Workflow State Machine

Implement WorkflowRunner, an in-memory job tracker for dependent work. Jobs move through a small set of states:

Requirements

• add_job(job_id, deps=None, max_attempts=1) registers a job.
• add_job raises ValueError on duplicate ids, max_attempts < 1, or dependencies that are not already registered.
• ready() returns pending jobs whose dependencies have succeeded, in insertion order.
• transition(job_id, state, reason=None) validates state transitions and returns the final state.
• Starting a job increments its attempt count.
• transition to running is allowed only when the job is currently pending and appears in ready(); otherwise raise ValueError (message must include "dependencies" when the job is blocked on unsatisfied dependencies).
• Other illegal edges raise ValueError whose message includes "invalid transition".
• If a running job fails and still has attempts left, move it to retrying; otherwise move it to failed. Requesting "failed" may therefore return "retrying" when attempts remain.
• retrying jobs are not ready. The caller must explicitly transition(..., "pending") to re-queue them.
• status(job_id) returns {"state": str, "attempts": int, "history": list[dict]} where each history entry is {"state", "reason", "attempt"} in transition order. attempt is the job's attempt count at the time of that transition.
• Raise ValueError for invalid transitions or unknown jobs.

Example

Constraints

• Keep it single-process and in memory.
• Use deterministic ordering.
• Raise ValueError for invalid transitions or unknown jobs.