BlameGPT

The problem

AI increases developer productivity, but PR reviews fall behind and are a bottleneck. Senior engineers drown in 20+ file PRs, that may have subtle bugs and vulnerabilities that may slip through.

AI-generated code hides issues and security bugs

PR reviews bottleneck on senior engineers

Bugs and vulnerabilities can be hard to spot, especially from AI generated code

How it works?

50–70% faster merges, fewer bugs, and consistent quality reducing review bottlenecks.

Step 1

Create a PR and comment @BlameGPT review

Step 2

Get instant PR feedback with issues that you might have missed

Step 3

Push more commits → re-review by commenting - @BlameGPT review

Step 4

Clean PR ready for an engineer's final review

Security-first architecture

Your code, your infrastructure. Choose your LLM provider, ephemeral data handling.

Minimal infra: 1GB RAM, 1 vCPU Linux server

Self-hosted with local SQLite. No database setup required

Temporary clones, encrypted APIs, no permanent code storage

Direct LLM API keys or via Azure foundary or AWS Bedrock

PR on Github

BlameGPT server on your infra

LLM Provider

Installation in 10 minutes

Self-hosted with minimal infrastructure (any 1 GB RAM Linux server).

1
Step 1

Select repositories

Choose which repositories you want BlameGPT to do PR code reviews.

2
Step 2

Deploy server: clone repo and docker compose up

Clone our repository and start the server with a simple docker compose command.

3
Step 3

Create a LLM API key (OpenAI, Claude, Azure OpenAI or AWS Bedrock)

Add your preferred AI provider's API keys to enable intelligent code analysis.

Dependency vulnerability scanning

Continuous checks across multiple databases with actionable alerts and compliance mapping.

Cross-checks GitHub Advisories, OSV.dev, and NVD
Alerts on vulnerable dependencies added in PRs
Dashboard visibility for existing vulnerabilities
Optional GitHub Issues or Slack alerts
CWE mapping for compliance

Security First

Comprehensive vulnerability detection

Blame analysis for production issues

Trace incidents to the offending commit and resolve production issues faster.

Trace errors back to specific PRs/commits
Faster root cause analysis and reduced MTTR
Visibility into when and where regressions were introduced

Debug Faster

Trace issues to their source