pip (no graphify)Best for: anyone trying NeuralMind for the first time, CI jobs, and fresh/locked-down machines where installing a second indexing tool is friction or impossible.
Primary goal: go from nothing to a queryable index in one pip install
and one build — no external graph tool.
Before v0.15.0 you had to install and run a separate tool (graphify)
before neuralmind build would do anything. That extra step was the #1
onboarding drop-off. NeuralMind now ships a built-in tree-sitter graph
backend, so the whole flow is two commands.
pip install neuralmind
cd /path/to/your-repo
neuralmind build .
On the first build with no graphify-out/graph.json present, NeuralMind
parses your code with the bundled tree-sitter backend and prints:
[neuralmind] generated code graph via the built-in tree-sitter backend → graphify-out/graph.json
Then query it like normal:
neuralmind query "how does authentication work?" .
neuralmind wakeup . # ~400-token project orientation
That’s it. No second install, no separate graph step.
The built-in backend produces a graphify-compatible graph — symbol-level
code nodes (files, classes, functions/methods), contains / imports_from
/ inherits / calls edges, and a docstring-derived rationale layer. The
entire retrieval pipeline downstream — progressive L0–L3 disclosure, the
synapse layer, the graph view, the MCP tools — works exactly the same. Only
the graph producer changed.
SUPPORTED_SUFFIXES seam.calls/inherits are best-effort by name (no type
resolution). Whether that costs retrieval quality is measured by the
backend parity gate, not guessed — and an optional LSP/SCIP precision pass
is on the roadmap.Install it and it takes priority automatically — no flag needed:
pip install neuralmind
neuralmind build .
Precedence rules:
neuralmind build --force only regenerates graphs we wrote — it never
clobbers a graphify build.You can switch either direction later without losing your .neuralmind/
state — it’s on disk in the project, not in the install.
Because the swap is proven at parity, not asserted. Every NeuralMind PR
runs a backend parity gate (evals/parity/run.py) that builds the reference
fixture with both graphify and the built-in backend, runs the faithfulness
eval and derives the token-reduction ratio on each, and fails the build if
the built-in backend drifts outside tolerance of graphify (within 25%
reduction, within 10 points faithfulness). If a backend change ever made
retrieval meaningfully worse, CI catches it before it ships.
Measure it on your own code in the same breath:
neuralmind benchmark . # your actual reduction ratio + per-query tokens
build — backend precedence + options