Calybris Core

A deterministic decision engine: it selects under explicit constraints, and makes the decision verifiable afterwards.

Given a frozen catalog, a policy snapshot, and a typed request, Calybris returns one action plus an audit bundle that replays to the same answer.

catalog + policy + request  ->  decision + audit bundle

Integer-only Rust hot path. No hosted dependency. No unsafe in project code.

What is this?

Calybris is a proof-carrying decision kernel: not an OMS, not an LLM gateway, not a matching engine. You bring the catalog — suppliers, carriers, venues, models — and the kernel evaluates hard constraints, picks the best eligible candidate, and emits digests you can replay and verify offline.

The kernel does not know what a candidate is. Supplier routing, carrier selection, model routing and pre-trade admission are reference mappings onto one API, not four products.

Two claims it does not make. It does not find the best commercial outcome; it selects by the rules you wrote, and a wrong rule produces a wrong decision you can at least see. And it proves the integrity of the trail, not the truth of your inputs.

In this release — 0.6.0

A typed decision surface on the kernel that was already there, and a way to ask what a policy change would have done.

0.5.8 and 0.5.9 were never published, so this is the whole distance from 0.5.7. Units, identities and the exact limits of the rejection trace are in docs/DECISIONS_0.6.0.md; the full list is in the CHANGELOG.

When to use / when not to

Quickstart (~5 minutes)

pip install calybris

The shortest path to a decision is the typed adapter: fixed quotes in, one selected candidate plus a replay-verified proof out.

from calybris import Candidate, DecisionEngine, DecisionRequest, EngineConfig

day = 86_400_000
catalog = [
    Candidate(candidate_id=1, provider_id=0, quality_bps=9000, risk_ceiling_bps=8000,
              lead_time_ms=8 * day, region_mask=1, quoted_cost_microunits=80_000_000),
    Candidate(candidate_id=2, provider_id=1, quality_bps=9500, risk_ceiling_bps=8000,
              lead_time_ms=3 * day, region_mask=1, quoted_cost_microunits=100_000_000),
]

# Lead time is a hard deadline here, not something to trade off against price,
# so its scoring penalty is switched off. Leave the default on and a delivery
# measured in days outweighs any realistic order value.
engine = DecisionEngine(catalog, config=EngineConfig(latency_penalty_microunits_per_ms=0))

request = DecisionRequest(
    request_sequence=1,
    budget_microunits=120_000_000,
    business_value_microunits=200_000_000,
    maximum_lead_time_ms=5 * day,
)
result = engine.decide(request)

result.status                     # "selected"
result.selected_candidate_id      # 2 — the cheaper quote misses the deadline
engine.verify(request, result)    # recomputed against the same catalog and policy

Quotes, budget and business value are integers in one currency and scale that you choose; nothing converts between currencies. Lead time is milliseconds in a u32, which caps it at roughly 49.7 days. When no candidate clears every gate, status is "rejected" and selected_candidate_id is None — never a fictitious zero.

verify does not read a self-declared flag. It rebuilds the whole result from the catalog, policy and request you hand it, which is why those are what you persist alongside the decision.

python examples/supplier_decision.py runs the full version offline, including a policy comparison. The candidates are suppliers there; the kernel does not know that.

Asking what a policy change would do

from calybris import compare_policies

stricter = DecisionEngine(catalog, config=EngineConfig(
    latency_penalty_microunits_per_ms=0, minimum_confidence_bps=9500,
))
comparison = compare_policies(engine, stricter, historical_requests)

comparison.total            # requests replayed
comparison.changed          # outcomes that moved
comparison.newly_rejected   # requests that now clear nothing
comparison.policy_changed   # True — compares native identity, not just config

This is a replay, not a forecast. It says what the two rule sets do to the same inputs; it does not demonstrate realized savings or supplier performance.

The Rust surface

git clone https://github.com/emirhuseynrmx/calybris-core.git
cd calybris-core
cargo run --example quickstart

That example builds a two-model policy, prescribes one request, verifies replay, and prints an audit bundle.

Budget control

AgentBudget sits beside the decision rather than in front of it: one shared budget across paid calls, with pre-call reservations, explicit uncertain-usage reconciliation, documented corrections for an overrun the budget could not absorb, and bounded immutable reports. python examples/agent_budget.py needs no provider credentials. See docs/AGENT_BUDGET.md for the same-process, non-streaming support boundary.

Use cases

Each is a runnable example in this repository, not a pitch.

Choosing among fixed quotes. Suppliers or subcontractors have quoted a already-priced job. Which one clears the budget, the deadline, the quality floor and the required capabilities — and can you show why, months later? → python examples/supplier_decision.py

Model and provider routing. A gateway picks among premium, fast, and budget providers under quality, latency, risk, provider, and budget ceilings. Every decision is verified before it enters the audited WAL, so "why did this request get the premium model" is answered from the proof rather than from log archaeology. → cargo run --example llm_routing

Pre-trade admission. A desk runs a VWAP algo at the cash open. Each child order clears a policy gate (an eligible venue under risk, latency, quality, and fee caps) and an exposure gate (notional reserved against the desk budget, routing fees committed on admit). The ledger carries the conservation proof remaining + reserved + committed == initial, in checked integers. Calybris owns the two gates and the proof — not the OMS, the market-data feed, or the matching engine. → cargo run --example pretrade_guard

Fulfillment and supplier routing at volume. 10,000 orders across 8 courier networks with distinct SLAs, regional coverage, and risk tolerances. Substitutions are recorded as decisions, so "why this courier, for this order" stays answerable months later. → python bindings/python/examples/orion_market.py

Defending a policy change with numbers. The same catalog evaluated under strict, medium, and relaxed profiles, reporting fulfillment and substitution rates, cost percentiles, batch and single-decision throughput, resident memory, and the audit success and tamper-detection counts for the resulting trail. → python bindings/python/examples/novamart_benchmark.py

The domain objects differ. The kernel, the digests, and the replay contract do not.

What gets proved

Calybris binds the full decision path:

policy digest + input digest + decision digest + replay result

The proof format is a written contract, not an implementation detail. docs/CALY_PROOF.md specifies every digest and chain byte-exactly, golden and conformance vectors pin them across versions and platforms so an independent reimplementation can prove itself against a fixed reference, and the bundled calybris-verify CLI lets an auditor check a decision trail without running your engine.

cargo install calybris-core   # ships the calybris-verify binary
calybris-verify chain decisions.wal.jsonl
calybris-verify chain decisions.wal.jsonl --anchor trusted-head.json
calybris-verify audit decisions.wal.jsonl --policy policy.json
calybris-verify audit rotated.wal.jsonl --policy policy-v1.json --policy policy-v2.json --json

What the trail carries:

• Receipts. receipt::verify_receipt_full verifies replay, claims, trusted signature, state anchor and WAL anchor as one fail-closed operation.
• Trusted policies. Construction canonicalizes catalog order, reserves model ID 0 as the rejection sentinel, and refuses catalogs that cannot fit the public decision counters.
• State trajectories. state_digest_before/after per decision; verify_complete_trajectory binds genesis and the expected terminal step, while verify_trajectory remains an unanchored fragment check.
• Ledger and checkpoints. Ledger digests bind WAL watermarks and reservation allocator state; coordinated checkpoints commit immutable snapshot and anchor generations behind one atomic manifest, with a loader that verifies the actual WAL.
• Policy rotation. Library and CLI WAL replay resolve the exact policy per record across rotations.
• Truncation. WalAnchor detects a cleanly removed WAL suffix when the trusted head is stored outside the WAL file.
• Signed provenance. Ed25519 policy signatures are domain-separated, so a signature is non-transferable across policies, signers and timestamps.

Python exposes the same signed policies, state-chain transitions, decision receipts, keyed audited WAL, durable anchors and replay verification. The verification path builds for wasm32-unknown-unknown (--no-default-features).

docs/THREAT_MODEL.md is explicit about scope: the system proves trail integrity, not confidentiality, policy quality, or input truth.

Architecture at a glance

On the Python side, calybris.decisions is the typed fixed-quote adapter and calybris.agent is the shared budget. Both call the Rust implementations rather than reimplementing security-sensitive logic.

Ships a calybris-verify auditor CLI (chain / audit / policy, --json) so a third party can verify a decision trail without running your engine.

Stability model

Rust owns correctness and replay semantics. The core Python package exposes the production trust boundary and is tested as an installed abi3 wheel. The Python API remains pre-1.0, so pin minor versions even though its runtime integrity guarantees match the Rust core. See the 0.5.7 trust-release migration for the canonical production path and the CALY-PROOF v1 compatibility boundary.

Install

# Rust (stable surface)
cargo add calybris-core

# Python (production-capable core binding; pre-1.0 API)
pip install calybris

Local Python build: maturin develop --release or see docs/PYTHON.md.

Examples & adapters

Reference integrations that map domain objects onto the kernel:

Full command list and code samples: docs/ADAPTERS.md

Performance

CodSpeed CI (Linux x86_64, release): ~8.6M prescribe/sec, ~115 ns/decision, 22-model synthetic catalog. Hardware and workload dependent — provenance and a reproduction recipe are in docs/BENCHMARKS.md; run cargo bench --bench kernel_bench on your own hardware.

A release-blocking production torture suite, introduced in 0.5.7 and still enforced, covers a 64-model checked kernel, state trajectories, signed receipts, keyed audited WAL, suffix-truncation detection, contended budgets, and a 25,000-tenant ledger.

Security posture

• #![forbid(unsafe_code)] — no unsafe in project code.
• Fail-closed audit boundaries: verified_audit_bundle / append_verified_audited refuse to emit or log a decision that does not replay exactly.
• Tamper-evident WAL: SHA-256 hash chain, optional HMAC-SHA256 with constant-time comparison (subtle). Keyed WAL APIs reject keys shorter than 32 bytes.
• Trusted WalAnchor verification detects a cleanly removed WAL suffix; the hash chain alone validates only the records still present.
• Anchored recovery APIs refuse to build a recovery plan from a valid but incomplete WAL prefix.
• visit_verified_wal* streams verified entries, so CLI audit and recovery planning do not retain the complete log in memory.
• Sync and async WAL writers enforce one active writer per file.
• prescribe_checked and the checked batch and trace APIs validate untrusted Rust inputs.
• Signed decision receipts bind optional state and WAL evidence to the exact replay-verified decision.
• Byte-exact proof contract (docs/CALY_PROOF.md) locked by golden and conformance vectors, cross-checked in Rust and Python.
• Concurrency and UB: 7 Loom exhaustive interleavings on budget ops; Miri on nightly for the library tests.
• Security CI: Semgrep Rust/Python/secrets/security-audit, cargo-audit, and cargo-deny; feature matrix covers default / no-default / async / full.
• Documented boundaries: docs/THREAT_MODEL.md (what it does not guarantee) and docs/KEY_MANAGEMENT.md (key custody and rotation).

Deployment security remains the caller's job: key storage, tenant isolation, inventory/capacity freshness, and an external audit.

Deep dive

License

Apache-2.0. See LICENSE.