NeuralMemory
Your AI agent forgets everything between sessions. Neural Memory gives it a brain.
Website · Quickstart · MCP Tools · Pro · Changelog
Memories are stored as interconnected neurons and recalled through spreading activation — the same way the human brain works. No vector database. No API calls. No monthly embedding bill.
pip install neural-memory
Restart your AI tool. Your agent now remembers — no init needed, the MCP server auto-initializes on first use.
Already installed? nmem update upgrades in place and detects whether you installed via pip or from source. nmem update --check only reports what is available.
The CLI is
nmem(or the longerneural-memory). There is nonmbinary.
3 Tools. That's It.
63 MCP tools are available, but you only need three:
| Tool | What it does |
|---|---|
nmem_remember |
Store a memory — auto-detects type, tags, and connections |
nmem_recall |
Recall through spreading activation — related memories surface naturally |
nmem_health |
Brain health score (A–F) with actionable fix suggestions |
Everything else — sessions, context loading, habit tracking, maintenance — works transparently in the background.
What Makes This Different
Most memory tools are search engines. Neural Memory is a graph that thinks.
When you ask "Why did Tuesday's outage happen?", a vector database returns the most similar sentence. Neural Memory traces the chain:
outage ← CAUSED_BY ← JWT expiry ← SUGGESTED_BY ← Alice's review
Relationships are explicit — CAUSED_BY, LEADS_TO, RESOLVED_BY, CONTRADICTS — so your agent doesn't just find memories, it reasons through them.
| Search-based (RAG) | Neural Memory | |
|---|---|---|
| Retrieval | Similarity score | Graph traversal |
| Relationships | None | 24 explicit types |
| LLM required | Yes (embedding) | No — fully offline |
| Multi-hop reasoning | Multiple queries | One traversal |
| Memory lifecycle | Static | Decay, reinforcement, consolidation |
| Cost per 1K queries | ~$0.02 | $0.00 |
Cloud Sync — Your Data, Your Infrastructure
Sync your brain across every machine. Unlike other memory tools, we never store your data.
Laptop ←→ Your Cloudflare Worker ←→ Desktop
↕
Your Phone
You deploy the sync hub to your own Cloudflare account (free tier). Your D1 database, your encryption key, your data. We provide the code — you own the infrastructure.
nmem sync # push/pull changes
nmem sync --auto # auto-sync after every remember/recall
Sync uses Merkle delta — only diffs travel, not the full brain. Fast, efficient, private.
Features
Memory & Recall
- 14 memory types — fact, decision, error, insight, preference, workflow, instruction, and more
- Spreading activation — memories surface by association, not keyword match
- Cognitive reasoning — hypothesize, submit evidence, make predictions, verify with Bayesian confidence
- Workload presets —
nmem config preset {balanced,safe-cost,max-recall,chat-heavy}tune the brain for SaaS, frugal mode, deep retention, or conversational agents - Temporal recall —
nmem_causalexposestemporal_rangeandtemporal_neighborhoodactions; see the Temporal Recall Recipes guide
Knowledge Ingestion
- Train from documents — PDF, DOCX, PPTX, HTML, JSON, XLSX, CSV ingested into permanent brain knowledge
- Import adapters — migrate from ChromaDB, Mem0, Cognee, Graphiti, LlamaIndex in one command
Lifecycle & Storage
- Memory consolidation — episodic memories mature into semantic knowledge over time
- Compression tiers — full → summary → essence → ghost → metadata (reclaim storage, keep meaning)
- Brain versioning — snapshot, rollback, diff, transplant memories between brains
Community
- Brain Store — browse, import, and publish pre-built brains to the community marketplace
- 3 seed brains — Python Best Practices, Git Workflows, Docker Essentials (ready to import)
Ecosystem
- Web dashboard — 7-page React UI with graph visualization, health radar, timeline, mindmap, Brain Store
- VS Code extension — memory tree, graph explorer, CodeLens, WebSocket sync (Marketplace →)
- Safety — Fernet encryption, sensitive content auto-detection, parameterized SQL, path validation
- Telegram backup — send brain
.dbfiles to Telegram for offsite backup
Quick Examples
# Store memories (type auto-detected)
nmem remember "Fixed auth bug with null check in login.py:42"
nmem remember "We decided to use PostgreSQL" --type decision
nmem todo "Review PR #123" --priority 7
# Recall
nmem recall "auth bug"
nmem recall "database decision" --depth 2
# Brain management
nmem brain list && nmem brain health
nmem brain export -o backup.json
# Sync across devices
nmem sync --full
# Web dashboard
nmem serve # http://localhost:8000/dashboard
import asyncio
from neural_memory import Brain
from neural_memory.storage import InMemoryStorage
from neural_memory.engine.encoder import MemoryEncoder
from neural_memory.engine.retrieval import ReflexPipeline
async def main():
storage = InMemoryStorage()
brain = Brain.create("my_brain")
await storage.save_brain(brain)
storage.set_brain(brain.id)
encoder = MemoryEncoder(storage, brain.config)
await encoder.encode("Met Alice to discuss API design")
await encoder.encode("Decided to use FastAPI for backend")
pipeline = ReflexPipeline(storage, brain.config)
result = await pipeline.query("What did we decide about backend?")
print(result.context) # "Decided to use FastAPI for backend"
asyncio.run(main())
Neural Memory Pro
Free Neural Memory is complete — 63 tools, unlimited memories, fully offline. You never have to pay.
But past 10K memories, things change. Keyword matching misses semantically related content. Consolidation slows to minutes. Storage grows unbounded. If your agent's brain is getting big, Pro makes it smart.
Free recalls by keyword. Pro recalls by meaning.
Query: "authentication improvements"
Free (FTS5): 2 results — exact matches only
Pro (HNSW): 7 results — includes "JWT rotation", "session hardening", "OAuth migration"
What Pro adds
| Free (SQLite) | Pro (InfinityDB) | |
|---|---|---|
| Recall | Keyword match (FTS5) | Semantic similarity (HNSW) |
| Speed at 1M neurons | ~500ms | <5ms |
| Scale tested | ~50K neurons | 2M+ neurons |
| Compression | Text-level trimming | 5-tier vector compression (97% savings) |
| Consolidation | O(N²) brute-force | O(N×k) HNSW clustering |
| Storage per 1M | ~5 GB | ~1 GB |
| Cloud sync | Manual push/pull | Merkle delta (auto, diffs only) |
Pro-exclusive features
- Cone Queries — adjustable semantic recall. Narrow the cone for precision, widen for exploration
- Smart Merge — consolidation that scales to 1M+ neurons using HNSW neighbor clustering
- Directional Compression — compress along multiple semantic axes while preserving meaning
- 5-Tier Auto Lifecycle — memories flow from float32 → float16 → int8 → binary → metadata. Auto-promote on access
Get Pro
pip install neural-memory # Pro features included
nmem shared activate --key NM-PRO-XXXX-XXXX-XXXX # activate license
nmem shared status # verify: Pro: Active
$9/mo — 30-day money-back guarantee. All free tools keep working. Downgrade anytime, keep your data.
Setup by Tool
Claude Code (Plugin)
/plugin marketplace add nhadaututtheky/neural-memory
/plugin install neural-memory@neural-memory-marketplace
Cursor / Windsurf / Other MCP Clients
pip install neural-memory
Add to your editor's MCP config:
{
"mcpServers": {
"neural-memory": { "command": "nmem-mcp" }
}
}
OpenClaw (Plugin)
pip install neural-memory && npm install -g neuralmemory
Set memory slot in ~/.openclaw/openclaw.json:
{ "plugins": { "slots": { "memory": "neuralmemory" } } }
Upgrade to Pro
Already using Neural Memory? Just activate your key:
nmem shared activate --key NM-PRO-XXXX-XXXX-XXXX # activate license
Then enable InfinityDB (semantic search engine):
# ~/.neuralmemory/config.toml
storage_backend = "infinitydb"
Restart your MCP server. Existing memories are auto-migrated from SQLite to InfinityDB on first startup.
Installation extras
pip install neural-memory[server] # FastAPI server + dashboard
pip install neural-memory[extract] # PDF/DOCX/PPTX/HTML/XLSX extraction
pip install neural-memory[nlp-vi] # Vietnamese NLP
pip install neural-memory[embeddings] # Local embedding models
pip install neural-memory[embeddings-openai] # OpenAI embeddings
pip install neural-memory[all] # Everything
Benchmarks vs alternatives
| Metric | NeuralMemory | Mem0 | Cognee |
|---|---|---|---|
| Write 50 memories | 1.2s | 148.2s (121x slower) | 290.6s (80x slower) |
| Read 20 queries | 1.8s | 2.9s | 34.6s |
| API calls | 0 | 70 | 149 |
Zero LLM calls, zero API cost. Full benchmarks →
Documentation
| Guide | Description |
|---|---|
| Quickstart Guide | Interactive guide with animated demos |
| Pro Quickstart | Get started with Pro features |
| CLI Reference | All 82 CLI commands |
| MCP Tools Reference | All 63 MCP tools with parameters |
| Cloud Sync | Multi-device sync setup |
| Brain Health Guide | Understanding and improving brain health |
| Embedding Setup | Configure embedding providers |
| Architecture | Technical design deep-dive |
Development
git clone https://github.com/nhadaututtheky/neural-memory
cd neural-memory && pip install -e ".[dev]"
nmem doctor --dev # Verify contributor setup
pytest tests/ -v # 7600+ tests
ruff check src/ tests/ # Lint
See CONTRIBUTING.md for guidelines.
Support
If Neural Memory helps your AI agent remember, please consider giving it a star — it helps others discover the project and keeps development going.
You can also sponsor the project.
License
MIT — see LICENSE.
1 maintainer
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