MINT SDK (Python)

SDK for building analysis plugins that integrate with the MINT platform.

Full Documentation: See the comprehensive docs for detailed API reference and guides.

• API Reference
• Plugin Development Guide
• MINT SDK 1.1 Migration Guide
• Exception Handling

Installation

# From PyPI (when published)
uv add mint-sdk

# From git
uv add git+https://github.com/MorscherLab/MINT#subdirectory=packages/sdk-python

MINT SDK 1.1 requires Pydantic >=2.12.0. New mint init projects include that minimum automatically.

Quick Start

Create a Python-only plugin whose standard UI is supplied by the SDK:

mint init my-plugin --mode generated --yes
cd my-plugin
mint dev

Generated mode uses typed Pydantic inputs with @generated_ui() and @job. It does not create a Vue project and mint build does not require Bun.

Use standard mode when the plugin needs a custom Vue page:

mint init my-plugin --mode standard --yes
cd my-plugin
mint sdk generate
mint dev

Standard mode starts with a small PluginWorkspaceView, a working form, and a generated typed client. That workspace is starter code, not a required layout; it can be replaced or removed without making mint doctor reject the mode. mint init --yes defaults to generated mode. Pass --mode standard when the plugin needs a custom Vue frontend.

mint doctor --strict is a pre-commit and CI validation step, not a startup requirement for mint dev.

mint sdk generate writes the frontend contract and typed client from backend routes and Pydantic schemas, so Vue code can call plugin endpoints without hand-writing route prefixes or request/response types. Use mint docs contract inside a plugin to inspect the generated endpoint and client-call contract without writing files. Use mint sdk generate --check --json in CI or editor tasks when you need machine-readable drift status. Use mint doctor --json for machine-readable project health checks and safe-fix status. When adding backend pieces, pass --generate to supported mint add commands to refresh the generated client in the same step.

Declarative plugin contracts

New plugins keep metadata and lifecycle hooks explicit with decorators while remaining regular AnalysisPlugin subclasses:

from pydantic import BaseModel

from mint_sdk import (
    AnalysisPlugin,
    ConfigChange,
    PluginHealth,
    health_check,
    mint_plugin,
    on_config_change,
)

class PeakQcSettings(BaseModel):
    threshold: float = 0.05

@mint_plugin(
    analysis_type="metabolomics",
    routes_prefix="/peak-qc",
    config=PeakQcSettings,
)
class PeakQcPlugin(AnalysisPlugin):
    def get_routers(self):
        return []

    async def initialize(self, context=None) -> None:
        self._context = context

    async def shutdown(self) -> None:
        pass

    @health_check(timeout=2.0)
    async def health(self) -> PluginHealth:
        return PluginHealth(message="ready")

    @on_config_change(fields={"threshold"})
    def threshold_changed(self, change: ConfigChange[PeakQcSettings]) -> None:
        self._threshold = change.current.threshold

Package identity comes from PEP 621 metadata and the sole mint.plugins entry point. @mint_plugin declares only runtime behavior. @health_check accepts sync or async handlers; sync handlers run in a worker thread, and timeouts or exceptions produce an unhealthy result. @on_config_change handlers are synchronous, run after the settings commit in declaration order, and receive only actual top-level field changes. Existing method-based plugins remain compatible, but a plugin must not declare both styles for the same contract.

Notifications and calendar feeds

Async plugin methods may return typed notification or calendar values. The decorators validate each result and publish it when the plugin instance is bound to an integrated platform context:

from datetime import UTC, datetime

from mint_sdk import (
    AnalysisPlugin,
    CalendarEvent,
    NotificationEvent,
    NotificationSeverity,
    calendar_event,
    notify,
)


class InstrumentPlanner(AnalysisPlugin):
    @notify(channels={"email", "teams"})
    async def report_stop(self, run_id: str) -> NotificationEvent:
        return NotificationEvent(
            event_key=f"run:{run_id}:stopped",
            severity=NotificationSeverity.CRITICAL,
            title="Acquisition stopped",
            message=f"Run {run_id} stopped before completion.",
            occurred_at=datetime.now(UTC),
        )

    @calendar_event
    async def schedule(
        self,
        run_id: str,
        user_id: int,
        start: datetime,
        end: datetime,
    ) -> CalendarEvent:
        return CalendarEvent(
            event_key=f"run:{run_id}",
            title=f"Run {run_id}",
            start=start,
            end=end,
            participant_user_ids=(user_id,),
        )

An instrument probe may call a normal, plugin-owned API and then invoke an async @notify method. The plugin owns and validates the probe token; MINT does not issue, store, rotate, or read it. MINT receives only the method's structured notification result.

Only unacknowledged Critical notifications are delivered externally. MINT selects email recipients and owns the SMTP, Teams, and Slack targets. Calendar events use stable keys for upsert or cancellation and appear in read-only ICS feeds. Each decorator accepts at most 100 typed results per call.

An unbound or standalone direct call returns the method result without publishing. An integrated host-bound direct call can publish. Stacking @notify with @job requests completed/failed owner email only for a managed Job; calling that function directly does not create a terminal Job email. See the plugin guide and 1.1 migration guide for the full contracts.

Backend-only generated UI

Plugins with simple controls and results can omit frontend source. Declare an optional Pydantic config model with @mint_plugin(config=...), calculations with @job, and the plugin class with @generated_ui. MINT then serves a standard workspace in standalone and integrated modes. An existing plugin Vue frontend always takes precedence.

from mint_sdk import (
    AnalysisPlugin,
    TableResult,
    generated_ui,
    job,
    mint_plugin,
)
from pydantic import BaseModel, Field

class Inputs(BaseModel):
    threshold: float = Field(1.0, ge=0.0)

class PluginConfig(BaseModel):
    method: str = "default"

@mint_plugin(
    analysis_type="test",
    routes_prefix="/simple-analysis",
    config=PluginConfig,
)
@generated_ui(title="Simple analysis")
class Plugin(AnalysisPlugin):
    @job(cpu=1)
    def run(self, inputs: Inputs) -> TableResult:
        return TableResult(
            columns=["threshold"],
            rows=[{"threshold": inputs.threshold}],
        )

Input v1 supports typed scalar fields, Pydantic models, enums, toggles, string arrays, and ordinary Path file/directory inputs. Result v1 supports text, JSON, tables/DataFrames, images, artifacts, Matplotlib figures, and interactive Plotly figures. Artifact v1 embeds its download bytes. Arbitrary HTML, JavaScript, and frontend callbacks are rejected.

The generic workspace cannot infer domain-specific part batching. @generated_ui therefore rejects a class containing @job(profile=StagedJob(...)); use a standard/custom frontend and its generated usePluginJobs() client for that lifecycle.

Call a decorated job like an ordinary synchronous Python method. The platform schedules the same job through its HTTP routes. @job, JobContext, JobManager, and the result types are stable SDK APIs. JobContext.report() publishes percent/stage/message plus structured current-file progress; JobContext.warn() records bounded non-fatal warnings without replacing the progress message.

When one definition processes different datasets, use presentation to derive the persisted Job name and source folders from each validated submission:

from mint_sdk import JobPresentation, job

def run_presentation(inputs: Inputs) -> JobPresentation:
    return JobPresentation(
        name=inputs.dataset_name,
        folders=tuple(inputs.dataset_ids),
    )

@job(title="Analyze", presentation=run_presentation)
def analyze(inputs: Inputs) -> TableResult:
    ...

The callback is synchronous, receives a detached copy of the validated input, and must return JobPresentation. If it omits name, the static job title is used. The canonical in-memory Job state remains available after clients refresh, but it is not restored after a daemon restart.

The generated workspace schema is versioned but remains experimental in this minor release. See the backend-only example.

Runtime configuration and concurrency

@mint_plugin(config=PluginConfig) makes the decorator the single config-model declaration for standalone and integrated settings. Add config_requires_admin=True when shared settings control server resources, credentials, provisioning, or storage paths. Integrated mutation and reset routes then require a platform administrator; reads and standalone operation keep their existing behavior. Custom form schemas and out-of-process runtime metadata inherit the same declaration. Effective values resolve in this order:

model defaults < saved store < explicit .env < process environment < startup override

Environment variables use MINT_PLUGIN_<NORMALIZED_PLUGIN_NAME>__<FIELD>; nested fields add another double underscore. For example, MINT_PLUGIN_MY_ANALYSIS__DATABASE__HOST. Values supplied by .env, the process environment, or startup overrides remain effective. Writes still replace the saved backing value; that value appears in stored_settings and is marked inactive until the higher-priority runtime override is removed. Secret fields are never returned or persisted by the public settings API.

Full replacement uses optimistic concurrency. Pass the opaque revision that backs the candidate; do not parse or calculate it:

candidate = self.settings.model_copy(update={"threshold": 0.1})
await self.save_settings_transactionally(
    candidate,
    expected_revision=self.settings_revision,
)

A full save makes one compare-and-swap attempt. It does not merge a stale candidate and does not retry it, because the SDK cannot infer which fields the caller intended to replace. A stale revision raises SettingsConflictError with committed=False; reload before rebuilding the candidate.

For a partial update, use await self.patch_settings_transactionally({"threshold": 0.1}). A patch shallow-merges into the latest durable backing value and retries compare-and-swap conflicts up to three times, so concurrent requests changing different fields do not overwrite each other. A patch does not take an expected_revision. Validation or preflight failure does not change durable or in-memory state. A preflight may run again during a patch retry, so it must be repeatable and side-effect free; @on_config_change handlers run once, only after a successful commit. A post-commit handler failure uses SettingsTransactionError with committed=True. If a CAS response is lost, the SDK reads the authoritative snapshot before deciding the result. A matching target is treated as committed. If neither the commit nor the readback can be confirmed, the error has commit_state="unknown" and committed=None; settings reads and new writes are fenced until an authoritative read succeeds, hot-applies the exact snapshot, and runs @on_config_change once.

The settings GET response carries the same opaque typed-settings content token as revision in its JSON body and as a quoted ETag. Each managed GET reads a fresh snapshot from authoritative persistence, but it does not hot-apply that snapshot to the running plugin or call @on_config_change. Full HTTP writes (PUT /settings and POST /settings/reset) return 428 Precondition Required without If-Match: "<revision>", and 409 Conflict when that revision is stale. The token is a CAS value, not a monotonic generation: equal durable content can produce the same token, so it does not detect an A-to-B-to-A transition. An unconfirmed commit returns 503 Service Unavailable with committed: null; reload before deciding whether to retry the mutation.

SettingsTransactionError.commit_state is committed, not_committed, or unknown; committed remains its True, False, or None compatibility projection. Readback uses strict JSON identity, so true and 1 are distinct, and non-finite numbers are rejected before persistence.

PUT {} and reset clear the durable typed backing instead of materializing model defaults. The effective runtime model still receives those defaults with DEFAULT provenance, so a later plugin release can change a default without an older saved copy shadowing it.

Every resolver-backed runtime uses the public synchronous SettingsCompareAndSwapAdapter: standalone apps use JsonSettingsStore, in-process platform plugins use the central platform store, and isolated or Docker plugins use RemotePlatformContext to reach that same store. An integrated host must implement PlatformContext.get_plugin_settings_cas_adapter() with the complete plugin-owned key scope. Missing or unreachable CAS support fails app startup before plugin initialization instead of falling back to a best-effort read-modify-write.

Config models use a strict, fail-fast storage contract. Supported fields are named BaseModel fields composed from the standard scalar types, dates and times, Decimal, UUID, Path, Enum, Pydantic secret types, nested BaseModel values, explicit mappings/sequences, Literal, unions, and Annotated with ordinary Field constraints or discriminators. Enum values must survive a JSON round trip without type drift. Mapping keys must be str or string-valued Literal annotations; JSON object storage does not preserve integer or Enum key identity. Validators may normalize values, but the returned runtime shape must still match the annotation. Dynamic Any values are limited to string-keyed JSON-shaped trees and recognized secret leaves. Do not union SecretStr or SecretBytes with UUID, date/time, Decimal, Path, bytes, or another scalar that uses the same JSON string representation; use a small BaseModel with separate public identity and secret fields.

MINT rejects RootModel, TypedDict, dataclasses, Pydantic dataclasses, NamedTuple, extra="allow", arbitrary types, unsafe mapping keys, and serialization-changing features. The latter include field/model serializers, json_encoders, GetPydanticSchema, SerializeAsAny, plain/wrap serializers, and field exclude/exclude_if. Broad Pydantic helper types such as Json and ImportString are not config scalars; use a supported storage type plus a validator. Numeric ser_json_temporal modes are rejected. Legacy ser_json_timedelta="float" is supported, while non-UTF-8 byte serialization requires the same ser_json_bytes and val_json_bytes mode. See the 1.1 migration guide for the complete migration list.

The platform owns allowed_experiment_types; a decorator config model and a plugin-supplied owned_keys scope cannot claim it. The value must be list[str] | None, and corrupt persisted policy denies every experiment type until repaired. Typed and platform-owned fields are updated separately.

The user-facing plugin-config GET returns platform-owned public fields plus a fresh, sanitized typed snapshot only when the loaded provider, plugin name, resolver, and exact decorator model all match. The typed snapshot is read from authoritative persistence for each request without hot-applying it to the running plugin. It does not fall back to raw stored values for unloaded, untyped, external, or Docker-hosted plugins. The response revision and ETag cover only the plugin-owned typed snapshot; they do not cover allowed_experiment_types or other platform-owned fields. The plugin-scoped internal transport can still read raw backing values for an isolated runtime, and every write through that transport must supply explicit owned_keys without platform-owned fields.

Integrated plugin frontends should let the injected plugin.settings_api capability choose the settings route. Isolated subprocess, Docker-managed, external HTTP, and dev-proxy runtimes receive "plugin" and use their proxied plugin-local /settings API. In-process plugins receive "platform" and use the platform config facade; an absent field keeps that legacy platform behavior. The frontend SDK composables perform this selection automatically.

Full saves and shallow patches prepare a secret-free backing value together with its exact effective snapshot. Environment/startup overrides are included in preflight and the live commit but are not materialized into storage. A patch replaces only its explicit top-level keys and uses bounded compare-and-swap retries. Preflight and host persistence callbacks may be synchronous or return one awaitable; nested awaitables fail the transaction instead of leaving work running after an uncommitted result.

Secret-bearing list items need at least one stable non-secret field, such as an ID or a discriminating Literal, so masked values can be associated after a reorder. If a positional item becomes entirely secret after masking while other items remain, a public full save returns 422 instead of storing an ambiguous placeholder. Use a small BaseModel item with a stable public field instead of mixing bare secret values with ordinary list values. A locked secret override also keeps the pre-existing lower backing branch; unlike a locked non-secret scalar, its masked effective branch is never copied down. If a different locked branch occurs inside a positional collection, public full replacement returns 422 instead of silently discarding sibling edits.

Each browser tab gets an ephemeral session. Jobs are independent and may run concurrently across a user's tabs. Process and staged jobs snapshot input and effective config at submission time. Service jobs snapshot input and expose the submitted config as JobContext.config, but run against the initialized host object: self.settings, plugin attributes, and services are live when the handler executes. Sessions and jobs are not persisted and do not survive a daemon restart.

@job works for both custom Vue and @generated_ui plugins. Generated contracts expose typed usePluginJobs() overloads for definition inputs, job handles, and outputs, plus ready, refresh, jobsFor, deleteJob, clearFinishedJobs, pause, resume, and idempotent dispose lifecycle helpers. Use ordinary Path parameters for file or directory inputs; HTTP submissions stage opaque owner/session-scoped uploads into per-job writable workspaces, while direct Python calls keep the caller's local paths. PluginTestHarness runs the real scheduled path without test-owned sessions, actors, polling, or temporary-file plumbing.

Use @job(requires_admin=True) for maintenance or provisioning work that must not be submitted by ordinary platform users. Integrated submission and all associated Path/staged upload and seal operations then require either a platform administrator or an administrator of the current plugin. jobs.manage_all alone does not grant admission. Direct Python calls and standalone mode remain local operations, and the original job owner may still cancel or delete an existing job after losing the role. Generated clients bind Path uploads to the selected definition automatically; custom callers can pass the optional definition ID to uploadPath(input, definitionId).

For large outputs, write beneath JobContext.output_path() and return ManagedFileResult. The host adopts and hashes the file without base64 or a worker-IPC copy. It either exposes an expiring authenticated download or runs a trusted @job_finalizer with ManagedFile, the original actor, immutable input/config snapshots, and a stable retry idempotency key. The finalizer's return annotation becomes the typed value inside the generated wire-result envelope. Finalization is async and cannot be cancelled by clients. Each attempt has a configured timeout; blocking libraries must run through JobFinalizationContext.run_blocking() so timeout cancellation drains the thread before the actor scope, managed file, quota, or live plugin services are released. A thread that never returns can therefore extend wall-clock teardown beyond that timeout. Platform mutation APIs remain responsible for current authorization. See the 1.1 migration guide.

The shared scheduler enforces CPU slots globally and a concurrent-job limit per user across all of that user's sessions. Each synchronous job runs in a fresh worker process. The pool defaults to one less than the host CPU count. A plugin's explicit @job(cpu=...) request is reduced to the pool size when the deployment has fewer CPUs, and the effective value is available as JobContext.cpu and applied to common Python and Rust thread-pool environment variables. Configure the two primary limits with MINT_JOB_CPU_SLOTS and MINT_JOB_MAX_CONCURRENT_PER_USER, or the matching mint daemon flags.

The in-memory runtime also bounds queued work, retained terminal jobs, serialized payloads, and result lifetime. Configure these limits with MINT_JOB_MAX_QUEUED, MINT_JOB_MAX_QUEUED_PER_USER, MINT_JOB_MAX_SESSIONS, MINT_JOB_MAX_SESSIONS_PER_USER, MINT_JOB_MAX_RETAINED, MINT_JOB_MAX_RETAINED_PER_USER, MINT_JOB_MAX_INPUT_BYTES, MINT_JOB_MAX_TEMP_BYTES_TOTAL, MINT_JOB_MAX_TEMP_BYTES_PER_USER, MINT_JOB_MAX_RESULT_BYTES, MINT_JOB_MAX_MANAGED_RESULT_BYTES, MINT_JOB_MAX_RESULT_BYTES_TOTAL, MINT_JOB_MAX_RESULT_BYTES_PER_USER, and MINT_JOB_RESULT_TTL_HOURS.

The temporary-byte limits are shared service-wide across plugins, ordinary path storage, staged parts, and multipart request spools. During upload adoption, both the request spool and retained workspace copy may count.

Process workers reconstruct the zero-argument plugin class and inject the submission-time settings snapshot. Jobs should depend on typed input, self.settings, and JobContext. The trusted worker snapshot contains effective secret values, but job state, events, and the public settings API never expose them.

For Docker, Linux, and WSL, run one foreground host process:

mint daemon \
  --platform-dir . \
  --app mint_sdk.runtime:create_plugin_app \
  --port 8000 \
  --cpu-slots 4 \
  --max-concurrent-per-user 2

Forwarded headers are trusted only from loopback by default. When a reverse proxy runs on another address, pass its explicit IP or CIDR with --forwarded-allow-ips; do not use a wildcard on an exposed deployment.

The previous mint platform daemon ... service-management commands remain available for compatibility.

Monorepo plugins must declare non-standard project paths so every CLI command uses the same frontend and generated client:

[tool.mint]
frontend_dir = "packages/ui"
generated_dir = "packages/contracts/client"

Both values are project-relative. generated_dir defaults to <frontend_dir>/src/generated when omitted. Conventional single-package plugins need no path settings.

mint doctor can also inspect split Python packages and several plugin projects from one repository root:

[tool.mint]
python_source_dirs = ["packages/leaf"]

[tool.mint.workspace]
plugin_members = ["plugins/*"]

[[tool.mint.doctor.import_rules]]
source = "leaf.analyzer"
forbid = ["mint_sdk", "fastapi", "leaf.api"]

Workspace members are explicit globs. Directories without a mint.plugins entry point are reported as skipped, and each plugin receives a separate text/JSON result group. Run mint doctor --strict when warnings must also fail CI. Import rules are static checks of normal Python imports; they do not import project code or guess dynamic imports.

For R-backed analyses:

mint init drp-r --mode standard
mint add r-analysis drp-fit --page
mint doctor --r --explain
mint sdk generate

This creates an RAnalysisBridge service, FastAPI route, typed frontend composable, optional starter page, and a small mint_bridge.R helper for reading inputs, writing outputs, accessing the current experiment id, and writing analysis artifacts while keeping Python/Pydantic as the frontend contract source of truth.

For standard biology design data:

mint add data-template --list --json
mint docs template plate-map
mint add data-template plate-map --page

Built-in templates include plate-map, sample-sheet, sample-prep, dose-response, calibration-curve, time-course, protocol-steps, assay-matrix, reagent-list, flow-cytometry-panel, instrument-run, and qpcr-plate. Generated template routes expose schema/default endpoints and merge multiple templates under design_data.templates, so a plugin can combine plate layouts, sample metadata, sample prep, reagents, protocols, calibration curves, time courses, readout matrices, cytometry panels, instrument run queues, and qPCR plates without clobbering prior template data.

Use create_template_collection() / save_template_collection() when a backend route needs to persist a coordinated set of templates, and load_template_collection() when a route needs all envelopes stored for an experiment. Single-template save_template() / load_template() remains available for narrow routes.

mint add data-template-pack <name> --page generates those collection routes and the matching frontend composable for curated packs, so plugin authors can save a whole experiment design scaffold with one API call.

For experiment object files, let the platform choose local, S3, or OpenStack Swift storage and keep only the returned reference in your experiment data. Standalone plugin runs use a local store under ~/.mint/plugins/<plugin-name>/objects.

class MyPlugin(AnalysisPlugin):
    async def initialize(self, context=None):
        self._context = context

    async def save_report(self, experiment_id: int, payload: bytes) -> dict:
        store = self.get_data_store(experiment_id)
        ref = await store.put_bytes(
            "reports/report.json",
            payload,
            content_type="application/json",
            metadata={"kind": "qc-report"},
        )
        return ref.to_dict()

The platform stores object bytes under {server.dataPath}/objects by default. Admins can switch the object backend between storage.objects.backend = "local" "s3", and "swift" and can set storage.objects.localPath for local storage. When the backend is S3, the same SDK calls write to storage.s3.objectBucket under storage.s3.objectPrefix. When the backend is Swift, they write to storage.swift.objectContainer under storage.swift.objectPrefix. The platform owns endpoint, region, access key, secret key, session token, SSL, path-style, Keystone auth URL, project/domain, and Swift password settings; saved credentials are encrypted at rest and redacted from admin config responses. Use Admin -> Configuration -> Object Storage to choose Local Path, S3 Bucket, or OpenStack Swift, and the Test Connection button to validate provider access. Isolated plugin uploads use multipart transfer for put_file / put_fileobj instead of base64 JSON.

S3-compatible provider settings may also come from environment variables. The platform reads endpoint/region names such as MINT_S3_ENDPOINT_URL, MINT_S3_REGION_NAME, S3_ENDPOINT_URL, and AWS_ENDPOINT_URL_S3; credential names such as AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_SESSION_TOKEN; and MINT-specific MINT_S3_* / MINT_STORAGE__S3__* variants for SSL and path-style addressing. Native Swift settings may come from MINT_SWIFT_* / MINT_STORAGE__SWIFT__* variables or standard OpenStack variables such as OS_AUTH_URL, OS_USERNAME, OS_PASSWORD, OS_PROJECT_NAME, OS_USER_DOMAIN_NAME, OS_PROJECT_DOMAIN_NAME, OS_AUTH_VERSION, and OS_REGION_NAME.

Scripts can use MINTClient.objects for the same storage API by passing plugin_id explicitly. get_s3_connector() and platform S3 credential access were removed in 1.1. Integrated plugins call get_data_store(experiment_id). A standalone script may construct S3Connector with an explicit S3ConnectionConfig or client.

At the Python layer, plugins implement the AnalysisPlugin interface:

from mint_sdk import AnalysisPlugin, PluginCapabilities, mint_plugin
from fastapi import APIRouter

router = APIRouter()

@router.get("/hello")
async def hello():
    return {"message": "Hello from my plugin!"}

@mint_plugin(
    analysis_type="metabolomics",
    routes_prefix="/my-plugin",
    capabilities=PluginCapabilities(
        requires_auth=True,
        requires_experiments=True,
    ),
)
class MyPlugin(AnalysisPlugin):
    def get_routers(self):
        return [(router, "")]

    async def initialize(self, context=None):
        self._context = context

    async def shutdown(self):
        pass

Plugin Package Structure

mint-plugin-example/
├── pyproject.toml
├── README.md
└── src/mint_plugin_example/
    ├── __init__.py
    └── plugin.py

pyproject.toml

[project]
name = "mint-plugin-example"
version = "0.1.0"
dependencies = ["mint-sdk>=1.1.0"]

[project.entry-points."mint.plugins"]
example = "mint_plugin_example.plugin:MyPlugin"

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["src/mint_plugin_example"]

The entry point mint.plugins is how the platform discovers your plugin.

Platform Context

When running integrated with the platform, your plugin receives a PlatformContext that provides access to:

• Authentication dependencies (get_current_user_dependency())
• Capability-scoped repositories and shared plugin database sessions
• Persisted configuration for the current plugin
• Platform-managed experiment object storage
• Typed notification and calendar publication

The context does not expose full platform settings or platform secrets. Use self.settings for the plugin's typed configuration and get_data_store(experiment_id) for experiment files.

async def initialize(self, context=None):
    self._context = context
    if context:
        # Running integrated - use platform services
        self.experiment_repo = context.get_experiment_repository()
    else:
        # Running standalone
        pass

Installation Commands

These commands are for a developer environment or the trusted in-process plugin path. In-process installation retains normal package, Git, editable/local-source, wheel, and source-distribution support.

# Install from GitHub
uv add git+https://github.com/org/mint-plugin-example

# Install specific version
uv add git+https://github.com/org/mint-plugin-example@v1.0.0

# Install from PyPI
uv add mint-plugin-example

# Install local plugin for development
uv add --editable ./my-plugin

Subprocess installation

A subprocess plugin source must be an administrator-trusted, existing local .whl. MINT rejects package requirements, Git URLs, editable installs, source trees, source archives, and source distributions for this path. Dependencies must have compatible binary wheels; source builds are disabled.

Keep the selected wheel at its persisted path. If it is missing at startup, MINT leaves the plugin disabled and does not use a registry, network download, or source fallback. This release does not include a trusted wheel builder or a content-hash artifact cache.

The virtual environment and separate process isolate dependencies and ordinary failures, not hostile code. They are not an OS sandbox. Deploy untrusted code through an external HTTP or Docker-managed runtime with explicit operating system or container controls. Snapshot rollback can remove packages added after the snapshot, but MINT refuses to restore missing or changed packages without immutable trusted wheels.

For an external HTTP runtime, the deployment operator sets MINT_EXTERNAL_PLUGIN_<NORMALIZED_NAME>_TOKEN on MINT and passes the same secret to the external service as MINT_PLUGIN_TOKEN. The normalized name is uppercase with runs of non-alphanumeric characters replaced by _. Use at least 32 non-whitespace characters. MINT reads the value at startup and does not register the runtime when it is missing or weak. It does not store, expose, or rotate this secret. Normalized token keys must be unique; all colliding runtimes remain disabled. Targets must be absolute http or https URLs with a hostname and valid optional port, without userinfo, query strings, or fragments. Plain HTTP is accepted only for localhost, 127.0.0.0/8, or ::1; remote targets require HTTPS. Tokens used by an instrument to authenticate to a plugin-owned API are separate credentials owned entirely by that plugin.