NeuralMemory stores experiences as interconnected neurons and recalls them through spreading activation, mimicking how the human brain works. Instead of searching a database, memories are retrieved through associative recall - activating related concepts until the relevant memory emerges.
npx skills add https://github.com/nhadaututtheky/neural-memory --skill memory-intakeInstallieren Sie diesen Skill über die CLI und beginnen Sie mit der Verwendung des SKILL.md-Workflows in Ihrem Arbeitsbereich.
Your AI agent forgets everything between sessions. Neural Memory gives it a brain.
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.
60 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.
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 |
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.
.db files to Telegram for offsite backup# 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())
Free Neural Memory is complete — 60 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.
Query: "authentication improvements"
Free (FTS5): 2 results — exact matches only
Pro (HNSW): 7 results — includes "JWT rotation", "session hardening", "OAuth migration"
| 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) |
pip install neural-memory # Pro features included
nmem pro activate YOUR_LICENSE_KEY # activate license
nmem pro status # verify: Pro: Active
$9/mo — 30-day money-back guarantee. All free tools keep working. Downgrade anytime, keep your data.
/plugin marketplace add nhadaututtheky/neural-memory
/plugin install neural-memory@neural-memory-marketplace
pip install neural-memory
Add to your editor's MCP config:
{
"mcpServers": {
"neural-memory": { "command": "nmem-mcp" }
}
}
pip install neural-memory && npm install -g neuralmemory
Set memory slot in ~/.openclaw/openclaw.json:
{ "plugins": { "slots": { "memory": "neuralmemory" } } }
Already using Neural Memory? Just activate your key:
nmem pro activate YOUR_LICENSE_KEY # 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.
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
| 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 →
| Guide | Description |
|---|---|
| Quickstart Guide | Interactive guide with animated demos |
| Pro Quickstart | Get started with Pro features |
| CLI Reference | All 66 CLI commands |
| MCP Tools Reference | All 60 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 |
git clone https://github.com/nhadaututtheky/neural-memory
cd neural-memory && pip install -e ".[dev]"
pytest tests/ -v # 7100+ tests
ruff check src/ tests/ # Lint
See CONTRIBUTING.md for guidelines.
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.
MIT — see LICENSE.