Ouroboros

You will either love this or bounce off it in ten minutes. The deciding trait in daily use is friction, deliberately applied: nothing starts until you have answered an interview about what you actually want, so the tool slows down the exact moment where most agent work quietly goes wrong. If your last three failures were the model misunderstanding the task, that friction is the feature.

Pick it when specification is your bottleneck. Pick Kiro if you want spec-driven work with an editor around it, or skip both if your tasks are small enough to describe in a sentence.

The economics are the failure mode here. Work is verified by several models voting, and each vote is a full inference over the same material, so the price of a two-line fix scales with the size of the panel rather than the size of the change. Worse, a consensus of models that share a training lineage can be confidently and unanimously wrong, which is the failure that looks most like success.

What it does right: everything is written to a ledger, so a disputed run can be replayed and examined instead of reconstructed from memory.

The vocabulary is quantitative and the definitions are absent. 1. Work is gated on a numeric ambiguity measure with no stated range and no evidence that it correlates with downstream failure. 2. Iteration stops when a similarity threshold is met, with no account of how similarity is computed or how the threshold was chosen. 3. Verification is split into mechanical, semantic and consensus stages, and only the mechanical stage is reproducible by a reader.

Naming a number does not make a thing measured. The structure is thoughtful and the calibration is undocumented, which are separable problems and only one of them is hard.

The revenue line is donations. That is honest and it is also the entire commercial thesis, so there is no pricing power, no enterprise tier and no incentive structure beyond the maintainer's own interest. Five thousand stars indicates the idea travelled further than the funding did.

The asset, if there is one, is the methodology rather than the code, and methodologies are not acquirable; a larger vendor that liked this would implement the pattern rather than buy the Python. Likely outcome is continued sponsored maintenance or absorption of the idea by an agent vendor. Position: adopt the workflow, keep the specs portable, expect no support contract.

The irony is hard to miss. This is a verification layer, and there is no headless mode, so it cannot run as a required check on a pull request where a verification layer belongs. It sits on an engineer's machine instead, needing Python 3.12 or newer, with native Windows marked experimental, which for my fleet means a supported path for some engineers and a caveat for the rest.

No SSO, no SCIM, no audit trail attached to a person. Onboarding is a day because the workflow is the product. Approved with conditions: volunteer teams only, and not on the release path.

MIT and installable with pip and an extras marker, which tells you the shape of it: Python I can read, patch and vendor. The part I actually want is per-stage model pinning through LiteLLM, because it means the interview, the execution and the verification can each point at a different endpoint, including servers running on my own machine, rather than one provider for the whole pipeline.

It registers as an MCP server with a single serve command, so whichever agent I am using this month picks it up as a tool. Nothing here holds my keys and a fork would compile the first time.