Graphify vs Supermemory: Which Context Layer Wins?

Graphify vs Supermemory is a comparison between two developer tools that both feed an AI coding assistant more context, yet do it by opposite mechanisms. Graphify parses your repository on your own machine and turns it into a knowledge graph you can query. Supermemory sits behind an API, reads the conversations and documents you give it, and serves back the facts it thinks are relevant. Both plug into Claude Code, Cursor and Codex. Both are pitched at the same frustration — the assistant that forgets, or that never understood the codebase in the first place. But one maps the code you have, and the other remembers what you say, so "better" depends entirely on which of those two problems is costing you time.

What is Graphify and how does it work?

Graphify is an open-source engine from Graphify Labs, a Y Combinator S26 company founded solo by Safi Shamsi in India. It reads a codebase locally using tree-sitter AST parsing and builds a queryable knowledge graph out of it — not just source files, but the docs, SQL schemas, config files and PDFs that sit around them. Its own GitHub description frames the job plainly: turn any codebase, with its docs, SQL schemas, configs and PDFs, into a queryable knowledge graph.

The notable part is what it leaves out. There are no embeddings, no vector store, and no LLM calls in the code-parsing step. The graph is built deterministically from structure, and every edge is labeled with its provenance: EXTRACTED when the relationship is explicit in the source, INFERRED when the engine deduced it. You can therefore see why the tool believes two things are connected, instead of trusting a similarity score.

In practice you invoke it as a /graphify skill or command from inside your coding assistant. The CLI is free and self-hosted under Apache-2.0/MIT licensing and installs with a single command; there is also a hosted platform at app.graphify.com for teams that would rather not run it themselves. According to its Y Combinator company page, the open-source repo reached 116K+ GitHub stars and 6.5M+ PyPI downloads in roughly five months.

What is Supermemory and what does it actually remember?

Supermemory, built by Dhravya Shah in San Francisco and backed by a Google-supported seed round, describes itself as context infrastructure for AI agents. Rather than parsing a repository, it ingests conversations and documents, automatically extracts facts from them, and assembles user profiles over time. It also handles the two hard parts that naive memory systems skip: contradictions, when a newer statement overrides an older one, and forgetting, so the store does not grow into noise. It serves relevant context back in about 50 milliseconds.

Delivery is broader than a CLI. Supermemory ships as an API and SDK, as an MCP server, and as plugins for Claude Code, Cursor, Codex and other clients. It can also be self-hosted, though the hosted service is the default path. Its competitive set is other AI memory products — chiefly Mem0 and Zep — and the company claims 97% recall, which it puts 26 points ahead of Zep.

Graphify vs Supermemory: how do they actually differ?

The cleanest way to hold the difference is by what each tool takes as input. Graphify's input is your repository, a fixed artifact that already encodes its own relationships: imports, calls, table references, config keys. Supermemory's input is your stream of interactions, an unbounded and contradictory thing that has to be distilled before it is useful.

That difference propagates everywhere else. Graphify's retrieval is deterministic graph traversal, so the same query returns the same path through the same edges, and you can inspect whether each edge was extracted or inferred. Supermemory's retrieval is probabilistic — it decides which stored facts matter for the current prompt, and it publishes recall benchmarks precisely because that decision can be wrong.

The trust model differs too. Graphify's parsing step runs locally and calls no model, so a private codebase stays where it is. Supermemory is primarily a hosted service with a self-host option; you are sending conversational material to it to be stored and profiled.

And they age differently. A Graphify graph is only as current as your last parse of the repository, and it knows nothing about a decision you made in chat yesterday. A Supermemory store knows exactly that, and nothing at all about the call graph of a function you have never discussed.

Is a knowledge graph better than vector search for code?

This is the argument Graphify is really making, and it is worth separating from the Supermemory comparison. The default way to give an assistant repository context has been embedding-based retrieval — chunk the code, embed the chunks, search by similarity. Graphify rejects that for code specifically, on the grounds that source code already carries explicit, machine-readable structure. A function either calls another function or it does not; a migration either touches a table or it does not. Turning that into a floating-point neighborhood discards information you already had.

The trade-off is coverage. Graph traversal follows edges that exist, which means a question phrased in terms the code never uses may find nothing, where similarity search would at least return something plausible. Provenance tagging is Graphify's hedge here: INFERRED edges extend reach beyond what is literally written, while still flagging themselves as deductions rather than facts. Supermemory sits outside this debate entirely — it is not trying to index your repository, it is trying to retain what you told it.

Graphify vs Supermemory: which one should you pick?

Pick Graphify if the pain is that your assistant does not understand the codebase. Large repository, unfamiliar service, schemas and configs and docs scattered across formats, an agent that keeps editing the wrong layer — that is a structural problem, and a deterministic graph addresses it directly. The CLI is free, open source, installs in one command, and the parsing never leaves your machine, so the cost of trying it is close to zero.

Pick Supermemory if the pain is that your assistant does not remember you. Every session restarting from nothing, the same preferences and project conventions retyped, decisions from last week gone — that is a memory problem, and no amount of code parsing fixes it. You are buying an API with sub-100ms retrieval, contradiction handling, and plugins for the clients you already use.

They are not really rivals, despite being marketed into the same gap. Graphify answers "how does this system fit together." Supermemory answers "what have we already established." A developer with both problems can reasonably run both, because neither one occupies the other's slot. If you have to choose one, choose by which question you find yourself re-answering for your assistant every day.

FAQ

Is Graphify free?

The Graphify CLI is free and open source under Apache-2.0/MIT licensing, and you self-host it — it runs on your machine and installs with a single command. Graphify Labs also operates a hosted platform at app.graphify.com for teams that prefer not to run it themselves.

Does Graphify use embeddings or a vector database?

No. Graphify's code-parsing step uses tree-sitter AST parsing and makes no LLM calls, stores no embeddings and requires no vector store. Retrieval is deterministic traversal of the resulting graph, and each edge is tagged EXTRACTED or INFERRED so you can see where a relationship came from.

How does Supermemory compare to Mem0 and Zep?

Mem0 and Zep are the products Supermemory is most often benchmarked against, since all three are AI memory layers rather than code indexers. Supermemory claims 97% recall, which it states is 26 points ahead of Zep.

Can I use Graphify and Supermemory together?

Yes — they occupy different slots. Graphify supplies structural knowledge about your repository, while Supermemory supplies retained facts from your conversations and documents. Both integrate with assistants like Claude Code, Cursor and Codex, Supermemory via its MCP server and plugins and Graphify as a /graphify command.

Does either tool keep my code private?

Graphify's parsing runs locally with no model calls, so the codebase stays on your machine unless you opt into the hosted platform. Supermemory is primarily a hosted API that stores what you send it, though it can also be self-hosted.