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Getting Started - Plan-Execute From YAML

The plan_execute_from_yaml.py workflow is useful for longer tasks that benefit from explicit planning, execution, checkpointing, and restart prompts.

Current status

This workflow currently lives in the examples directory. It is useful today for long, complex jobs, and future URSA versions are expected to integrate this workflow more directly into the main URSA interface.

Prerequisites

  • URSA is installed.
  • You have access to an LLM endpoint.
  • You are running from a clone of the URSA repository or otherwise have the examples/two_agent_examples/plan_execute/ files available.
  • You have a dedicated workspace for generated files.

1. Choose an example YAML file

The repository includes example task files under:

examples/two_agent_examples/plan_execute/

A good first run is:

examples/two_agent_examples/plan_execute/example_from_yaml.yaml

Other examples include pi_multiple_ways.yaml, openchami_boot_docs_example.yaml, and larger scientific demos.

2. Run the workflow

python examples/two_agent_examples/plan_execute/plan_execute_from_yaml.py \
  --config examples/two_agent_examples/plan_execute/example_from_yaml.yaml \
  --workspace ./plan-execute-workspace

The runner may ask you to:

  • select or confirm an LLM model,
  • choose single or hierarchical planning,
  • resume from a checkpoint or start fresh.

For a first run, choose the simplest/single-planning option and start fresh.

3. Review the generated plan

The planning stage decomposes the YAML task into steps. Read the plan before allowing a long workflow to proceed, especially if the execution step may write files, install packages, or run commands.

4. Review outputs in the workspace

The workspace contains generated code, outputs, checkpoints, and artifacts created during the run. Treat it as the durable record of the job.

5. Resume or inspect a run

For detailed checkpointing, resume behavior, and troubleshooting, see the Plan-Execute checkpointing reference.

Where next?