Every AI agent needs memory.
None of them have the right database.
The way AI agents store and retrieve information is broken. Not because developers are doing it wrong — but because the databases they're using were never designed for this. Here's the honest case.
You're managing a memory system, not building one
The current approach: Redis for recent context, PostgreSQL for structured facts, Pinecone for semantic search. You wire them together yourself. You write the decay logic. You decide what to delete. You build the relevance scoring.
This is not building an AI application. This is operating a memory infrastructure. Every hour spent managing that infrastructure is an hour not spent on your actual product.
Relevance decays exponentially. Floor never reaches zero — memories persist at low priority, not deleted.
Old facts are drowning out new ones
In a traditional database, a fact stored six months ago has identical weight to one stored this morning. Your AI agent has no way to distinguish between them without you explicitly managing timestamps and relevance scores.
Ebbinghaus proved in 1885 that human memory decays exponentially — but retains a floor of relevant context. MuninnDB implements this same curve, continuously. Fresh memories are strong. Old ones fade. Nothing is ever fully lost.
Your agent is always behind
Traditional databases are passive. They wait to be queried. Your AI agent knows what it knows only when it asks. Between queries, new relevant information could appear — and your agent will miss it.
MuninnDB's semantic triggers flip this model. You subscribe to a context — "alert me if anything about user billing preferences becomes relevant" — and MuninnDB pushes a notification the moment it detects a match. Your agent gets critical context at the right moment, without polling.
// Subscribe once
mem.Subscribe(ctx, &muninn.TriggerRequest{
Context: "billing preferences",
Threshold: 0.8,
Callback: myHandler,
})
// Get pushed when relevant
// No polling. No cron jobs.
// MuninnDB finds you.Nothing else does this
These aren't missing features. These features didn't exist in a database before MuninnDB — because they were never designed for cognitive memory.
| Capability | PostgreSQL | Redis | Pinecone | Neo4j | ★ cognitive MuninnDB |
|---|---|---|---|---|---|
| Memory decay (Ebbinghaus) | ✗ | ✗ | ✗ | ✗ | ✓ |
| Hebbian auto-learning | ✗ | ✗ | ✗ | ✗ | ✓ |
| Semantic push triggers | ✗ | ✗ | ✗ | ✗ | ✓ |
| Bayesian confidence | ✗ | ✗ | ✗ | ✗ | ✓ |
| Full-text search (BM25) | ✓ | ✗ | ✗ | ✗ | ✓ |
| Vector / semantic search | ✗ | ✗ | ✓ | ✗ | ✓ |
| Graph traversal | ✗ | ✗ | ✗ | ✓ | ✓ |
| Zero external dependencies | ✗ | ✗ | ✗ | ✗ | ✓ |
| Single binary deploy | ✗ | ✓ | ✗ | ✗ | ✓ |
Comparisons reflect core native capabilities. PostgreSQL pgvector extension provides limited vector support but no cognitive primitives.
The bottom line
MuninnDB doesn't add cognitive features to a storage system. It starts from cognitive principles and implements storage to serve them. This difference — not a surface-level one — is what makes it the right database for AI applications.
Start in 5 minutes.
No ops. No dependencies.
Download a single binary, run it, and your AI agent has a cognitive memory system. When you're ready to scale, it scales with you.
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