By mimfort
Automate PR reviews with retrieval-augmented generation and code graph analysis. Get AI-driven feedback on maintainability risks (complexity, duplication) and performance issues (N+1 queries, blocking I/O) by indexing your codebase into a vector store and Neo4j graph, then reviewing diffs with full repository context.
Review code changes only for maintainability risks: unnecessary complexity, poor readability, duplication, weak separation of concerns, and misalignment with repository conventions. Use when the user explicitly asks for maintainability review, code quality review, clean code review, complexity review, readability review, simplification review, or how to simplify changed code without changing behavior.
Review code changes only for performance and efficiency risks (N+1 queries, repeated work, bad asymptotics, missing batching/caching, blocking I/O, memory growth). Use when the user explicitly asks for a performance review of a diff/PR.
Review a GitHub pull request with the RAG + code-graph pipeline (reviewer MCP server). Use when the user asks to review a PR ("review PR 123", "Π·Π°ΡΠ΅Π²ΡΡΠΉ PR", a PR URL). Requires ParadeDB/Neo4j running and a built base index.
Gather disciplined context for solving a task, then hand off to development. Use when the user asks to solve/implement a task ("solve PRI-4", "/reviewer_solve-task <key or description>", "ΡΠ΅ΡΠΈ Π·Π°Π΄Π°ΡΡ X"). Reads the task from a connected board (if a key + board), pulls related/similar tasks and relevant code, distills a brief, and enters brainstorming. Requires the reviewer MCP server (and optionally a board MCP).
Build or update the reviewer base index (vector store + code graph) from a local repo clone. Use when the user asks to "index the codebase", "build the index", "sync the code", "rebuild the graph", "ΠΏΡΠΎΡΠΈΠ½Π΄Π΅ΠΊΡΠΈΡΡΠΉ ΠΊΠΎΠ΄", "ΠΏΠΎΡΡΡΠΎΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ".
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π·πΊ Π ΡΡΡΠΊΠ°Ρ Π²Π΅ΡΡΠΈΡ: README.ru.md
An agent that automatically reviews pull/merge requests using RAG + a code graph + Claude Code.
Plain linters catch syntax and style but miss meaning and relationships: a broken function contract, the impact of a change on its callers, a removed guard, a contradiction with an existing test. This agent gives an LLM the same context a human reviewer has β semantic + lexical retrieval over the whole repository, structural code-graph expansion, and an agentic tool loop β then posts the result back to GitHub as inline comments on diff lines plus a summary.
A single PR review runs as three stages:
prepare_review (MCP) β analyze (Claude subagents) β publish_review (MCP)
GitHubProvider pulls the PR (base/head SHA) and changed files; changed .py
files are chunked (tree-sitter) and embedded (Voyage) into an ephemeral overlay ref="pr:N";
policy and per-file review units are assembled.search_code, get_related_symbols,
read_file, get_definition, find_callers, get_changed_file_diff.Status: working v1. Target analysis language is Python; VCS is GitHub (behind a
VCSProviderinterface). Proven live: it catches real bugs and sees the impact on calling code and existing tests.
The core is the reviewer/ library, assembled in reviewer/app.py::build_components(settings)
from Settings (pydantic-settings, .env). Entry points are reviewer/entrypoints/cli.py (Click)
and reviewer/entrypoints/mcp_server.py (FastMCP). Three pieces work together:
pgvector (HNSW ANN) and
pg_search (BM25). A query embeds with Voyage, runs both ANN and BM25 search, and the result
lists are merged with Reciprocal Rank Fusion (RRF), then reranked with Voyage rerank-2.5.graph/backend.py) picks a backend via GRAPH_BACKEND
(auto|scip|treesitter): SCIP (@sourcegraph/scip-python) gives a precise, type-aware graph
with CALLS + IMPLEMENTS edges; tree-sitter is a fast fallback with CALLS-by-name only.
Retrieval expands the changed symbols 1β2 hops to surface callers/callees/implementations/tests.reviewer-mcp server exposes prepare_review,
publish_review, and the agent tools. The Claude Code plugin (plugin/) drives the review: it
calls prepare_review, runs analysis subagents against those MCP tools, then calls
publish_review.The single key linking RAG and the graph is node_id = "path#fqn" (e.g.
rag/embedder.py#VoyageEmbedder.embed_query). Both the chunk in Postgres and the node in Neo4j use
it, so graph expansion and chunk retrieval are stitched together without any mapping table.
Index freshness: a stable base + a PR overlay. A full reindex of a large repo is expensive, so the index keeps a persistent base and layers PR changes on top:
ref="base:<branch>" β the persistent index of a tracked branch (e.g. "base:main",
"base:master"). Each tracked branch in REVIEW_BRANCHES has its own isolated index. Updated
incrementally by reviewer index --ref <branch> (only changed files are chunked; only chunks
with a new content_hash are re-embedded β embeddings are reused across branches by hash,
saving Voyage quota).ref="pr:N" β an ephemeral overlay of just the PR's changed files at its HEAD.retrieval = (base:<branch> where path β changed) βͺ overlay. For changed files
the agent sees the new version; for everything else, the stable base.base_ref from the
PR). A PR targeting an untracked branch is skipped (prepare_review returns
{"status":"skipped",...}). The code graph (Neo4j :Symbol) is also branch-scoped via a
branch property, with unique constraint (repo, branch, id).npx claudepluginhub mimfort/rag_for_gitAgentic PR review: hybrid RAG + code graph via MCP, review skills for Claude Code
Agentic PR review: hybrid RAG + code graph via MCP, review skills for Claude Code
Persistent incremental knowledge graph for token-efficient, context-aware code reviews with Claude Code
Review pull requests with structured analysis and approve with confidence
Automated code review for pull requests using multiple specialized agents with confidence-based scoring
Multi-lens code review pipeline: deep review (Claude or Codex), automated fix loop, interactive walkthrough, manual promote, external-finding injection.
Multi-agent code review for Claude Code β parallel review by Codex (GPT-5.5), Gemini 3.1 Pro, and five Claude specialist subagents (one run blind), then verified synthesis.