Flywheel

One platform: routing, verification, the lane layer, the closed loop, and the projected world. The native desktop surface for accountable AI infrastructure.

Flywheel is the engine and native client for a verified-inference loop: a model perceives only through witnessed organs, acts only through a gate it cannot talk past, journals everything, and verifies its own work by re-perceiving.

The flagship tools (gather, crucible, index, forum, learn, telos) are lanes inside Flywheel, each a provisioned, health-checked organ reachable through one surface. Every agent tool call carries a sealed, chain-linked receipt a third party re-verifies offline.

Proof before trust.

What is in this repo

This is a monorepo containing both halves of the platform:

• harness/ is the Python engine: the gateway (localhost HTTP API), the agent loop, the receipt discipline, the lane layer, the verified-inference loop, the tool-call receipt system. Zero runtime dependencies (stdlib only).
• desktop/ is the Flutter native client: 24 views, 50 widgets, zero webview embedding. Talks to the gateway over localhost. Launches a bundled frozen engine by absolute path on a clean machine (no Python, no PATH, no network).
• site/ is a dev/CI fallback browser shell (not the primary UI).

Run it now

Start the gateway (the engine keeps the loop, receipts, lanes, and routing):

flywheel app --port 8799

The native surface is Flywheel Desktop. From a dev checkout:

cd desktop
flutter run -d windows --release

The gateway also serves a /site/index.html shell as a dev/CI fallback.

The lane model

Flywheel encompasses the tool family. Each flagship is a lane:

Check their health through one surface:

flywheel lanes
flywheel lanes --probe    # live MCP handshake per lane

The receipt discipline

Every agent tool invocation carries a sealed receipt binding:

• what the tool was (capability class: read / write / exec / external-mcp)
• what it was allowed to do (admission decision from the gate)
• what it actually did (witnessed args + output sha256 digests, never raw content)
• whether a stranger can re-walk it (offline-verifiable, chain-linked)

Receipts compose into a transitive-witness DAG where a drifted action degrades exactly its downstream dependents. The five flagships emit organ-bundle entries on a shared proof-surface spine so cross-tool receipts compose end-to-end.

The organizational learning loop

The layer above audit. The receipt discipline records what happened at machine resolution. The learning loop feeds forward: it derives lessons from witnessed divergences (an allowed action that rolled back, a memory whose source drifted, a graded failure), stores them in a durable, hash-chained, append-only memory, and surfaces recurring patterns as improvement candidates for human admission. A lesson is not a note an operator wrote; it is a claim bound by hash to its evidence, re-checkable offline, fail-closed when the evidence is gone. See docs/LESSON-LOOP.md.

Offline-first

The Flutter desktop GUI launches a bundled engine by absolute path and serves its UI menu on localhost only. No external web address is contacted to show the GUI. The gateway serves /api/* and the UI on http://127.0.0.1:8799.

Install

pip install flywheel-verify
flywheel up

flywheel-verify is the PyPI distribution name (the bare flywheel name is an unrelated package); the installed command is flywheel. Zero runtime dependencies, stdlib only.

Or from source:

git clone https://github.com/HarperZ9/flywheel.git
cd flywheel
pip install -e .
python scripts/run_harness_cli.py app --port 8799

Documentation

• QUICKSTART.md: first ten minutes
• WALKTHROUGH.md: guided tour
• docs/LESSON-LOOP.md: the organizational learning loop (architecture)
• docs/GUIDE-LESSON-LOOP.md: the organizational learning loop (full guide and spec)
• docs/ASSESSMENT-AGENTIC-SECURITY-2026-08.md: Flywheel against the July 2026 agentic security convergence
• CREDO.md: the belief

License

FSL-1.1-MIT (Functional Source License). See LICENSE.