What is Git AI?
Git AI is an open-source project for bringing AI context closer to Git workflows and software repositories. Teams evaluating a Git AI alternative often compare it with Blamely AI and Oobeya when they need more enterprise-ready analytics.
As teams use AI coding tools more frequently, Git history alone does not always explain whether a change was human-authored, AI-assisted, or shaped by generated code.
Oobeya connects Git AI context with delivery, review, quality, and team-level engineering metrics so leaders can evaluate AI-assisted development with operational visibility.
Why connect Git AI to Oobeya?
Git AI captures AI-assisted development activity. Oobeya connects that activity with review, quality, delivery, and team data so adoption can be evaluated through outcomes, not usage counts alone.
- Which repositories contain the most AI-assisted development activity?
- How does AI-assisted code move through review and merge workflows?
- Where does AI-generated or AI-assisted work create rework, churn, or quality risk?
- Which teams need clearer governance around AI coding practices?
- How can leaders connect AI attribution with delivery and quality outcomes?
What Oobeya analyzes from Git AI
| Git AI signal area | Oobeya visibility |
|---|---|
| AI attribution | AI-assisted, AI-generated, and human-authored code context connected to engineering workflows. |
| Repository activity | Commits, branches, pull requests, and code changes interpreted with attribution context. |
| Review behavior | Review patterns, pass rates, rework, and merge flow around AI-assisted changes. |
| Quality and risk | Attribution context compared with defects, vulnerabilities, churn, and quality gates. |
| Governance reporting | Team-level visibility for AI adoption, policy boundaries, and leadership decisions. |
Engineering performance metrics from Git AI data
Git AI signals become more valuable when AI-assisted development is evaluated with review, quality, delivery, and team context. Use this page to connect AI coding activity with engineering outcomes so adoption can be governed with evidence.
AI-assisted contribution patterns
Use Git AI context to understand where AI-assisted development appears in engineering workflows and how teams adopt it.
Review and quality impact
Connect AI coding signals with pull requests, rework, code quality, testing, and security context.
Delivery outcome analysis
Evaluate Git AI usage alongside cycle time, throughput, lead time, and bottleneck trends instead of relying on usage counts alone.
Governance and reporting
Give leaders a practical view of AI impact, adoption, cost or attribution context, and engineering risk where supported data is available.
Git AI Integration Setup Guide
The Git AI integration documentation explains setup requirements, authentication details, and how to activate AI attribution and code-origin signals in Oobeya.
Open Git AI integration documentation
