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: 0.0.9.
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.
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