pyrunir-mcp

pyrunir-mcp is a typed Python API for planning-and-learning agents. Callers keep domains, tasks, candidates, validation results, and history in memory; filesystem artifacts are written only at the dump boundary.

See docs/api.md for the API and docs/index.md for workflow/output tables.

Basic Flow

from pyrunir_mcp import (
    ValidationHistory,
    create_domain_context,
    create_policy,
    create_task_context,
    dump_result,
    execute_policy,
    generate_tasks,
)

domain = create_domain_context("domain.pddl")
task = create_task_context(domain, "problem.pddl")
policy = create_policy(domain, "policy.txt")
history = ValidationHistory()

result = execute_policy(task, policy)
feedback = history.fold(result.observation)
dump_result(result, "artifacts/execute-policy")

Entry Points

Context/candidates:

  • create_domain_context(domain_file)
  • create_task_context(domain_context, problem_file, num_threads=1)
  • create_policy(domain_context, policy_file); pass None for an empty policy
  • write_empty_policy(domain_context, policy_file) for canonical empty policy text
  • create_module_program(domain_context, module_program_file); pass None for the built-in empty module program
  • create_classifier(domain_context, classifier_file); pass None for an empty classifier

runir.task_generation:

  • describe_generator(domain_name)
  • get_generator_path(domain_name) / get_generator_domain_path(domain_name)
  • generate_tasks(domain_name, output_dir, batch_name, configs, allow_invalid=False)

Validation:

  • execute_policy(task_context, policy, classifier=None, ...)
  • prove_policy(task_context, policy, evidence_classifier=None, ...)
  • execute_module_program(task_context, module_program, classifier=None, ...)
  • prove_module_program(task_context, module_program, evidence_classifier=None, ...)
  • prove_classifier(task_context, classifier, ...)

Dumping:

  • dump_result(result, output_dir, formats=(DumpFormat.JSON,))
  • dump_validation_history(history, output_dir, formats=(DumpFormat.JSON,))

DumpFormat.PSV, DumpFormat.MD, and DumpFormat.JSON request LLM-readable PSV, human-readable Markdown, and machine-readable JSON. Validation calls do not write files.

Key dates

PyPI data
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  • Released:
Newer release available (0.0.33)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for DominikDrexler from gravatar.com DominikDrexler

Credits

Author: Dominik Drexler

Requires

Python >=3.13

Provides Extra

dev

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