Blamely AI vs Git AI
Compare AI code attribution for your team.
Evaluate Blamely AI and Git AI across code-origin capture, Git workflows, reporting, and team rollout. Blamely runs locally, including in air-gapped environments.
Focus your evaluation
- Capture coverage and attribution history
- Local and air-gapped workflows
- Attribution with engineering outcomes
Compare capture, workflow, and rollout.
Both tools record AI code provenance rather than relying on a retrospective guess from code style. Blamely captures supported assistant activity locally and stores commit attribution in Git notes. Git AI also records line-level attribution in Git notes and offers a separate teams product.
Capability comparison
Blamely AI and Git AI, side by side.
Compare the published product scope, then validate the details with the same real-world scenario.
Scroll horizontally to view the full comparison.
| Area | Blamely AI | Git AI | What to validate |
|---|---|---|---|
| Attribution capture | A local daemon captures supported assistant edits and matches changed lines at commit. | Agents record checkpoints; Git AI stores line-level attribution at commit. | Test the assistants, completion modes, and editors your developers actually use. |
| Git storage | Attribution is stored under refs/notes/blamely and can travel with pushed notes. | Git notes link AI-written lines to agent, model, and session context. | Test rebases, squash merges, cherry-picks, and note synchronization in your workflow. |
| Developer workflow | Editor gutter attribution, provenance on hover, change history, and local reporting. | A CLI for attribution plus team-level capabilities for usage, quality, and impact. | Separate local CLI capabilities from hosted or team product capabilities. |
| Engineering outcomes | Connect Blamely attribution to AI impact, team performance, review, quality, and delivery in Oobeya. | The teams offering connects attribution with token spending, PRs, rework, and incidents. | Review integrations, data flow, feature scope, and commercial terms for each team deployment. |
| Data control | Local capture and attribution; air-gapped operation. Oobeya integration is a separate data flow. | The teams product offers self-hosting or the vendor’s cloud. | Inspect code, prompt, and metadata handling separately for CLI and team deployments. |
Official product references
Updated
Oobeya in practice
Put code origin alongside engineering outcomes.
Review AI usage, delivery outcomes, and team performance in the same engineering context.
AI Usage
Explore featurePerformance Overview
AI attribution alongside delivery and usage
75,403 of 123,666 added lines are AI-attributed.
- Delivery velocity
158commits/week
- Time per commitderived
36.2min
- Reclaimed capacityderived
1,401hours
- Developers using AIderived
17developers
- Coding time split
- 219.4h AI40.8h Human
- Avg. lines / AI commit
- 114.4 AI83.5 Human
- Token spend
- 905.5M
- Cache efficiency
- 99.3%
Maya Patel
Alex Morgan
Sam Taylor
Jordan LeeTry the same scenario
Follow a mixed human and AI change
Use a branch containing human edits, accepted AI suggestions, and a review-driven rewrite.
Capture changes with the assistants and editors in your rollout.
Commit, rebase, and squash the branch, then inspect the surviving line attribution.
Review the data stored locally and the additional data shared with a team analytics integration.
Common questions
Evaluating AI code attribution
Need to discuss your team or technical setup? Contact us.
How do Blamely and Git AI capture code origin?
Both use captured assistant activity and Git attribution records. Test the exact assistants, editors, completion modes, and Git operations used by your team. Capture coverage matters more than comparing a single AI percentage.
Can Blamely run in an air-gapped environment?
Yes. Blamely capture and attribution run locally, including in air-gapped environments. Review how attribution records will be shared if you separately connect them to Oobeya for team analytics.
Can we compare AI adoption with engineering results?
In Oobeya, review AI code share and assistant usage alongside review time, rework, quality, and delivery trends. Higher AI usage alone does not establish a productivity improvement, and correlation does not prove causation.
How should we verify the comparison?
Use the linked official sources as a starting point. Confirm current product scope, plan limits, integrations, deployment options, and commercial terms directly with Git AI and Blamely. Validate the capabilities that matter using your own evaluation scenarios.
How should we compare cost and rollout effort?
Use the same scope for both evaluations. Include licensing, hosting, integration setup, onboarding, upgrades, maintenance, and support. Separate included product capabilities from any custom implementation effort.
What should we bring to an Oobeya evaluation?
Bring your toolchain, deployment constraints, shortlist, and two or three concrete questions. A review bottleneck, production delivery trend, or AI rollout question helps focus the walkthrough on the decisions your team needs to make.
Continue your evaluation
All comparisonsEvaluate Oobeya with your own engineering questions.
Bring your toolchain, shortlist, and deployment requirements. We’ll focus the walkthrough on the decisions your team needs to make.