mgm152002 / infra.ai_backend
A fully autonomous Agentic AI platform built with FastAPI and LangChain to intelligently manage and resolve infrastructure incidents with minimal human intervention. This system leverages natural language interfaces, real-time context integration, and operational tooling to drastically reduce MTTR (Mean Time to Resolution) and manual workloads.
Code health
mgm152002/infra.ai_backend scores 6.0 out of 10 on defect risk, which Repowise rates Fair. Maintainability scores 4.5 out of 10. Static performance risk scores 8.8 out of 10. The three are scored separately and never blended into a single number. 1 of 97 files are git hotspots, the places where changes and bug fixes concentrate.
Full health report- Files
- 97
- Symbols
- 895
- Modules
- 23
- Doc pages
- 0
- Dead exports
- 11
Where the risk concentrates
All 1 hotspotsRanked by prior bug fixes and change frequency, mined from the full git history rather than from the code alone.
| File | Churn | Prior fixes | Maintainers | Commits 90d |
|---|---|---|---|---|
| conftest.pytests | 94.7th %ile | 7 | 1bus factor 1 | 11 |
Read more about mgm152002/infra.ai_backend
- OverviewFiles, symbols, languages, packages, git intelligence
- ArchitectureDependency graph, layered architecture, symbol index, and third-party dependencies
- Code healthPer-file health scores, hotspots, test coverage, dead code, and refactoring targets
- AI docsProModule-by-module documentation generated from source
Add a badge to your README
Both badges are public, cached, and update on their own after every index. Nothing to install.
Repowise badge
Links to this page. Adding it also keeps the index refreshing weekly.
[](https://repowise.dev/repo/mgm152002/infra.ai_backend)Code health badge
Average health across every file, straight from the latest index.
[](https://repowise.dev/repo/mgm152002/infra.ai_backend)How mgm152002/infra.ai_backend works
A fully autonomous Agentic AI platform built with FastAPI and LangChain to intelligently manage and resolve infrastructure incidents with minimal human intervention. This system leverages natural language interfaces, real-time context integration, and operational tooling to drastically reduce MTTR (Mean Time to Resolution) and manual workloads. This page is an auto-generated, always-fresh map of the mgm152002/infra.ai_backend repository, written primarily in Python. Repowise indexes the source, parses every symbol, computes a dependency graph, scores per-file code health from complexity, duplication, test coverage and churn, mines git history for hotspots and ownership, and lifts the resulting architectural decisions into a wiki you can read or query through MCP.
The codebase has 97 source files, 895 symbols, and 7 languages, organised into 23 modules, led by python, sql, markdown. Static analysis leaves 682 open findings across the three health dimensions.
Use the links above to open the interactive dashboards, or connect this repo to your editor via the Repowise MCP server for grounded answers inside Claude, Cursor, or VS Code.