Routing note: For ambiguous user intents, use the shared clarification templates in references/intent-clarification.md.

TrueFoundry Workflows

Create, configure, and deploy workflows on TrueFoundry. Workflows are built on Flyte, an open-source orchestration platform, and use Python decorators (@task, @workflow) to define structured sequences of tasks as directed acyclic graphs (DAGs).

Workflow definition uses Python SDK (@task/@workflow decorators). Workflow deployment uses tfy deploy workflow CLI command. Alternative: tfy apply with a YAML manifest. REST API fallback when CLI unavailable.

When to Use

• User wants to create a data processing or ML training pipeline
• User says "create a workflow", "deploy a workflow", "run a pipeline"
• User wants to schedule recurring batch operations (cron workflows)
• User wants to orchestrate multi-step tasks: ETL, feature engineering, model training, batch inference
• User asks about Flyte tasks, map tasks, conditional tasks, or Spark tasks on TrueFoundry

When NOT to Use

• User wants to deploy a web service or API -> prefer deploy skill; ask if the user wants another valid path
• User wants to deploy a one-off job (not a multi-step pipeline) -> prefer deploy skill; ask if the user wants another valid path with job type
• User wants to check running applications -> prefer applications skill; ask if the user wants another valid path
• User wants to monitor job runs -> prefer jobs skill; ask if the user wants another valid path
• User wants to serve an ML model behind an endpoint -> prefer llm-deploy or deploy; ask which path they want

Prerequisites

Always verify before creating a workflow:

1. Credentials — TFY_BASE_URL and TFY_API_KEY must be set (env or .env)
2. Workspace — TFY_WORKSPACE_FQN required. Never auto-pick. Ask the user if missing.
3. SDK — Install the TrueFoundry SDK with workflow extras (required for defining tasks/workflows):

   pip install "truefoundry[workflow]"
   

4. CLI login — Authenticate the CLI (required for deployment):

   tfy login --host "$TFY_BASE_URL"
   

5. Python — Python 3.9+ required for workflow definition code
6. Cluster setup — The Flyte data plane must be installed on the target cluster. The control plane ships with TrueFoundry (no additional setup). If the user gets errors about Flyte not being available, they need to contact their platform admin to install the data plane components.

For credential check commands and .env setup, see references/prerequisites.md.

CLI Detection

tfy --version

Core Concepts

• Task -- The smallest unit of execution. A Python function decorated with @task. Each task runs in its own container with configurable resources and dependencies.
• Workflow -- A composition of tasks arranged as a DAG using the @workflow decorator. Defines execution order and data flow between tasks.
• Task Config -- Specifies the container image, Python version, pip packages, and compute resources for each task.
• Execution Config -- Controls scheduling (cron), launch plans, and runtime parameters.

Critical rule: The workflow function must contain only task calls and control flow. Do not put business logic directly in the workflow function -- all computation must live inside @task functions.

Basic Workflow Example

from truefoundry.workflow import (
    PythonTaskConfig,
    TaskPythonBuild,
    conditional,
    task,
    workflow,
)
from truefoundry.deploy import Resources

# Define task configuration
cpu_task_config = PythonTaskConfig(
    image=TaskPythonBuild(
        python_version="3.9",
        pip_packages=["truefoundry[workflow]"],
    ),
    resources=Resources(cpu_request=0.5, memory_request=500),
)

@task(task_config=cpu_task_config)
def fetch_data(source: str) -> dict:
    """Fetch and return raw data."""
    data = {"records": 1000, "source": source}
    return data

@task(task_config=cpu_task_config)
def process_data(raw_data: dict) -> dict:
    """Clean and transform the data."""
    processed = {
        "records": raw_data["records"],
        "source": raw_data["source"],
        "status": "processed",
    }
    return processed

@task(task_config=cpu_task_config)
def train_model(data: dict) -> str:
    """Train a model on processed data."""
    return f"model_trained_on_{data['records']}_records"

@workflow
def ml_pipeline(source: str = "default") -> str:
    """End-to-end ML pipeline."""
    raw = fetch_data(source=source)
    processed = process_data(raw_data=raw)
    result = train_model(data=processed)
    return result

Note: Workflows REQUIRE the Python SDK for definition -- this is the exception to the CLI-first approach. Only the deploy step uses CLI.

Task Configuration

PythonTaskConfig

Every task needs a PythonTaskConfig that defines its execution environment:

from truefoundry.workflow import PythonTaskConfig, TaskPythonBuild
from truefoundry.deploy import Resources

# CPU task
cpu_task_config = PythonTaskConfig(
    image=TaskPythonBuild(
        python_version="3.9",
        pip_packages=[
            "truefoundry[workflow]",
            "pandas==2.1.0",
            "numpy",
        ],
        # Or use a requirements file:
        # requirements_path="requirements.txt",
    ),
    resources=Resources(
        cpu_request=0.5,
        memory_request=500,
    ),
)

# GPU task (for training or inference steps)
gpu_task_config = PythonTaskConfig(
    image=TaskPythonBuild(
        python_version="3.9",
        pip_packages=[
            "truefoundry[workflow]",
            "torch",
            "transformers",
        ],
    ),
    resources=Resources(
        cpu_request=2.0,
        cpu_limit=4.0,
        memory_request=8192,
        memory_limit=16384,
        devices=[
            GPUDevice(name="T4", count=1),
        ],
    ),
)

Key points:

• truefoundry[workflow] must always be in pip_packages (or in the requirements file)
• pip_packages takes a list of pip-installable package specifiers
• requirements_path can point to a requirements file instead of inline packages
• Resource memory_request and memory_limit are in MB
• Different tasks can have different configs (e.g., lightweight preprocessing vs GPU-heavy training)

Container Tasks

For tasks that need a pre-built Docker image instead of a Python build:

from truefoundry.workflow import task, ContainerTask

container_task = ContainerTask(
    name="my-container-task",
    image="my-registry/my-image:latest",
    command=["python", "run.py"],
    resources=Resources(
        cpu_request=1.0,
        memory_request=2048,
    ),
)

Use container tasks when:

• You need a custom base image with system-level dependencies
• The task runs non-Python code
• You have a pre-built image with all dependencies baked in

Security: Verify container image sources before using them in workflow tasks. Pin image tags to specific versions — do not use :latest. For pip_packages, pin package versions to avoid supply-chain risks from unvetted upstream changes.

Cron Workflows (Scheduling)

Schedule workflows to run at fixed intervals using cron syntax. The schedule is always in UTC timezone.

from truefoundry.workflow import workflow, ExecutionConfig

@workflow(
    execution_configs=[
        ExecutionConfig(schedule="0 6 * * *"),  # Every day at 6:00 AM UTC
    ]
)
def daily_etl_pipeline() -> str:
    raw = fetch_data(source="production_db")
    processed = process_data(raw_data=raw)
    return processed["status"]

Common Cron Patterns

Cron format: minute hour day-of-month month day-of-week

Advanced Patterns

For map tasks (parallel execution), conditional tasks (branching logic), and Spark tasks (large-scale data processing), see references/workflow-advanced-patterns.md.

Deploying Workflows

After the user writes their workflow code (Python file with @task and @workflow decorators), deploy using tfy deploy workflow.

Approach A: Via tfy deploy workflow (CLI -- Primary)

tfy deploy workflow \
  --name my-ml-pipeline \
  --file workflow.py \
  --workspace_fqn "$TFY_WORKSPACE_FQN"

Important: After deployment, the workflow must be triggered manually. The TrueFoundry UI shows a yellow banner indicating the workflow is deployed but not yet running. The user can trigger it from the dashboard or via a launch plan.

Approach B: Via tfy apply (YAML Manifest -- Alternative)

1. Generate the workflow deployment manifest:

# workflow-manifest.yaml
name: my-ml-pipeline
type: workflow
workflow_file: workflow.py
workspace_fqn: "YOUR_WORKSPACE_FQN"

2. Preview:

tfy apply -f workflow-manifest.yaml --dry-run --show-diff

3. Apply:

tfy apply -f workflow-manifest.yaml

Approach C: Via Python SDK

from truefoundry.workflow import WorkflowDeployment

deployment = WorkflowDeployment(
    name="my-ml-pipeline",
    workflow_file="workflow.py",
    workspace_fqn="your-workspace-fqn",
)
deployment.deploy()

Deployment Checklist

Before deploying, confirm with the user:

• Workflow name -- a descriptive name for the workflow
• Workspace -- TFY_WORKSPACE_FQN is set (never auto-pick)
• Task configs -- each task has appropriate resources, Python version, and packages
• Pip packages -- truefoundry[workflow] is included in every task's packages
• Schedule -- if cron, confirm the cron expression and timezone (always UTC)
• Workflow file -- path to the Python file containing @workflow and @task definitions

Post-deploy: Remind the user that the workflow must be triggered manually after deployment. The TrueFoundry dashboard shows a yellow banner for newly deployed workflows that have not been triggered yet.

Monitoring Workflow Runs

After deployment, monitor runs through:

1. TrueFoundry Dashboard -- Navigate to Workflows in the dashboard to see run history, task status, logs, and DAG visualization.
2. Applications skill -- Use the applications skill to list workflow applications:

   tfy_applications_list(filters={"application_type": "workflow"})
   

3. Jobs skill -- Use the jobs skill to inspect individual workflow run details and status.

Run States

<success_criteria>

• The user has a workflow file with properly decorated @task and @workflow functions
• The agent has confirmed that all @task functions include truefoundry[workflow] in their pip_packages
• The workflow was successfully deployed to the specified workspace using tfy deploy workflow (or tfy apply as alternative)
• The user can monitor workflow runs via the dashboard or applications skill
• The agent has set up a cron schedule if the user requested recurring execution

</success_criteria>

Composability

• Check credentials first: Use status skill to verify TrueFoundry connection
• Find workspace: Use workspaces skill to list available workspaces
• List workflows: Use applications skill with application_type: "workflow"
• Monitor runs: Use jobs skill to check run status and history
• View logs: Use logs skill to inspect task-level logs
• Manage secrets: Use secrets skill to set up secret groups for workflow tasks that need API keys or credentials

Error Handling

CLI Errors

tfy: command not found
Install the TrueFoundry CLI:
  pip install 'truefoundry==0.5.0'
  tfy login --host "$TFY_BASE_URL"

Manifest validation failed.
Check:
- YAML syntax is valid
- Required fields: name, type, workflow_file, workspace_fqn
- Workflow file path is correct and accessible

SDK Not Installed

The truefoundry[workflow] package is required for defining tasks and workflows:
  pip install "truefoundry[workflow]"

TFY_WORKSPACE_FQN Not Set

TFY_WORKSPACE_FQN is required. Get it from:
- TrueFoundry dashboard -> Workspaces
- Or use the `workspaces` skill to list available workspaces
Do not auto-pick a workspace.

Flyte Data Plane Not Available

Flyte data plane is not installed on this cluster.
The control plane ships with TrueFoundry, but the data plane must be installed
on each cluster separately. Contact your platform admin to set up the Flyte
data plane components on the target cluster.

Business Logic in Workflow Function

Error: Workflow function contains non-task code.
The @workflow function must only contain task calls and control flow.
Move all computation into @task-decorated functions.

Bad:
  @workflow
  def my_wf():
      data = pd.read_csv("file.csv")  # NOT allowed in workflow function
      return process(data)

Good:
  @workflow
  def my_wf():
      data = load_data()              # Call a @task instead
      return process(data)

Missing truefoundry[workflow] in Task Packages

Each task's PythonTaskConfig must include "truefoundry[workflow]" in pip_packages.
Without it, the task container cannot communicate with the Flyte backend.

Fix: Add "truefoundry[workflow]" to the pip_packages list in every PythonTaskConfig.

Task Resource Errors

Task failed due to resource limits (OOMKilled or CPU throttled).
Increase memory_limit or cpu_limit in the task's Resources config.
Check the task logs in the TrueFoundry dashboard for details.

Cron Schedule Not Triggering

Cron schedules use UTC timezone. Verify your cron expression accounts for
UTC offset from your local timezone.
Use https://crontab.guru to validate your cron expression.

REST API Fallback Errors

401 Unauthorized — Check TFY_API_KEY is valid
404 Not Found — Check TFY_BASE_URL and API endpoint path
422 Validation Error — Check manifest fields match expected schema