Why Mnemo?

Every AI memory solution stores memories. Mnemo forgets intelligently.

Humans don't remember everything equally — important memories consolidate, trivial ones fade, frequently recalled knowledge strengthens. Mnemo models this with:

• Weibull decay — stretched-exponential forgetting: exp(-(t/λ)^β) with tier-specific β
• Triple-path retrieval — Vector + BM25 + Knowledge Graph fused with RRF
• Three-layer contradiction detection — regex signal → LLM 5-class → dedup pipeline
• 10-stage retrieval pipeline — from preprocessing to context injection

The result: your AI agent's memory stays relevant instead of drowning in noise.

Feature Highlights

| Capability | Core (Free) | Cloud | |:---|:---:|:---:| | Vector + BM25 + Knowledge Graph | ✅ | ✅ | | Weibull forgetting model | ✅ | ✅ | | Memory tiers (Core/Working/Peripheral) | ✅ | ✅ | | Cross-encoder rerank | ✅ | ✅ | | Contradiction detection | ✅ | ✅ | | Multi-backend (LanceDB, Qdrant, Chroma, PGVector) | ✅ | ✅ | | Scope isolation (multi-agent) | ✅ | ✅ | | $0 local deployment (Ollama) | ✅ | ✅ | | Cloud managed API + adaptive retrieval | — | ✅ (details) |

Architecture

  Store ──→ Embedding ──→ Vector DB (LanceDB / Qdrant / Chroma / PGVector)
                              │
  Recall ──→ Multi-path retrieval ──→ Rerank ──→ Decay ──→ Top-K results
                              │
  Lifecycle: Weibull decay + memory tiers + contradiction detection

Quick Start

Option 1: npm (simplest)

npm install @mnemoai/core

import { createMnemo } from '@mnemoai/core';

// Auto-detect: uses OPENAI_API_KEY from env
const mnemo = await createMnemo({ dbPath: './memory-db' });

// Or use a preset for Ollama ($0, fully local)
// const mnemo = await createMnemo({ preset: 'ollama', dbPath: './memory-db' });

// Store a memory
await mnemo.store({
  text: 'User prefers dark mode and minimal UI',
  category: 'preference',
  importance: 0.8,
});

// Recall — automatically applies decay, rerank, MMR
const results = await mnemo.recall('UI preferences', { limit: 5 });

Available presets: openai, ollama, voyage, jina — see docs

Option 2: Python

pip install mnemo-memory
npx @mnemoai/server   # start the REST API

from mnemo import MnemoClient

client = MnemoClient()
client.store("User prefers dark mode", category="preference")
results = client.recall("UI preferences")

Option 3: 100% Local ($0, no external API)

ollama pull bge-m3               # embedding
ollama pull qwen3:8b             # smart extraction LLM
ollama pull bge-reranker-v2-m3   # cross-encoder rerank

const mnemo = await createMnemo({ preset: 'ollama', dbPath: './memory-db' });

Full Core functionality — embedding, extraction, rerank — all running locally. Zero API cost.

Option 4: Docker (full stack)

git clone https://github.com/Methux/mnemo.git
cd mnemo
cp .env.example .env     # add your API keys
docker compose up -d     # starts Neo4j + Graphiti + Dashboard

Packages

| Package | Platform | Install | |:---|:---|:---| | @mnemoai/core | npm | npm install @mnemoai/core | | Mnemo Cloud | Managed API | Register at m-nemo.ai | | @mnemoai/server | npm | npx @mnemoai/server | | @mnemoai/vercel-ai | npm | npm install @mnemoai/vercel-ai | | mnemo-memory | PyPI | pip install mnemo-memory |

Core vs Cloud

Mnemo Core — Free, MIT License

The open-source foundation. Full retrieval engine, no restrictions.

| Feature | Details | |:---|:---| | Storage | Pluggable backend — LanceDB (default), Qdrant, Chroma, PGVector | | Retrieval | Triple-path (Vector + BM25 + Graphiti) with RRF fusion | | Rerank | Cross-encoder (configurable provider) | | Decay | Weibull stretched-exponential, tier-specific β | | Tiers | Core / Working / Peripheral — tier-specific parameters optimized through ablation testing | | Contradiction | Three-layer detection (regex + LLM + dedup) | | Extraction | Smart extraction (configurable LLM) | | Graph | Graphiti/Neo4j knowledge graph | | Scopes | Multi-agent isolation | | Noise filtering | Embedding-based noise bank + regex |

Mnemo Cloud

Everything in Core, plus adaptive intelligence and zero-ops hosting. Learn more →

Pricing

| Plan | Price | Description | |:---|:---|:---| | Core | Free forever | Self-hosted, MIT licensed, unlimited | | Cloud Free | $0 | Managed API — 1,000 memories, 5,000 recalls/mo | | Cloud Pro | Coming soon | Unlimited, priority support |

Try Mnemo Cloud →

API Configuration Guide

Mnemo is a framework — you bring your own models. Choose a setup that fits your budget:

| Setup | Embedding | LLM Extraction | Rerank | Est. API Cost | |:---|:---|:---|:---|:---:| | Local | Ollama bge-m3 | Ollama qwen3:8b | Ollama bge-reranker | $0/mo | | Hybrid | OpenAI text-embedding-3-small | GPT-4.1-mini | Jina reranker | ~$5/mo | | Cloud | Voyage voyage-4 | GPT-4.1 | Voyage rerank-2 | ~$45/mo |

These are your own API costs, not Mnemo subscription fees. All setups use the same Core/Cloud features — the difference is model quality.

Cognitive Science

Mnemo's design maps directly to established memory research:

| Human Memory | Mnemo | |:---|:---| | Ebbinghaus forgetting curve | Weibull decay model | | Core vs peripheral memory | Tier system with differential decay rates | | Interference / false memories | Deduplication + noise filtering | | Metamemory | mnemo-doctor + Web Dashboard |

Documentation

Full documentation at docs.m-nemo.ai

• Quick Start
• Local Setup ($0 Ollama)
• Configuration Reference
• Storage Backends
• Retrieval Pipeline
• API Reference

Tools

| Tool | Description | Run | |:---|:---|:---| | mnemo init | Interactive config wizard | npm run init | | mnemo-doctor | One-command health check | npm run doctor | | validate-config | Config validation gate | npm run validate | | Dashboard | Web UI for browsing, debugging, monitoring | http://localhost:18800 |

License

This project uses a dual-license model:

• MIT — Core framework (SPDX-License-Identifier: MIT)
• Commercial — Cloud features and advanced strategies

See LICENSE for details.

Contributing

We welcome contributions to Mnemo Core (MIT-licensed files). See CONTRIBUTING.md.

Areas where we'd love help:

• Benchmark evaluation (LOCOMO, MemBench)
• New storage adapters and embedding providers
• Retrieval pipeline optimizations
• Documentation and examples