MemantoMemory that AI agents love.
Open-source agentic memory built on Moorcheh.
13 typed memory categories · deploys with Moorcheh in your VPC
At a glance
Memanto agent quick-demo → Moorcheh namespace memanto_agent_quick-demo
LLMs forget between sessions
LLMs inherently lack persistent memory. Agents need a way to remember context, preferences, and decisions across conversations, sessions, and workflows.
- Remember user preferences across sessions
- Track decisions and commitments made during conversations
- Store facts and context for faster, smarter responses
- Manage long-running workflows with consistent state
- Learn from interactions over time
Without Memanto
User: “Set theme to dark mode.”
— new session —
User: “Use my preferred theme.”
Agent: “Which theme do you prefer?”
With Memanto
recall → preference: dark mode
Agent: “Applying dark mode from your saved preference.”
State-of-the-art memory accuracy
Evaluated on long-horizon agent benchmarks against leading memory systems.
LongMemEval
500 samplesMulti-session recall over long conversation horizons.
LoCoMo
10 samplesLong-context conversational memory accuracy.
Evaluation playground
Test the benchmarks yourself
Run the same LongMem and LoCoMo questions used in our published results. Pick a ground-truth prompt, send it through Memanto with your model, and score the answer with an LLM judge.
01
Pick a question
Choose from LongMem or LoCoMo ground-truth benchmarks.
02
Run inference
Query Memanto with your LLM and API key — nothing stored locally.
03
Judge accuracy
A secondary LLM scores the answer against ground truth.
Six gaps Memanto closes
Each gap maps to a production principle — built from real agent failure modes, not theoretical RAG limitations.
The problem
Irrelevant memory dumps
Memory arrives as one giant blob dumped into context. The agent cannot search, filter, or pull only what matters for the task.
Memanto closes it
Relevant results only
Instead of overloading your agent with data, Memanto finds only the exact information needed for the current task.
The problem
Outdated memories
A preference from six months ago carries the same weight as a deadline from yesterday. No timestamps, no recency signals.
Memanto closes it
Prioritizes new info
Memanto automatically keeps memories up to date by making sure new information always outranks older, stale notes.
The problem
Unknown memory sources
The agent cannot tell facts you stated from inferences it made, or data that has simply gone stale.
Memanto closes it
Verifiable sources
Memanto tracks where every memory came from, so your agent knows if a fact was told by you or just inferred from context.
The problem
All memories grouped together
Facts, habits, events, and instructions sit in one pile — no type labels, hierarchy, or filtering.
Memanto closes it
Smartly categorized
We organize memories into 13 clear types like facts and preferences, so agents only search for what is actually useful.
The problem
Memory contradiction
New information that conflicts with old memory is never resolved. Both versions live on side by side.
Memanto closes it
Resolves contradictions
Memanto catches and flags conflicting information immediately, preventing your agent from storing two different versions of the truth.
The problem
Long overhead ingestion
Traditional RAG needs indexing pipelines and latency before a memory is actually available for recall.
Memanto closes it
Instant memory ingestion
New memories are ready to use the second they are written. There is no waiting for indexing, and recall happens in less than 90ms.
Episodic, semantic, and procedural memory are no longer collapsed into one undifferentiated blob. Typed categories mean cleaner retrieval, better conflict detection, and controllable filtering.
Results are backed by peer-reviewed research — Memanto outperforms Mem0, Zep, and Letta on long-horizon agent memory benchmarks.
Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents