Deployment Artifact Format (tar.gz)

When a workflow is deployed, Agent Studio packages it into a tar.gz archive that the workflow engine can execute independently. This format differs from the template ZIP — it contains runtime-resolved data rather than template blueprints.

Archive Structure

artifact.tar.gz
├── workflow.yaml                           # REQUIRED: artifact type descriptor
├── collated_input.json                     # REQUIRED: complete runtime definition
└── studio-data/                            # Tool code and workflow files
    └── workflows/
        └── {workflow_directory_name}/
            └── tools/
                └── {tool_slug}/
                    ├── tool.py
                    ├── requirements.txt
                    └── [additional files]

workflow.yaml

A simple descriptor that tells the workflow engine how to load the artifact:

type: collated_input
input: collated_input.json

Currently, collated_input is the only supported artifact type. The input field points to the JSON file containing the full workflow definition.

collated_input.json

The complete runtime representation of the workflow. See CollatedInput Schema for the full specification.

studio-data/ Subtree

The studio-data/ directory is a filtered copy of the project’s studio-data/:

• Only the specific workflow’s directory under studio-data/workflows/ is included
• tool_templates/, temp_files/, and deployable_workflows/ are excluded
• Within tool directories, the following are excluded:
  • .venv/
  • .next/
  • node_modules/
  • .nvm/
  • .requirements_hash.txt

How the Engine Consumes Artifacts

1. The engine extracts the tar.gz to a temporary directory
2. Reads workflow.yaml to determine the artifact type
3. Loads collated_input.json and parses it into the CollatedInput Pydantic model
4. Resolves tool paths relative to the extracted directory
5. Creates virtual environments for each tool from their requirements.txt
6. Executes the workflow using CrewAI or LangGraph based on the artifact contents

LangGraph Artifact Alternative

LangGraph workflows use a different artifact structure, detected by the presence of langgraph.json:

langgraph_artifact/
├── langgraph.json                          # LangGraph graph definition
├── pyproject.toml                          # Dependencies (installed via pip)
├── .env                                    # Optional environment variables
└── src/
    └── graph.py                            # Graph implementation

The langgraph.json schema:

{
  "graphs": {
    "graph_name": "src/graph.py:graph_symbol"
  }
}

Currently limited to a single graph per artifact. Dependencies are installed via pip install . from the artifact root.