cyandoor 0.1.0

Decision intelligence for AI agents.
Better decisions, learned from outcomes.

CyanDoor is building an outcome-trained decision layer for AI agents: connect decision-time evidence and eligible candidate actions with downstream outcomes, then use trustworthy history to guide the next choice.

Current release

Version 0.1.0 is an early, deliberately narrow Decision-Evidence foundation. It provides typed, deterministic candidate-set manifests: describe candidate identities, freeze the ordered set before outcomes are observed, calculate its canonical digest, and verify later that the sealed manifest has not changed.

It does not yet include runtime connectors, persistence, Decision Audit/headroom, policy learning, cloud sync, or automatic agent optimization. Coding agents are the initial evidence environment, not the long-term product boundary.

Install

Requires Python 3.12 or newer.

pip install cyandoor

Real example

from datetime import UTC, datetime

from cyandoor import (
    CandidateSetMember,
    DraftCandidateSetManifest,
    seal_candidate_set,
    verify_candidate_set,
)

now = datetime.now(UTC)
draft = DraftCandidateSetManifest(
    candidate_set_id="verification-strategies",
    candidate_set_version="1",
    decision_space_id="execution_strategy",
    members=(
        CandidateSetMember(
            order_index=0,
            candidate_id="run-tests-first@1",
            action_identity_digest="sha256:" + "1" * 64,
        ),
        CandidateSetMember(
            order_index=1,
            candidate_id="inspect-failure-first@1",
            action_identity_digest="sha256:" + "2" * 64,
        ),
    ),
    created_at=now,
)

sealed = seal_candidate_set(
    draft,
    eligibility_rule_id="default",
    eligibility_rule_version="1",
    evidence_eligibility_cutoff_at=now,
    sealed_at=now,
)

assert verify_candidate_set(sealed)

Why Decision Evidence?

Traces reconstruct a run. CyanDoor focuses on the choice inside it: what was available and eligible, what was selected, what evidence existed at the cutoff, and whether that record remains verifiable after the outcome is known.

The long-term relationship is:

decision-time evidence + eligible candidate actions + historical outcomes
-> better next decision

Product breadth belongs in neutral architecture. Public availability belongs only in tested capabilities.

Design principles

• agent-agnostic contracts, with integrations claimed only after validation;
• explicit provenance and evidence-strength states;
• immutable, strictly validated models;
• deterministic canonical serialization and digests;
• no causal or improvement claim without supporting evidence.

The public website and expanded documentation are being developed in cyandoor/cyandoor-web. Until its canonical domain is live, this README remains the release-accurate technical starting point.

Contributing and status

The repository is in early alpha. Use GitHub Issues for focused bug reports and technical discussion.

License

CyanDoor is licensed under the Apache License 2.0.

Evidence is the beginning. Better decisions are the outcome.