pytyr-mcp

pytyr-mcp is a typed Python API for planning-and-learning agents. Callers keep parsed domains, task contexts, and search results in memory; filesystem artifacts are written only at the dump boundary.

Current release: 0.0.7.

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

Basic Flow

from pytyr_mcp import (
    DumpFormat,
    SearchBudget,
    create_domain_context,
    create_task_context,
    find_satisficing_plan,
)

domain = create_domain_context("domain.pddl")
task = create_task_context(domain, "problem.pddl")

result = find_satisficing_plan(task, search_budget=SearchBudget(100_000, 5.0))
dump = result.dump("artifacts/find-plan", formats=(DumpFormat.JSON,), include_plan_text=True)

Entry Points

Context:

  • create_domain_context(domain_file)
  • create_task_context(domain_context, problem_file, num_threads=1)

Planning:

  • find_satisficing_plan(task_context, search_budget=PROVE_SEARCH_BUDGET)

Dumping:

  • result.dump(...)

Planning calls return typed result objects and do not write files.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.0.11)

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

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