🧠 NeuralMind

Persistent codebase memory for AI coding agents — Claude Code, Codex, Cursor, Cline, Continue, and any MCP client.

Your agent learns your codebase the way a senior engineer would — what goes together, what you usually touch next — and remembers it across sessions. Local-first, no telemetry. Side effect: much cheaper code questions — 46–263× fewer tokens than pasting every source file, at 95% mean gold-file recall, on a public 40-query benchmark that publishes every miss.

After install, your agent:

• Boots with SYNAPSE_MEMORY.md (learned associations, strongest hub files)
• Recalls related files for each prompt you send (Claude Code UserPromptSubmit hook)
• Queries your codebase in ~800 tokens instead of ~50,000
• Gets health checks, synapse pruning, audit queries, and code/doc type filtering (v3.1.4+)
• Gets a pre-commit warning when a change skips a pattern its own peers share — the eleventh handler that forgot the auth check the other ten have (v3.2.0+)
• Gets compliance annotations it can actually trust — a version string or an SVG path is no longer reported as a SOC 2 control (v3.3.0+)
• Searches your prose too: ingest-content indexes a book or docs tree into its own project, re-embeds only what changed, and shows a progress bar with an ETA while it works (v3.4.0+)
• Thinks with your brain, not just your code: 6 SOTA synaptic learning techniques (STC, SAMPL, resource STDP, FOK, lateral inhibition, replay) plus intent-aware ranking that reads "how does X implement Y" as a question about code, and ranks implementation above docstrings for it (v3.9.0+)
• New in v4.8.2: the low-severity bug-hunt fixes. A mistyped project path is an error instead of a new empty project (MCP tools return project_not_found); the daemon and serve answer malformed requests with a 4xx; ingesting a Markdown file build already indexed stores it once; decisions restore/invalidate report an unknown id or a database error; doctor and feedback say what actually turns query memory on (release notes)
• v4.8.1: bug-hunt fixes. The installers stop destroying config they can't parse — one trailing comma used to make install-hooks --global drop your permissions, model and env (and --uninstall delete the file), and install-mcp drop every other MCP server; now the file is left untouched and the entry to paste is printed. Hooks do nothing in a project without .neuralmind/ (which neuralmind build creates), instead of building a full index from the prompt hook. The Bash output cache redacts the AWS CLI's SecretAccessKey/SessionToken JSON and {"password": …}; a Latin-1 doc no longer fails the build; rebuilds stop duplicating doc edges; watch --reindex stops serving deleted symbols; neuralmind export . works without --output. The medium-severity fixes from the same pass: hooks keep working after the agent cds into a subdirectory; neuralmind_review and neuralmind_impact no longer return security_denied under the default roles; calls, Python relative imports and C++ headers link to the right nodes; savings stops counting builds as queries; negative feedback weakens what it targets (release notes)
• Also in v4.8.1: neuralmind audit verify checks the whole log. It now fails on a record without a hash once the chain has started, on a line that isn't a JSON object, and on a prev_sha256 that doesn't match the record before it; v4.8.0 and earlier passed all three. audit export -o reports the events it wrote, not the size of the whole log. It still can't detect records deleted from the end (release notes)
• v4.8.0: a fresh or cleared Claude Code session starts with a short recap of the previous one: its first prompt, its last three, and the files it edited, marked as context, not instructions. It's built from what the hooks already see, with no model call; resumed and compacted sessions don't get it. Prompts are credential-redacted before they're stored in .neuralmind/recaps/; neuralmind recap shows what the next session will see, --clear deletes the records, and NEURALMIND_SESSION_RECAP=0 turns it off. Claude Code only — other hosts don't run its hooks (walkthrough · release notes)
• Also new in v4.8.0: decision search finds a decision worded differently from the question. neuralmind decisions query and the MCP decision tools rank by meaning as well as by shared words (hybrid, the default), using the embedding model the code index already runs locally. Search never downloads it; without it, you get keyword results and a notice. "what happens if someone steals a copy of our db?" now finds "Hash API tokens before storing them". Measured on a synthetic set before it became the default: on 20 paraphrased questions that share no word with their answers, hybrid finds 9 in its top 5 and keyword search 0; recall on 20 ordinary questions stays at 1.00 and every exact title still ranks first. Half the paraphrases are still missed, and the misses are published. --mode semantic|keyword and NEURALMIND_DECISION_SEARCH choose another ranking (release notes)
• Also new in v4.8.0: policy mistakes that used to mean the default policy, admin included, now refuse every MCP call with reason: "config": a neuralmind-backend.yaml that doesn't parse but names a security setting, and a security: or roles: key left empty. neuralmind doctor names the setting to fix (release notes)
• v4.7.0: access control for CMMC environments. security.identity: os takes each MCP caller's identity from the OS account the server runs as, and its role from security.users, instead of trusting the role a call declares; security.require_encrypted_storage refuses to build, query, serve MCP tools, or run hooks unless FileVault, BitLocker, or LUKS is verified on the project's volume. neuralmind doctor reports both (release notes)
• v4.6.1: the MCP server applies your security settings. security.roles and security.rate_limit in neuralmind-backend.yaml used to be ignored, so a caller could declare admin and reach every tool even where the policy left admin out; now a role gets only the tools the policy lists, and a policy without admin caps what any caller can reach; roles: {} grants nothing and a value of the wrong type refuses calls, though a file that doesn't parse still means the defaults (v4.8.0 refuses it). No security: block, no change — if you have one, check it before upgrading, because it replaces the defaults (release notes)
• v4.6.0: one keyword index for docs and code, on by default — so on "how does X work" questions the code that does X can win an L3 slot on keywords too, not just the README that mentions it. Measured before it shipped (reproducible on demand, not a CI gate; one of the six repos is private): mean hit@5 72.8% → 79.4% across 30 questions on each of six repos — pre-registered and committed for the five public repos, plus a private 383-file repository — and public-benchmark gold-file recall 93.75% → 95%. Losses published: requests −1 question, rich MRR 0.71 → 0.60, a new click public-benchmark miss (click 100% → 85.71%), and the private 383-file repo at 73% / 0.60, short of its 80% / 0.65 target. query --explain now shows the query intent L3 ranked with; five more ranking changes were measured, lost, and ship off behind flags. Run neuralmind build once after upgrading (release notes)
• Also in v4.6.0: what the docs described, the product now does. A repeat read of an unchanged file in the same Claude Code session comes back as a short stub, and the next read is always full; decisions go STALE when a commit changes their files (neuralmind init-hook . runs decisions scan after every commit); team governance is enforced on memory publish, and remove-edge retracts an association for the whole team. The unused co-access module is gone, and cognition-loop was rebuilt so it no longer deletes learned memory (release notes)
• v4.5.1: decision memory answers questions. neuralmind decisions query and the MCP decision tools match any word of a question, best match first, where every word used to be required; neuralmind_memory_search, neuralmind_memory_timeline and neuralmind_memory_get work for the default builder and reader roles instead of returning security_denied; and neuralmind decisions eval no longer touches your project's decisions (release notes)
• v4.5.0: numbers measured on your project. neuralmind eval . scores retrieval against the questions and gold files in your .neuralmind.eval.yaml (hit@1 / hit@5 / MRR, with a history); every reduction ratio divides by the measured size of your code instead of a fixed 50K guess; queries can be read-only (--no-learn, MCP learn: false, NEURALMIND_NO_LEARN=1) so evals never train on their own test; and the index covers what git covers — .gitignore is honoured (release notes)
• v4.4.0: never answers from a stale index without saying so. doctor, health, build and the agent's first neuralmind_wakeup compare the code graph with the files on disk; Index is stale: 51 files missing from graph… arrives before any answer does. build --regenerate-graph escapes an old graphify graph, every build purges vectors for code that no longer exists, and queries load the index without rebuilding it or printing a line (release notes)

Works with every IDE your team already uses.

Website: neuralmind.uk · Docs: docs.neuralmind.uk · Changelog: CHANGELOG.md · Release notes: docs/releases/

The Problem

Every large engineering organization has the same AI spend problem: token costs compound as the codebase grows, context is re-discovered from scratch on every query, and nobody can explain the ROI.

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

❌ Naive:  Load entire codebase → 50,000 tokens → $0.15-$3.75/query
✅ NeuralMind: Smart context → ~800 tokens → $0.002-$0.06/query

Engineering leads are stuck between two bad options: let agents burn tokens loading whole files, or hand-curate context windows. Neither scales.

The Solution

NeuralMind is a code intelligence layer that deploys in your infrastructure — not a SaaS wrapper, not a model swap. It sits between your agent and your code, learning how your team actually works.

Two cooperating brains:

The agent asks a question. NeuralMind retrieves only the relevant slice (~800 tokens). The more you use it, the smarter the retrieval gets — Hebbian co-activation strengthens edges between code that's used together; unused edges decay.

NeuralMind sends no telemetry and transmits no repository content off your machine. It processes locally and hands only the relevant code slice to your AI tool on the same machine — what that tool then sends to its own model provider is between you and it. Its one outbound request is a one-time download of a public embedding model on first build — pre-seedable, see Run NeuralMind air-gapped.

Who This Is For

If your team uses Claude Code, Cursor, Cline, or any MCP agent — NeuralMind makes every agent remember your codebase.

Theoretical = MCP is standard protocol. All MCP-compatible agents should work. We haven't physically tested display-server-dependent IDEs (Cursor, Cline, Continue) — Xvfb is not available in our CI. Hermes-Agent, OpenClaw, and Agent Zero are covered by host-specific notes in skills/neuralmind/SKILL.md and a CI check (tests/test_skill_manifest.py) that keeps the portable skill's identity in sync with each registry's rules, but none has been physically driven end-to-end yet either.

Benefits

1. Cheaper context

The fixture number is the floor of a floor: small repo, conservative gate. The mechanism is what scales — the bigger the codebase, the more whole-file context you avoid.

2. Learns how you work (the differentiator)

NeuralMind's moat is usage memory: a Hebbian synapse layer that learns what your team edits together and surfaces it on future queries.

Both are budget-neutral by design: recalled nodes displace the weakest hits rather than adding tokens.

Why a range, not a number. Both A/Bs run against a ~500-line fixture through a ChromaDB HNSW index, so the deltas are small and jitter between runs — CI averages the onboarding lift over three runs for exactly that reason. What CI guarantees is the direction; the magnitude is whatever your own repo produces. Run python -m tests.benchmark.run for yours.

3. Context budget management (v3.13.0+)

Every query gets a fixed token budget (default 8,000 tokens, matching lean-ctx). If the assembled context would exceed it, lower-priority layers are trimmed first — L3 search results, then L2 on-demand modules, then L1 summary. L0 identity is never trimmed. Budget warnings log at 80% usage.

result = mind.query("How does auth work?", context_budget=6000)
# Context trimmed to fit 6000 tokens, L3 removed first

4. Session summaries (v3.13.0+)

Periodic session digests (every 25 tool calls) capture what was done, key decisions, files touched, and commands run. Stored as markdown under .neuralmind/summaries/, semantically recallable via the vector index. Auto-pruned (max 100 per project). They're built from the event log that neuralmind watch and neuralmind serve write, so with the hooks alone they don't reflect the session; there, a new Claude Code session gets the lighter session recap instead (v4.8.0+).

neuralmind status .  # shows recent summaries

5. Read dedup for repeat reads (v4.6.0+)

When your agent reads a file it already read in this Claude Code session and the content hasn't changed, the Read hook replaces the repeat with a short stub that says so: the earlier result is still current. The next read of that file always goes through in full, so an agent whose earlier copy has left its context gets it back by reading again. Reads are tracked per session and per subagent, keyed on the exact text and range returned. A new, resumed, cleared or compacted session starts fresh. Reads under 2,000 characters are never stubbed.

It uses Claude Code's PostToolUse updatedToolOutput, keeping the Read tool's own output shape. Claude Code ignores a replacement that doesn't match and delivers the original read, so a shape change on Claude Code's side means no dedup rather than a broken read. MCP-only agents have no PostToolUse hook and are unaffected. Off with NEURALMIND_READ_DEDUP=0. Not benchmarked yet, so no savings figure here.

The v3.13 co-access module (graph_traversal) was removed rather than wired in: every query already strengthens the edges between the results it returns together, and the module's copies went to a namespace recall never read. The "preload related files" idea it described was never built.

6. Cognition loop (on demand; rebuilt in v4.6.0)

neuralmind cognition-loop . runs one maintenance pass over the learned memory: the synapse store's half-life decay (it charges only the time since the last decay, so running it often never decays anything twice) and cleanup of expired read-dedup rows.

neuralmind cognition-loop .   # run a pass now; safe to put in cron

Nothing schedules it, by design: Claude Code's session-start hook already decays at every session start, and neuralmind watch every 10 minutes. Use it when you run neither, for example with an MCP-only client.

A miss, published: before v4.6.0 this command ran its own SQL that deleted most learned edges idle for more than about two days, replayed recent queries into edges recall never read, and deleted session summaries older than 30 days. If you ran it, neuralmind memory reset --namespace traversal clears the leftover rows.

7. Decision memory + stale-decision guard (v4.1.0+; automatic invalidation v4.6.0+)

Store architecture decisions with rationale, evidence, affected files and the commit they came from (neuralmind decisions record), and search them (neuralmind decisions query). The commit history retires them for you: the post-commit hook from neuralmind init-hook . runs neuralmind decisions scan, which marks a decision STALE when the commit changed one of its files after the decision was recorded. Any change counts; there's no diff analysis. The commit that lands the change a decision describes leaves it ACTIVE.

Before your agent edits a file, a PreToolUse hook surfaces any decision governing it that is STALE or INVALIDATED, with the reason, the decision's id, and neuralmind decisions restore <id> for one that still holds. Re-run neuralmind init-hook . on an existing checkout to pick up the scan. Pulls and rebases don't run post-commit hooks, so a decision whose files changed only in pulled commits stays ACTIVE until one of your own commits touches them.

Search takes keywords or a question, and ranks by meaning as well as by shared words (v4.8.0+), so a question finds a decision worded differently from it. The embedding model is the one the code index already runs locally, and search never downloads it. --mode keyword ranks by shared words only: any word can match, and decisions matching more of the words rank first (v4.5.1+). neuralmind decisions eval --queries FILE scores every mode against questions with known answers; the Memory Layer wiki has the results, misses included.

Walkthroughs: Keep decision memory honest across commits · Find the decision behind the code when you don't know its words.

8. Finds the right code (not just less of it)

95% mean gold-file recall (85.71–100% per repo) across 40 pre-registered queries on four pinned OSS repos (requests, click, flask, rich) — every miss published, not rounded away. Reproducible — python -m evals.public.run. A separate, off-by-default eval on requests/click only put retrieval ranking at MRR 0.96 against the incumbent codebase-memory-mcp's 0.23; that one has not been re-verified against the current four-repo corpus.

9. Answer grounding vs. naive truncation (currently a loss)

At a matched token budget, the faithfulness eval asks whether NeuralMind's selected context carries more of the gold facts than naive truncation. On the ~500-line reference fixture, which mixes code with prose chapter summaries, truncation currently wins slightly: 0.451 vs 0.505 expected-fact recall, a −0.054 delta at v4.3.4. CI fails the build if the mean delta drops below −0.10; earlier releases measured +0.013 to +0.143. We publish it as a loss rather than drop the eval.

10. Tool-output compression (measured, and removed)

Through v4.4.0, NeuralMind's PostToolUse hooks handed Claude Code compressed copies of Bash and Grep output. Claude Code adds a hook's additionalContext next to the tool result rather than replacing it, so the copies cost tokens instead of saving them: +17.5% on Bash calls and +22.1% on content-mode Grep, and the Read hook never saw Claude Code's payload at all (compression benchmark). From v4.5.0 the hooks inject nothing, so Claude sees exactly the tool result. The compressors themselves would cut 66–87%, but a Read replaced by its skeleton would keep none of the file's source lines. So nothing replaces tool output until something keeps what the agent needs.

Use Cases

Limitations (Read Before Installing)

What NeuralMind is NOT:

• NOT a SaaS wrapper. It's a code intelligence layer that runs in your infrastructure. We never see your code.
• NOT a model swap. It works with whatever agent you already use — Claude, GPT, Gemini, or any MCP-compatible agent.
• NOT a replacement for Copilot/Cursor. It composes with them. It's the memory layer that makes every agent smarter.
• SOC 2-ready posture, certification on the roadmap. Our architecture supports SOC 2 deployment patterns (an engine that transmits no repository content, hash-chained audit log, per-tool MCP permissions). See commercial-terms.json.
• NOT SSO/SAML today. This is a roadmap feature. See commercial-terms.json do_not_market list.

Technical limits:

• Per-language answer quality is Python-first. Structural coverage (symbol extraction) is 100% across all 10 bundled languages. Answer quality (faithfulness, grounding) is only measured on Python fixtures.
• Synapse learning needs sessions. The Hebbian layer learns from co-activation over time. A fresh install has no learned associations — they accumulate over days/weeks of real use.
• No real-time cross-machine sync today. Team memory uses a commit-and-pull model (neuralmind memory publish). Real-time sync is roadmap-only.

How to Use

Install (pick your path)

Quick start

cd your-project
neuralmind build .          # index the codebase (tree-sitter, ~seconds to minutes)

neuralmind wakeup .         # what the agent sees at session start
neuralmind query . "How does authentication work?"  # ~800 tokens, not 50,000

neuralmind install-hooks .  # Claude Code: session memory, prompt recall, stale-decision guard
neuralmind serve .          # Obsidian-style graph view in your browser
neuralmind savings . --cost # measured token savings, priced for your model
neuralmind doctor           # verify the install end to end — incl. graph vs files on disk

Is the index in step with the code? (v4.4.0+)

A code graph that lags the code still "works" — it just answers from code that no longer exists. NeuralMind now checks, both ways, whatever tool built the graph:

neuralmind doctor .                     # [FAIL] Code graph: ... 51 files on disk not in graph
neuralmind health .                     # exit 1 when the graph and the code disagree
neuralmind build . --regenerate-graph   # replace a stale graphify graph with the built-in one
neuralmind build . --strict             # CI: exit 3 instead of embedding a failing graph

Your agent sees it too: its first neuralmind_wakeup starts with Index is stale: … Run neuralmind build . --regenerate-graph. whenever the graph lags. Pin the graph source with graph_source: auto | builtin | graphify in .neuralmind.yaml. Walkthrough: Recover from a stale code graph.

Measure it on your own questions (v4.5.0+)

neuralmind eval . --suggest --write   # draft .neuralmind.eval.yaml: questions + the file that answers each
neuralmind eval .                     # hit@1 / hit@5 / MRR, read-only — never trains on its own test
neuralmind eval . --report            # the run history as a markdown table

Every reduction ratio (benchmark, savings, cost, build --dry-run) now divides by the measured token count of the code the index covers — prose like a changelog is left out, and it is counted at the same ~4 chars/token as the context — and the index covers what git covers. benchmark prints the old fixed-50K ratio beside it, so older numbers stay comparable. Walkthrough: Measure retrieval on your own repo.

Test a ranking change before you trust it (v4.6.0+)

neuralmind build .                                         # writes the v4.6.0 docs + code keyword index
NEURALMIND_BM25_UNIFIED=0 neuralmind eval . --no-history   # v4.5.0 ranking, same questions
neuralmind eval .                                          # v4.6.0 ranking
NEURALMIND_L3_PER_FILE=2 neuralmind eval . --no-history    # an off-by-default research flag

Every ranking flag is read at query time, so one build serves every variant. v4.6.0's own default was chosen this way, across six repos and a keep rule fixed in advance — raw output in bench/retrieval/. Walkthrough: A/B-test a ranking change on your own repo.

Index prose, not just code (v3.4.0+)

A book, a docs tree, a research folder — ingest-content indexes a corpus of Markdown/text into its own project, so it never gets folded into the enclosing repo:

neuralmind ingest-content chapters --dry-run       # preview: files, sizes, chunk counts
neuralmind ingest-content chapters --content-only  # index the prose, skip the code graph
neuralmind status book                             # nodes indexed, chunks, last ingest

Re-runs are incremental — only files whose content changed are re-embedded, and chunks from shortened or deleted files are evicted. Long embeds show a progress bar with an ETA on a terminal, and plain milestone lines off one (CI, an agent shell), so a slow run is never mistaken for a hung one.

Wire up your agent

# Claude Code, Cursor, Cline, VS Code, Claude Desktop — registers with every detected client
neuralmind install-mcp --all
# Codex, Continue, or any other MCP client: point it at the stdio server
codex mcp add neuralmind -- neuralmind-mcp

# Claude Code: install lifecycle hooks (SessionStart, UserPromptSubmit, PreCompact, PostToolUse, PreToolUse, Stop, SessionEnd)
neuralmind install-hooks .

# Team memory: commit learned weights (no source code) for teammates
neuralmind memory publish

Run the benchmark

# Measure YOUR repo — not a fixture, not a demo
neuralmind benchmark .

# Reproduce the public benchmark (requests, click, flask, rich) — needs a source checkout
neuralmind benchmark . --public

# Retrieval self-probe: does the index find YOUR symbols?
neuralmind probe .

⚡ 30-Second Proof

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 fresh clone:

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

Output looks like:

  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)

The fixture is intentionally tiny (~500 lines) — it runs in CI as a regression gate. Before v4.5.0, neuralmind benchmark reported 12–50× on real repos against a fixed 50K-token estimate; it now divides your measured code, so the ratio grows with the repo. The public benchmark measures 46–263× against every source file (benchmarks · production field report).

Then get your own number:

pip install neuralmind
cd /path/to/your-repo
neuralmind build .
neuralmind benchmark .

🧠 What You Get

• Progressive context disclosure (L0–L3). A question costs ~800 tokens, not your whole repo. The agent asks for more depth only where it needs it.
• A synapse layer that learns. Hebbian co-activation strengthens edges between code that's used together; unused edges decay. Recall is spreading activation over that graph — your agent's context gets better the more you work.
• Session memory. SYNAPSE_MEMORY.md is exported for Claude Code so every session boots already knowing the hub files and learned associations. A fresh or cleared session also gets a short recap of the previous one (neuralmind recap prints it; walkthrough).
• Read dedup. In Claude Code, a repeat read of an unchanged file comes back as a short stub instead of the whole file again; the next read is always full.
• neuralmind last. The Bash hook caches the latest successful command's output, credentials redacted, so it can be printed again without re-running the command. A failing command fires a different hook event and isn't cached.
• Team memory. neuralmind memory publish writes a learned-weights bundle (no source code); commit it and teammates' agents inherit it on their next session — a fresh clone starts with the team's earned intuition. With team governance set up, publish honours the admin's scope and weight threshold, team governance remove-edge retracts an association for every teammate, and each publish, import and review is audited (walkthrough).
• Commit-time drift guard. neuralmind drift reads your staged diff, maps changed lines to graph symbols, and flags one that skips a pattern a strong majority of its siblings share — before it ships, not after a query happens to surface the cluster. neuralmind init-hook wires it into pre-commit automatically (warn by default; --strict to block).
• MCP server for any agent. Claude Code, Codex, Cursor, Cline, Continue, or anything MCP-compatible: neuralmind install-mcp --all.
• Graph view. neuralmind serve renders the index as a force-directed, community-coloured graph with the synapse overlay — backlinks, semantic quick-switcher, clickable neighbours. There's also a VS Code extension.
• Ten-language code graph. tree-sitter indexes Python, TypeScript, Go, Rust, Java, C, C++, C#, Ruby, and PHP out of the box.
• Business-context synapse seeding. seed_from_documents() builds deterministic, LLM-free associations between business documents (decisions, SOPs, meeting notes, policies) and your code graph — adjacency-matched compounds, title-reference cross-links, frequency-capped tags. 56 tests.
• Team tier ($29/user/mo). The license buys seats and support: a multi-seat license (5-50), priority support, and an annual invoice. The features themselves — shared-memory governance, append-only hash-chained audit log, self-hosted deployment — run under the auto-issued free license at 1 seat, so you can evaluate everything before paying. MIT core stays MIT; tier2 is source-available, not MIT — see LICENSING.md and pricing.

How it works under the hood: Architecture · brain-like learning.

📊 Benchmarks

Measured, not marketed. The fixture numbers are produced by CI on every commit (every merged PR carries a sticky benchmark comment) and reproduce locally with python -m tests.benchmark.run; the public benchmark reproduces on demand with python -m evals.public.run, raw per-query data committed:

• 85.71–100% gold-file recall (95% mean) at 46–263× fewer tokens than pasting every source file on the public benchmark — 3 of 40 queries missed, every one published, and a bare vector-RAG baseline matches or beats it on recall at fewer tokens (where NeuralMind loses).
• Synapse recall A/B: lifts top-k hit rate at ±0 token cost — +3.5 to +14 points across runs; CI gates the direction, not the magnitude.
• Onboarding lift: lifts top-k module hit-rate from a committed team baseline — +0.9 to +11.6 points across runs (a distinct eval from the synapse recall A/B above — see evals/onboarding/).
• Real production rebuild: 48.8× average reduction, 1,033 tokens/query, against the fixed 50K-token estimate the CLI used before v4.5.0 (full field report).
• 5.1× token reduction on the CI fixture at v4.3.4 (500-line, deliberately tiny — the floor of a floor; the build fails below 4.0×).
• Retrieval quality (N-15): graded relevance (0-3), nDCG@5, MRR, recall@k, precision@k + RAGAS faithfulness scoring — 8 CI regression gates, per-shape breakdowns.
• Content QA (N-16): book/markdown content retrieval — 30 queries, 11 chapters, 150K-word corpus. N-15 IR metrics + RAGAS on long-form content. ingest-content CLI + benchmark --content end-to-end command.
• Backend parity gate: the built-in tree-sitter backend is held within tolerance of the legacy graphify backend on every PR.
• Tool-output compression: measured on v4.3.4, the PostToolUse hooks added 17.5% to Bash calls and 22.1% to content-mode Grep rather than saving anything; they now inject nothing (compression benchmark).

Methodology, gold sets, and community submissions: benchmarks/ · public methodology and results · tool-output compression.

3 submission(s). vs fixed 50K: a fixed 50,000-token estimate over tokens per question — the one ratio every row shares, so it tracks context size, not repo size. vs measured code: the submitter's measured code over tokens per question (v4.5.0+ submissions). See the JSON data for notes and verification commands, or the interactive dashboard for scatter + by-language charts.

🔒 Security & Compliance

• Local engine, no telemetry by default. NeuralMind transmits no repository content and ships no telemetry in its default configuration. Only the minimal relevant slice of code ever reaches your AI tool. The only outbound requests it makes by default are a one-time fetch of a public embedding model on first build (carrying nothing about your code; pre-seed it and the install never reaches the network at all) and, only if you explicitly opt in with NEURALMIND_LLM_SEED=1 + ANTHROPIC_API_KEY, a call that sends README/architecture-doc prose (never code, never other files) to Anthropic to seed synapse edges. No code path ingests video, image, or audio files at all. Full disclosure: docs/compliance/THIRD_PARTY_LLM_DISCLOSURE.md.
• CycloneDX SBOM per release, hash-chained audit logs (the query trail in the core; the governance trail in the Team modules, free at 1 seat), signed licenses (Ed25519), tarball integrity instructions on every release.
• Live posture page: neuralmind.uk/security · Policy: SECURITY.md · Compliance summary · Third-party LLM & media disclosure · SDLC policy

Behavior toggles: NEURALMIND_BYPASS=1 (switch off every NeuralMind hook action), NEURALMIND_SYNAPSE_INJECT=0 (skip prompt-time recall), NEURALMIND_SYNAPSE_EXPORT=0 (skip memory export), NEURALMIND_TEAM_MEMORY=0 (skip team-bundle import), NEURALMIND_STALE_GUARD=0 (skip the PreToolUse stale-decision guard), NEURALMIND_READ_DEDUP=0 (never stub a repeat read), NEURALMIND_SESSION_RECAP=0 (don't record prompts and edited files, and show no session recap), NEURALMIND_DECISION_SCAN=0 (skip the post-commit decision scan), NEURALMIND_DECISION_SEARCH=keyword (rank decisions by shared words only, as in v4.6). All fail-open.

📚 Documentation

❓ FAQ

How is this different from RAG? RAG retrieves similar text. NeuralMind maintains a weighted graph of your code and learns from use — retrieval is spreading activation over structural edges plus Hebbian synapses, disclosed progressively so the agent pays only for the depth it needs.

Does my code leave my machine? NeuralMind itself sends no telemetry and transmits no repository content off your machine; its only default outbound request is a one-time public embedding-model download on first build (pre-seedable). Your agent still sends its selected slice to its own model — NeuralMind just makes that slice smaller.

What if it doesn't help on my repo? Run neuralmind benchmark . and read the number. If it's not worth it, uninstall — and see the use cases for guidance on when NeuralMind is the right fit.

Is the paid tier required? No. The core is MIT and complete, and the auto-issued free license runs every feature — governance and audit included — at 1 seat. The Team tier ($29/user/mo, 5–50 seats) adds seats beyond one, priority support, and an annual invoice.

What about SOC 2? Our architecture supports SOC 2 deployment patterns (no repository content transmitted, audit log, per-tool MCP permissions). Certification is on the roadmap. See commercial-terms.json.

What about CMMC 2.0? CMMC assesses a defense contractor's environment, not a tool, so there is nothing for NeuralMind to be certified against. If you index CUI source code, NeuralMind is in your assessment scope. It provides per-tool permission sets and a hash-chained audit log. Since v4.7.0, security.identity: os binds MCP roles to the caller's OS account, and security.require_encrypted_storage refuses to run on a volume it can't verify as encrypted. The encryption itself stays with your host. The Level 2 practice mapping is in docs/COMPLIANCE-SUMMARY.md.

What about SSO/SAML? Roadmap-only. Not available today. See commercial-terms.json do_not_market list.

🤝 Contributing

Contributions welcome — see CONTRIBUTING.md, CODE_OF_CONDUCT.md, and SUPPORT.md. Tests live in tests/; pytest tests/ must pass (the synapse layer's tests are stdlib-only). Security reports: see SECURITY.md.

📄 License

MIT for the core — see LICENSE. The optional Team tier is licensed separately — see LICENSE-COMMERCIAL.md.