Flywheel is software you run on your own machine: a coding harness that can route work to a hosted or local model, check tool requests, record runs, and compare answers with sources you supply. Its receipts and checker make activity more inspectable; they do not prove that a model’s answer is true or that a chosen source is authoritative.
What Flywheel does
Flywheel combines a Python engine with a local gateway and, in the package description, a Flutter desktop client. The engine routes tasks, evaluates tool requests, runs checks, records a ledger, and serves the gateway. It is not described in these sources as a hosted workstation service.
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The September 19, 2026 article labels the subject Flywheel 1.0.1. The PyPI page accessed October 5, 2026 lists the distribution flywheel-verify at version 0.6.2, uploaded September 11, 2026. The relationship between those version labels is not established, so check the specific release artifact before installing. The article introducing Flywheel 1.0.1 and the PyPI project page describe the product from different publication contexts.
How to install and start it
The article’s command-line workflow installs the Python distribution named flywheel-verify; the installed command is flywheel. The package description specifies Python 3.11 or later and says the core engine has no runtime dependencies.
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Install the package:
pip install flywheel-verify. -
Start the local gateway and browser shell:
flywheel up. -
Open the gateway at
http://127.0.0.1:8799.
The package page also describes a Windows desktop release with the engine bundled. It does not establish that this release and the article’s 1.0.1 label are the same artifact. The package page lists the license expression as FSL-1.1-MIT; consult the linked project’s license text for the terms that apply to your intended use.
Choose a hosted or local model route
Flywheel can use hosted provider APIs or a local model route. A local model is optional, not a prerequisite for installing the engine. For local inference, the package page describes Ollama over HTTP and separate 14B and 32B weight downloads, but gives no hardware-sizing guidance. Do not treat those model sizes as a guarantee that a particular computer will run them well.
| Route | What the sources establish | Practical trade-off |
|---|---|---|
| Hosted provider | Flywheel supports hosted model APIs. [Flywheel article, September 19, 2026; PyPI page, accessed October 5, 2026] | Prompts and the context needed for a task go to the selected provider. Provider-specific privacy guarantees and costs are not established here. |
| Local model | The package page describes Ollama over HTTP, with separate 14B and 32B weight downloads. [PyPI page, accessed October 5, 2026] | Requires separate model setup and suitable compute; the retrieved description does not quantify hardware needs. |
What the coding agent’s permission check can—and cannot—do
The article calls the coding agent relay. It can operate on folders and use local or hosted models. When the agent requests shell commands, Flywheel parses the request and can allow it, refuse it with a reason returned to the model, or escalate it.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThis capability check is not a complete sandbox. The project says its executable-name map is curated by hand: a command whose executable has not been seen before is admitted and logged as unknown. Treat the check as a control and record of tool requests, not as a guarantee that every command is safe or blocked appropriately.
What run ledgers and sealed receipts prove
According to the PyPI description, routed runs can record tool names, arguments, and outputs in a ledger. Optional sealed tool-call receipts include the capability, outcome, hashes of arguments and outputs, and a hash linking to the previous receipt. If an earlier receipt is invalid, later receipts in that chain become unverifiable.
This structure can help someone inspect or recheck what was recorded. It does not independently prove that a tool action was safe, that an output was accurate, or that a cited source was authoritative. A verifiable record is evidence about the recorded sequence, not a verdict on the truth of its contents.
How check-output evaluates an answer
The checker compares values in an answer with the source selected to decide them. The article’s example command is:
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flywheel check-output --contract task.contract.json --answer answer.json --allow-commands
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The contract defines what to check; the answer file supplies the values. The reported exit codes distinguish the outcomes:
| Exit code | Meaning |
|---|---|
| 0 | Confirmed against the selected source |
| 1 | Disagreement |
| 3 | Nothing could confirm the value |
The article also describes result labels RELEASE, RELEASE_WITH_CAVEAT, and HOLD. The essential distinction is that unconfirmed values remain unconfirmed; they should not be presented as verified. As the package page puts it, “An unchecked value never reads as a confirmed one.”
Domain packs do not supply the authority
Finance, medicine, and law packs provide field templates and arithmetic, not the authoritative underlying domain data. You must choose and supply the source that determines a value. A checker can compare a result with that source, but it cannot establish that the source itself is correct, current, or suitable.
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With Lean verification, the part settled by the kernel can be expressed as a theorem, while external decisions remain named axioms. In practical terms, formal verification can establish that a stated relation follows from its premises; it does not turn an external premise into a verified fact.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the published benchmarks do—and do not—show
The following figures are reported by the Flywheel project on its PyPI page, not independently evaluated results:
| Project-reported result | Interpretation |
|---|---|
| Continued pretraining on the workspace corpus changed general code completion by −3.05 percentage points over 164 tasks, with p = 0.4049. [Flywheel project, PyPI page, 2026] | The project says it claims no capability uplift. |
| A retired arms benchmark reported verified inference at 9/10 versus single-shot at 8/10, a difference of +0.100 with a 95% confidence interval of [−0.236, +0.420]. [Flywheel project, PyPI page, 2026] | The project notes that the arms were not independent and that the interval includes zero; it does not claim uplift. |
| The page lists offline benchmark summaries including six scenarios for governed-agent and agent-recovery suites, and 26 cases for source-mined checks. [Flywheel project, PyPI page, 2026] | These are project-run measurements, not independent evidence of user productivity or provider reliability. |
These results do not support a claim that Flywheel makes coding faster or improves model capability. Its documented value is in routing, controls, recorded runs, and checks against chosen sources—not a demonstrated productivity gain.
Who should consider Flywheel
Flywheel may suit someone who wants a self-hosted environment for connecting an agent to models and tools, retaining a record of tool activity, and checking selected answer fields against supplied sources. The trade-offs are operational: hosted routes send task context to a provider, local routes require model setup and adequate compute, and the executable-name check has an explicit unknown-command limitation.
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