Bean Recipe Adapt vs Code Graph RAG: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Bean Recipe Adapt and Code Graph RAG — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Bean Recipe Adapt
Bean
Personal kitchen assistant that discovers, adapts, and helps cook recipes tailored to user preferences and ingredients.
Key features
- Recipe Adaptation: Adjusts ingredient quantities and cooking steps to match target serving sizes while maintaining proportions and timing.
- Ingredient Substitution: Suggests pantry-friendly or diet-compliant substitutes for missing or restricted ingredients, including vegan and allergy-safe alternatives.
- Dietary Customization: Transforms recipes to accommodate dietary preferences or restrictions (e.g., vegetarian, gluten-free, dairy-free) and highlights changes made.
- Step-by-Step Guidance: Generates clear, adjusted cooking directions that reflect substituted ingredients and scaled quantities to reduce user confusion.
- Shopping List Generation: Compiles an itemized shopping list from adapted recipes, grouping items and indicating quantities required for the adjusted servings.
- Waste Reduction Suggestions: Recommends ways to repurpose leftover ingredients or scale recipes to minimize waste and optimize ingredient usage.
- Recipe discovery and browsing
- Cooking assistance and guidance
- Meal suggestion functionality
Best for
- Adapting a 6-person casserole recipe down to a 2-person portion while recalculating ingredient amounts and oven times.
- Converting a recipe containing dairy into a dairy-free version with suggested plant-based substitutes and adjusted texture instructions.
- Generating a shopping list and step-by-step plan for a weeknight meal using only items detected in the user's pantry and a few suggested purchases.
- Modifying dessert recipes to accommodate common allergies (nuts, gluten) and providing safe ingredient swaps and preparation notes.
- Scaling up a dinner party menu across multiple dishes while ensuring ingredient quantities align and combined shopping lists are produced.
- Providing quick substitution options when a user is missing a specific ingredient, including notes on flavor and texture differences.
- Discover new recipes and meal ideas
- Follow step-by-step cooking guidance
- Plan meals and explore dishes
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.