Your team uses Claude Code? Now it remembers your codebase.
Persistent neural memory for AI agents. Your agent opens the right file first — 93.75% gold-file recall across 40 pre-registered queries on four public repos, at 45–257× fewer tokens than pasting those files in. Local-first, no telemetry.
- 93.75%
- Gold-file recall
- 45–257×
- vs. pasting files
- 40
- Pre-registered queries
- Auto-provisioned
- Free tier
mean, 79–100% per repo
fewer tokens
four public repos
no signup
How it works
Three steps, then it runs forever on every git commit via neuralmind init-hook .
- 01 / 03
Index
tree-sitter parses your codebase into a graph — functions, classes, imports, comments. Ten languages, zero config. TurboVec compresses vectors 4-bit.
~15 min - 02 / 03
Query
Your agent asks a question. Progressive L0–L3 disclosure pulls exactly the right amount of context — a local index lookup, not another model call.
local lookup - 03 / 03
Remember
A Hebbian synapse layer learns co-activations from how you actually use the codebase. Budget-neutral, runs in the background.
Forever
Benchmarks
neuralmind benchmark . for your own.- Gold-file recall
93.75%
40 pre-registered queries, 4 pinned OSS repos (79–100% per repo)
- Tokens vs. pasting files
45–257×
same 40 queries; cheaper than ripgrep on every repo
- Learned recall
Never worse
CI asserts synapse recall ≥ no-recall on the same warm graph, at a neutral token budget
- vs. naive truncation
Never worse
CI asserts our selection beats truncation at an equal budget. Both magnitudes vary by repo — run them for yours
- Field report, one repo
48.8×
~9,300-node private TypeScript codebase — method reproducible, not CI-gated
- Setup time
~15 min
one CLI command; post-commit hook keeps it current
Plus one labeled field report (measured with the CLI, not CI-gated): 48.8× on a real ~9,300-node TypeScript SaaS platform, through a major rebuild. Read the field report →
See it in 30 seconds
Clone the repo, run the demo, get numbers on YOUR codebase. Then email the output to [email protected] with your team size for a free full spend model.
Prove it in 5 minutes
30-second demo on a fresh clone
One script: isolated venv, index build, three real questions against the bundled fixture project.
$ git clone https://github.com/dfrostar/neuralmind && cd neuralmind
$ bash scripts/demo.sh
Then measure YOUR codebase
The fixture is tiny (~500 lines, ~5.5×). Real repos consistently hit 12–50× on the same pipeline — run the benchmark on your own code and read your own number. A maintainer field report on a ~9,300-node private TypeScript codebase measured 48.8×.
$ pip install neuralmind
$ cd /path/to/your-repo
$ neuralmind build .
$ neuralmind benchmark .
Verify what you installed
Check the SBOM, release integrity, and audit trail on the security page — and the full production before/after data on the effectiveness page.
What the demo prints (bundled fixture)
Q: How does authentication work in this codebase?
naive = 4,736 tok neuralmind = 829 tok reduction = 5.7×
Average reduction: 5.5× across 3 queries
Avg context size: 859 tokens (vs 4,736 naive)
Small fixture, small multiplier — by design. It runs in CI on every commit as a regression gate. The 12–50× headline comes from real repos; your own number is one neuralmind benchmark . away.
The savings are free. The tier is control.
The savings are free
- Token compression ships in the free MIT core — the savings cost nothing, and you can measure them on your own repo in ~15 minutes.
- Modeled at 30 code questions per developer per day, a 50-developer team gets back ~$310/mo on inference alone. The figure barely moves with the exact ratio — 48.8× and 65.6× differ by under 1% of the saving, because both already remove ~98% of the tokens.
- The bigger line is time: ~$1,650/mo per 50-dev team recovered from context-limit thrashing and re-prompting, at a $50/hr fully-loaded rate.
- Recall is a local index lookup, not another model call — nothing in the loop between "I need to know X" and "I know X" waits on an API. That compounds across every developer, every day.
Fewer wrong answers, faster teams
- 93.75% gold-file recall across 40 pre-registered queries on four public repos — 79–100% per repo, with every miss published rather than dropped.
- Team dashboard shows synapse memory health, ingestion status, savings, latency trends — all read-only, all local.
- Self-documenting code: DocEvolver finds undocumented methods and evolves JSDoc that actually improves retrieval.
- The engine makes no network calls of its own and sends no telemetry — verifiable on the wire. Works with the agents you already run: Claude Code, Cursor, Cline, any MCP agent. No rip-and-replace.
Features
Everything persistent memory should be — and nothing it shouldn't.
Local Retrieval, No Model Call
Recall is a local index lookup, not another round trip to a model. The ChromaDB-free TurboVec backend keeps 4-bit quantized vectors 8–16× smaller with fact recall 0.800 vs 0.744 for float32 — parity gated in CI.
Hebbian Synapse Layer
Associations strengthen when you use them together — like a real hippocampus. Budget-neutral, no cost until it activates.
Progressive L0–L3 Disclosure
Retrieves the exact bytes needed. Never pastes the whole repo. 45–257× fewer tokens than full-file context across 40 pre-registered queries on four public repos.
Team Dashboard
Read-only web UI: synapse memory, ingestion status, savings, latency trends, community distribution, recent queries.
Self-Documenting Code
DocEvolver finds undocumented methods, generates JSDoc variants, evolves them against retrieval fitness. Winning variants patched back into source.
Business-Context Synapse Seeding
seed_from_documents() builds deterministic, LLM-free associations between business documents (decisions, SOPs, meeting notes) and your code graph. Adjacency-matched compounds, title-reference cross-links.
MCP Server
First-class MCP integration. Works with Claude Code, Cursor, Cline, Continue, and any MCP-compatible agent.
Ten-Language Code Graph
tree-sitter indexes Python, TypeScript, Go, Rust, Java, C, C++, C#, Ruby, and PHP out of the box.
Tool Output Recovery
Caches dropped tool output from context windows. When your agent forgets, neuralmind remembers.
Free Tier — Auto-Provisioned
pip install neuralmind && neuralmind wakeup . writes the license on first run. Zero signup wall. Default tier is "free", identity auto-issued.
100% Local Engine
NeuralMind makes zero network calls of its own — only the minimal relevant slice ever reaches your AI tool, never your whole codebase. No telemetry.
One-Command Project Init
neuralmind init auto-detects project structure, installs hooks, builds the index — all at once. Your project is ready in seconds.
Compliance Annotation Engine
Scans code and docs for annotations across CMMC 2.0, NIST SP 800-53, SOX ITGC, HIPAA, SOC 2 and ISO 27001. Each framework carries its own marker on the line — # NIST AC-1:, # CMMC AC.L2-3.1.1:. Ingest CMMC assessment guides, export control mappings, gate CI on annotation health.
Audit Export & CI/CD Check
neuralmind export --controls produces control-to-code mappings as CSV for evidence submission, or an SSP report with --format pdf. neuralmind ci-check gates builds on annotation health.
Compliance Report MCP Tool
neuralmind_compliance_report surfaces live compliance stance from any MCP-compatible agent. Ask "are we compliant on access control?" and get an answer grounded in real annotations.
See your savings before you spend a dollar
pip install neuralmind and you have the whole product. For teams evaluating at scale, we run a free AI-spend assessment: measured on your code, modeled in your numbers, no obligation.What the assessment gives you
- A measured token-reduction ratio on one of your own repos — run locally, with no network calls of its own
- A spend model in your numbers: per-seat subscriptions, usage-based API (OpenRouter, Bedrock, Vertex), and self-hosted GPU
- A productivity model: hours lost to context-limit thrashing and re-prompting, valued at your fully-loaded rate
- An honest fit verdict — if your workload is generation-heavy or caching already covers you, we say so
The software is MIT-licensed and free. Commercial support for deployment and integration is available.
Common questions
Stop paying for tokens you don't use.
One command to install. Seconds to verify. Your agent remembers what matters.
$pip install neuralmind- No cloud calls
- CI-verified
- 100% local