Graph Memory vs RAG: Structured Graphs vs Text Chunks

If you're building AI-powered developer tools, you've probably considered RAG (Retrieval-Augmented Generation). Graph Memory takes a different approach. Here's how they compare and when to use each.

How RAG works

Traditional RAG splits your codebase into text chunks, embeds them into vectors, and retrieves the most similar chunks for a given query. It's simple, well-understood, and works reasonably well for many use cases.

But it has limitations:

• No structure — a function definition is just text, no different from a comment
• No relationships — RAG doesn't know that AuthService calls TokenManager
• No cross-references — the doc explaining auth and the code implementing it are unrelated chunks
• No persistence — you can't store decisions, track tasks, or build team knowledge

How Graph Memory works

Graph Memory builds six typed graphs from your project:

Graph	What it understands
Docs	Heading hierarchy, cross-file links, code blocks
Code	AST symbols, imports, call relationships
Knowledge	Notes, typed relations, cross-graph links
Tasks	Kanban status, priorities, assignees
Skills	Steps, triggers, usage frequency
Files	Directory structure, languages, metadata

Every entity is embedded for vector search, but it's also connected to related entities through typed edges. When you search for "authentication", you don't just get text chunks — you get the auth module's functions, the docs explaining the auth flow, notes about auth decisions, and tasks related to auth work.

Key differences

Aspect	RAG	Graph Memory
Data model	Flat text chunks	Typed nodes + edges in typed graphs
Code understanding	Text similarity	AST-parsed symbols + import graph
Relationships	None	Typed edges (calls, imports, blocks, relates_to)
Search	Vector similarity	Hybrid BM25 + vector + graph expansion
Persistence	Read-only index	Read-write (notes, tasks, skills)
Cross-domain	Separate indices	Cross-graph links (code ↔ docs ↔ notes)

When to use what

Use RAG when:

• You need a quick, simple solution for text retrieval
• Your content is mostly unstructured prose (blog posts, wikis)
• You don't need to track relationships between entities

Use Graph Memory when:

• You're working with codebases (structure matters)
• You want AI to understand relationships (what calls what, what documents what)
• You need persistent team memory (notes, decisions, procedures)
• You want task tracking integrated with code context
• You want your AI to build and maintain knowledge over time

The best of both worlds

Graph Memory actually includes vector search — every node is embedded and searchable via cosine similarity. But it adds BM25 keyword search and BFS graph expansion on top. You get the recall of RAG plus the precision of structured graphs.

The result: when your AI assistant asks "how does authentication work?", it gets:

1. The AuthService class and its methods (from Code Graph)
2. The authentication docs with step-by-step flow (from Docs Graph)
3. Team decisions about auth architecture (from Knowledge Graph)
4. Open tasks related to auth improvements (from Task Graph)
5. The "setup auth for new service" skill (from Skill Graph)

That's something flat RAG simply can't do.

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