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neural-memory

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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.

240 stars PythonOthers Updated Aug 30, 2026

Documentation

NeuralMemory

GitHub stars
PyPI
Downloads
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Python 3.11+
License: MIT
VS Code
OpenClaw Plugin

Your AI agent forgets everything between sessions. Neural Memory gives it a brain.

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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.

bash
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 longer `neural-memory`). There is no `nm` binary.


3 Tools. That's It.

63 MCP tools are available, but you only need three:

ToolWhat 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.

> All 63 MCP tools →


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:

code
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
RetrievalSimilarity scoreGraph traversal
RelationshipsNone24 explicit types
LLM requiredYes (embedding)No — fully offline
Multi-hop reasoningMultiple queriesOne traversal
Memory lifecycleStaticDecay, 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.

code
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.

bash
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.

> Cloud Sync setup guide →


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_causal` exposes `temporal_range` and `temporal_neighborhood` actions; 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 `.db` files to Telegram for offsite backup

Quick Examples

bash
# 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
python
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.

code
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)
RecallKeyword match (FTS5)Semantic similarity (HNSW)
Speed at 1M neurons~500ms** Pro quickstart → · Full comparison → · Pricing →

Setup by Tool

Claude Code (Plugin)

bash
/plugin marketplace add nhadaututtheky/neural-memory
/plugin install neural-memory@neural-memory-marketplace

Cursor / Windsurf / Other MCP Clients

bash
pip install neural-memory

Add to your editor's MCP config:

json
{
  "mcpServers": {
    "neural-memory": { "command": "nmem-mcp" }
  }
}

OpenClaw (Skill or Plugin)

Skill — one click via ClawHub. Published on every release:

clawhub.ai/skills/neural-memory

Plugin — memory slot replacement. Use this if you want NeuralMemory to *be*

OpenClaw's memory provider rather than a skill it calls:

bash
pip install neural-memory && npm install -g neuralmemory

Set memory slot in `~/.openclaw/openclaw.json`:

json
{ "plugins": { "slots": { "memory": "neuralmemory" } } }

Upgrade to Pro

Already using Neural Memory? Just activate your key:

bash
nmem shared activate --key NM-PRO-XXXX-XXXX-XXXX   # activate license

Then enable InfinityDB (semantic search engine):

toml
# ~/.neuralmemory/config.toml
storage_backend = "infinitydb"

Restart your MCP server. Existing memories are auto-migrated from SQLite to InfinityDB on first startup.

> Get a license → · Pro quickstart →

Installation extras

bash
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

MetricNeuralMemoryMem0Cognee
Write 50 memories1.2s148.2s (121x slower)290.6s (80x slower)
Read 20 queries1.8s2.9s34.6s
API calls070149

Zero LLM calls, zero API cost. Full benchmarks → ·

Cognitive Efficiency release evidence →


Documentation

GuideDescription
Quickstart GuideInteractive guide with animated demos
Pro QuickstartGet started with Pro features
CLI ReferenceAll 82 CLI commands
MCP Tools ReferenceAll 63 MCP tools with parameters
Cloud SyncMulti-device sync setup
Brain Health GuideUnderstanding and improving brain health
Embedding SetupConfigure embedding providers
ArchitectureTechnical design deep-dive

Development

bash
git clone https://github.com/nhadaututtheky/neural-memory
cd neural-memory && pip install -e ".[dev]"
nmem doctor --dev        # Verify contributor setup
pytest tests/ -v          # 7800+ 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.

Frequently asked questions

What is neural-memory?

neural-memory is 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.

How do I install neural-memory?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is neural-memory open source?

Yes — it is hosted on GitHub at https://github.com/nhadaututtheky/neural-memory and has 240 stars.

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