Topos
Structural code quality metrics for agent-written programs
Details
- External ID
- 48678841
- Source
- HN
- Company
- —
- Product
- Topos
- Website domain
- krv.ai
- Launched
- June 25, 2026
- Cohort
- —
- Upvotes
- 5
- Upvotes percentile
- 0.12568306010928962
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Code review is the new bottleneck. "Tests passing" is no longer sufficient to trust the changes, and the (human) cost of evaluating the quality and robustness of new agent-written contributions is skyrocketing. We built Topos to evaluate code quality based on the structural properties of the programs themselves. We map your files to graphs (AST, CFG, CPG, MDG) and calculate metrics that can characterize how simple, composable, or secure your programs are. Agents can use this tool as they write and optimize based on your preferences. And yes, the inspiration for the repository is from category theory: this is inspired by a topos for program evaluation, that comes equipped with objects (program representations), morphisms (maps on graphs), probes, profunctors, test-coverage metrics, and a custom subobject classifier (Heyting algebra generated by the 3 pillars of evaluation) that affords a highly structured framework for awarding medals for program quality.GitHub: https://github.com/Krv-Labs/toposOpen Source. BSD-3 License. CLI, MCP Server & VS Code Extension.
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Horizontal
- Function
- Observability & eval
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- code quality metrics for ai-generated programs
- Manually corrected
- False
Could you build this?
No Topos relies on formal program analysis, abstract syntax graph extraction, and Heyting algebra/category theory to compute mathematical structural quality metrics on arbitrary codebases.
What it would actually take: The system parses source code across languages using tree-sitter into uniform ASTs and control-flow/data-flow graphs. It then evaluates structural graph properties using custom algorithms grounded in lattice theory and static analysis to score modularity and coupling. This requires specialized expertise in compiler design, static program analysis, and theoretical computer science.
Discussion
1 comment analyzed.
Concerns raised: No benchmarks or data on regression rates vs human review
Competitors
Other products that read as similar to this one — 84 launches clear the similarity bar, closest 8 shown.
Attention rank: #68 of 85 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 233 days after the earliest competitor.
- We scored 50k PRs with AI · hn · 2026-03-30 · 11 upvotes · similarity 0.44
- code-quality · github · 2026-09-21 · 12 upvotes · similarity 0.42
- Every Developer in the World, Ranked · hn · 2026-03-12 · 11 upvotes · similarity 0.41
- RepoGym · ph · 2026-09-23 · 1 upvotes · similarity 0.41
- Continue · hn · 2026-02-17 · 44 upvotes · similarity 0.41
- Cheddar-bench · hn · 2026-02-22 · 9 upvotes · similarity 0.40
- Benchmark your team's AI coding security posture · hn · 2025-11-05 · 5 upvotes · similarity 0.39
- Devthropology · hn · 2026-07-09 · 37 upvotes · similarity 0.38
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a observability & eval tool for Media & entertainment yet.