tuicr

CodeJury is a terminal-first, knowledge-grounded multi-agent software delivery pipeline for scoping requirements, implementing changes, running tests, and gating pull requests. It divides delivery into six terminal-driven stages and uses isolated development branches, human approval gates, deterministic QA, and ensemble code review. The system indexes a repository into a persistent code graph and uses semantic search to retrieve relevant code during later work. Its review stage sends the same change to independent models from different providers; both must approve it for the change to pass, while a rejection returns the reason to the developer. A configurable panel can use up to six specialist reviewers and a foreperson to reconcile their findings. The project is distributed as a Python package installable with pip and documents support for macOS, Linux, and Windows. Its repository describes a one-command run and operation without an API key.

Jev Review is a local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev. It runs as a local Node.js server over MCP stdio and exposes a focused `jev_review` tool: an agent submits a task, focused diff, selected files, or repository context, and Jev returns structured per-dimension scores and confidence values for areas such as correctness, complexity, readability, modularity, changeability, testing, reliability, security, and documentation. When a previous evaluation is supplied, the plugin reports metric deltas, improvements, regressions, and weak dimensions rather than producing a blended overall score or prose diagnosis; the coding agent remains responsible for identifying causes and changing the code. It supports Claude Code, Codex, Cursor, and OpenCode, and is distributed directly from its GitHub repository rather than npm. The process keeps the API key locally and sends only explicitly supplied review context directly to the configured Jev API; it has no hosted backend, database, telemetry service, or author-operated proxy. The repository is licensed under MIT.

Code Review Graph is an open-source, local-first code intelligence tool that provides a command-line interface and MCP server. It builds a persistent structural map of a codebase with Tree-sitter, representing relationships such as functions, classes, imports, calls, inheritance, and test coverage, while tracking changes incrementally. The graph supplies focused repository context to AI coding tools so they can read relevant code instead of repeatedly scanning large portions of a project. It also supports code-review workflows such as blast-radius analysis and risk-scored pull-request reviews. The Python package can be installed with pip or pipx; its install command detects supported AI coding platforms and configures MCP settings, hooks, skills, and graph-aware instructions.

GitWarren is a desktop application for reviewing local Git changes made by coding agents before they are committed. It reads the worktree directly and combines staged, unstaged, and untracked files into one diff for local review, with comments and threaded discussions that can be edited or resolved. An MCP server over stdio lets Claude Code, Codex, or another MCP client open reviews, inspect discussions, reply in threads, comment on lines, and resolve comments; agent messages are attributed and separate MCP sessions distinguish concurrent agents. Reviews and comments are stored in a local SQLite file, while repository state is read from Git when displayed. The application requires no account, runs on macOS, Windows, and Linux, and is free and open source under GPL-3.0.

Open Code Review is an open-source, AI-powered command-line code-review tool developed from Alibaba Group’s internal review assistant. It reads Git diffs and sends changed files to a configurable LLM through an agent with tool-use capabilities; the agent can read complete files, search the repository, and inspect other changed files for context before producing structured comments tied to source lines. Its hybrid architecture combines deterministic engineering with dynamic agent decisions: deterministic stages select and bundle files, match rules to file characteristics, and apply independent comment-positioning and reflection modules, while scenario-specific prompts and tools guide context retrieval and review. The `ocr scan` command audits complete files or directories without requiring a meaningful diff, and the project provides multilingual rules for issues including null-pointer exceptions, thread safety, cross-site scripting, and SQL injection. It supports configurable model endpoints, including OpenAI- and Anthropic-compatible endpoints, and requires Git 2.41 or newer.