tangle-cli 0.1.0

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 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/static. It contains checked-in generated Pydantic models, generated endpoint operation methods, and the official OpenAPI snapshot.

The default public tangle-cli package depends on the matching tangle-api package, so normal installs include the checked-in generated bindings used by static API-backed commands and the handwritten TangleApiClient wrapper:

pip install tangle-cli

The native extra remains as a compatibility no-op alias for older install instructions. 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

Custom API/codegen users can still run codegen from the fully capable install; generating bindings does not require removing the official tangle-api package. For project-local generated APIs, generate into a local source tree such as src/tangle_api/generated (and src/tangle_api/schema/openapi.json when you want tangle api --schema-source official) and run from that project so local src/tangle_api shadows site-packages. For packaged custom APIs, publish/provide a distribution named tangle-api with a version compatible with this tangle-cli release (for example 0.1.0+yourorg for a tangle-cli dependency on tangle-api==0.1.0) via a private index, --find-links, or uv sources. As an expert escape hatch, --no-deps installs only tangle-cli and skips all dependencies, so that environment must manually provide every required runtime dependency plus its generated/custom tangle_api; this is acceptable for controlled codegen/custom scenarios but not normal UX.

Agent skills

This repo includes the Tangent agent-skill bundle under skills/tangent/. The bundle was ported from tangle-cli-lab and now treats this repository as the canonical source. It drives the public tangle / tangle-cli command surface, assumes the default tangle-cli install includes tangle-api, and keeps relative references (agents/*.md, references/*.md) self-contained for Pi-style skill loaders. The source distribution includes skills/** so downstream source-based consumers can inspect or vendor the skill docs with the release.

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. Normal tangle-cli installs include the tangle-api package needed for official operations; custom API projects can shadow or replace that package as described in the codegen section.
• --schema-source official: only the checked-in official static schema from tangle-api (or a compatible custom tangle-api package on your environment's import path).
• --schema-source cache: only the schema previously written by tangle api refresh for the selected base URL. This is the custom/source-checkout fallback when a consumer environment does not provide an importable tangle_api.schema 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 static bindings eagerly. Normal installs include tangle-api; source checkouts or downstream embeddings may instead 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

For a project-local custom API package, write both the schema snapshot and generated modules under that project's source tree, then run tools/tests from the project environment so src/tangle_api is earlier on sys.path than the official site-packages package:

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

That project-local tangle_api package can be an editable/package source tree. If you ship the custom API bindings as a wheel or source distribution, use the distribution name tangle-api and a compatible version for the tangle-cli release you are using. A PEP 440 local version such as 0.1.0+yourorg can satisfy a public ==0.1.0 dependency while distinguishing your private build. Provide that package through your private index, --find-links, or uv source configuration so the resolver chooses it instead of the public official package.

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, runtime.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-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 local or packaged tangle_api package containing tangle_api.generated and, for official-schema CLI discovery, tangle_api.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.

The supported workaround hierarchy for custom API consumers is: prefer a project-local src/tangle_api package that shadows site-packages for that project; if distributing bindings, prefer a compatible private tangle-api distribution; reserve --no-deps installs or manual uninstalls of the official package for controlled expert environments where you manually provide all dependencies and the generated/custom tangle_api package.

Runtime generated model extension pattern

tangle_api.generated.models is a leaf package and codegen emits plain generated Pydantic models directly:

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

Generated models do not import tangle_cli and codegen does not bake downstream extension modules into tangle_api. Downstream packages compose their own extended model namespace at runtime. In tangle_cli.models, the default CLI mixins are declared in tangle_cli.generated_model_extensions:

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

tangle_cli.models.compose_models(...) reads those mappings and creates subclasses in the tangle_cli.models namespace, e.g. ComponentSpec(ComponentSpecExtensions, tangle_api.generated.models.ComponentSpec), without mutating tangle_api.generated.models. The generated operations layer also calls _response_model(model_name, default) so TangleApiClient can deserialize responses into the CLI-composed classes while the base GeneratedTangleApiOperations remains downstream-agnostic.

Downstream projects can use the same pattern in their own namespace: import base classes from tangle_api.generated.models, define method/property-only mixins plus a MODEL_EXTENSIONS mapping, and compose subclasses locally. Avoid global monkey-patching of tangle_api.generated.models.

Built-in --model-alias defaults still keep stable public model names such as ComponentSpec even when a backend schema uses names like ComponentSpecOutput or ComponentSpecInput.

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