Your codebase,
40–70× smaller
for AI agents
NeuralMind turns any repository into a queryable neural index. AI coding agents answer code questions in ~800 tokens instead of loading 50,000+ tokens of raw source.
LLMs are flying blind
on large codebases
Without NeuralMind, every code question forces an AI agent to load raw source files — burning tokens and budget on irrelevant context.
Without NeuralMind
Raw file loading on every query
With NeuralMind
Smart semantic context retrieval
4-layer progressive
disclosure
NeuralMind loads only what's relevant to each query. Static orientation layers always load; dynamic layers respond to your specific question.
Identity — Always Loaded
Project name, description, graph size, entry points, main patterns
Architecture Summary — Always Loaded
Module overview, key components, dependencies, data flow, top clusters
Relevant Modules — Query-Specific
Code clusters most semantically similar to your question via community detection
Semantic Search — Query-Specific
Direct vector similarity hits, reranked by learned cooccurrence patterns
Cut tokens at the
source and the output
Most tools optimize only retrieval. NeuralMind compresses both what agents fetch and what they consume from tool outputs.
What to fetch
What agents see
See exactly what
agents receive
Every response includes a token footer showing real-time savings. No guesswork — you always know the efficiency of context.
Automatic on session start
Run neuralmind wakeup . once. The agent orients itself without reading a single source file.
Query-aware context
Different questions get different context. Asking about auth returns auth clusters. Asking about payments returns payment logic.
Gets smarter over time
The cooccurrence reranker learns which modules appear together in your queries and boosts their relevance automatically.
What this means for
your API bill
Based on 100 queries/day. NeuralMind runs entirely offline — no additional API costs beyond your model provider.
| Model | Without NeuralMind | With NeuralMind | Monthly Savings |
|---|---|---|---|
| Claude 3.5 Sonnet | $450 / mo | $7 / mo | $443 saved |
| GPT-4o | $750 / mo | $12 / mo | $738 saved |
| Claude Opus | $2,250 / mo | $36 / mo | $2,214 saved |
| GPT-4.5 | $11,250 / mo | $180 / mo | $11,070 saved |
Works directly in
Claude Desktop & Cursor
Native Model Context Protocol server. Call NeuralMind tools directly from your AI agent session — no wrappers, no middleware.
neuralmind_wakeup
Session-start orientation. Returns project context in ~365–600 tokens without reading any source files.
~400 tokensneuralmind_query
Answer any code question. Returns L0–L3 structured context with token count and reduction ratio.
~800–1100 tokensneuralmind_skeleton
Explore a file's functions, call graph, and cross-file dependencies without loading full source.
5–15× cheaperneuralmind_search
Semantic entity search. Finds functions, classes, and routes by concept — ranked by similarity.
ranked resultsneuralmind_build
Incremental index update. Only re-embeds changed nodes — fast after small code changes.
incrementalneuralmind_benchmark
Measure per-query token counts and reduction ratios on your actual codebase.
metricsNeuralMind vs.
Heuristic-only retrieval
Both approaches reduce context. The tradeoff is retrieval quality vs. zero dependencies. NeuralMind runs fully offline — no API calls, no cloud services, no data leaves your machine.
| Feature | Heuristic-only | 🧠 NeuralMind |
|---|---|---|
| Token reduction | ~33× (97% fewer tokens) | 40–70× |
| Retrieval accuracy | 70–80% top-5 | Higher (semantic) |
| External dependencies | ✓ None | ChromaDB (local) |
| Runs offline | ✓ Yes | ✓ Yes |
| Learns from usage | ✗ No | ✓ Cooccurrence reranking |
| MCP server | ✗ No | ✓ Native |
| PostToolUse compression | ✗ No | ✓ Phase 2 hooks |
| File skeleton view | ✗ No | ✓ Call graph + deps |