A cognitive science-based memory framework for AI agents. Runs 100% local at $0 or with cloud APIs. Weibull forgetting, triple-path retrieval, 10-stage pipeline.
Other memory solutions store everything forever. Your agent drowns in noise. Context windows fill with stale facts. Retrieval degrades as data grows. The more your agent remembers, the worse it performs.
Mnemo models human memory: important memories consolidate, trivial ones fade, frequently recalled knowledge strengthens. Built on decades of cognitive science research, not naive vector search.
From raw conversation to durable, retrievable memory in milliseconds.
Every feature grounded in cognitive science and real-world agent workloads.
Stretched-exponential forgetting with tier-specific beta parameters. Memories fade naturally unless reinforced through recall.
Vector similarity, BM25 full-text, and knowledge graph traversal fused with Reciprocal Rank Fusion for robust recall.
Three-layer LLM detection pipeline. When facts conflict, old versions auto-expire and new truths consolidate.
Per-agent memory with configurable access rules. Each agent operates in its own namespace with controlled sharing.
Voyage rerank-2 precision ranking ensures the most relevant memories surface first, every time.
35 ablation tests prove every module earns its place. No cargo-cult features — each component has measurable, verified impact on retrieval quality.
Core is free (MIT). Pro unlocks production features. API costs are separate — you bring your own keys.
$0/mo API · 100% Offline
~$5/mo API · MIT License
~$45/mo API · MIT License
From $69/mo + API · Commercial
Start free with Core. Upgrade when you need production features.
For solo developers and side projects
For teams building production agents
For organizations with custom needs
Mnemo pricing covers software licensing only. Embedding, LLM, and rerank API costs are separate — you bring your own API keys. Run 100% locally with Ollama for $0 API cost, or use cloud providers like OpenAI and Voyage (~$5-45/mo depending on usage). Ecosystem: Vercel AI SDK, REST Server, Python SDK.
# Install
npm install @mnemoai/core
# Pull Ollama model
ollama pull bge-m3
# Done! Use preset in your code:
const mnemo = await createMnemo({
preset: 'ollama',
dbPath: './memory-db'
});# Install
npm install @mnemoai/core
# Auto-detect from env var
const mnemo = await createMnemo({
dbPath: './memory-db'
});
# Or use a preset
const mnemo = await createMnemo({
preset: 'openai',
dbPath: './memory-db'
});import { createMnemo } from '@mnemoai/core';
// Initialize — auto-detect, preset, or full config
const mnemo = await createMnemo({
preset: 'openai', // or 'ollama', 'voyage', 'jina'
dbPath: './memory-db',
});
// Store a memory
await mnemo.store({
text: 'User prefers dark mode and concise responses',
category: 'preference',
});
// Retrieve — decay, dedup, and ranking applied automatically
const memories = await mnemo.recall('user preferences');
console.log(memories);
// [{ text: "User prefers dark mode...", score: 0.94, category: "preference" }]How Mnemo compares to other AI memory frameworks on architecture and features.
| Feature | Mnemo | Mem0 | Zep | Letta |
|---|---|---|---|---|
| Weibull Decay | Yes | No | No | No |
| Triple-Path Retrieval | Vec+BM25+Graph | Vector only | Vector+temporal | Vector only |
| Contradiction Detection | 3-layer LLM | Basic | No | No |
| Cross-encoder Rerank | Voyage/Jina/Ollama | No | No | No |
| 100% Local ($0) | Ollama preset | No | No | Partial |
| Open Source | MIT | Paid ($99+) | Paid | Freemium |
| Preset Config (1 line) | 4 presets | No | No | No |