tangle-cli 0.0.1a1

CLI for Tangle, the open-source ML pipeline orchestration platform

tangle-cli

CLI for Tangle, the open-source ML pipeline orchestration platform.

This repository contains the public Tangle CLI package. The CLI is built with Cyclopts and is intentionally split into two command families:

• tangle api ... — pure OpenAPI wrappers around Tangle backend endpoints.
• tangle sdk ... — hand-written SDK, local, and compound commands that may call the API or may run entirely locally.

Start here:

uv run tangle quickstart
uv run tangle --help
uv run tangle api --help
uv run tangle sdk --help

Command families

tangle api ...: direct OpenAPI wrappers

tangle api commands are generated/dynamic wrappers for backend HTTP endpoints. They are useful when you want to call the API directly with minimal CLI behavior layered on top.

API command sources are:

• Official static schema: the checked-in OpenAPI snapshot packaged in tangle_api.schema and generated into tangle_api.generated.
• Dynamic cache: live schemas fetched with tangle api refresh and merged in by default as cached-only extension commands.

By default tangle api uses --schema-source auto, which means official static operations plus cached live-backend extensions when a cache exists. Official operations win if a cached schema has the same method/path.

tangle sdk ...: hand-written SDK commands

tangle sdk commands are hand-written workflows. They can be:

• local-only: no generated/native API bindings required, e.g. pipeline validation/layout and component generation;
• API-backed: use the generated client but add domain behavior, e.g. pipeline-run submit payload construction, hydration, artifact lookup, publishing/version checks, or config batching.

Current SDK groups include:

uv run tangle sdk artifacts --help
uv run tangle sdk components --help
uv run tangle sdk pipelines --help
uv run tangle sdk pipeline-runs --help
uv run tangle sdk published-components --help
uv run tangle sdk secrets --help

Common parameters and environment

API-backed commands commonly accept these options. Explicit CLI options win over config-file values, and config-file values win over environment defaults.

Option / env	Purpose
--base-url, TANGLE_API_URL	API origin. Defaults to local development API URL when omitted.
--token, TANGLE_API_TOKEN	Bearer token shorthand.
--auth-header, TANGLE_API_AUTH_HEADER, TANGLE_AUTH_HEADER	Full Authorization value such as Bearer ... or Basic ....
-H, --header, TANGLE_API_HEADERS	Extra headers. Repeatable as CLI flags; env accepts a JSON object or newline-separated Name: value entries.
--config	YAML/JSON defaults. Many commands accept a single object, a list of objects, or _defaults + configs.
--log-type	SDK progress logs: console, none, or file. Logs go to stderr or a temp log file so structured stdout stays parseable.
TANGLE_VERBOSE=1	Redacted HTTP request/response diagnostics only. This is separate from normal progress logging.

Examples for protected APIs:

uv run tangle api refresh --base-url https://api.example \
  --auth-header 'Bearer ...' \
  -H 'X-Gateway-Auth: ...'

uv run tangle api pipeline-runs list --base-url https://api.example \
  --auth-header 'Basic ...' \
  -H 'X-Api-Key: ...'

uv run tangle sdk pipeline-runs submit pipeline.yaml \
  --base-url https://api.example \
  --auth-header 'Bearer ...' \
  -H 'X-Gateway-Auth: ...' \
  --log-type console

Use --log-type none for quiet machine-readable runs, and --log-type file to capture progress logs in a temporary file while keeping stdout clean.

Installation and package split

The repository contains two Python import packages with different responsibilities:

• tangle_cli is hand-written. It contains CLI wiring, SDK/business helpers, local pipeline/component workflows, dynamic API discovery, codegen, shared runtime classes, logging, and extension classes.
• tangle_api is generated/native. It contains checked-in generated Pydantic models, generated endpoint operation methods, and the official OpenAPI snapshot.

The default tangle-cli package keeps the top-level import and local-only SDK commands native-free. Install the native extra when you want static API-backed commands and the handwritten TangleApiClient wrapper to use the checked-in generated bindings:

pip install 'tangle-cli[native]'

In this workspace, uv installs the workspace tangle-api package for development and tests:

uv run tangle api --help
uv run tangle sdk pipelines validate pipeline.yaml

If you are embedding tangle_cli in a downstream project, you can provide your own local tangle_api.generated package produced from your backend schema instead of using this repo's official generated package.

Quick command examples

Local-only SDK commands:

uv run tangle sdk pipelines validate pipeline.yaml
uv run tangle sdk pipelines diagram pipeline.yaml
uv run tangle sdk pipelines layout pipeline.yaml --recursive
uv run tangle sdk pipelines hydrate pipeline.yaml --output hydrated.yaml
uv run tangle sdk components generate from-python path/to/component.py --image python:3.12
uv run tangle sdk components bump-version path/to/component.yaml

API-backed SDK commands:

uv run tangle sdk published-components search transformer --base-url https://api.example
uv run tangle sdk published-components inspect transformer --base-url https://api.example
uv run tangle sdk published-components publish components/my-component.yaml --dry-run
uv run tangle sdk pipeline-runs submit pipeline.yaml --dry-run --log-type none
uv run tangle sdk pipeline-runs submit pipeline.yaml --base-url https://api.example --log-type console
uv run tangle sdk pipeline-runs status RUN_ID --base-url https://api.example
uv run tangle sdk artifacts get --run-id RUN_ID --query '{"artifact_ids":["artifact-id"]}'
uv run tangle sdk secrets list --base-url https://api.example

Direct API commands:

uv run tangle api refresh --base-url https://api.example
uv run tangle api pipeline-runs list --base-url https://api.example
uv run tangle api pipeline-runs get RUN_ID --base-url https://api.example
uv run tangle api components get DIGEST --base-url https://api.example
uv run tangle api published-components list --base-url https://api.example

Path parameters are positional arguments and query parameters become options. Check generated help for the exact options exposed by the active schema source:

uv run tangle api pipeline-runs list --help
uv run tangle api pipeline-runs list --include-execution-stats
uv run tangle api pipeline-runs create --body @pipeline-run.json

Responses are printed as JSON when the backend returns JSON.

Config files

Implemented API-backed commands and many SDK commands accept --config path/to/config.yaml (or JSON). Config files may contain a single object, a list of objects, or a _defaults + configs object; with multiple config entries, the command runs once per entry.

_defaults:
  base_url: https://api.example
  auth_header: Bearer ...
  header:
    - "X-Gateway-Auth: ..."
  log_type: none

configs:
  - filter: active
    limit: 10
  - filter: finished

uv run tangle api pipeline-runs list --config api-config.yaml --limit 5
uv run tangle sdk published-components search --config components.yaml
uv run tangle sdk pipeline-runs submit --config submit.yaml

For generated tangle api commands, config keys use generated CLI parameter names such as base_url, schema_source, body, and endpoint parameters like limit, filter, or id.

API schema cache and dynamic commands

Refresh the local schema cache for a live backend with:

uv run tangle api refresh --base-url http://localhost:8000
uv run tangle api refresh --base-url https://api.example --auth-header 'Bearer ...'

refresh fetches:

<base-url>/openapi.json

Schemas are cached under the OS-specific user cache directory via platformdirs, with an openapi subdirectory. Override that directory with:

export TANGLE_CLI_CACHE_DIR=/path/to/openapi-schema-cache

Delete a cached live schema without touching the checked-in official snapshot:

uv run tangle api reset-cache --base-url https://api.example

Schema source modes are:

• --schema-source auto (default): official static operations plus cached-only backend extensions when a cache exists. Requires the native tangle-api package for official operations.
• --schema-source official: only the checked-in official static schema. Requires the native tangle-api package.
• --schema-source cache: only the schema previously written by tangle api refresh for the selected base URL. Does not require the native package.

For resource help, put --schema-source on the resource group:

uv run tangle api published-components --schema-source official --help
uv run tangle api published-components --schema-source cache --help

For endpoint calls, put it on the endpoint command:

uv run tangle api published-components experimental-search \
  --schema-source cache \
  --base-url https://api.example \
  --body @query.json

SDK command details

Local components

generate from-python converts a local Python function into a component YAML using inline source by default, or --mode bundle to embed local dependency modules. Common options include --function, --output, --name, --image, --dependencies-from, --strip-code, --use-legacy-naming, and --resolve-root.

bump-version increments or sets component version metadata in YAML and updates/regenerates a referenced Python source when the component contains python_original_code_path annotations.

Generation and version-bump commands accept --config YAML/JSON files via tangle_cli.args_container. Use keys such as python_file, image, function, mode, resolve_root, yaml_file, set_version, and update_timestamp; explicit CLI values take precedence.

Published components

Published/registry component operations live under sdk published-components so local component authoring and registry calls do not share a command group.

uv run tangle sdk published-components publish components/my-component.yaml \
  --base-url https://api.example \
  --image python:3.12 \
  --name "My component"

uv run tangle sdk published-components publish components/my-component.yaml --dry-run
uv run tangle sdk published-components deprecate sha256:old --superseded-by sha256:new

publish accepts --image, --name, --description, --annotations (JSON), --dry-run, --published-by, generic git metadata fields, generic API auth fields, --log-type, and --config. By default it scopes version checks and automatic old-version deprecation to the current authenticated user via users_me(); use --published-by to supply an explicit owner/publisher filter. Publishing fails closed if no owner can be determined.

There is no separate OSS publish-all command. To publish multiple components, pass a YAML/JSON config list, or _defaults + configs, to the same published-components publish command; the command aggregates results and exits nonzero if any component errors.

_defaults:
  base_url: https://api.example
  image: python:3.12
configs:
  - component_path: components/first.yaml
    name: First component
  - component_path: components/second.yaml
    name: Second component

Batch publish-all, notification integrations, dbt generation, from-container generation, and backend-specific advanced search workflows remain out of this OSS CLI package.

Pipelines and pipeline runs

Local pipeline commands live under sdk pipelines:

uv run tangle sdk pipelines validate pipeline.yaml
uv run tangle sdk pipelines hydrate pipeline.yaml --output hydrated.yaml
uv run tangle sdk pipelines diagram pipeline.yaml
uv run tangle sdk pipelines layout pipeline.yaml --recursive

Pipeline run API/submit commands live under sdk pipeline-runs:

uv run tangle sdk pipeline-runs submit pipeline.yaml --dry-run
uv run tangle sdk pipeline-runs submit pipeline.yaml --arg key=value --annotation owner=team
uv run tangle sdk pipeline-runs wait RUN_ID --max-wait 600 --poll-interval 10
uv run tangle sdk pipeline-runs logs EXECUTION_ID
uv run tangle sdk pipeline-runs annotations set RUN_ID key value
uv run tangle sdk pipeline-runs export RUN_ID --output pipeline.yaml

submit hydrates refs by default and builds an API submit payload with root_task.componentRef.spec. Use --no-hydrate to submit the local YAML structure as-is. Use --dry-run to print the payload without creating a run.

Programmatic client

The stable public wrapper for downstream Python tools is:

from tangle_cli.client import TangleApiClient

client = TangleApiClient("http://localhost:8000")
run = client.pipeline_runs_get("run-id")
existing = client.find_existing_components(
    ["component-name"],
    published_by_substring="[e-mail]",
)

TangleApiClient is handwritten in tangle_cli.client and inherits generated endpoint methods from tangle_api.generated.operations.GeneratedTangleApiOperations. The generated endpoint methods call the handwritten transport/request logic. Handwritten semantic helpers such as find_existing_components(...) return domain models and normalize common compatibility cases.

The top-level import tangle_cli is lightweight and does not import native static bindings. Install the native extra or otherwise provide a local tangle_api.generated package before importing tangle_cli.client.

Codegen/autogen from OpenAPI

Use codegen when you want to update the checked-in official generated package or generate bindings for your own Tangle-compatible API instance.

Official backend/submodule flow:

git submodule update --init --recursive
uv sync --group codegen
uv run --group codegen python -m tangle_cli.openapi.codegen
uv run pytest

With no source flags, codegen loads OpenAPI from the default official backend submodule at third_party/tangle, writes packages/tangle-api/src/tangle_api/schema/openapi.json, and regenerates packages/tangle-api/src/tangle_api/generated. The backend import creates a database engine at import time; codegen points it at a temporary SQLite database unless --backend-database-uri is provided.

Regenerate from the checked-in API-package snapshot:

uv run python -m tangle_cli.openapi.codegen --from-snapshot

Fetch a remote OpenAPI JSON document directly:

uv run python -m tangle_cli.openapi.codegen \
  --openapi-url https://api.example/openapi.json \
  --out src/tangle_api/generated

Generate from a backend checkout explicitly:

uv run --group codegen python -m tangle_cli.openapi.codegen \
  --backend-path /path/to/tangle/backend \
  --backend-database-uri sqlite:////tmp/tangle-openapi.sqlite

Important codegen options:

• --out: directory that receives __init__.py, models.py, and operations.py. Defaults to packages/tangle-api/src/tangle_api/generated.
• --operations-class-name: generated operations mixin class name. Defaults to GeneratedTangleApiOperations.
• --model-extension-module: importable module with MODEL_EXTENSIONS; repeat to compose modules.
• --model-alias: expose a stable public model name from one or more source schema names, e.g. ComponentSpec=ComponentSpecOutput,ComponentSpecInput.
• --request-body-schema / --request-body-schema-file: override a specific operation's JSON request-body schema without mutating the fetched OpenAPI document.

At runtime, more tangle api ... commands become available in two ways:

1. Static codegen: regenerate and install/provide a tangle_api.generated package for the schema.
2. Dynamic cache: run tangle api refresh --base-url ... and use --schema-source auto or --schema-source cache to expose cached-only operations through the dynamic CLI.

Generated model extension pattern

Generated models use a generated implementation base plus a stable public subclass. For example, codegen emits this shape for a model with a handwritten extension:

class _ComponentSpecGenerated(TangleGeneratedModel):
    name: Any = None
    # generated OpenAPI fields...

class ComponentSpec(ComponentSpecExtensions, _ComponentSpecGenerated):
    pass

The public class is a subclass rather than an alias because the public class name is the stable contract while the generated base can be regenerated. Subclassing lets the public class keep the OpenAPI/Pydantic fields from _ComponentSpecGenerated and add or override behavior through normal Python MRO.

Extension bases are placed to the left of the generated base:

class ComponentSpec(ComponentSpecExtensions, _ComponentSpecGenerated):
    pass

That means extension methods/properties override generated-base behavior when names overlap, while generated fields and TangleGeneratedModel runtime helpers such as to_dict() remain available.

The built-in default extension module is:

tangle_cli.generated_model_extensions

It defines:

MODEL_EXTENSIONS = {
    "ComponentSpec": "ComponentSpecExtensions",
    "GetExecutionInfoResponse": "GetExecutionInfoResponseExtensions",
    "GetGraphExecutionStateResponse": "GetGraphExecutionStateResponseExtensions",
}

During codegen, tangle_api.generated.models imports those extension classes from tangle_cli.generated_model_extensions. This preserves the package boundary: tangle_api remains generated bindings, while tangle_cli owns handwritten runtime and extension behavior.

Downstream projects can layer their own extensions:

# my_project/tangle_model_extensions.py
class MyComponentSpecExtensions:
    @property
    def owning_team(self) -> str | None:
        return (self.metadata or {}).get("annotations", {}).get("team")

MODEL_EXTENSIONS = {
    "ComponentSpec": "MyComponentSpecExtensions",
}

uv run python -m tangle_cli.openapi.codegen \
  --openapi-url https://api.example/openapi.json \
  --out src/tangle_api/generated \
  --model-extension-module my_project.tangle_model_extensions

The default module is applied first. Repeated --model-extension-module values are applied in order, and later/downstream modules become leftmost in the generated public class MRO, so they override earlier/default extensions. If two modules export the same extension class name, codegen imports them with deterministic aliases.

Pass an empty string to disable built-in default extensions:

uv run python -m tangle_cli.openapi.codegen \
  --from-snapshot \
  --model-extension-module ""

The same empty-string sentinel can disable built-in --model-alias defaults. Built-in aliases keep stable public model names such as ComponentSpec even when a backend schema uses names like ComponentSpecOutput or ComponentSpecInput.

Extension classes should be importable from their modules and should not import generated model classes. They should be mixins over generated data, not replacements for generated schemas.

Extending SDK behavior

The CLI exposes small explicit seams rather than requiring downstream forks.

Hydrator resolvers

packages/tangle-cli/src/tangle_cli/pipeline_hydrator.py exposes a resolver registry:

from tangle_cli.pipeline_hydrator import PipelineHydrator, register_component_resolver


def resolve_from_catalog(hydrator: PipelineHydrator, value, path: str, base_dir):
    # return (digest, component_spec_dict) or None
    return "sha256:...", {"name": "Resolved", "implementation": {"container": {"image": "python:3.12"}}}

register_component_resolver("catalog", resolve_from_catalog)

Resolvers receive the hydrator instance, the reference value, a display path, and the current base directory. They can use hydrator._api_client() for API-backed lookups, hydrator.log for progress logs, and hydrator.resolution_overrides for template/config variables. There is also an instance method hydrator.register_component_resolver(...) for per-hydrator overrides. Built-in kinds include digest, name, url, file, resolve, http, https, local, and local_from_python.

Downstream-only features such as Docker/from-container materialization or cloud storage can be added by registering new resolvers while the OSS default remains explicit about unsupported kinds.

Pipeline run hooks

packages/tangle-cli/src/tangle_cli/pipeline_runs.py defines PipelineRunHooks, passed into PipelineRunManager. Subclass it to customize submit/load/wait/log behavior:

from tangle_cli.pipeline_runs import PipelineRunHooks, PipelineRunManager


class MyRunHooks(PipelineRunHooks):
    def read_pipeline_yaml(self, pipeline_path):
        if str(pipeline_path).startswith("s3://"):
            return load_from_s3(pipeline_path)
        return super().read_pipeline_yaml(pipeline_path)

    def extra_submit_annotations(self, *, pipeline_spec, pipeline_path, run_as=None):
        annotations = super().extra_submit_annotations(
            pipeline_spec=pipeline_spec,
            pipeline_path=pipeline_path,
            run_as=run_as,
        )
        annotations["submitted_by"] = "my-tool"
        return annotations

    def fetch_logs(self, client, execution_id):
        return client.executions_container_log(execution_id)

manager = PipelineRunManager(client=my_client, hooks=MyRunHooks())

Available hooks include:

• read_pipeline_yaml(...)
• hydrate_pipeline(...)
• prepare_run_arguments(...)
• extra_submit_annotations(...)
• before_submit(...)
• after_submit(...)
• after_wait(...)
• fetch_logs(...)

Use these for generic downstream behavior such as alternate storage, extra annotations, scheduling/time input defaults, mutex checks, notifications, or alternate log providers. The OSS defaults intentionally exclude provider-specific cloud, notification, and scheduler behavior.

Component publish hooks

packages/tangle-cli/src/tangle_cli/component_publisher.py defines ComponentPublishHook with:

• before_batch(components_config)
• after_component(component_path, result)
• after_batch(results)

ComponentPublisher(..., hooks=[...]) calls these around publish batches. Use them for downstream summaries, audit records, or notifications while keeping OSS publishing generic.

Shared CLI helpers and logging

cli_options.py centralizes shared Cyclopts annotations such as BaseUrlOption, TokenOption, AuthHeaderOption, HeaderOption, ConfigOption, and LogTypeOption. cli_helpers.py centralizes config loading, JSON printing, credential-isolation helpers, and the native-safe LazyTangleApiClient proxy. logger.py provides ConsoleLogger, NullLogger, CaptureLogger, logger_for_log_type(...), and run_with_logging(...).

Use these helpers for new SDK commands so top-level imports remain native-free, --config behavior stays consistent, credentials from config do not accidentally mix with ambient environment auth, and progress logs stay off structured stdout.

Development checks

Common validation commands:

uv run --frozen pytest -q
uv build --sdist --wheel
uv build --sdist --wheel --package tangle-api
git diff --check

Targeted CLI smoke:

uv run tangle quickstart
uv run tangle api --help
uv run tangle sdk --help