We Got Asked "Isn't This Just Graphify?" — So We Benchmarked Both on the Linux Kernel
We entered Synapse MCP into the OpenAI × Product Hunt Astra Challenge and started talking to developers. One question kept coming up:
"Isn't this just Graphify?"
Honestly? It sent me down a rabbit hole. If there's already a YC-backed code graph tool with 119k stars and a full engineering team, does the world need another one? Are we just building something that already exists with a smaller budget and fewer people?
Instead of guessing, I installed both tools on the same machine and ran them head-to-head against the same codebases — including the full Linux kernel (74,619 code files across 96,000 paths).
Here is what the physical data showed.
1. They are fundamentally different tools
Graphify is a repository knowledge plane. It's a Python CLI that extracts your codebase into a static graph.json file. You query it, and it returns a list of what’s there. It's read-only, handles binary docs and DB sockets, and it's very good at what it does.
Synapse is an AI agent execution guardrail. It runs as a persistent local engine in memory with sub-millisecond query latency.
The core difference isn't just speed: Synapse simulates edits in memory and validates them before they ever touch disk.
Graphify can tell you who calls a function.
Synapse can tell you whether it's safe to delete that function — with compiler-level confidence scores, blast-radius risk levels, and a definitive safety verdict.
One gives your agent a list. The other gives your agent a decision.
2. The Linux Kernel Benchmark (74,619 files)
We cloned torvalds/linux locally and ran cold index extraction and queries on both engines:
Filesystem Discovery: Synapse 2.0s vs Graphify 1m 15s (37.5× faster)
Peak RAM Consumption: Synapse 877 MB vs Graphify 5.21 GB (5.9× lower footprint)
Cold Query Latency: Synapse 148 ms vs Graphify 59.3 seconds (400× faster)
Availability During Indexing: Synapse serves provisional queries immediately from in-memory tables. Graphify completely locks out queries until its multi-gigabyte JSON graph finishes building.
3. Head-to-Head on Developer Queries
When testing real-world engineering tasks across each other’s codebases:
10× to 15× Fewer Tokens: Graphify's breadth-first search regularly blew through token limits (dumping 4,678 tokens of raw edge dumps for a single function). Synapse delivered the exact symbol definition and calling context in 167 to 307 tokens. That's 90%+ less context noise for your LLM.
Compiler-Aware Context: Graphify returned No affected nodes found because its tree-sitter parser has no module alias resolver. Synapse tracks aliases, macros, and arity variants (foo/1 vs foo/2).
Write Safety: When an agent proposes a refactor, Synapse's Shadow Graph runs linters and typechecks against an in-memory overlay. If it fails, the write is aborted before your working tree gets corrupted.
4. The Answer to the Rabbit Hole Question
Yes, there is room for both — because they solve different problems.
If you want a company-wide map to browse your organisation’s architecture, Graphify is great.
If you want your AI coding agent to be smarter, stop hallucinating/guessing when working on your codebase and corrupting builds, Synapse is built for you.
Pricing & Availability:
Free Tier is the full product: 50+ languages, unlimited repos, in-memory ETS engine, zero cloud telemetry, no time limit and most of the functionality.
Pro Tier ($19/mo): Adds atomic write-safety (write_safely), stack trace crash resolution, test coverage and compiler-backed dead code deletion safety.
Would love your feedback. I wrote Synapse to selfishly make me and my agents more efficient when coding - I hope that it helps you too. It's fast, handles multiple repos, and makes LLMs understand your code in a very few calls, saves you tokens, and gets the job done faster. Try it yourself
All benchmark data published. Have a go yourself it is free.
What's the most destructive or annoying thing an AI agent has broken in your codebase when you gave it write permissions?
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