cadora 0.5.0

Ship agent-built software you can prove — an audit-grade conductor for coding-agent CLIs (Claude Code, Codex): deterministic gates, tamper detection, run evidence, per-node cost attribution.

Cadora

Ship agent-built software you can prove — deterministic gates, tamper detection, run evidence, and per-node cost attribution, across coding-agent CLIs.

Coding agents can build real software. Cadora is the audit-grade conductor that proves what they built: it drives headless coding-agent CLIs (Claude Code, OpenAI Codex) through a declared multi-step workflow, refuses to take the agent's word for the result, and captures the whole run — artifacts, events, human decisions, and to-the-node cost — as inspectable evidence.

Cadora is the conductor, not the agent: it doesn't implement the agent loop (the backend CLI does), and it isn't an in-IDE assistant. It sits above the vendors, which is exactly what makes its verdicts neutral and its cost ledger cross-vendor.

Why it exists

A vendor's tool verifying that vendor's agent is the fox auditing the henhouse. Cadora verifies from the outside:

• Deterministic, fail-closed gates — Cadora re-runs your build and tests itself and reads exit codes and test counts, never the agent's claims. A test runner that executes zero tests is reported vacuous and blocks the run. A missing toolchain is blocked_prerequisite, not a fake failure — classified for Python, Node, Go, and Rust.
• Tamper detection — cadora integrity detects generated packages and scripts that impersonate real tools, unrecognized build substitutions, and tests run against another project's environment. Modes: audit (record), enforce (block), repair (one constrained fix session, then re-verify).
• Fail-closed human review — mark nodes review: true and run --hitl: the operator must approve, request bounded revisions, or abort; closed stdin aborts rather than silently approving. Every decision, comment, and revision cost is archived. The review surface is pluggable over MCP (Claude Code, Claude Desktop, Codex CLI, or any MCP client).
• Per-node cost attribution, cross-vendor — every node records its backend, model, tokens, and dollars, split by funding source (subscription vs metered API). cadora usage and the local dashboard's FinOps panel aggregate by model / backend / funding / day — one ledger even when design runs on Claude and code runs on Codex.

The evidence of a run is the archive: runs/<id>/manifest.json + per-stage artifacts + the event stream + gate/integrity/review outcomes + cost. Inspect with cadora archive ls / show or the dashboard.

Backends

Backend	Drive	Notes
claude (default)	claude -p, structured stream-json	subscription-funded by default; metered API is explicit opt-in (--funding api)
codex	codex exec --json, structured JSONL	uses your Codex login/plan
fixture	local, deterministic, offline	demos, CI smoke, policy-safe HITL walkthroughs — no model call

Both live backends run the same topology, gates, integrity evaluation, and archive, so their results A/B-compare directly — including phase-split runs (--executor claude --construction-executor codex). The NodeExecutor seam makes a new backend one class.

Methods are packs — AI-DLC is the flagship

Cadora ships the AWS AI-DLC method (AI-Driven Development Life Cycle, MIT-0) as its built-in flagship workflow: cadora aidlc-init installs the rule-set into your workspace (CLAUDE.md for Claude Code, AGENTS.md for Codex — existing project instructions are preserved outside a managed block), and the example topologies drive 🔵 Inception → 🟢 Construction → Build & Test from a vision.md. The method is a pack, not the product: any workflow you can express as a topology of gated nodes conducts the same way.

Install

Requires Python 3.10+ and at least one authenticated backend CLI (claude or codex):

pip install cadora

From source: git clone https://github.com/yeychenne/cadora.git && cd cadora && python3 -m venv .venv && source .venv/bin/activate && pip install -e ".[dev]".

Quickstart

# 1. Set up a workspace from your product vision (installs the AI-DLC method pack).
cadora aidlc-init ./my-project --vision vision.md

# 2. Drive the workflow on Claude Code — autonomous, gated, subscription-funded.
cadora run examples/aidlc.topology.yaml --vision vision.md --cwd ./my-project

# 3. Read the evidence.
cadora archive ls
cadora archive show <run-id>
cadora usage            # tokens + dollars by model / backend / funding
cadora dashboard        # local cockpit: DAG cost/quality map + FinOps panel

A/B the same spec on Codex:

cadora run examples/aidlc.topology.yaml \
  --executor codex --model gpt-5.5 \
  --integrity-mode repair \
  --vision vision.md --cwd ./my-project

Split phases across vendors (design on Claude, code on Codex):

cadora run examples/aidlc-phased.topology.yaml \
  --executor claude \
  --construction-executor codex --construction-model gpt-5.5 \
  --vision vision.md --cwd ./my-project

Scan any existing workspace for toolchain tampering — no agent run required:

cadora integrity ./my-project [--json]

Gate mechanics worth knowing

The gate distinguishes a real failure from an unavailable prerequisite. Python workspaces that declare dev requirements get a cached isolated gate environment (.cadora/gate-venv); your --gate-cmd runs unchanged inside it. If provisioning is impossible, the archive records blocked_prerequisite + the missing packages instead of misreporting the application as broken. Offline: --gate-wheelhouse /path/to/wheels; opt out with --gate-setup off.

Autonomous runs pass --dangerously-skip-permissions to the backend (an agentic workflow edits files and runs commands) — point Cadora only at workspaces you trust, prefer a dedicated worktree/container, and keep credentials out of the workspace environment.

The dashboard binds localhost only, no authentication — keep it on loopback or front it with TLS + auth. See docs/dashboard.md.

Status

v0.5.0 — the multi-backend + repositioning release: multi-backend phase routing (--construction-executor), per-node executor cost attribution in cadora usage and the dashboard FinOps panel, and the audit-grade repositioning (AI-DLC becomes the flagship method pack). On top of v0.4.0's gate substance checks (vacuous-pass blocking), cross-stack prerequisite classification, and the topology/FinOps dashboard. 120+ tests, ruff clean, CI on Python 3.10–3.12.

Roadmap: cadora report — a portable, self-contained evidence pack per run (gates, integrity findings, review trail, per-node cost); cadora compare — side-by-side measured verdicts across backends/models; additional backend and method packs as they earn verification.

License

MIT. The vendored AI-DLC rule-set is MIT-0 (awslabs/aidlc-workflows).