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tirth8205/code-review-graph

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Code Review Graph

This project is a static code analysis tool and local-first code indexer that builds a persistent dependency graph of functions, classes, and imports. It functions as an AI context optimizer and codebase dependency graph, designed to reduce token usage by providing AI assistants with only the most relevant code fragments and impact analysis for a given change.

The system implements a Model Context Protocol server that exposes code intelligence and architectural graph queries to external AI coding tools. It distinguishes itself by computing the change blast radius and risk scores of modifications, tracing all affected callers and dependent components to isolate the minimum necessary context for review.

The tool includes capabilities for automated architectural documentation generation, structural code visualization in formats such as HTML and SVG, and a syntax-aware parsing pipeline with support for custom languages. It maintains performance in large repositories through bounded structural analysis and incremental hash-based updates that re-parse only modified entities.

Features

  • Change Impact Analysis - Traces callers, dependents, and tests affected by a change to isolate the minimum necessary review context.
  • Model Context Protocol Integrations - Implements a Model Context Protocol server to deliver compact summaries and impact reports to AI coding assistants.
  • Review Context Optimizers - Optimizes AI code reviews by providing only relevant code fragments and impact analysis to reduce token usage.
  • AI Context Optimization - Assembles a minimal set of relevant code fragments to optimize the context window and reduce token consumption.
18,822 stars·2,013 forks·Python·MIT·20 views
  • Codebase Impact Analysis - Computes blast radius and risk scores to determine how modifications propagate through the codebase.
  • Context Window Optimizations - Filters codebase data to reduce token consumption by delivering only the most relevant fragments and impact reports to LLMs.
  • MCP Protocol Integrations - Exposes graph query capabilities to AI assistants through the Model Context Protocol for structured tool execution.
  • Code Analysis Tools - Exposes architectural graph queries and hotspot detection tools to AI assistants via the Model Context Protocol.
  • Code Intelligence Tools - Provides a suite of code intelligence tools and prompts integrated through a standardized protocol for AI assistants.
  • Codebase Dependency Mapping - Creates a searchable structural map of functions and dependencies to visualize and understand architectural relationships.
  • MCP Servers - Provides an MCP server implementation that exposes structural codebase graphs and tool queries to AI assistants.
  • Prompt-Optimized Context Retrieval - Selects the minimal set of relevant structural fragments to optimize prompt size and maintain technical accuracy.
  • Token Reduction Pipelines - Provides a filtering mechanism to serve only relevant structural data, minimizing tokens required for automated reviews.
  • Local Code Indexers - Operates as a background service that monitors repositories and incrementally updates a local persistent graph of code relationships.
  • Software Dependency Mapping - Maintains a persistent map of functions, classes, and imports to analyze the blast radius of architectural changes.
  • Local-First Architectures - Maintains a persistent, local-first dependency graph of codebase entities for low-latency retrieval.
  • Syntax Parsing Engines - Converts source code into structured nodes and edges using a syntax-aware parsing pipeline.
  • Structural Graph Generation - Implements a parsing pipeline that converts source code into a structural map of functions, classes, and imports.
  • Documentation Generation - Automatically creates markdown wikis and architectural overviews by identifying code communities and patterns.
  • Incremental Updates - Uses checksums to incrementally update the structural map, re-parsing only modified entities.
  • Bounded Graph Traversals - Limits graph traversal depth to maintain performance and stability when analyzing very large repositories.
  • Code Graph Synchronization - Ships a streaming system for importing source files and pull request diffs to keep the local structural map current.
  • Large-Scale Codebase Analysis - Implements a bounded structural analysis mode to ensure stability and performance when processing expansive codebases.
  • Graph - Provides a background monitoring system that tracks repositories and triggers automatic graph rebuilds upon detecting changes.
  • Automated Architecture Analysis - Generates high-level system overviews by detecting structural patterns and code communities in large projects.
  • Static Code Analysis Tools - Implements a static analysis pipeline that parses source code into structural maps and automated architectural documentation.

    • Star history

    Star history chart for tirth8205/code-review-graphStar history chart for tirth8205/code-review-graph

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does tirth8205/code-review-graph do?

    This project is a static code analysis tool and local-first code indexer that builds a persistent dependency graph of functions, classes, and imports. It functions as an AI context optimizer and codebase dependency graph, designed to reduce token usage by providing AI assistants with only the most relevant code fragments and impact analysis for a given change.

    What are the main features of tirth8205/code-review-graph?

    The main features of tirth8205/code-review-graph are: Change Impact Analysis, Model Context Protocol Integrations, Review Context Optimizers, AI Context Optimization, Codebase Impact Analysis, Context Window Optimizations, MCP Protocol Integrations, Code Analysis Tools.

    Which projects share features with tirth8205/code-review-graph?

    Projects with overlapping indexed features include: colbymchenry/codegraph — Codegraph is a local codebase indexer and static analysis graph database that serves as a context provider for AI… mikeyobrien/ralph-orchestrator — This project is an autonomous workflow engine and orchestration platform designed to coordinate specialized AI agents.… tencent/weknora — WeKnora is a multi-tenant retrieval-augmented generation (RAG) knowledge platform and autonomous AI agent framework.… affaan-m/ecc — ECC is an LLM agent orchestration framework and cross-platform AI tooling suite designed to coordinate multi-model… parcadei/continuous-claude-v3 — This project is an agentic development framework and autonomous software engineering system. It utilizes a coordinated… shashankss1205/codegraphcontext — CodeGraphContext is a code graph indexer and visualization tool that analyzes source code to build graphs of…

    Projects sharing features with Code Review Graph

    These projects share indexed features with Code Review Graph. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • colbymchenry/codegraphcolbymchenry avatar

      colbymchenry/codegraph

      50,154View on GitHub↗

      Codegraph is a local codebase indexer and static analysis graph database that serves as a context provider for AI agents. It parses multiple programming languages into a searchable knowledge graph of symbols and dependencies, exposing these relationships to AI tools through the Model Context Protocol. The project distinguishes itself by aggregating relevant code snippets and symbol flows to reduce token usage for large language models. It automates the configuration of server settings and steering instructions across various AI agent platforms and command line editors to enable automatic code

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    • mikeyobrien/ralph-orchestratormikeyobrien avatar

      mikeyobrien/ralph-orchestrator

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      This project is an autonomous workflow engine and orchestration platform designed to coordinate specialized AI agents. It functions as a development framework that manages the end-to-end lifecycle of complex, multi-step tasks, including persona definition, persistent memory management, and the execution of automated coding workflows. By acting as a Model Context Protocol server, it enables standardized communication between development tools and external AI models. The platform distinguishes itself through an event-driven architecture that routes typed messages between agent personas, allowin

      Rustaiai-agentsai-agents-framework
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    • affaan-m/eccaffaan-m avatar

      affaan-m/ECC

      221,981View on GitHub↗

      ECC is an LLM agent orchestration framework and cross-platform AI tooling suite designed to coordinate multi-model workflows. It provides a system for managing specialized agent roles, reusable skills, and structured planning to execute complex software development tasks across different AI-powered code editors. The project distinguishes itself as a Model Context Protocol manager, providing a configuration layer to integrate external servers and audit tool execution. It further implements an agentic security sandbox that restricts sensitive file access and scans for secret leakage to secure a

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    • tencent/weknoraTencent avatar

      Tencent/WeKnora

      16,974View on GitHub↗

      WeKnora is a multi-tenant retrieval-augmented generation (RAG) knowledge platform and autonomous AI agent framework. It transforms raw documents into queryable knowledge bases and integrates large language models with vector databases to provide grounded AI responses. The system also functions as a Model Context Protocol (MCP) tool server, exposing knowledge search and agentic capabilities to external AI clients. The platform distinguishes itself through an autonomous agent framework that utilizes iterative reasoning, tool calling, and web search to solve multi-step tasks. It implements a sta

      Goagentagenticai
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