🧠 NeuralMind

Persistent memory for AI coding agents.

Your AI coding agent learns your codebase the way a senior engineer would — what files go together, what you usually edit next, what patterns matter. The memory persists across sessions and surfaces automatically. No MCP tool call required: NeuralMind writes a SYNAPSE_MEMORY.md file that Claude Code loads on every session start, so the agent boots with what it's learned about your code already in context.

Works with Claude Code, Cursor, Cline, Continue, and any MCP-compatible agent. 100% local — your code never leaves your machine. (Side effect: ~5–10× cheaper agent sessions because the agent stops re-loading context it already understood. Benchmarks below ↓.)

🆕 New in v0.24.0 — memory namespaces & branch isolation. The learned synapse layer is now namespace-aware: branch:<name> / personal / shared / ephemeral memory live separately in the same store, so a feature-branch spike can't pollute what the agent learned about main. Recall stays smart by default — a transparent merged view weights the active branch at 1.0×, your long-term personal memory at 0.8×, and an imported shared team baseline at 0.5× (published constants, attributed per-namespace in query --trace). New neuralmind memory {inspect,reset,export,import} shows contribution by namespace, clears exactly one namespace, and moves memory as versioned JSON bundles (the PRD 8 team-memory on-ramp). Existing learned memory migrates in place, losslessly into personal — single-transaction rebuild, rollback on any failure, proven by a no-data-loss test. Release notes

v0.23.0 — versioned index contract (IR), retrieval-quality harness, debug traces, and a local daemon. Four future-proofing foundations. (PRD 1) A canonical, schema-versioned intermediate representation of your code graph, validated on every build — a new neuralmind validate command checks the contract without a vector backend; the embedder still reads graph.json unchanged (the IR round-trips back identically), so retrieval is unaffected. (PRD 2) A new neuralmind benchmark --quality mode measures whether retrieval finds the right code — precision@k / recall@k / MRR / answerability over 30 golden queries across Python/TS/Go — and fails CI on a regression. (PRD 3) neuralmind query --trace shows why a result came back — per-layer candidates, cluster scoring with vector-vs-synapse attribution, and final hits. (PRD 5) An experimental neuralmind daemon holds project state warm so repeat query/stats skip cold backend init (auto-preferred when running, transparent direct-mode fallback). Release notes

v0.22.0 — turbovec becomes the default (when available). import neuralmind no longer requires ChromaDB, and the default backend is now auto: prefer the ChromaDB-free turbovec path when its deps are installed, else fall back to chroma. Safe by construction — a plain pip install neuralmind is unchanged; only installs with the [turbovec] extra flip, with a one-time auto-reindex (the old ChromaDB index is left as a fallback, nothing deleted). neuralmind doctor now shows the resolved backend. The staged middle step toward retiring ChromaDB. Release notes

v0.21.0 — ChromaDB-free retrieval. NeuralMind can now embed and search with zero ChromaDB: the opt-in turbovec backend pairs Google Research's TurboQuant compressed index (8–16× smaller vectors) with a bundled OnnxMiniLMEmbedder that produces vectors byte-identical to ChromaDB's (all-MiniLM-L6-v2 on just onnxruntime + tokenizers). At/above retrieval parity on the gold set (fact-recall 0.744→0.800). Enable with backend: turbovec in neuralmind-backend.yaml. This retires the dependency behind the recurring CVE-2026-45829 advisory. Release notes

v0.20.0 — Measure the onboarding lift. neuralmind eval --onboarding turns NeuralMind's headline differentiator into a number: does an agent that inherits a committed team memory retrieve better on its first queries than a cold agent with none? The headline is the top-k module hit-rate lift (a deterministic +6.5 points on the reference fixture), with fact-recall + grounding as honest secondaries; budget-neutral by design, gated in CI at lift ≥ 0. Release notes

v0.19.0 — One-command MCP setup. neuralmind install-mcp --all auto-detects your installed agents — Claude Code, Cursor, Cline, Claude Desktop — and registers NeuralMind's MCP server with each (non-destructive merge, idempotent). The agent then onboards onto your codebase through NeuralMind's tools instead of grepping cold. Distribution is half the moat; the learned synapse layer (usage memory) is the other half. Release notes

v0.18.0 — Incremental updates. Re-index just the file you edited, not the whole repo: neuralmind watch --reindex re-parses each changed file and re-embeds only its nodes (unchanged files stay byte-for-byte identical, so the embedder skips them). Your index stays fresh as you type. Release notes

v0.17.0 — Optional SCIP precision. Set NEURALMIND_PRECISION=1 with a SCIP index (scip-python/scip-typescript/scip-go) and NeuralMind folds in compiler-accurate calls/inherits edges, replacing the heuristic ones for covered files. Off by default, dependency-free, proven by a precision check in the CI gate. Release notes

v0.16.0 — Multi-language: TypeScript + Go. The built-in tree-sitter backend now indexes Python, TypeScript, and Go out of the box — neuralmind build . works standalone on a TS or Go repo, no graphify. Proven at parity per language by the CI gate (100% symbol coverage vs graphify). Release notes

v0.15.0 — No graphify needed. A built-in tree-sitter graph backend, so pip install neuralmind && neuralmind build . just works — no second, external tool. graphify still takes priority where present. Proven at parity by a CI gate (reduction + faithfulness within tolerance). Release notes

v0.14.0 — Measure faithfulness. A new contributor/CI command — neuralmind eval — turns "does the memory actually help?" into a number: it scores whether NeuralMind's selected context contains more of the facts a correct answer needs than a naive baseline at the same token budget (a faithfulness delta, plus grounding and contradiction checks). 100% local by default; the LLM-as-judge is opt-in. Release notes

v0.13.0 — Measurement foundation. The offline faithfulness dataset + expected-fact-recall scorer and polyglot (TypeScript + Go) retrieval fixtures that neuralmind eval is built on. Release notes

v0.12.0 — Install Doctor. One command — neuralmind doctor — inspects your setup (code graph, semantic index, synapse memory, MCP server, Claude Code hooks, query-memory consent) and prints each piece with a status and the exact command to fix it. Exits non-zero on a real failure so you can gate CI or an agent's setup step on it; --json gives a stable machine-readable snapshot. Release notes

v0.11.0 — Directional Synapses. The brain-like layer now learns what comes next, not just what goes together. A new synapse_transitions table records ordered (from_node, to_node) observations every time the watcher flushes a batch, so the agent can ask neuralmind next <file> (or call the neuralmind_next_likely MCP tool) and get a probability distribution over what's typically edited after that file. Additive — the existing undirected synapse graph keeps doing its job. Release notes

v0.9.0 — Enterprise-Ready. GHCR auto-built multi-platform container image (docker pull ghcr.io/dfrostar/neuralmind:latest), CycloneDX SBOM attached to every release, air-gapped install walkthrough, and a compliance one-pager consolidating NIST AI RMF + SOC 2 + GDPR claims. Release notes

v0.8.0 — Always-On. neuralmind watch + neuralmind serve run as first-class services with committed systemd + launchd templates + a Windows Task Scheduler walkthrough in the Scheduling Guide + a /healthz endpoint for Docker HEALTHCHECK and systemd ExecStartPost probes. Release notes

v0.7.0 — Install anywhere. Five install paths now in the README: pip, pipx, uv, Docker, and source. Same package every path; smoke-test verified. Release notes · Install matrix ↓

v0.6.0 — Obsidian-style graph view with a live activity feed. neuralmind serve streams synapse + file events to the canvas in real time, so you can watch the brain learning your codebase. Release notes · Graph view section ↓

🌐 Visit the landing page • 📖 Read the About page • ⚖️ Not affiliated with NeuralMind.ai

🧠 What changes when an agent has memory

Three concrete behaviors appear once the synapse layer has watched a few coding sessions on your project. None of them require the agent to call extra tools — the memory primes the model on session start and surfaces predictions automatically.

Without NeuralMind	With NeuralMind
Every session, the agent starts cold. You re-explain your auth flow, your billing module, your naming convention.	Session starts with `SYNAPSE_MEMORY.md` already in context: "strongest associations: `middleware.py` ↔ `handlers.py` ↔ `session.py` — auth flow. Hub: `middleware.py`." The agent boots knowing the shape of your code.
Agent finishes editing `payment_service.py`, asks "anything else?"	Agent: "after `payment_service.py` you usually update `webhook_handler.py` (45%) and the test file (28%) — want me to do those too?" Learned from your edit history, no manual hint needed. (v0.11.0+)
`npm test` floods the conversation with 800 lines. Agent re-reads 50K tokens.	Hook auto-summarizes Bash output to errors + repeated-line patterns + last 3 lines. `neuralmind last` recovers the raw cache when needed. (v0.10.0+)

The brain layer learns continuously from how you actually work — file co-edits via a watcher daemon, query intent from MCP calls, tool-use patterns from PostToolUse hooks. Decays unused associations like a real brain so stale knowledge doesn't crowd out current patterns.

This is not "another RAG tool." It's the memory layer that everything else assumes the agent already has.

⚡ 30-second proof — see the memory work

The clearest evidence the memory is working is the measurable side effect: the agent stops re-loading context it already understood. Reproduce it on a freshly cloned checkout:

git clone https://github.com/dfrostar/neuralmind && cd neuralmind
bash scripts/demo.sh

The script creates an isolated venv, installs the deps, builds the index for the bundled fixture (tests/fixtures/sample_project/), and runs three real questions. Each question loads only the relevant code instead of the whole repo — exactly the kind of context-economy a senior engineer applies without thinking. Output looks like:

  Q: How does authentication work in this codebase?
     naive = 4,736 tok   neuralmind =  829 tok   reduction =   5.7×
  Q: What are the main API endpoints?
     naive = 4,736 tok   neuralmind =  923 tok   reduction =   5.1×
  Q: Explain the billing flow from a user perspective.
     naive = 4,736 tok   neuralmind =  826 tok   reduction =   5.7×

  Average reduction:   5.5×  across 3 queries
  Avg context size:    859 tokens  (vs 4,736 naive)
  Est. monthly saved:  ~$34.89  @ 100 queries/day on Claude 3.5 Sonnet
  Wall time:           0.85s

The fixture is intentionally small (~500 lines) — it catches regressions in CI. Real repos consistently hit 40–70× on the same pipeline (benchmarks · community submissions · interactive dashboard). Once the demo convinces you, run it on your own code:

pip install neuralmind          # built-in tree-sitter backend — no graphify needed (v0.15.0+)
cd /path/to/your-repo
neuralmind build .
neuralmind benchmark . --contribute

Prefer graphify's richer graph? Install it and it takes priority automatically: pip install neuralmind graphifyy && graphify update . && neuralmind build .

📊 The fact-based case

Two docs you should read before forming an opinion. Both are linked from this section so you can pick what you need:

docs/BUSINESS-CASE.md — the compelling pitch, with provable numbers. Every claim is a single command away from being verified on your own code. ROI math with assumptions you can change. Three concrete scenarios. Read this if you're evaluating whether to bring NeuralMind to your team.
docs/HONEST-ASSESSMENT.md — the skeptic's companion. When NeuralMind isn't worth installing. What "40–70×" actually means (and doesn't). Where the community-benchmark sample is too small to extrapolate. Read this if you want to know what could go wrong before adopting.

The headline you can stand on: retrieval reduction is measured in CI on every commit (open any closed PR in the PR list — each one has a sticky benchmark comment with current numbers) and reproduces in 30 seconds on a fresh clone via bash scripts/demo.sh. Real-world repos have submitted 46–66× but n=2 — your number comes from neuralmind benchmark . --contribute on your code.

🔒 Security & Compliance

For enterprises and regulated industries:

100% Local Processing – Your code never leaves your machine. All embeddings are generated and stored locally using ChromaDB.
No External APIs – NeuralMind runs completely offline. No cloud services, no telemetry, no data exfiltration.
Explainable AI – Every context decision is auditable. Know exactly which code was retrieved (Extracted) vs. inferred by the model.
Open-Source & MIT Licensed – Full transparency. No hidden clauses, no vendor lock-in. Audit the code yourself.
GDPR/HIPAA-Friendly – Process sensitive code without compliance concerns. All data stays under your control.

For CTOs & Security Teams:

✅ Zero external dependencies for code storage
✅ Runs behind your firewall or on-premise
✅ No collection of usage metrics or code patterns
✅ Predictable, reproducible results (not a black box)
✅ Works with proprietary and sensitive code

See full security policy

📌 For AI Coding Agents

If you are Claude Code, Codex, Cursor, Cline, or any AI agent with NeuralMind installed, start here.

Check if NeuralMind is active

neuralmind stats .

Expected output when ready:

Project: your-project
Built: True
Nodes: 241

If Built: False, run neuralmind build . first (the built-in backend generates the graph automatically; no graphify needed).

Startup workflow

Every new session, do this first:

neuralmind wakeup .

Or via MCP:

neuralmind_wakeup(project_path=".")

This returns ~365–600 tokens of structured project context:

Project name and description (from CLAUDE.md, mempalace.yaml, or README.md first line)
How many code entities and clusters are indexed
Architecture overview: top 10 code clusters with their entity types and sample names
Sections from graphify-out/GRAPH_REPORT.md if present

Use this output as your orientation before writing any code. It replaces reading the entire repository.

Decision tree — which tool to call

Need to understand the project?
  └─► neuralmind wakeup .               (MCP: neuralmind_wakeup)      ~400 tokens

Answering a specific code question?
  └─► neuralmind query . "question"     (MCP: neuralmind_query)       ~800–1100 tokens

About to open a source file?
  └─► neuralmind skeleton <file>        (MCP: neuralmind_skeleton)    ~5–15× cheaper than Read
      → Only fall back to Read when you need the actual implementation body
      → Use NEURALMIND_BYPASS=1 when you truly need raw source

Answering a complex, multi-part question?
  └─► neuralmind recursive-query . "q"  (MCP: neuralmind_recursive_query)  decomposes + synthesizes

Question about reference documents (PDFs, legal, clinical)?
  └─► neuralmind query-docs . "q"       (MCP: neuralmind_query_docs)       searches doc index only

Searching for a specific function/class/entity?
  └─► neuralmind search . "term"        (MCP: neuralmind_search)      ranked by semantic similarity

Made code changes and need to update the index?
  └─► neuralmind build .                (MCP: neuralmind_build)       incremental — only re-embeds changed nodes

Understanding the output

`wakeup` / `query` output format

## Project: myapp

Full-stack web app for task management. Uses React 18, Node.js, and PostgreSQL.

Knowledge Graph: 241 entities, 23 clusters
Type: Code repository with semantic indexing

## Architecture Overview

### Code Clusters
- Cluster 5 (45 entities): function — authenticate_user, hash_password, verify_token
- Cluster 12 (23 entities): class — UserController, AuthMiddleware, SessionStore
- Cluster 3 (18 entities): function — createTask, updateTask, deleteTask
...

## Relevant Code Areas        ← query only; absent from wakeup
### Cluster 5 (relevance: 1.73)
Contains: function entities
- authenticate_user (code) — auth.py
- verify_token (code) — auth.py

## Search Results             ← query only
- AuthMiddleware (score: 0.91) — middleware.py
- jwt_handler (score: 0.85) — auth/jwt.py

---
Tokens: 847 | 59.0x reduction | Layers: L0, L1, L2, L3 | Communities: [5, 12]

Layer meanings:

Layer	Name	Always loaded	Content
L0	Identity	✅ yes	Project name, description, graph size
L1	Summary	✅ yes	Architecture, top clusters, GRAPH_REPORT sections
L2	On-demand	query only	Top 3 clusters most relevant to the query
L3	Search	query only	Semantic search hits (up to 10)

`skeleton` output format

# src/auth/handlers.py  (community 5, 8 functions)

## Functions
L12   authenticate_user   — Validates credentials and issues JWT
L45   verify_token        — Checks JWT signature and expiry
L78   refresh_token       — Issues new JWT from a valid refresh token
L102  logout              — Revokes refresh token in DB

## Call graph (within this file)
authenticate_user → verify_token, hash_password
refresh_token → verify_token

## Cross-file
verify_token imports_from → utils/jwt.py (high 0.95)
authenticate_user shares_data_with → models/user.py (high 0.91)

[Full source available: Read this file with NEURALMIND_BYPASS=1]

Use skeleton to understand what a file does, how its functions relate, and which other files it depends on — without consuming tokens on the full source body.

`search` output format

1. authenticate_user (function) - score: 0.92
   File: auth/handlers.py  Community: 5

2. AuthMiddleware (class) - score: 0.87
   File: auth/middleware.py  Community: 5

3. hash_password (function) - score: 0.81
   File: utils/crypto.py  Community: 5

PostToolUse hooks — what happens automatically

If neuralmind install-hooks has been run for this project (check for .claude/settings.json), Claude Code automatically compresses tool outputs before you see them:

Tool	What happens	Typical savings
Read	Raw source → graph skeleton (functions, rationales, call graph)	~88%
Bash	Full output → error lines + warning lines + last 3 lines + summary	~91%
Grep	Unlimited matches → capped at 25 + "N more hidden" pointer	varies

This is fully automatic — you do not need to call any extra tools.

What the agent actually sees on a compressed Bash call (v0.10.0+)

After every Bash call, NeuralMind appends a content-aware footer so the agent can tell at a glance what was dropped — not just how many bytes:

[neuralmind: dropped 23 lines (12 info, 8 debug, 3 other);
 repeated: 5× 'Gamma API error 503'  ·  4298 B stdout total  ·
 `neuralmind last` for cached raw  ·  NEURALMIND_BYPASS=1 to disable]

This lets the agent judge whether the dropped middle was log noise (safe to ignore) or a buried error (worth recovering). No second query needed to find out.

Recovery without re-running (v0.10.0+)

Every compressed Bash call also stashes its raw stdout/stderr to <project>/.neuralmind/last_output.json (single-slot, 2 MB cap, atomic writes). Recover it with:

neuralmind last           # human-readable raw output
neuralmind last --json    # full payload (ts, command, exit, stdout, stderr)

This turns NEURALMIND_BYPASS=1 from a re-run-from-scratch cost into a free lookup — meaningful on npm test (~28s), non-deterministic API calls, or any destructive command that can't be re-run safely.

To bypass compression on a future command (the new full output goes into the cache instead of being summarized):

NEURALMIND_BYPASS=1 <your command>

To disable the recovery cache entirely:

export NEURALMIND_OUTPUT_CACHE=0

Predict the next file (v0.11.0+)

After neuralmind watch has been running for a handful of sessions, the synapse layer learns directional edit patterns — not just what files go together but what file usually follows what. Ask it directly:

$ neuralmind next . src/auth/handlers.py
After src/auth/handlers.py:
   45.2%  tests/test_auth.py
   28.4%  src/auth/middleware.py
   12.1%  docs/auth.md
    8.3%  src/auth/__init__.py
    6.0%  src/main.py

The same prediction is available three ways depending on who's asking:

Surface	How an agent reaches it	Where it's read
MCP tool `neuralmind_next_likely`	Cursor / Cline / Continue / any MCP client	After editing a file, prefetch the likely next one
CLI `neuralmind next <dir> <file>`	Shell, scripts, status lines	Quick lookup or piping into a fzf picker
Auto-memory `SYNAPSE_MEMORY.md`	Claude Code (loads on session start, no user action)	"What typically comes next" section primes the model with the top transitions before any tool call

Why this matters for agents: the auto-memory section is the single highest-leverage surface — Claude Code sees SYNAPSE_MEMORY.md on every session start without anyone asking. An agent priming on "after src/auth/handlers.py, the human usually opens tests/test_auth.py" can proactively offer to update the test, rather than waiting to be asked. Zero user prompts; the prediction is just in context.

The transition signal needs a long observation window to converge (N files edited together yield only N-1 ordered pairs), so running the watcher as a service via the always-on guide shortens time-to-useful-predictions from weeks to days.

Branch-isolated memory (v0.24.0+)

Every learned association now carries a namespace. On main nothing changes — your memory is the personal namespace, read at full weight. The moment you git checkout -b feature-x, new activations land in branch:feature-x, and recall reads a merged view: branch-local context at 1.0×, long-term personal at 0.8×, an imported shared team baseline at 0.5× (explicit constants — query --trace attributes every boost to the namespace that drove it). Session scratch goes to ephemeral, which decays fast and is cleared at the next SessionStart.

$ neuralmind memory inspect .
Synapse memory — .neuralmind/synapses.db
Active namespace: branch:feature-x  (schema v1)
Namespace                  Edges    Weight  Transitions   Nodes
branch:feature-x              34      6.20           12      28
personal                     412     88.71          120     310
shared                        96     31.40           41       0

$ neuralmind memory reset . --namespace branch:feature-x   # branch merged? drop its memory
$ neuralmind memory export . --namespace personal -o team-baseline.json
$ neuralmind memory import team-baseline.json --namespace shared   # teammate's machine

Existing learned memory migrates in place and losslessly into personal on first open (single transaction, rollback on any failure). See the branch-isolated memory walkthrough and Release Notes v0.24.0.

After making code changes

The index does not auto-update unless a git post-commit hook was installed with neuralmind init-hook .. After significant code changes, rebuild manually:

neuralmind build .          # incremental — only re-embeds changed nodes
neuralmind build . --force  # full rebuild — re-embeds everything

MCP tool quick reference

Tool	When to call	Required params	Returns
`neuralmind_wakeup`	Session start	`project_path`	L0+L1 context string, token count
`neuralmind_query`	Code question	`project_path`, `question`	L0–L3 context string, token count, reduction ratio
`neuralmind_search`	Find entity	`project_path`, `query`	List of nodes with scores, file paths
`neuralmind_skeleton`	Explore file	`project_path`, `file_path`	Functions + rationales + call graph + cross-file edges
`neuralmind_recursive_query`	Complex question	`project_path`, `question`	Synthesized answer, sub-queries, gaps, sources
`neuralmind_query_docs`	Reference docs	`project_path`, `question`	Relevant doc chunks with source files and relevance scores
`neuralmind_stats`	Check status	`project_path`	Built status, node count, community count
`neuralmind_build`	Rebuild index	`project_path`	Build stats dict
`neuralmind_benchmark`	Measure savings	`project_path`	Per-query token counts and reduction ratios

⚡ Two-phase optimization

┌─────────────────────────────────────────────────────────────┐
│ Phase 1: Retrieval — what to fetch                          │
│   neuralmind wakeup .    →  ~365 tokens (vs 50K raw)        │
│   neuralmind query "?"   →  ~800 tokens (vs 2,700 raw)      │
│   neuralmind_skeleton    →  graph-backed file view          │
├─────────────────────────────────────────────────────────────┤
│ Phase 2: Consumption — what the agent actually sees         │
│   PostToolUse hooks compress Read/Bash/Grep output          │
│   File reads → graph skeleton (~88% reduction)              │
│   Bash output → errors + summary (~91% reduction)           │
│   Search results → capped at 25 matches                     │
└─────────────────────────────────────────────────────────────┘

Combined effect: 5–10× total reduction vs baseline Claude Code.

🎯 The Problem

You: "How does authentication work in my codebase?"

❌ Traditional: Load entire codebase → 50,000 tokens → $0.15–$3.75/query
✅ NeuralMind: Smart context → 766 tokens → $0.002–$0.06/query

💰 Realistic savings

The dollar figures depend on your workload. Run neuralmind benchmark . --contribute to get numbers for your codebase and query volume. Order-of-magnitude expectations:

You today	NeuralMind likely saves	Setup pays back in
<$50/mo on LLM, small repo	$5–15/mo	months — probably skip
$50–500/mo, 10K+ line repo	$20–200/mo	days
$500–5,000/mo team workload	hundreds–thousands/mo	hours
Already using prompt caching + long context	smaller marginal win	measure first

These are directional. The Honest Assessment explains why retrieval-token reduction (40–70×) ≠ end-to-end cost reduction (3–10× typical), and when NeuralMind is and isn't worth installing.

✅ Does it work on your code? Prove it in 5 minutes.

NeuralMind benchmarks itself in CI on every PR. But your codebase isn't our fixture. The only way to know what it does for you is to measure it on your code.

pip install neuralmind        # built-in tree-sitter backend — no graphify needed (v0.15.0+)
cd /path/to/your-project
neuralmind build .
neuralmind benchmark .

You'll get back your actual reduction ratio and per-query token count — typically 30–80× on real repos. No telemetry, nothing uploaded, nothing committed. If the numbers don't justify it, pip uninstall neuralmind and move on — 5 minutes lost.

Want the dollar figure for your team?

neuralmind benchmark . --contribute

That flag produces a ready-to-share JSON blob with your project's numbers, the exact command that produced them, and an estimated monthly savings at your query volume. Paste it into Slack, a design doc, a PR — or optionally contribute it to the public leaderboard.

Full walkthrough: Does NeuralMind work on your codebase?

🚨 When do I reach for NeuralMind?

Two ways to decide: start with what's annoying you (symptoms), or start with what you're trying to achieve (goals).

Symptoms — "This is happening to me"

What you notice	Reach for	Why it fixes it
Agent starts every session not knowing my codebase	`neuralmind install-hooks .` + `neuralmind watch .`	`SYNAPSE_MEMORY.md` auto-loads on session start with learned associations + transitions
I keep telling the agent "after you edit X, also update Y"	`neuralmind watch .` (v0.11.0+)	Directional transitions learn the pattern; agent proactively suggests the follow-up
Multi-agent setup (Claude Code + Cursor + Cline) — each one has its own context	NeuralMind MCP server	Shared synapse store; learning from one agent benefits the others
Claude Code hits context limits mid-task	`neuralmind install-hooks .`	Auto-compresses Read/Bash/Grep before the agent sees them (~88–91%)
My monthly LLM bill is climbing	`neuralmind query` + hooks	40–70× fewer tokens per code question; 5–10× per session combined
I start every session re-pasting project structure	`neuralmind wakeup .`	~400 tokens of orientation; pipe into any chat
Agent reads a 2,000-line file to answer about one function	`neuralmind skeleton <file>`	Functions + call graph, no body; ~88% cheaper than `Read`
`grep` floods the agent with hundreds of matches	`neuralmind install-hooks .`	Caps at 25 matches with "N more hidden" pointer
The agent is confidently wrong about what my code does	Start session with `wakeup`; ask with `query`	Grounds the model in real structure instead of guessing
I want to query my codebase from ChatGPT / Gemini	`neuralmind wakeup . \| pbcopy`	Model-agnostic output; paste into any chat
Retrieval feels random across similar questions	`neuralmind learn .`	Cooccurrence-based reranking adapts to your patterns
Index feels out of date after a refactor	`neuralmind build .` (or `init-hook` once)	Incremental — only re-embeds changed nodes

Goals — "What am I trying to solve for?"

If your goal is…	Do this	Expected outcome
Give the agent persistent memory of your codebase	`install-hooks` + `neuralmind watch .`	`SYNAPSE_MEMORY.md` primes the agent every session — associations + transitions learned from how you actually work
Predict the next file the agent should open	`install-hooks` + `neuralmind watch .` (v0.11.0+)	After observing edit patterns, agent surfaces "after X, you usually edit Y (45%)" without being asked
Cut LLM spend on code Q&A	`install-hooks` + use `query` for questions	5–10× total reduction (the measurable side effect of better memory)
Faster, more grounded agent responses	`wakeup` at session start → `query` / `skeleton` during	Fewer hallucinations; less re-exploration
Keep all code local (no SaaS, no telemetry)	Default install — no extra config	100% offline; nothing leaves the machine
Work across Claude + GPT + Gemini with one index	Build once, pipe output into any model	Same context quality, model-agnostic
Make retrieval adapt to how your team queries	Enable memory (TTY prompt) + `neuralmind learn .`	Relevance improves on repeat patterns
Measure savings for a manager or stakeholder	`neuralmind benchmark . --json`	Per-query tokens, reduction ratios, dollar estimate
Auto-refresh the index as code changes	`neuralmind init-hook .` (git post-commit)	Every commit rebuilds incrementally

Still not sure?

You probably don't need NeuralMind if:

Your codebase is under ~5K tokens total (just paste the whole thing in).
You don't use an AI coding agent.
You only want inline completions — use Copilot or Cursor directly.

You almost certainly want NeuralMind if your AI coding agent feels amnesiac — it doesn't remember what it learned about your code, doesn't predict what you'll need next, doesn't carry context between sessions. The brain layer is what closes that gap. The token-savings are the receipts.

See the use-case walkthroughs for step-by-step guides matched to your situation.

The honest version — strengths and rough edges

We'd rather you trust the numbers than be wowed by them, so here's the candid take a feature list won't give you.

What genuinely sets it apart

It measures whether the memory actually helps — and gates it in CI. Most code-RAG tools assert; NeuralMind ships a faithfulness eval (+0.143 vs naive at a matched token budget), an onboarding-lift eval (+6.5 pts), and a synapse-recall A/B (+12 pts), each failing the build on regression. That measurement discipline is the real moat. (Benchmarks)
Learned usage memory, not just embeddings. The synapse layer learns what your team edits together and what you touch next — and the onboarding-lift metric proves it's not decoration.
100% local, and now optionally ChromaDB-free (v0.21.0) with byte-identical embeddings and an 8–16× smaller index.

Where it's still rough — set expectations accordingly

The headline 40–70× is a real-repo extrapolation. What's measured in CI is a deliberately conservative 6.2× on a 500-line fixture. The mechanism scales with repo size, but a large-repo benchmark isn't in CI yet — so prove it on your code with benchmark-your-repo.
ChromaDB is still the default. The slim, advisory-free stack is opt-in (backend: turbovec) while it bakes; the default-flip + dependency removal are staged for later releases.
It's beta, single-maintainer, fast-moving. Lots of surface area (hooks, watcher, serve, MCP, evals) and frequent releases — expect occasional churn; pin a version for CI.
The compressed backend is approximate. TurboQuant parity is gated on the reference fixture; large-repo recall under 2/4-bit quantization is "trust the gate," not yet measured at scale.

If those trade-offs are acceptable, the upside is real and receipts are included. If your repo is tiny or you don't use an agent, you don't need this — and we'd rather say so.

🏢 For organizations evaluating NeuralMind

If you're building a pitch for your team — finance, healthcare, legal, government, internal-platform, or just a large engineering org with a climbing LLM bill — start with docs/BUSINESS-CASE.md for the fact-based ROI argument and docs/ENTERPRISE.md for the regulated/on-premise/multi-team scenarios.

Both docs ground every claim in something you can verify with one command on your own code.

⚖️ NeuralMind vs. Alternatives

Short answers to "why not just use X?". Each row links to a deeper page.

Compared against	Short verdict
Cursor `@codebase`	Works only in Cursor; NeuralMind works in any agent and adds tool-output compression
Aider repo-map	Aider is syntactic only; NeuralMind adds semantic retrieval and compression
Sourcegraph Cody	Cody is server-hosted and org-wide; NeuralMind is local and per-project
Continue / Cline	Those are agent runtimes; NeuralMind is the context/compression layer underneath
GitHub Copilot	Copilot is hosted completions; NeuralMind is local context for any agent
Windsurf / Codeium	Vertically integrated IDE; NeuralMind is editor- and model-agnostic
Claude Projects	Projects reload all files every turn; NeuralMind retrieves only what the query needs
Prompt caching	Caching amortizes a big prompt; NeuralMind makes the prompt small — combine both
LangChain / LlamaIndex for code	Frameworks you assemble; NeuralMind is the assembled default for code agents
Long context windows (1M/2M)	Possible ≠ cheap — NeuralMind gives ~60× cost reduction on the same model
Generic RAG over a codebase	Text chunking loses structure; NeuralMind keeps the call graph
Tree-sitter / ctags / grep	Deterministic but syntactic; use alongside NeuralMind, not instead of

Full comparison index: docs/comparisons/.

🚀 Quick Start (humans)

Install — pick your path

NeuralMind installs five ways. The CLI, semantic indexing, and the MCP server (for Claude Code, Cursor, Cline, Continue, and any MCP client) come in every path.

Method	Command	When to pick
pip	`pip install neuralmind`	Default. Drops it in your active env.
pipx	`pipx install neuralmind`	Global CLI, no env pollution. Recommended if you want `neuralmind` available everywhere.
uv	`uv pip install neuralmind`	Modern, fast Python tooling. ~10× faster install than pip.
Docker	`docker pull ghcr.io/dfrostar/neuralmind:latest && docker run --rm -v "$PWD:/project:ro" ghcr.io/dfrostar/neuralmind:latest neuralmind --help`	Containerized — no Python on the host. Multi-platform (`linux/amd64` + `linux/arm64`); auto-published to GHCR on every release since v0.9.0. To build locally instead: `docker build -t neuralmind:dev .` and substitute that tag.
From source	`git clone … && pip install -e .`	Hacking on NeuralMind itself.

No external tools required. Since v0.15.0 a built-in tree-sitter backend indexes Python, TypeScript, and Go out of the box — pip install neuralmind && neuralmind build . just works, no graphify install. Optional extras:

pip install graphifyy — use the legacy graphify graph backend instead of the built-in one (held at parity by a CI gate).
pip install "neuralmind[turbovec]" — the ChromaDB-free vector backend (v0.21.0+): smaller deps, 8–16× smaller index, same answer quality. See ChromaDB-free local.

Verify install:

neuralmind --help     # works for every install path

# For pip / uv / source (a Python env where neuralmind is importable):
python -c "import neuralmind; print(neuralmind.__version__)"

The python -c line is skipped for pipx and Docker — pipx isolates the package in its own venv, and Docker doesn't expose the in-container Python.

Walkthrough with pros/cons of each path: docs/use-cases/install-paths.md.

Index a project

# Install via any path above, then:

# Go to your project
cd your-project

# Build the index — the built-in tree-sitter backend generates the
# code graph automatically (Python / TypeScript / Go). No graphify needed.
neuralmind build .

# (Optional) Prefer the legacy graphify graph? Install graphifyy and run
# `graphify update .` before `neuralmind build .` — it takes priority where present.

# (Optional) Install Claude Code PostToolUse compression hooks
neuralmind install-hooks .

# (Optional) Auto-rebuild on every git commit
neuralmind init-hook .

# Start using
neuralmind wakeup .
neuralmind query . "How does authentication work?"
neuralmind skeleton src/auth/handlers.py

# Or browse it: Obsidian-style graph view of your codebase + learned synapses
neuralmind serve .

🕸️ Graph view (`neuralmind serve`)

neuralmind serve opens a local web UI that makes the same index your AI agent queries inspectable by a human. Same ChromaDB index, same synapses.db, just made navigable.

Force-directed graph of code nodes coloured by community.
Structural edges (calls / imports) layered with the Hebbian synapse overlay — edges thicken as the brain learns which nodes co-activate.
Backlinks, outgoing links, and synaptic neighbours for any node you click, Obsidian-style.
Semantic quick-switcher — type a phrase, jump to the node.
Open in editor — click a node, opens $EDITOR (or --editor code/cursor/ vim/subl/idea) at the right file and line.
Local-first: stdlib HTTP server, vanilla-JS canvas, no CDN, per-session access token bound to 127.0.0.1 by default.

neuralmind serve .                       # opens http://127.0.0.1:8765/?token=…
neuralmind serve . --editor "code -n"    # override the editor
neuralmind serve . --no-auth             # skip the token (trusted hosts only)

Why it matters: the agent-facing brain has always been a black box — you couldn't see what NeuralMind retrieved, whether the graph was reasonable, or what the synapse layer had actually learned. The graph view exposes all three.

Coming next (graph-view Phase B): a replay-last-query overlay that highlights the L3 hits the agent received, edge tooltips + a min-weight synapse slider answering "why are these two nodes related?", pin UX, and a Cmd/Ctrl-K quick-switch. Then Phase C: a live activity feed of synapse co-activations. Full plan in ROADMAP.md.

🔧 How It Works

NeuralMind wraps a knowledge graph (graphify-out/graph.json) in a ChromaDB vector store. Since v0.15.0 that graph is produced by a built-in tree-sitter backend when no graphify output exists, so neuralmind build . works with no external tool; a real graphify graph still takes priority where present. When you query it, a 4-layer progressive disclosure system loads only the context relevant to your question.

┌─────────────────────────────────────────────────────────────┐
│ Layer 0: Project Identity (~100 tokens) — ALWAYS LOADED     │
│   Source: CLAUDE.md / mempalace.yaml / README first line    │
├─────────────────────────────────────────────────────────────┤
│ Layer 1: Architecture Summary (~500 tokens) — ALWAYS LOADED │
│   Source: Community distribution + GRAPH_REPORT.md          │
├─────────────────────────────────────────────────────────────┤
│ Layer 2: Relevant Modules (~300–500 tokens) — QUERY-AWARE   │
│   Source: Top 3 clusters semantically matching the query    │
├─────────────────────────────────────────────────────────────┤
│ Layer 3: Semantic Search (~300–500 tokens) — QUERY-AWARE    │
│   Source: ChromaDB similarity search over all graph nodes   │
└─────────────────────────────────────────────────────────────┘
Total: ~800–1,100 tokens vs 50,000+ for the full codebase

Prerequisites (v0.15.0+): none beyond pip install neuralmind — the bundled tree-sitter backend generates graphify-out/graph.json automatically on first neuralmind build .. Installing graphify is optional and, where present, takes priority. Either way you end up with:

graphify-out/graph.json — the knowledge graph (auto-generated by the built-in backend, or by graphify update .)
graphify-out/GRAPH_REPORT.md — architecture summary (enriches L1, optional; graphify only)
graphify-out/neuralmind_db/ — ChromaDB vector store (created by neuralmind build)

🖥️ Complete CLI Reference

`neuralmind build`

Build or incrementally update the neural index from graphify-out/graph.json.

neuralmind build [project_path] [--force]

Argument/Option	Default	Description
`project_path`	`.`	Project root containing `graphify-out/graph.json`
`--force`, `-f`	off	Re-embed every node even if unchanged

neuralmind build .
neuralmind build /path/to/project --force

Output: nodes processed, added, updated, skipped, communities indexed, build duration.

`neuralmind wakeup`

Get minimal project context for starting a session (~400–600 tokens, L0 + L1 only).

neuralmind wakeup <project_path> [--json]

neuralmind wakeup .
neuralmind wakeup . --json
neuralmind wakeup . > CONTEXT.md

`neuralmind query`

Query the codebase with natural language (~800–1,100 tokens, all 4 layers).

neuralmind query <project_path> "<question>" [--json]

neuralmind query . "How does authentication work?"
neuralmind query . "What are the main API endpoints?" --json
neuralmind query /path/to/project "Explain the database schema"

On first run from a TTY, you will be prompted once to enable local query memory logging. Disable with NEURALMIND_MEMORY=0.

`neuralmind search`

Direct semantic search — returns code entities ranked by similarity to the query.

neuralmind search <project_path> "<query>" [--n N] [--json]

Option	Default	Description
`--n`	10	Maximum number of results
`--json`, `-j`	off	Machine-readable JSON output

neuralmind search . "authentication"
neuralmind search . "database connection" --n 5
neuralmind search . "PaymentController" --json

`neuralmind skeleton`

Print a compact graph-backed view of a file without loading full source (~88% cheaper than Read).

neuralmind skeleton <file_path> [--project-path .] [--json]

Option	Default	Description
`--project-path`	`.`	Project root (where the index lives)
`--json`, `-j`	off	Machine-readable JSON output

neuralmind skeleton src/auth/handlers.py
neuralmind skeleton src/auth/handlers.py --project-path /my/project
neuralmind skeleton src/auth/handlers.py --json

Output: function list with line numbers and rationales, internal call graph, cross-file edges (imports, data sharing), and a pointer to the full source for when you need it.

`neuralmind benchmark`

Measure token reduction using a set of sample queries.

neuralmind benchmark <project_path> [--json]

neuralmind benchmark .
neuralmind benchmark . --json

`neuralmind stats`

Show index status and statistics.

neuralmind stats <project_path> [--json]

neuralmind stats .
neuralmind stats . --json   # {"built": true, "total_nodes": 241, "communities": 23, ...}

`neuralmind learn`

Analyze logged query history to discover module cooccurrence patterns. Improves future query relevance automatically.

neuralmind learn <project_path>

neuralmind learn .

Reads .neuralmind/memory/query_events.jsonl, writes .neuralmind/learned_patterns.json. The next neuralmind query applies boosted reranking automatically.

`neuralmind install-hooks`

Install or remove Claude Code PostToolUse compression hooks.

neuralmind install-hooks [project_path] [--global] [--uninstall]

Option	Description
`--global`	Install in `~/.claude/settings.json` (affects all projects)
`--uninstall`	Remove NeuralMind hooks only; preserves other tools' hooks

neuralmind install-hooks .                       # project-scoped
neuralmind install-hooks --global                # all projects
neuralmind install-hooks --uninstall             # remove project hooks
neuralmind install-hooks --uninstall --global    # remove global hooks

`neuralmind init-hook`

Install a Git post-commit hook that auto-rebuilds the index after every commit. Safe and idempotent — coexists with other tools' hook contributions.

neuralmind init-hook [project_path]

neuralmind init-hook .
neuralmind init-hook /path/to/project

🔌 MCP Server

NeuralMind ships a Model Context Protocol server (neuralmind-mcp) that exposes all tools to MCP-compatible agents.

Starting the server

neuralmind-mcp
# or
python -m neuralmind.mcp_server

Claude Desktop configuration

{
  "mcpServers": {
    "neuralmind": {
      "command": "neuralmind-mcp",
      "args": ["/absolute/path/to/project"]
    }
  }
}

Config file locations:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

Claude Code / Cursor project-scoped auto-registration

Drop a .mcp.json at your project root:

{
  "mcpServers": {
    "neuralmind": {
      "command": "neuralmind-mcp",
      "args": ["."]
    }
  }
}

Hermes-Agent (Nous Research)

Hermes-Agent is a self-improving agent framework that supports MCP servers. NeuralMind has been verified end-to-end against Hermes-Agent v0.12.0 (build 2026.4.30) — the agent discovered all 11 NeuralMind tools (4-second handshake) when registered as shown below.

Prerequisite: install NeuralMind. The MCP server (neuralmind-mcp) ships with the default install:

pip install neuralmind

Older pip install "neuralmind[mcp]" commands still work — the mcp extra is preserved as a no-op for backwards compatibility.

Two ways to register the server. Both end up in ~/.hermes/config.yaml:

Option A — CLI (recommended for first-time setup):

hermes mcp add

Option B — edit the config directly (~/.hermes/config.yaml, add under the mcp_servers top-level key):

mcp_servers:
  neuralmind:
    command: "neuralmind-mcp"
    args: ["/absolute/path/to/project"]

Verify the server is registered and reachable:

hermes mcp list                     # neuralmind should appear, status ✓
hermes mcp test neuralmind          # ✓ Connected, ✓ Tools discovered: 11

If you haven't installed Hermes-Agent yet, the upstream installer is:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
source ~/.bashrc

After editing the YAML directly, run /reload-mcp from the running hermes CLI to pick up the change without restarting (the hermes mcp add flow does this automatically). Both stdio (shown above) and HTTP transports are supported — see the upstream MCP integration docs for the full schema (command, args, env, url, headers, enabled, per-server tools filtering, timeout, connect_timeout).

v0.6.0 graph view works identically here. Run neuralmind serve in the same project and any tool call from Hermes-Agent will pulse the corresponding nodes on the canvas. The synapse store is shared with Claude Code, Cursor, OpenClaw, and any other agent pointed at this project — see docs/use-cases/multi-agent.md.

OpenClaw

OpenClaw is a personal AI assistant that registers MCP servers via its CLI. Verified against OpenClaw 2026.5.2 — mcp set / mcp list / mcp show round-trip the documented JSON schema into ~/.openclaw/openclaw.json exactly as expected.

Prerequisite: install NeuralMind (the MCP server ships with the default install):

pip install neuralmind

Register NeuralMind:

openclaw mcp set neuralmind '{"command":"neuralmind-mcp","args":["/absolute/path/to/project"]}'

Verify it landed:

openclaw mcp list                  # neuralmind should appear
openclaw mcp show neuralmind       # echoes the JSON you stored

Remove with openclaw mcp unset neuralmind. Definitions are stored under the mcp.servers key in ~/.openclaw/openclaw.json.

v0.6.0 graph view works identically here. Run neuralmind serve in the same project and any tool call from OpenClaw will pulse the corresponding nodes on the canvas. OpenClaw and Claude Code talking to the same project reinforce the same synapse store — see docs/use-cases/multi-agent.md.

If you haven't installed OpenClaw yet:

npm install -g openclaw@latest   # or: pnpm add -g openclaw@latest
openclaw onboard --install-daemon

OpenClaw's MCP support covers stdio (shown above), SSE, HTTP, and streamable-http transports — see the upstream MCP CLI reference for details on url/transport config and the inverse direction (openclaw mcp serve, which exposes OpenClaw's own channels as an MCP server to other clients).

Agent Zero

Agent Zero is a self-organising AI agent framework with first-class MCP support — both as a client (it consumes MCP servers) and as a server (it exposes its own tools to other MCP clients). NeuralMind plugs in via the standard MCP client path.

Prerequisite: install NeuralMind (the MCP server ships with the default install):

pip install neuralmind

Register NeuralMind via Agent Zero's Web UI:

Open Agent Zero → Settings → MCP/A2A → External MCP Servers → Open
Paste this into the JSON editor:

{
  "mcpServers": {
    "neuralmind": {
      "command": "neuralmind-mcp",
      "args": ["/absolute/path/to/your-project"]
    }
  }
}

Click Apply now. Agent Zero discovers NeuralMind's tools at handshake and registers them into the normal tool registry.

The schema is the standard MCP command / args / env shape — see the upstream MCP setup guide for HTTP/SSE transports, OAuth, and per-server tool filtering.

If you haven't installed Agent Zero yet, the upstream README has the Docker and Python install paths.

v0.6.0 graph view works identically here. Run neuralmind serve in the same project and any tool call from Agent Zero will pulse the corresponding nodes on the canvas. The synapse store is shared with Claude Code, Cursor, Cline, Continue, OpenClaw, Hermes-Agent, and any other agent pointed at this project — see docs/use-cases/multi-agent.md.

Coming soon — one-click install. NeuralMind is being submitted to the agent0ai/a0-plugins registry so users can discover and install it from inside Agent Zero's Plugin Hub. The manual JSON path above continues to work either way.

Skill (OpenClaw, Agent Zero, Hermes, any SKILL.md host)

The MCP server gives an agent the actions. The skill at skills/neuralmind/SKILL.md gives it the playbook — when to call neuralmind_query vs. neuralmind_skeleton vs. neuralmind_search, what the outputs look like, and which env-var escape hatches exist. It is a portable Anthropic-style SKILL.md (frontmatter + markdown body) so the same file works in any host that implements the spec.

OpenClaw. Drop the directory into your ClawHub local skills path, or ship it as part of an OpenClaw plugin by listing skills/ in openclaw.plugin.json:

cp -r skills/neuralmind ~/.openclaw/skills/
openclaw skills list   # neuralmind should appear

The skill is description-matched on triggers like "how does X work" or "find function Y", so you don't need to load it explicitly.

Agent Zero. Drop the same directory into the Agent Zero skills folder:

cp -r skills/neuralmind /path/to/agent-zero/skills/

Agent Zero auto-discovers SKILL.md files by description and tag, then uses its code_execution_tool to call the MCP tools the skill names in its allowed_tools frontmatter.

Hermes-Agent. Hermes has a first-class skills system that reads the same SKILL.md spec. Drop the directory into the category-organised tree:

mkdir -p ~/.hermes/skills/code-intelligence
cp -r skills/neuralmind ~/.hermes/skills/code-intelligence/

Hermes loads skills on demand based on the frontmatter description, so no further wiring is needed. You can also publish the directory as a Hermes tap (a GitHub repo of skill directories) for one-command install across machines. This layers on top of the MCP integration documented in the Hermes-Agent section above — the MCP server still does the work; the skill teaches Hermes when to call it.

Claude Code, Cursor. These already have richer integrations (lifecycle hooks for Claude Code, MCP wiring for Cursor), so the skill is optional. It still works as a portable "agent operating manual" if you want a single file that travels with the project.

The skill duplicates none of NeuralMind's logic — it points the agent at MCP tools that already exist. Edit it like documentation.

Troubleshooting

"Connection closed" / "Connection failed" right after register. Almost always means an old NeuralMind install (≤ 0.4.x) where the MCP server was gated behind the [mcp] extra. From 0.5.0 onward the MCP SDK is bundled. Fix:

pip install --upgrade neuralmind

Then re-run the host's verify step (hermes mcp test neuralmind or openclaw mcp list).

neuralmind-mcp: command not found. The package installed but the console script wasn't put on PATH — usually because pip installed into a user site-packages dir that isn't on PATH. Add ~/.local/bin to PATH or reinstall in a venv where the entry point is on PATH.

The host shows neuralmind in mcp list but no tools when you query. Run neuralmind build /path/to/project first — the index has to exist before the MCP tools can answer queries. The hooks (SessionStart, UserPromptSubmit, PreCompact from neuralmind install-hooks) need a built index too.

MCP tool schemas

`neuralmind_wakeup`

{
  "project_path": "string (required) — absolute path to project root"
}

Returns:

{
  "context": "string",
  "tokens": 412,
  "reduction_ratio": 121.4,
  "layers": ["L0", "L1"]
}

`neuralmind_query`

{
  "project_path": "string (required)",
  "question":     "string (required) — natural language question"
}

Returns:

{
  "context": "string",
  "tokens": 847,
  "reduction_ratio": 59.0,
  "layers": ["L0", "L1", "L2", "L3"],
  "communities_loaded": [5, 12],
  "search_hits": 8
}

`neuralmind_search`

{
  "project_path": "string (required)",
  "query":        "string (required)",
  "n":            10
}

Returns array of:

{ "id": "node_id", "label": "authenticate_user", "file_type": "code",
  "source_file": "auth/handlers.py", "score": 0.92 }

`neuralmind_skeleton`

{
  "project_path": "string (required)",
  "file_path":    "string (required) — absolute or project-relative path"
}

Returns:

{ "file": "src/auth/handlers.py", "skeleton": "# src/auth/handlers.py ...", "chars": 620, "indexed": true }

`neuralmind_recursive_query`

Recursively decompose and explore complex questions. Breaks multi-part questions into focused sub-queries, executes them, identifies gaps, and synthesizes results. Searches both code and document indexes.

{
  "project_path": "string (required)",
  "question":     "string (required) — compound question to decompose",
  "max_depth":    3,
  "include_docs": true
}

Returns:

{
  "question": "string",
  "answer": "string — synthesized answer",
  "sub_queries": [{"query": "string", "results": [...], "source": "string"}],
  "depth_reached": 2,
  "gaps_identified": ["string"],
  "total_queries": 6,
  "token_estimate": 4156,
  "sources": ["file1.ts", "file2.ts", "doc.md"]
}

When to use: Multi-faceted questions spanning multiple files or concepts, like "How does auth work and what security measures are in place?" or "What is the deployment architecture and how do Cloudflare and Render interact?"

Benchmark: 6x more tokens than standard query, but decomposes compound questions and achieves full term coverage on 3/5 test questions. See graphify-out/RECURSIVE_QUERY_BENCHMARK.md after running benchmark_report.py.

`neuralmind_query_docs`

Search reference documents (legal, clinical, strategic PDFs/DOCX converted to markdown). NOT for code — use neuralmind_query for code questions.

{
  "project_path": "string (required)",
  "question":     "string (required) — question about reference documents",
  "n":            5
}

Returns:

{
  "results": [
    {
      "content": "string — relevant text chunk",
      "source_file": "docs/reference/filename.md",
      "file_name": "filename.md",
      "chunk": "3/12",
      "relevance": 0.719
    }
  ],
  "total_doc_chunks": 241,
  "query": "string"
}

Setup: Documents must be converted to markdown and indexed first:

# Convert documents (PDF, DOCX, TXT, HTML → .md)
pip install pypdf mammoth
python doc_indexer.py build /path/to/project

# Or use the doc-ingest skill for batch conversion

Auto-rebuild: A git post-commit hook can rebuild the doc index when files in docs/reference/ change.

Search reference docs via CLI:

python doc_indexer.py query /path/to/project "HIPAA compliance"
python doc_indexer.py stats /path/to/project

`neuralmind_build`

{
  "project_path": "string (required)",
  "force":        false
}

Returns:

{
  "success": true,
  "nodes_total": 241,
  "nodes_added": 5,
  "nodes_updated": 2,
  "nodes_skipped": 234,
  "communities": 23,
  "duration_seconds": 3.1
}

`neuralmind_stats`

{ "project_path": "string (required)" }

Returns:

{ "built": true, "total_nodes": 241, "communities": 23, "db_path": "..." }

`neuralmind_benchmark`

{ "project_path": "string (required)" }

Returns:

{
  "project": "myapp",
  "wakeup_tokens": 341,
  "avg_query_tokens": 739,
  "avg_reduction_ratio": 65.6,
  "results": [...]
}

🪝 PostToolUse Compression

When neuralmind install-hooks has been run, Claude Code automatically applies these transforms to every tool output before the agent sees it.

Read → skeleton

Raw source files are replaced with the graph skeleton (functions + rationales + call graph + cross-file edges). This is ~88% smaller and contains the structural information agents need most.

To get the full source anyway:

NEURALMIND_BYPASS=1 <command>

Bash → filtered output

Long bash output is reduced to:

All error/ERROR/FAIL/traceback/warning lines
All summary lines (=====, passed, failed, Finished, Done in, etc.)
Last 3 lines verbatim
Header: [neuralmind: bash compressed, exit=N]

All errors and failures are always preserved. Routine pip/npm/build chatter is dropped.

Grep → capped results

Search results are capped at 25 matches with a [N more hidden] note appended. Prevents context flooding from repository-wide searches.

Tunable thresholds

Variable	Default	Description
`NEURALMIND_BYPASS`	unset	Set to `1` to disable all compression
`NEURALMIND_BASH_TAIL`	`3`	Lines to keep verbatim from end of bash output
`NEURALMIND_BASH_MAX_CHARS`	`3000`	Below this size, bash output is not compressed
`NEURALMIND_SEARCH_MAX`	`25`	Max grep/search matches before capping
`NEURALMIND_OFFLOAD_THRESHOLD`	`15000`	Chars above which content is written to a temp file

🧠 Continual Learning

NeuralMind optionally learns from your query patterns to improve future relevance.

How it works

Collect — Each neuralmind query logs which modules appeared in the result to .neuralmind/memory/query_events.jsonl (opt-in, local only, zero overhead)
Learn — neuralmind learn . analyzes cooccurrence: which clusters appear together across queries
Improve — The next neuralmind query applies a +0.3 reranking boost to modules that co-occur with the current query's top matches
Repeat — The system gets smarter as you use it

Opt-in / consent

On first TTY query:

NeuralMind can keep local query memory (project + global JSONL) to improve future retrieval.
Enable? [y/N]:

Consent saved to ~/.neuralmind/memory_consent.json. Disable at any time:

export NEURALMIND_MEMORY=0     # disable query logging
export NEURALMIND_LEARNING=0   # disable pattern application

File locations

~/.neuralmind/
├── memory_consent.json             # consent flag
└── memory/
    └── query_events.jsonl          # global event log

<project>/.neuralmind/
├── memory/
│   └── query_events.jsonl          # project-specific events
└── learned_patterns.json           # created by: neuralmind learn .

Privacy

100% local — nothing is sent to any server. Delete ~/.neuralmind/ and <project>/.neuralmind/ at any time to remove all learning data.

⏰ Keeping the Index Fresh

Automatic — Git post-commit hook (recommended)

neuralmind init-hook .

After every commit, the hook runs:

neuralmind build . 2>/dev/null && echo "[neuralmind] OK"

Manual

neuralmind build .   # built-in backend regenerates the graph; add `graphify update .` first only if you use graphify

Scheduled — cron

0 6 * * * cd /path/to/project && neuralmind build .

CI/CD — GitHub Actions

- run: pip install neuralmind
- run: neuralmind build .
- run: neuralmind wakeup . > AI_CONTEXT.md

🔌 Compatibility

Component	Works With	Notes
CLI	Any environment	Pure Python, no daemon required
MCP Server	Claude Code, Claude Desktop, Cursor, Cline, Continue, any MCP client	Bundled with `pip install neuralmind`
SKILL.md	OpenClaw (ClawHub), Agent Zero, Hermes-Agent, any SKILL.md host	Portable agent playbook at `skills/neuralmind/SKILL.md` — pairs with the MCP server
PostToolUse Hooks	Claude Code only	Uses Claude Code's `PostToolUse` hook system
Git hook	Any git workflow	Appends to existing `post-commit`, idempotent
Copy-paste	ChatGPT, Gemini, any LLM	`neuralmind wakeup . \| pbcopy`

Quick-start by tool

Claude Code — full two-phase optimization

pip install neuralmind
cd your-project
neuralmind build .            # built-in tree-sitter backend — no graphify needed
neuralmind install-hooks .    # PostToolUse compression
neuralmind init-hook .        # auto-rebuild on commit (optional)

Then use MCP tools in sessions: neuralmind_wakeup, neuralmind_query, neuralmind_skeleton.

Cursor / Cline / Continue — MCP server

pip install neuralmind
neuralmind build .            # built-in tree-sitter backend — no graphify needed

Add to your MCP config:

{ "mcpServers": { "neuralmind": { "command": "neuralmind-mcp" } } }

ChatGPT / Gemini / any LLM — CLI + copy-paste

neuralmind wakeup . | pbcopy      # macOS — paste into chat
neuralmind query . "question"     # get context for a specific question

✨ What's New in v0.5.4 — Graph view

neuralmind serve ships in v0.5.4 — see the Graph view section above. The next patch release (v0.5.5) lands graph-view Phase B: the replay-last-query overlay (#105), edge tooltips + min-weight synapse slider (#106), pin UX, and a Cmd/Ctrl-K quick-switch. Phase C after that: a live activity feed of synapse co-activations. Full plan in ROADMAP.md.

✨ What's New in v0.4.0 — Brain-like Synapse Layer

NeuralMind now runs as a second brain alongside the LLM: a persistent associative memory that learns continuously from how the agent and the codebase actually interact. See the release notes for the full story.

Feature	Details
Synapse store	SQLite-backed weighted graph; Hebbian reinforce, decay, long-term potentiation
Spreading activation	`mind.synaptic_neighbors(query)` — usage-based recall complementing vector search
`neuralmind watch` daemon	File edits become co-activation signals; brain learns even when no query runs
Three new Claude Code hooks	SessionStart (decay+export), UserPromptSubmit (recall injection), PreCompact (hub normalization)
Auto-memory export	Writes `SYNAPSE_MEMORY.md` to Claude Code's auto-memory dir so associations surface natively
Three new MCP tools	`synaptic_neighbors`, `synapse_stats`, `synapse_decay`, `export_synapse_memory`
3× fewer embedder calls per query	Selector caches one search per query and slices for L2/L3/synapses
Directional transitions (v0.11.0+)	Parallel `synapse_transitions` table tracks ordered `(from, to)` observations; `next_likely(node)` returns probability distribution over what follows; new `neuralmind next` CLI + `neuralmind_next_likely` MCP tool

Earlier (v0.3.x)

Feature	Version	Details
Memory Collection	v0.3.0	Local JSONL storage for query events
Opt-in Consent	v0.3.0	One-time TTY prompt, env var overrides
EmbeddingBackend abstraction	v0.3.1	Pluggable vector backend (Pinecone/Weaviate ready)
Pattern Learning	v0.3.2	`neuralmind learn .` analyzes cooccurrence
Smart Reranking	v0.3.2	L3 results boosted by learned patterns
Accurate Build Stats	v0.3.3	Correctly distinguishes added vs updated nodes
Documentation polish	v0.3.4	CLI flags sync, Setup Guide, agent guidance in README

📊 Benchmarks

NeuralMind benchmarks itself on every pull request. A hermetic fixture (tests/fixtures/sample_project/) plus a committed query set (tests/fixtures/benchmark_queries.json) runs through the full retrieval pipeline, and CI fails if aggregate reduction drops below a conservative floor (currently 4× on the small fixture — the fixture is intentionally tiny, real repos consistently hit 40–70× as shown below).

📊 All measured numbers in one place: the Benchmarks & Results page collects token reduction, faithfulness delta, synapse +12 pts, onboarding +6.5 pts, and the v0.21 ChromaDB-free parity — each CI-gated, each with a reproduce command and an honest "what we don't claim."

What CI measures on every PR

Phase 1 — Reduction. Naive baseline (every .py file in the fixture concatenated) vs NeuralMind.query() output, per query. All tokens counted with tiktoken.
Phase 2 — Learning uplift. Same queries run cold, then after seeding memory and running neuralmind learn. Reports the delta in reduction ratio and top-k retrieval hit rate. On a 500-line fixture the numerical uplift is modest by design — the test proves the mechanism persists, not that it's magic.
Per-model breakdown. GPT-4o and GPT-4/3.5 counts are measured via real tiktoken encodings. Claude uses the Anthropic SDK tokenizer when available, else a clearly-labeled estimate derived from published vocab ratios. Llama is always estimated. No fabricated numbers anywhere.
Memory persistence. tests/test_memory_persistence.py asserts events are logged, neuralmind learn produces a patterns file, and subsequent queries load it without error.

Community benchmarks

Real-world numbers submitted by users. Your code never leaves your machine — you submit a PR (or an issue, which a maintainer converts to a PR) with only the numbers. CI validates every entry against the schema and re-renders this table automatically.

Project	Lang	Nodes	Wakeup	Avg Query	Reduction	Model	Submitted
cmmc20	JavaScript	241	341	739	65.6×	Claude 3.5 Sonnet	@dfrostar · 2025-10-01
mempalace	Python	1,626	412	891	46.0×	Claude 3.5 Sonnet	@dfrostar · 2025-10-01

2 submission(s). See the JSON data for notes and verification commands, or the interactive dashboard for scatter + by-language charts.

Submit yours:

Easy path: open a benchmark submission issue — fill out a form, a maintainer converts it to a PR.
PR directly: add an entry to docs/community-benchmarks.json and run python scripts/render_community_table.py --inject README.md to regenerate the table. Schema: community-benchmarks.schema.json.

All entries include the exact neuralmind command that produced them, so reviewers (and any reader) can audit the numbers.

Reproduce locally (on our fixture)

pip install . tiktoken matplotlib graphifyy
graphify update tests/fixtures/sample_project
neuralmind build tests/fixtures/sample_project --force
python -m tests.benchmark.run          # phase 1 + phase 2
python -m tests.benchmark.multi_model  # per-model breakdown
python scripts/generate_chart.py       # refreshes the PNG above

Full machine-readable results land in tests/benchmark/results.json, human-readable report in tests/benchmark/report.md.

Reproduce on your code

Don't just trust numbers from our fixture — run it on your repo:

pip install neuralmind
neuralmind build .            # built-in tree-sitter backend — no graphify needed
neuralmind benchmark . --contribute

Output shows your reduction ratio, tokens per query, and estimated monthly savings at Claude 3.5 Sonnet pricing. Full walkthrough: Does NeuralMind work on your codebase?

Retrieval quality baseline

Heuristic-only baseline (community-reported): 70–80% top-5 retrieval accuracy
NeuralMind target on the same query set: exceed that baseline via semantic retrieval + learned cooccurrence reranking

The pytest regression gate (tests/test_benchmark_regression.py) currently enforces ≥50% top-k hit rate on the fixture plus ≥4× reduction (low because the fixture is tiny; real repos measure 10× higher).

❓ FAQ

How much does NeuralMind reduce Claude / GPT token costs?

Measured on real repos: 40–70× reduction per query (see Benchmarks). For a team running 100 queries/day on Claude Sonnet, that is roughly $450/month → $7/month. Exact savings depend on codebase size and model pricing.

Does NeuralMind work outside Claude Code?

Yes. The CLI works anywhere Python runs; the MCP server works with Cursor, Cline, Continue, Claude Desktop, and any MCP-compatible agent. For non-MCP tools like ChatGPT or Gemini, neuralmind wakeup . | pbcopy pipes context into a regular chat window. Only the PostToolUse compression hooks are Claude-Code-specific.

Does my code leave my machine?

No. NeuralMind is fully offline — no API calls, no cloud services. Embeddings run locally via ChromaDB, and the knowledge graph is stored in graphify-out/ in your project. Query memory (optional, opt-in) is written to .neuralmind/ on disk.

Is this RAG? How is it different from LangChain or LlamaIndex?

It is a form of RAG, but specialized for code. Instead of chunking text, NeuralMind retrieves over a knowledge graph of code entities (functions, classes, clusters) with a fixed 4-layer structure. That keeps the call graph intact and produces a token-budgeted output instead of a flat list of chunks. See vs. LangChain/LlamaIndex.

I have a 1M context window now — do I still need this?

Long context makes it possible to stuff a whole repo in; it does not make it cheap. You still pay per input token, so a 50K-token repo at Claude Sonnet rates costs ~$0.15 every turn. NeuralMind drops that to ~$0.002. See vs. long context.

Does it support my language?

The built-in tree-sitter backend indexes Python, TypeScript, and Go out of the box (v0.16.0+) — a mixed-language repo is indexed in one pass. More grammars slot in behind the same SUPPORTED_SUFFIXES seam. For languages beyond those, install graphify — any language it supports works, since NeuralMind consumes graphify-out/graph.json and graphify takes priority where present. Either producer feeds the same retrieval pipeline.

What is the difference between `wakeup`, `query`, and `skeleton`?

wakeup — ~400 tokens of project orientation (L0 + L1). Run it at session start.
query — ~800–1,100 tokens for a specific natural-language question (L0–L3).
skeleton — compact view of a single file (functions + call graph + cross-file edges). Use before Read.

How does the PostToolUse compression work?

When neuralmind install-hooks . has been run, Claude Code invokes NeuralMind after every Read/Bash/Grep tool call but before the agent sees the output. Read becomes a skeleton (~88% smaller), Bash keeps errors + last 3 lines (~91% smaller), Grep caps at 25 matches. Set NEURALMIND_BYPASS=1 on any command to opt out.

Can I use NeuralMind without a knowledge graph?

The knowledge graph (graphify-out/graph.json) is the source of truth — but as of v0.15.0 you no longer need a separate tool to produce it. neuralmind build . generates it automatically with the bundled tree-sitter backend. Installing graphify is optional and takes priority where present.

Does it auto-update when I change code?

Only if you install the git post-commit hook with neuralmind init-hook .. Otherwise run neuralmind build . manually; it is incremental and only re-embeds changed nodes.

What do I do if retrieval quality is poor on my repo?

Check that neuralmind stats . reports all your nodes indexed.
Run neuralmind benchmark . to see reduction ratios.
Enable query memory (it prompts on first TTY run) and periodically run neuralmind learn . — cooccurrence-based reranking improves relevance on your actual queries.
Open an issue with the query and expected result — retrieval quality is the thing we most want to improve.

📚 Documentation

Resource	Contents
Business Case	Fact-based ROI argument with provable claims, math you can plug your numbers into, and three concrete scenarios
Honest Assessment	Skeptic's companion — when NeuralMind isn't worth installing, what the headline numbers don't measure
Enterprise Use Cases	Regulated industries, on-premise, multi-team — what to know before pitching internally
Setup Guide	First-time setup for Claude Code, Claude Desktop, Cursor, any LLM
CLI Reference	All commands and options
Scheduling Guide	Automate audits with Windows Task Scheduler, GitHub Actions, or cron
Version Strategy	Versioning policy, breaking changes, support timeline, upgrade path
Compatibility Matrix	Version compatibility, Python/platform support, known issues, migration guides
Learning Guide	Continual learning details
API Reference	Python API (`NeuralMind`, `ContextResult`, `TokenBudget`)
Architecture	4-layer progressive disclosure design
Integration Guide	MCP, CI/CD, VS Code, JetBrains
Troubleshooting	Common issues and fixes
Roadmap	What's shipping next, where we want help, what's out of scope
Future-Proofing Plan	8-initiative engineering plan for sustainability and scale
Brain-like Learning	Design rationale for the learning system
Use Cases	Step-by-step walkthroughs: Claude Code, cost optimization, any-LLM, offline/regulated, growing monorepo, multi-agent (new in v0.6.0)
Release Notes v0.24.0	Memory namespaces & branch isolation (PRD 4) — every synapse/transition/activation carries a namespace (`personal` / `shared` / `branch:<name>` / `ephemeral`); lossless single-transaction v0→v1 schema migration (existing memory → `personal`); transparent merged reads (branch 1.0× > personal 0.8× > shared 0.5×, published constants, per-namespace `--trace` attribution); new `neuralmind memory {inspect,reset,export,import}` with versioned JSON bundles (PRD 8 on-ramp); per-namespace decay/TTL (`shared` sticky, `ephemeral` fast + cleared at session boundaries); branch auto-detection via stdlib git with safe `personal` fallback
Release Notes v0.23.0	Versioned index contract (IR) + retrieval-quality harness + debug traces + local daemon. PRD 1: builds materialize a canonical, schema-versioned `IndexIR` and validate it; new backend-free `neuralmind validate`; round-trip-faithful graphify⇄IR adapter; learned synapses folded in; IR metadata in `build`/`stats`. PRD 2: new `neuralmind benchmark --quality` measures precision@k / recall@k / MRR / answerability over 30 golden queries (Python/TS/Go), gated in CI. PRD 3: `neuralmind query --trace` attaches a per-layer retrieval trace (candidates, cluster scoring with vector-vs-synapse attribution, final hits, token budget); bounded + redactable; zero-overhead off by default. PRD 5: experimental `neuralmind daemon` keeps project state warm (project registry + per-project locks + background jobs + shared `dispatch()` API); CLI auto-prefers it with direct-mode fallback. Retrieval and existing indexes unchanged
Release Notes v0.22.0	turbovec becomes the default (when available) — `import neuralmind` no longer needs ChromaDB; default backend is now `auto` (turbovec when its deps are installed, else chroma); one-time auto-reindex with the old ChromaDB index kept as a fallback; `neuralmind doctor` shows the resolved backend; staged middle step toward retiring ChromaDB
Release Notes v0.21.0	ChromaDB-free retrieval — opt-in `turbovec` backend now embeds and searches with zero ChromaDB: TurboQuant compressed index (8–16× smaller vectors) + a bundled `OnnxMiniLMEmbedder` producing vectors byte-identical to ChromaDB's; at/above parity (fact-recall 0.744→0.800); enable via `backend: turbovec`; retires the CVE-2026-45829 dependency (default-flip staged for v0.22)
Release Notes v0.20.0	Onboarding-lift eval — `neuralmind eval --onboarding` measures the learned-synapse uplift (committed team memory vs a cold agent) as a cold/onboarded A/B over the gold queries; headline = top-k module hit-rate lift (fact-recall + grounding as secondaries); CI-gated at lift ≥ 0; `evals/onboarding/` mirrors the faithfulness eval
Release Notes v0.19.0	One-command MCP setup — `neuralmind install-mcp --all` auto-detects Claude Code / Cursor / Cline / Claude Desktop and registers the MCP server with each (non-destructive merge, idempotent); pure-stdlib `mcp_install` module
Release Notes v0.18.0	Incremental per-file graph updates — `graphgen.update_files()` / `NeuralMind.update_files()` re-parse only changed files (unchanged files stay byte-identical so the embedder skips them), wired to the watcher via `neuralmind watch --reindex`
Release Notes v0.17.0	Optional SCIP precision pass — `NEURALMIND_PRECISION=1` folds compiler-accurate `calls`/`inherits` edges from a `*.scip` index into the built-in graph (replacing heuristic ones for covered files); off by default, dependency-free stdlib SCIP reader, proven by a precision check in the CI gate
Release Notes v0.16.0	Multi-language built-in backend — TypeScript + Go extractors behind the `SUPPORTED_SUFFIXES` seam, `neuralmind build` indexes Python/TS/Go with no graphify; proven at parity per language by the CI gate (100% symbol coverage on the reference fixtures)
Release Notes v0.15.0	Built-in tree-sitter graph backend — `neuralmind build` works with no graphify install; graphify still takes priority where present; backend swap proven at parity by a new CI gate (reduction + faithfulness within tolerance)
Release Notes v0.14.0	`neuralmind eval` — measure answer faithfulness (expected-fact recall vs a matched-budget naive baseline) with grounding + contradiction checks and `--json`; 100% local by default
Release Notes v0.13.0	Measurement foundation — offline faithfulness eval (query + gold-fact dataset, expected-fact-recall scorer), polyglot retrieval fixtures (TypeScript + Go), and a standard documentation process
Release Notes v0.12.0	Install Doctor — `neuralmind doctor` health checks (graph, index, synapses, MCP, hooks, memory), `--json` output, friendlier "graph not built" error
Release Notes v0.11.0	Directional Synapses — `synapse_transitions` table, `next_likely()` API, `neuralmind next` CLI, `neuralmind_next_likely` MCP tool
Release Notes v0.10.0	Agent Ergonomics — content-aware compression footer, `neuralmind last` recovery cache, small-failure passthrough
Release Notes v0.9.0	Enterprise-Ready — GHCR auto-build, CycloneDX SBOM, air-gapped install walkthrough, compliance one-pager
Release Notes v0.8.0	Always-On — systemd + launchd templates, Windows Task Scheduler walkthrough, `/healthz` endpoint
Release Notes v0.7.0	Install anywhere — `pip` / `pipx` / `uv` / Docker / source, Dockerfile, event-log rotation fix
Release Notes v0.6.0	Live activity feed, cross-process JSONL bridge, pin UX, depth slider, replay overlay
Comparisons	NeuralMind vs. Cursor, Copilot, Cody, Aider, Claude Projects, LangChain, long context, prompt caching, RAG, tree-sitter
USAGE.md	Extended usage examples

🤝 Contributing

See CONTRIBUTING.md for guidelines and ROADMAP.md for what we're working on next and where help is most welcome.

📄 License

MIT License — see LICENSE for details.

⭐ Star this repo if NeuralMind saves you money!

Name		Name	Last commit message	Last commit date
Latest commit History 358 Commits
.github		.github
docs		docs
evals		evals
neuralmind		neuralmind
optimization		optimization
scripts		scripts
skills/neuralmind		skills/neuralmind
tests		tests
.gitignore		.gitignore
.release-please-manifest.json		.release-please-manifest.json
BENCHMARK_TURBOVEC.md		BENCHMARK_TURBOVEC.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
RELEASE_NOTES_v0.10.0.md		RELEASE_NOTES_v0.10.0.md
RELEASE_NOTES_v0.11.0.md		RELEASE_NOTES_v0.11.0.md
RELEASE_NOTES_v0.12.0.md		RELEASE_NOTES_v0.12.0.md
RELEASE_NOTES_v0.13.0.md		RELEASE_NOTES_v0.13.0.md
RELEASE_NOTES_v0.14.0.md		RELEASE_NOTES_v0.14.0.md
RELEASE_NOTES_v0.15.0.md		RELEASE_NOTES_v0.15.0.md
RELEASE_NOTES_v0.16.0.md		RELEASE_NOTES_v0.16.0.md
RELEASE_NOTES_v0.17.0.md		RELEASE_NOTES_v0.17.0.md
RELEASE_NOTES_v0.18.0.md		RELEASE_NOTES_v0.18.0.md
RELEASE_NOTES_v0.19.0.md		RELEASE_NOTES_v0.19.0.md
RELEASE_NOTES_v0.20.0.md		RELEASE_NOTES_v0.20.0.md
RELEASE_NOTES_v0.21.0.md		RELEASE_NOTES_v0.21.0.md
RELEASE_NOTES_v0.22.0.md		RELEASE_NOTES_v0.22.0.md
RELEASE_NOTES_v0.23.0.md		RELEASE_NOTES_v0.23.0.md
RELEASE_NOTES_v0.24.0.md		RELEASE_NOTES_v0.24.0.md
RELEASE_NOTES_v0.3.2.md		RELEASE_NOTES_v0.3.2.md
RELEASE_NOTES_v0.4.0.md		RELEASE_NOTES_v0.4.0.md
RELEASE_NOTES_v0.5.0.md		RELEASE_NOTES_v0.5.0.md
RELEASE_NOTES_v0.5.3.md		RELEASE_NOTES_v0.5.3.md
RELEASE_NOTES_v0.6.0.md		RELEASE_NOTES_v0.6.0.md
RELEASE_NOTES_v0.7.0.md		RELEASE_NOTES_v0.7.0.md
RELEASE_NOTES_v0.8.0.md		RELEASE_NOTES_v0.8.0.md
RELEASE_NOTES_v0.9.0.md		RELEASE_NOTES_v0.9.0.md
ROADMAP.md		ROADMAP.md
SECURITY.md		SECURITY.md
USAGE.md		USAGE.md
benchmark.py		benchmark.py
benchmark_turbovec.py		benchmark_turbovec.py
plugin.yaml		plugin.yaml
pyproject.toml		pyproject.toml
release-please-config.json		release-please-config.json
report_turbovec.py		report_turbovec.py
requirements-pinned.txt		requirements-pinned.txt

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🧠 NeuralMind

🧠 What changes when an agent has memory

⚡ 30-second proof — see the memory work

📊 The fact-based case

🔒 Security & Compliance

📌 For AI Coding Agents

Check if NeuralMind is active

Startup workflow

Decision tree — which tool to call

Understanding the output

wakeup / query output format

skeleton output format

search output format

PostToolUse hooks — what happens automatically

What the agent actually sees on a compressed Bash call (v0.10.0+)

Recovery without re-running (v0.10.0+)

Predict the next file (v0.11.0+)

Branch-isolated memory (v0.24.0+)

After making code changes

MCP tool quick reference

⚡ Two-phase optimization

🎯 The Problem

💰 Realistic savings

✅ Does it work on your code? Prove it in 5 minutes.

🚨 When do I reach for NeuralMind?

Symptoms — "This is happening to me"

Goals — "What am I trying to solve for?"

Still not sure?

The honest version — strengths and rough edges

🏢 For organizations evaluating NeuralMind

⚖️ NeuralMind vs. Alternatives

🚀 Quick Start (humans)

Install — pick your path

Index a project

🕸️ Graph view (neuralmind serve)

🔧 How It Works

🖥️ Complete CLI Reference

neuralmind build

neuralmind wakeup

neuralmind query

neuralmind search

neuralmind skeleton

neuralmind benchmark

neuralmind stats

neuralmind learn

neuralmind install-hooks

neuralmind init-hook

🔌 MCP Server

Starting the server

Claude Desktop configuration

Claude Code / Cursor project-scoped auto-registration

Hermes-Agent (Nous Research)

OpenClaw

Agent Zero

Skill (OpenClaw, Agent Zero, Hermes, any SKILL.md host)

Troubleshooting

MCP tool schemas

neuralmind_wakeup

neuralmind_query

neuralmind_search

neuralmind_skeleton

neuralmind_recursive_query

neuralmind_query_docs

neuralmind_build

neuralmind_stats

neuralmind_benchmark

🪝 PostToolUse Compression

Read → skeleton

Bash → filtered output

Grep → capped results

Tunable thresholds

🧠 Continual Learning

How it works

Opt-in / consent

File locations

Privacy

`wakeup` / `query` output format

`skeleton` output format

`search` output format

🕸️ Graph view (`neuralmind serve`)

`neuralmind build`

`neuralmind wakeup`

`neuralmind query`

`neuralmind search`

`neuralmind skeleton`

`neuralmind benchmark`

`neuralmind stats`

`neuralmind learn`

`neuralmind install-hooks`

`neuralmind init-hook`

`neuralmind_wakeup`

`neuralmind_query`

`neuralmind_search`

`neuralmind_skeleton`

`neuralmind_recursive_query`

`neuralmind_query_docs`

`neuralmind_build`

`neuralmind_stats`

`neuralmind_benchmark`

What is the difference between `wakeup`, `query`, and `skeleton`?

Packages