OP here. Last week I posted a discussion ("The Confident Idiot Problem") about why we need deterministic checks instead of just "LLM vibes" for reliability.
That thread [1] blew up, so I’m sharing the open-source implementation (v0.2) that solves it.
Steer is an active reliability layer for Python agents. It sits between your LLM and the user to enforce hard constraints.
Unlike passive observability tools that just log errors, Steer creates a feedback loop:
1. Catch: It uses deterministic verifiers (like Regex, AST parsing, JSON Schema) to block hallucinations in real-time.
2. Teach: You fix the behavior in a local dashboard (`steer ui`).
3. Train: v0.2 adds a "Data Engine" that exports these runtime failures into an OpenAI-ready fine-tuning dataset.
The goal isn't just to block errors; it's to use those errors to bootstrap a model that stops making them.
It is Python-native, local-first, and framework agnostic.
Repo: https://github.com/imtt-dev/steer
[1] https://news.ycombinator.com/item?id=46152838
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