triad-base

Primitives for building thinker / doer / checker agent triads with Claude (or any LLM).

What's in the box

Module What it gives you
triad_base.types AgentType / PlanStatus enums; Plan, AgentMetadata, ExecutionResult dataclasses; parse_agent_metadata() parser
triad_base.state.schema Pydantic v2 models for agent state JSON (ThinkerState / DoerState / CheckerState / Heartbeat / ExecutionRecord / interventions) + load_state / save_state / validate_state_file
triad_base.state.validation validate_file / validate_directory / migrate_file / migrate_directory / check_schema_compatibility
triad_base.preflight HealthChecker (disk space · state-dir writable · stale locks · optional config valid · Claude CLI present) + create_checkpoint / restore_checkpoint / clear_checkpoint (git-stash rollback)
triad_base.orchestrator TriadContext + build_thinker_prompt / build_doer_prompt / build_checker_prompt + format_prior_suggestions
triad_base.verdict Typed gate/judge verdict contract (schema v1) — Verdict / Finding / ReviewerError / ReviewerResult + to_json_schema(); pool-pure, pydantic-only

Design

Pure plumbing, no transport. The library assembles prompts and parses output; it does not call Claude. Consumers wire the prompts to their own transport (CLI subprocess, Anthropic SDK, direct API). Transport is intentionally out of scope so different consumers can diverge in dispatch (CLI vs SDK vs API) without dragging shared transport glue along.

Narrow surface. Five small modules. No CLI, no __main__, no opinionated workflow. You compose the parts you need.

Install

pip install triad-base

Usage sketch

from pathlib import Path
from triad_base.types import AgentType, Plan, PlanStatus
from triad_base.orchestrator import TriadContext, build_thinker_prompt
from triad_base.preflight import HealthChecker

# Preflight before kicking off
report = HealthChecker(
    portfolio_root=Path("/repo"),
    state_dir=Path("/repo/.state"),
).run_all_checks()
if not report.ok:
    raise RuntimeError(report.summary())

# Build a thinker prompt
plan = Plan(id="ex-001", target_project="myproject", status=PlanStatus.READY, ...)
ctx = TriadContext(
    plan=plan,
    state_dir=Path("/repo/.state"),
    working_dir=Path("/repo"),
    prior_suggestions=None,
    test_output="",
    thinking_context="",
    execution_context_tail="",  # add your own logging / state-persistence block
)
prompt = build_thinker_prompt(ctx)

# Hand `prompt` to your Claude transport of choice; you own that part.

Versioning

Semantic versioning. 0.x.y series — interface may change between minor versions during early use; pin in your consumer's requirements.txt or pyproject.toml accordingly.

Dependencies

  • pydantic>=2.0
  • pyyaml>=6.0

License

MIT

Project links

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Key dates

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  • Released:
Newer release available (0.2.2)

1 maintainer

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Credits

Author: Mike Donnelly

GitHub Statistics

Data verified by PyPI on Aug 22, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

dev

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