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ReviewCerberus
Pricing
- Model
- Free
Summary
Automated code review bots either drown you in generic lint warnings or miss the logic flaw that ships to production — ReviewCerberus is a focused, open-source tool that runs LLM-based analysis against your actual git diff and returns a structured severity-ranked report.
The tool pulls the diff between branches, sends it to AWS Bedrock or the Anthropic API with prompt caching for token efficiency, and outputs issues grouped by severity across logic, security, performance, and code quality. It runs as a GitHub Action, a CLI command, or inside Docker, so dropping it into an existing CI/CD pipeline is a config-file change rather than a platform migration. The output is structured — not a wall of prose — which means it lands in PR comments your team can act on immediately. The wall appears when your team needs providers beyond AWS Bedrock and Anthropic: the vendor states only those two integrations exist, so OpenAI or local model users are blocked without patching the source.
Bottom line: Pick this for a GitHub-hosted Python or polyglot repo where you want severity-ranked AI review comments on every PR without paying for a SaaS seat — but if your team runs OpenAI or Ollama and cannot route through Bedrock or Anthropic, you are forking the codebase before your first run.
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Pros
Sign in to edit- Runs as a native GitHub Action, so review automation lives in your workflow YAML alongside your existing CI steps — no separate dashboard, webhook configuration, or third-party account required.
- Prompt caching against Anthropic's API reduces repeated token spend on similar PR structures, which means review costs do not scale linearly with PR volume the way naive LLM integrations do.
- Structured output organized by severity, so reviewers see critical security or logic issues at the top rather than parsing a flat summary to find what actually needs fixing before merge.
- MIT license with Docker and CLI support, which means the tool runs identically in GitHub Actions, a local pre-commit hook, or a self-hosted Jenkins pipeline without vendor lock-in.
- Multi-dimensional analysis covers logic, security, performance, and code quality in a single pass, so you are not stitching together separate tools for each concern.
Cons
Sign in to edit- Provider support is limited to AWS Bedrock and the Anthropic API at the time of this listing. Teams whose infrastructure runs on OpenAI or Azure OpenAI hit a hard wall on day one — the path forward is forking the repository and implementing a new provider adapter, at which point you own that maintenance burden permanently.
- One-shot analysis per CI run means there is no iterative review: the tool cannot follow up on a developer's response to a comment, re-analyze only the changed lines in a follow-up commit, or track whether a flagged issue was actually resolved. Teams that need conversational review threads or incremental re-analysis after each push will find this model insufficient and switch to a SaaS code review agent that maintains session state across commits.
- No API surface of its own — the tool is invoked as a CLI or Action and returns output to the PR or stdout. Teams that want to pipe review results into a custom dashboard, ticketing system, or metrics pipeline must parse the output format themselves, since there is no programmatic query interface.
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About
- Platforms
- Docker, CLI, GitHub Actions
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-07-05T02:29:11.420Z
Best For
Who it's for
- Teams using GitHub for pull request reviews
- Developers seeking multi-LLM code analysis
- Projects requiring security and performance checks
What it does well
- Automated PR code reviews
- Analysis of logic, security, performance, and quality
- Generating structured review reports
- Integration into CI/CD workflows via GitHub Actions
Integrations
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Frequently Asked Questions
- Is ReviewCerberus free?
- Yes — ReviewCerberus is fully free to use. There is no paid tier.
- Is ReviewCerberus open source?
- Yes. ReviewCerberus is open source.
- Can I self-host ReviewCerberus?
- Yes. ReviewCerberus supports self-hosting on your own infrastructure.
- What platforms does ReviewCerberus support?
- ReviewCerberus is available on: Docker, CLI, GitHub Actions.
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Curated lists that include this category
ReviewCerberus analyzes the diff between a base branch and a feature branch, passes that diff to a large language model, and returns a review report structured by severity — covering logic issues, security concerns, performance problems, and general code quality. The core workflow is one-shot per run: invoke it via GitHub Action on a pull request event, via CLI for local review, or inside a Docker container in any CI pipeline, and it produces inline PR comments plus a summary. There is no stateful session, no iterative back-and-forth; analysis happens once per trigger.
The differentiating feature the docs describe is prompt caching for token efficiency. Rather than re-sending identical context on every run, the tool caches portions of the prompt to reduce API token consumption against Anthropic’s caching-compatible endpoints. For teams reviewing many PRs daily, that architectural choice keeps API costs from compounding, which is the failure mode that quietly kills SaaS-adjacent open-source tools in production.
ReviewCerberus fits teams already on GitHub who want AI review without onboarding another SaaS dashboard. The GitHub Action integration means the setup lives in `.github/workflows`, versioned with your code. The hard limit is provider coverage: the tool supports AWS Bedrock and the Anthropic API only. Teams using OpenAI, Azure OpenAI, or self-hosted models cannot use it without modifying source code. It is MIT-licensed, so the modification path is open — but that means you are maintaining a fork, not consuming a supported integration.
The repository ships with a Dockerfile, a Makefile, a `.env.example` for credential configuration, and a GitHub Actions workflow definition under `.github/workflows`. The `action` directory contains the composite action definition for direct GitHub Marketplace-style use. The project is written in Python and manages dependencies via Poetry.