Open-source semantic code intelligence for AI agents. Built for cost efficiency, security, and compliance.
Phase 3 of the release arc. Turn the v0.6.0 → v0.7.0 → v0.8.0 foundation into something a CTO, security team, or regulated-industry operator can actually adopt.
ghcr.io/dfrostar/neuralmind:vX.Y.Z and :latest, multi-platform (linux/amd64 + linux/arm64), non-root runtime, transitive deps pre-wheeled. :latest only moves for stable tags — pre-release tags like v1.0.0-rc1 are excluded from the floating tag.neuralmind-vX.Y.Z.sbom.json — ingestible by Grype, Trivy, Dependency-Track, and most enterprise SCA scanners.No production code changes — pure CI + docs. No migration. Same graph.json, same synapses.db, same hooks.
neuralmind watch and neuralmind serve are first-class production processes now. The synapse store accumulates 24/7 whether you're at the keyboard or not, and the graph view is always listening on 127.0.0.1:8765.
scripts/systemd/neuralmind-{watch,serve}.service (user-scope units, hardened), scripts/launchd/com.neuralmind.{watch,serve}.plist (macOS user agents with RunAtLoad + KeepAlive), Windows Task Scheduler section in the Scheduling Guide./healthz endpoint on neuralmind serve — unauthenticated, returns {"status":"ok","version":"..."}. Designed for Docker HEALTHCHECK and systemd ExecStartPost probes so a fresh container can be checked without threading a session token.HEALTHCHECK) with install + verify + uninstall + troubleshooting.The canvas still requires the per-session auth token by default; pass --no-auth in the templates or read the tokenized URL from the service logs.
Distribution release, not a features release. The brain is the same brain. What changed is how many ways you can install it.
pip install neuralmind graphifyy — the default. Works in any venv.pipx install neuralmind — global CLI, isolated env. neuralmind on PATH everywhere without activation.uv pip install neuralmind graphifyy — ~10× faster than pip, same wheel.docker build -t neuralmind:dev . — multi-stage Dockerfile in the repo root, non-root runtime, transitive deps pre-wheeled in the builder. GHCR auto-publish lands in a later release; build locally for now.All five paths deliver the same package: the neuralmind CLI, the neuralmind-mcp server (for Claude Code, Cursor, Cline, Continue, and any MCP client), and the live graph view from v0.6.0. Smoke test is identical: neuralmind --help works everywhere; python -c "import neuralmind" works for pip / uv / source paths.
graph-view, hebbian-learning, force-directed-graph, …) so PyPI search ranking finally matches what we ship.logrotate/copytruncate is fixed./api/queries test coverage (#116) — the replay-last-query route is now in the automated regression suite.No migration. Same graph.json, same synapses.db, same hooks. Upgrade is whatever your install path's --upgrade equivalent is.
The pitch flipped. v0.5.4 made the brain inspectable. v0.6.0 makes it legible. neuralmind serve now streams a live activity feed: synapse + file events pulse across the canvas in real time as the agent and the codebase interact. The graph stops being a static map and becomes a window into the hippocampus learning your codebase.
/api/events SSE stream subscribed to the in-process event bus. Affected nodes pulse on the canvas; sidebar log shows the most recent ~80 events.neuralmind watch daemon, or a hook-driven Claude Code session in another process, feeds the same live feed via <project>/.neuralmind/events.jsonl. The in-process bus stays the primary path; the JSONL is a deliberately boring side channel. Opt out with NEURALMIND_EVENT_LOG=0.Cmd/Ctrl-K and / jump-to-search from anywhere; Esc clears and blurs.The cross-process JSONL bridge means that if you run Claude Code, Cursor, OpenClaw, and Hermes-Agent against the same project — they reinforce the same synapse store, and the v0.6.0 graph view shows the union of their activity. Pre-v0.6.0, the synapse store was shared but the experience wasn't — three tools talking to a black box. Now the brain is visibly one brain. See the multi-agent walkthrough.
No migration: same graph.json, same synapses.db, same hooks. Upgrade is pip install --upgrade neuralmind. Then neuralmind serve and save a file — that's the demo and the verification in one motion.
NeuralMind exists to solve a fundamental problem: AI agents waste tokens loading raw source code when they only need small, semantic context.
Our mission is to make semantic code intelligence accessible, affordable, and trustworthy—without data exfiltration, vendor lock-in, or compliance headaches.
Phase 1 — Smart Retrieval: Instead of loading entire files, NeuralMind uses a 4-layer semantic index to surface only the ~800 tokens of code your question actually needs.
Phase 2 — Output Compression: PostToolUse hooks compress Read, Bash, and Grep output 88–91% smaller before agents see it.
Phase 3 — Brain-like Memory (v0.4.0): A second brain runs alongside the LLM — a persistent weighted graph that learns associations between code nodes from how the agent and the codebase actually interact. Stronger connections form between code that gets used together, weaker ones decay, and the agent's prompts trigger spreading-activation recall over the learned graph.
Phase 4 — Graph View (v0.5.4, made live in v0.6.0): neuralmind serve renders the whole system as an Obsidian-style force-directed graph in the browser. Structural edges, the learned synapse overlay, backlinks, a semantic quick-switcher, and one-click open-in-editor. v0.6.0 adds a live activity feed — synapse and file events pulse on the canvas in real time, with a cross-process JSONL bridge that lets Claude Code, Cursor, OpenClaw, and Hermes-Agent all feed the same canvas. The brain stops being a black box — you can finally watch what it's learning, live.
Result: 40–70× per-query token reduction (50K+ tokens of raw source compressed into ~800 tokens of structured context) and 40–70% bill drops on real codebases. Retrieval that gets sharper the longer the system runs on a codebase.
NeuralMind doesn't load code randomly. It uses a 4-layer index that progressively surfaces context:
The agent gets exactly what it needs, in order, without bloat.
NeuralMind learns from your actual queries. Over time, cooccurrence-based reranking improves retrieval quality based on how you ask questions. Better answers, without external training.
The newest layer is an associative memory inspired by how brains actually learn. NeuralMind tracks weighted "synapses" between code nodes, and applies three classic neuroscience principles:
The synapse store is local SQLite, project-scoped, and inspectable. It exports a markdown summary that Claude Code's auto-memory system loads natively, so the learned associations show up in every session — no MCP tool call required.
neuralmind serve (v0.5.4, made live in v0.6.0)The agent-facing brain is now also a human-facing tool. A stdlib HTTP server renders a force-directed graph of the entire codebase in the browser — structural edges (calls and imports) drawn together with the learned synapse overlay, where edge thickness encodes weight. Each node has Obsidian-style backlinks and outgoing-links panels, a "synaptic neighbours" list with weights and activation counts, and a one-click "open in editor" button (smart support for VS Code, Cursor, vim, sublime, JetBrains). A semantic quick-switcher lets you type a phrase and jump straight to the matching node. Zero CDN dependencies; per-session access token bound to 127.0.0.1.
v0.6.0 adds a live activity feed. The server now exposes a long-lived /api/events SSE stream subscribed to an in-process event bus. Every SynapseStore.reinforce() call and every coalesced file-edit batch publishes an event; affected nodes pulse on the canvas with short animated radial rings, and a sidebar log shows the most recent ~80 events. A cross-process JSONL bridge (<project>/.neuralmind/events.jsonl) lets a separate neuralmind watch daemon, a Claude Code session, an OpenClaw call, or any other process feed the same canvas. One canvas, every agent. Opt out with NEURALMIND_EVENT_LOG=0.
Why it matters: pre-v0.6.0 the graph view was inspectable but static — you had to refresh to see new state. With the live feed, you can sit there in real time and watch the hippocampus learn your codebase. "The agent has a learning memory" stops being a claim and becomes a visual.
Every query is logged with full provenance: which code was retrieved, why, which embeddings were used, code state (git commit). Export for NIST AI RMF, SOC 2, GDPR, HIPAA.
NeuralMind is MIT licensed and fully open source. No hidden business model, no vendor lock-in, no surprise rate limits.
Builds on v0.5.4's graph view. neuralmind serve now streams synapse + file events to the canvas in real time over SSE — affected nodes pulse, a sidebar log shows the most recent ~80 events. A cross-process JSONL bridge means a separate neuralmind watch daemon, or a hook-driven Claude Code session, feeds the same live feed via <project>/.neuralmind/events.jsonl. Pin UX, Cmd/Ctrl-K quick-switch, a 1–3-hop depth slider, replay-last-query overlay, edge tooltips, and a min-weight synapse slider round out the release. No migration — same index, same synapses.db, same hooks. See the v0.6.0 release notes.
neuralmind watch daemon with the in-process bus so a single-process serve+watch stays unified without the JSONL bridge.This is an independent, open-source project. No relationship to NeuralMind.ai (a different company). We chose the name because it reflects our philosophy: a "neural" index that learns your codebase.
Your code stays local. Zero cloud calls, zero telemetry, zero data exfiltration.
Open source, MIT licensed. Every decision is auditable, every result is explainable.
Built for regulated industries. NIST AI RMF, SOC 2, GDPR, HIPAA friendly.
Works with your tools. Claude Code, Cursor, ChatGPT, local LLMs—not locked in.
Smart context reduces per-query tokens 40–70×. Lower costs, better answers.
Built in public. Issues, discussions, and contributions welcome.
Ready to reduce your per-query token costs by 40–70×?