open-code-review vs pr-agent
Two of the top projects in Code review and testing, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
open-code-review
CLI that reviews Git diffs with an LLM agent and line-level comments
pr-agent
LLM pull request reviewer that comments via /review, /describe and /improve
Verdict
open-code-review ranks higher, 74 to 59. Of the 5 score parts we measure for both, open-code-review leads on 3, pr-agent on 1 and 1 is even.
- Adoptionopen-code-review leads by 44 points, 66 to 22: a wide gap.
- FreshnessEven at 100.
- Maintenancepr-agent leads by 8 points, 99 to 91.
- Easy to runopen-code-review leads by 17 points, 50 to 33: a wide gap.
- Agent-readyopen-code-review leads by 40 points, 70 to 30: a wide gap.
Pick open-code-review if you are
Teams adding LLM code review to local workflows or CI pipelines
- Deterministic file selection and bundling; each bundle runs as an isolated sub-agent
- Reviews workspace changes, branch ranges, single commits, or full files via `ocr scan`
Pick pr-agent if you are
Teams wanting self-hosted AI pull request review in CI or on their git host
- Supports GitHub, GitLab, Bitbucket, Azure DevOps and Gitea
- Model-agnostic via LiteLLM: OpenAI, Claude, Gemini, Bedrock, Ollama and others
| What we compare | open-code-review | pr-agent |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 66, popular | 22, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 91, healthy | 99, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 47.2k | 13.4k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Go | Python |
| Docker | No | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
open-code-review
Open Code Review is a Go CLI (`ocr`) that reads a Git diff, groups related files into bundles, and runs each bundle through a tool-using LLM agent that produces structured review comments. File selection, rule matching, comment positioning and reflection are done by deterministic code rather than prompts. `ocr scan` reviews whole files when there is no diff, and delegation mode lets an existing coding agent do the review with its own model.
Who it is for: Teams adding LLM code review to local workflows or CI pipelines
Strengths
- Deterministic file selection and bundling; each bundle runs as an isolated sub-agent
- Reviews workspace changes, branch ranges, single commits, or full files via `ocr scan`
- Sessions can be resumed and browsed in a viewer; JSON output for automation
- Plugins for Claude Code, Codex, Cursor, Kimi Code, OpenCode; CI docs for GitHub, GitLab, Gerrit
Weaknesses
- Requires Git >= 2.41 and a configured LLM endpoint, unless delegation mode is used
- Recall is lower than general-purpose agents by the README's own benchmark
- Benchmark figures are published by the project itself, not independently verified
- No Docker or Compose files detected
- no GPU
- Needs Git >= 2.41, LLM provider API
- Models: configurable LLM providers, custom providers
pr-agent
PR-Agent runs as a GitHub Action, CLI, Docker image or webhook service and posts AI-generated descriptions, reviews, code suggestions and Q&A on pull requests. Each tool makes a single LLM call, and a compression strategy handles large diffs. It supports GitHub, GitLab, Bitbucket, Azure DevOps and Gitea, and any model reachable through LiteLLM.
Who it is for: Teams wanting self-hosted AI pull request review in CI or on their git host
Strengths
- Supports GitHub, GitLab, Bitbucket, Azure DevOps and Gitea
- Model-agnostic via LiteLLM: OpenAI, Claude, Gemini, Bedrock, Ollama and others
- One LLM call per tool, about 30 seconds per command
- Review behavior is configurable through a TOML settings file and JSON prompts
Weaknesses
- Described as a community-maintained legacy project of Qodo
- /help_docs is disabled since v0.36.1 over a credential-exposure issue
- Needs your own LLM API key and per-provider setup
- Docker images moved to pragent/pr-agent from 0.34.2, so pinned references break
- no GPU
- Docker + Compose
- Needs LLM API key (e.g. OpenAI), Git provider token
- Models: OpenAI GPT, Anthropic Claude, Google Gemini, DeepSeek, Mistral
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