Introduction

ferroplan is a fast, data-parallel PDDL planner in Rust — a from-scratch reimplementation of the FF planner family, and a deterministic planning core for the age of AI.

The bet behind it: an LLM should be the author and supervisor of a planner, not its runtime. You don't ask a model to add a column of numbers — you have it emit code that does the arithmetic deterministically and for free; the same applies one level up. Rather than running a whole village of agents' decisions through an LLM every tick — expensive, non-reproducible, unbounded — the model authors a PDDL domain that plans deterministically, cheaply, inspectably, and at scale, and only nudges it at runtime. PDDL is the auditable interface between intent, the model's authoring, and a fast solver. You can read a domain and an axiom; you can't read a model's weights.

It combines:

• a delete-relaxation FF heuristic over a data-oriented task representation (bitset states, structure-of-arrays / CSR operator tables);
• data parallelism — parallel grounding and parallel batch heuristic evaluation, with bit-for-bit identical plans regardless of thread count;
• ADL (conditional effects, forall/exists, equality) and numeric fluents;
• derived predicates / axioms (:derived, static/stratified);
• PDDL3 preferences with anytime branch-and-bound metric optimization, and PDDL3 trajectory constraints ((:constraints ...)) — the six untimed modal operators enforced via monitor-automaton compilation, hard and soft alike;
• PDDL2.1 temporal planning — durative actions with constant or parameter-dependent durations and required concurrency (see Temporal planning);
• an optional SGPlan-style partition-and-resolve mode.

It is offered as a Rust library (with a structured, JSON-serializable API) and the ff command-line binary, a drop-in for Metric-FF.

Acknowledgments

ferroplan owes an enormous debt to the planners it learns from. Above all SGPlan (Chih-Wei Hsu and Benjamin W. Wah, University of Illinois), which has set the standard in satisficing planning with preferences and temporal/resource constraints for nearly two decades — coming even close to it on a slice of the benchmarks is genuinely an honor, and a tribute to the depth and durability of that team's research. And to Jörg Hoffmann's FF / Metric-FF, whose relaxed-plan heuristic and enforced hill-climbing are this engine's backbone, and to VAL (Derek Long & Maria Fox) for independent temporal-plan validation.