archify vs Code Graph RAG: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of archify and Code Graph RAG — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
a
archify
tt-a1i
Agent skill for Claude, Codex, and opencode that turns a plain-English description into a polished, themeable architecture diagram in a single HTML file.
Key features
- Prompt-to-Diagram: Describe your system in English and get a polished technical diagram back.
- Multiple Diagram Types: Handles architecture, workflow, sequence, data-flow, lifecycle, CI/CD, and state-machine diagrams.
- Single-File HTML Output: Diagrams are self-contained HTML files you can open, share, or embed anywhere.
- Dark / Light Theme Toggle: One-click theme toggle inside the diagram, with the choice persisted across sessions.
- Multi-Format Export: Copy PNG to clipboard plus download as PNG, JPEG, WebP, or SVG at up to 4x source resolution.
- Semantic Tech Labels: Recognizes labels like aws.lambda, postgres, redis, github-actions, openai and maps them to the right visual category without a manual icon library.
- Multi-Agent Compatibility: Installs as a skill for Claude, Codex CLI, and opencode with a single command.
Best for
- System Design Sketches: Turn a rough design description into a shareable architecture diagram in minutes.
- Runbook & Incident Docs: Generate sequence and data-flow diagrams for runbooks and incident reviews on the fly.
- CI/CD Documentation: Draw pipeline and workflow diagrams from an English description of your build/deploy flow.
- Onboarding Materials: Produce lifecycle and request-chain diagrams to onboard new engineers to a service.
- Slide-Ready Visuals: Export high-resolution PNG or SVG diagrams straight from your agent for decks and blog posts.
C
Code Graph RAG
vitali87
Multi-language monorepo RAG: Tree-sitter parses your codebase into a Memgraph knowledge graph so you can query, edit, and refactor in plain English.
Key features
- Multi-Language Graph Ingest: Tree-sitter parses Python, TypeScript, TSX, JavaScript, Rust, Go, Java, C, C++, C#, PHP, Lua, and Dart into a single language-agnostic Memgraph schema.
- Natural-Language Cypher: The interactive CLI turns plain-English questions into Cypher queries and answers grounded in the real code structure, not vector-only guesses.
- AST-Based Surgical Editing: The agent edits code through structural patches with a diff preview before any change is applied.
- Structural Search & Replace: ast-grep is exposed as an agent tool, so you match and rewrite by AST pattern across the whole codebase instead of regex.
- Pluggable ast-grep Tier: Add a new language from a single YAML pattern file — Ruby was added this way with Module/Function/Class nodes plus import edges.
- Data-Flow Tracing: FLOWS_TO taint edges follow values through assignments, function calls, and I/O sinks across C, Java, C#, and Go.
- Dead-Code Detection: Walk call and reference edges from entry points to find functions and modules nothing reaches.
- Shared Graph Across Projects: Index many repos into one shared graph and query across them; a `clean` subcommand resets from scratch with confirmation.
Best for
- Monorepo Q&A: Ask 'where is refund logic in this monorepo?' and get grounded answers from a graph of the real code, not stale docs.
- AI-Assisted Refactoring: Rename or restructure APIs across languages with AST patches and a diff preview before commit.
- Cross-Language Data-Flow Audits: Trace a value through assignments and function calls to see where sensitive data ends up.
- Dead-Code Cleanup: Find unreachable functions and modules by walking call edges from entry points.
- Codebase Onboarding: Give a new engineer or agent a queryable graph they can explore in natural language.
- Structural Migrations: Use ast-grep to rewrite deprecated patterns (imports, error handling, config lookups) across a polyglot codebase.