), which `add_skills` `env_ids` installs under its environment name (behind a gateway, each server's under that server's name). `env/gateway/gateway.py` is the env-side gateway server, container-only code. While its clock is armed it serves agents `get_time` (`env/gateway/get_time.py`), which answers in UTC and, given an IANA `time_zone`, also in that zone; executors' working `hours` (`env/gateway/triggers.py`) take a fixed `utc_offset` or an IANA `time_zone`. |
 | `artifact/` | Immutable versioned artifacts; every `put()` creates a new version. The document goes to the document store, binary payloads to the object store. Every artifact but an environment universe, whose `metadata` key holds its legacy metadata file ids, carries a `metadata` dict outside its versioned fields, changed by compare-and-swap (`merge_metadata`). Built-ins: `CliArtifact` (`cli`), `ComputerArtifact` (`computer`, a base computer: its architectures, OS, session user, settings and container form, whose image `put` reads from its registry into `metadata.validated_images`), `FileArtifact` (`file`), `FileArtifactUniverse` (`file_artifact_universe`), `DockerImageArtifact` (`docker_image`), `EnvironmentArtifact` (`environment`), `EnvironmentUniverseArtifact` (`environment_universe`), `SkillArtifact` (`skill`); `[artifacts] type_aliases` maps another stored spelling of a type onto one of them. A `DockerImageArtifact` holds a tar.gz of its image, or, written by `put_ref` (`--image-ref` on `env mcp-server put` and `a2a-agent put`), only a registry reference with its tag pinned to a digest, which sandboxes pull, or a published image's tag with its `image_digest` and its build context, which sandboxes pull by the digest and build from the context when they can't (the infra images, and a bundle agent built from a shipped agent's sources, `Plan.published`), or, written by `put_context`, only its build context, which a VM sandbox builds before running it (`VmSandbox.load_docker_images`) and Modal builds before creating its container (`ModalSandboxProvider.prepare_image`, outside a fallback chain's deadline); anything else that runs images by name (a container on the local provider, a lone server on any provider but Modal) refuses such an image. A build context (`utils/docker/context.py`) is what `docker build` would send, `.dockerignore` applied, written so equal files give equal bytes and an equal `source_digest`. |
-| `task/`, `task_step/` | A `Task` holds its `TaskStep`s inline; `Task.run()` executes them as a DAG. `depends_on` (None means all prior steps) gates a step, independent steps run concurrently, `fail_task_on_error` makes a failure fatal or tolerated, `retry_config` rolls a failed span back through the step journal and re-dispatches it. Built-in steps live in `task_step/task_steps/` (`deploy_env`, `deploy_agent`, `prompt_agent`, the verifiers under `verifiers/`, `start_computer_recording` and `end_computer_recording`, whose open recordings `teardown_run` and `teardown_sandboxes` end first, and more). `task/episode/` runs a task with some of its agents played from outside, as a training loop does (`EpisodeSpec`). |
+| `task/`, `task_step/` | A `Task` holds its `TaskStep`s inline; `Task.run()` executes them as a DAG. `depends_on` (None means all prior steps) gates a step, independent steps run concurrently, `fail_task_on_error` makes a failure fatal or tolerated, `retry_config` rolls a failed span back through the step journal and re-dispatches it. Built-in steps live in `task_step/task_steps/` (`deploy_env`, `deploy_agent`, `prompt_agent`, the verifiers under `verifiers/`, `start_computer_recording` and `end_computer_recording`, and more). `teardown_run` and the `teardown_sandboxes` step share one core (`task/teardown.py` `tear_down`): the recordings the run left open end with fresh grants, each env still up closes (`Env.close`; a computer env ends any recording still open), what stayed open is registered from its namespace, then every sandbox is terminated by id. `task/episode/` runs a task with some of its agents played from outside, as a training loop does (`EpisodeSpec`). |
 | `store/` | Four store ABCs with local and cloud implementations: `DocumentStore` (SQLite, MongoDB, DynamoDB, Firestore with MongoDB compatibility), `ObjectStore` (filesystem, S3, Cloud Storage), `ImageStore` (local OCI registry, ECR), `SecretStore` (env vars or file, AWS Secrets Manager, Google Cloud Secret Manager). `VersionedEntityStore` implements the shared versioned get/put logic, `QueryBuilder` is the immutable chained query API, `store/base.py` holds the error types. `store/routing.py` is the `@local` namespace: while the CLI or a `build_app` server runs, an `@local/…` id and what an `@local` task's run records go to local stores beside the configured ones, and that run's writes to the configured stores are refused. A new backend must pass the conformance kits in `tst/store/`. |
 | `config/` | The `Config` singleton (`get_config`, `configure`, `reset_config`) in `config/runtime.py`, file discovery in `config/loader.py`, and `load_impl`, which resolves `module:Class` pointers. `agent_env.store` re-exports the config names for compatibility. |
 | `providers/` | `providers/sandbox_providers/` holds the sandbox providers `local`, `modal`, `modal_vm`, `e2b`, `sail_vm` (the `sail` extra), `smol_vm` (the `smol` extra), `tensorlake` (the `tensorlake` extra), `tenki` (the `tenki` extra), `vercel` (the `vercel` extra), `boxd` (the `boxd` extra), `daytona` (the `daytona` extra), `boat` (the `boat` extra); `[sandbox] default` and `agent_default` accept a comma-separated fallback chain, whose deploys skip a provider that can't run them. A sandbox provider declares what it runs (`ON_THIS_MACHINE`, and for VMs on this machine the address their requests to it arrive at, `REACHES_HOST_AT`; `CONTAINER_ACCEPTS`, `PRIVATE_NETWORK`; `SUPPORTED_SANDBOX_MODES`, derived from whether it implements `create_sandbox` and `create_vm`; and `SANDBOX_ACCEPTS`, the images an agent's sandbox runs, which defaults to those a VM loads when it implements `create_vm` and to `CONTAINER_ACCEPTS` otherwise, so only a provider that implements `create_vm` yet gives an agent a container, like the local one, declares it), each sandbox carries the `platform` of the images it runs (linux/amd64 unless its provider sets another when it creates it; None, any, unchecked, for a local sandbox), and code outside `providers/sandbox_providers/` reads those, never which provider it has. `providers/env_providers/` holds the environment providers: `EnvironmentProvider` (an env's containers and state store), `EnvironmentGatewayProvider` (`gateway`, an env's default), which renders a docker-compose for the gateway and its MCP servers inside the sandbox, and `EnvironmentServerProvider` (`server`), one MCP server in its own container with no gateway in front and no state store; `providers/env_state/` holds env-state providers (`local_postgres` built in). |
@@ -123,8 +123,8 @@ and deserialized through a registry.
 | `cli/` | Click CLI with the groups `a2a-agent`, `artifact`, `bundle`, `config`, `env`, `eval`, `plugin`, `run`, `task`, `up`. `task run`, `task run-batch` and `eval run` hand their runs to the configured `[runner]` and follow them. |
 | `agents/` | The `agents` bundle: the agents agent-env ships, one folder each under `agents/agents/`, with a smoke task each under `agents/tasks/`. Its Claude Code, `@local/agentenv-framework/agents/claude-code`, is the default agent. An agent's Python packages are its folder's `requirements.txt`, installed in its image, never in agent-env's own environment. A run that pulls one of its agents reads the whole bundle, so `tst/unit/bundle/shipped_bundles_test.py` parses and checks every shipped bundle. |
 | `computers/` | The `computers` bundle: the base computers agent-env ships as `computer` artifacts, `ubuntu-24.04` (XFCE on Xvfb, supervisord as the session user, settings as `AGENTENV_SETTING_<NAME>`), aliased through `[artifacts]`. It has no tasks either. |
-| `drivers/` | The `drivers` bundle: the computer drivers agent-env ships as `computer_driver` envs, the reference driver (X11 on Linux, recording through `x11grab`) aliased `reference`. It has no tasks: a bundle with neither tasks nor evals is refused unless its `aliases.toml` names something for other bundles to use. `tst/integration/computer/` serves it on the `ubuntu-24.04` base in a scaffold image and runs the conformance probe and an agent against it. |
-| `examples/` | The example bundles agent-env ships (`hello`, and `echo-mcp`, which needs Docker). Tests run them, but they are examples for users, not fixtures. Every shipped bundle is registered under `agent_env.bundles` in `pyproject.toml`, `<name> = "<package>"` shipping `src/<package as a path>/<name>/`. |
+| `drivers/` | The `drivers` bundle: the computer drivers agent-env ships as `computer_driver` envs, the reference driver (X11 on Linux, recording through `x11grab`) aliased `reference`. It has no tasks: a bundle with neither tasks nor evals is refused unless its `aliases.toml` names something for other bundles to use. The shipped `desktop` example composes it with the `ubuntu-24.04` base into a computer env, which `tst/integration/computer/` deploys, probes, records and has an agent use. |
+| `examples/` | The example bundles agent-env ships (`hello`, and `echo-mcp`, which needs Docker, and `desktop`, a computer env of `ubuntu-24.04` and `reference` with a probe task and an agent task). Tests run them, but they are examples for users, not fixtures. Every shipped bundle is registered under `agent_env.bundles` in `pyproject.toml`, `<name> = "<package>"` shipping `src/<package as a path>/<name>/`. |
 
 ## Extension points