Why MenteDB
The first memory system designed specifically for AI agents. Not a database with AI bolted on.
Feature Comparison
Three approaches, compared
How cognitive memory stacks up against flat key-value stores and vector-based retrieval.
| Feature | Flat Key-Value | Vector Store | MenteDB |
|---|---|---|---|
| Semantic search | |||
| Exact key lookup | |||
| Contradiction detection | |||
| Pain signal feedback | |||
| Knowledge gap detection | |||
| Temporal reasoning | |||
| Memory decay & consolidation | |||
| Causal graph traversal | |||
| Write inference | |||
| Multi-agent support | |||
| Token budget optimization | |||
| Speculative pre-assembly | |||
| Works offline (local mode) | |||
| Cloud sync across devices | |||
| MCP native | |||
| Single process_turn call |
How It Works
Three steps, one API call
Store
Every conversation turn is automatically analyzed. Facts, preferences, decisions, and corrections are extracted and stored with rich metadata.
Connect
Memories form a knowledge graph. Contradictions are detected. Pain signals are recorded. Relationships between facts are inferred automatically.
Recall
On each turn, the most relevant context is assembled in milliseconds. Pain warnings surface before mistakes repeat. Knowledge gaps are flagged.
What Makes It Different
Built for how agents actually think
Cognitive, Not Just Storage
MenteDB doesn't just store and retrieve. It reasons about memory — detecting contradictions, tracking what went wrong, and predicting what you'll need next.
One Tool Call
process_turn handles everything: storage, retrieval, extraction, contradiction detection, and context assembly. No complex pipelines to build.
Pain Signals
When something goes wrong, MenteDB remembers. Next time a similar situation arises, it warns the agent before the mistake repeats.
Works Everywhere
MCP-native. Works with GitHub Copilot, Claude, Cursor, and any MCP-compatible client. Local-first with optional cloud sync.