Memory that AI Agents Love!
An open source memory layer for building, scaling, and deploying AI agents with persistent semantic recall in production.
Collecting memanto...
Successfully installed memanto-2.1.4
> Agent namespace [dev-agent] created. [OK] Memory nodes are listening.
remember · recall · answer
How it works
AI AGENTS | TOOLS
Your agent ecosystem
MEMANTO
Memory Agent
Namespaces
Memory Engine
Agent sends memory
Your AI agent stores context, facts, and preferences to MEMANTO via CLI or API. A single command is all it takes.
Identifying the gaps in
AI agent memory
When asked what causes agent memory to fail, Claude pointed to passive, static context. We unpacked that insight into six core challenges that MEMANTO was specifically engineered to solve.
“My memory exists as a static snapshot injected into context — useful, but fundamentally passive. I can't query it, update it mid-conversation, express confidence levels, or distinguish between ‘I know this’ versus ‘I was told this once.’”
Six gaps we designed around
Hover or tap a row — the diagram highlights the same theme.
Static injection
Memory arrives as a blob injected into context — can't query by relevance or filter by the current task.
No temporal decay
No provenance
Flat memory
No writeback
No scoping
Why agents love MEMANTO
Six principles. Zero compromises. Built from the failure modes of every system that came before.
6 design principles
Queryable, not injectable
Agents query memory by relevance to the current task — not a static blob injected at conversation start.
Temporally aware with decay
Confidence & provenance
Typed & hierarchical
Contradiction aware
Zero overhead ingestion
Interactive Dashboard
Visualize insights, resolve memory conflicts, and run complex RAG entirely from a native UI.
Works with your entire AI stack
Connect your favorite AI assistant — or build a MEMANTO-powered agent with your favorite framework.
$memantoconnectclaude-codeMemantoClaw
Persistent, long-horizon memory for NemoClaw — bringing full MEMANTO memory capabilities natively into your agentic workflows.
- Built-in MEMANTO memory layer on NemoClaw agents
- Semantic retrieval across sessions with zero-cost ingestion
- Agentic calls powered by Moorcheh's native LLM — no extra API keys needed
- Open-source and self-hostable
Memanto vs the field
Most memory layers stop at remember + recall. Memanto adds answer — LLM-grounded responses directly from your agent's memory, with no extra API keys.
| Feature | Mem0 | Zep | Letta | LangMem | MemantoBest |
|---|---|---|---|---|---|
| RememberStore agent memories | |||||
| RecallSemantic search & retrieval | |||||
| AnswerMemanto onlyLLM-grounded response from memory | |||||
| Zero Ingestion LatencyMemories available instantly after write | |||||
| Conflict ResolutionAutomated contradiction detection | |||||
| Semantic Memory Types13 built-in memory categories | |||||
| Multi-Agent NamespacesIsolated memory per agent | |||||
| No External API KeyBuilt-in LLM proxy — zero setup |
Traditional DBs vs Memanto
Memanto's no-indexing architecture fundamentally changes the economics and speed of AI memory.
Powerful CLI Built-in
Manage agents, store memories, and run RAG directly from your terminal.
SOTA on Agentic Memory Benchmarks
Memanto leads across LoCoMo and LongMemEval — the two most rigorous long-context memory benchmarks for AI agents.
Read about Memanto architecture, benchmark methodology, and results.
Setup in under a minute
Watch a quick demo of installing MEMANTO, activating an agent, and storing your first memories.
Free to run. Cheap to scale.
MEMANTO is open source and costs nothing. The only spend is your Moorcheh API key — billed per operation, not per token. 500 free Compute Units, no card required.
MEMANTO
- 500 free Compute Units (~100,000 operations)
- Memory storage — always free
- Unlimited retention
- Up to 5 agents
- Agentic memory mode
- Built-in RAG with model selection
- Direct AI mode — drop-in LLM proxy
- Open source — self-host anytime
- No credit card required
How far do 500 Moorcheh credits go?
500 credits ≈ 100,000 operations. Here's what that looks like in practice.
The free tier is plenty for heavy development and testing.