AI Assistant
Input
“I'm vegetarian, allergic to peanuts. Meetings before noon only. Use metric units.”
Extracted
3 beliefsVegetarian
Diet preference
97%
Peanut allergy
Locked — cannot override
100%
Before noon
Schedule preference
85%
AI that learns from its own mistakes.
Every AI today makes decisions it can't explain, repeats mistakes it should have learned from, and treats guesses the same as proven facts. Memgraph AI fixes all three.
Memory · Just now
“I'm allergic to peanuts, and I'm a vegetarian.”
health_allergy_peanutsfood_preference_vegetarian+2 beliefs saved
Decision #847
SUCCESS“Which restaurant should I visit tonight?”
3 memories used·76% confidence
Trust Score
89%+4%
Food & Dietary96%
Restaurant Recommendations89%
Tax Advice44%
847 verified decisions · improving
How it works
Every mistake makes it smarter.
One loop. No retraining required.
01
Capture
Conversations become structured beliefs with confidence scores. Not flat text in a vector store.
02
Decide
Full reasoning chain recorded. Which beliefs were used, how confident, and why.
03
Record
One API call. Did it work? Patient recovered. Deal closed. Recommendation failed.
04
Improve
Right beliefs get stronger. Wrong ones get weaker. Same mistake never happens twice.
Every outcome sharpens the next decision. The loop runs itself.
Capabilities
Storage is easy. Intelligence is hard.
We built both.
TYPED
Structured Beliefs
Typed knowledge with confidence scores. Not loose text in a vector store.
TRACEABLE
Decision Traces
Which beliefs were used. Why it chose that answer. Full audit trail.
CLOSED LOOP
Outcome Learning
Right answers reinforce beliefs. Wrong ones weaken them. Automatic.
AUTO-DETECT
Contradiction Alerts
New info conflicts with existing knowledge? Flagged instantly. No hallucination.
4 DOMAINS
Domain Profiles
Finance, healthcare, legal, agriculture. Pre-configured and ready.
BYOK
Any Model, Any Provider
OpenAI, Anthropic, Google, Ollama, self-hosted. Your keys. No lock-in.
Comparison
They store. We think.
Five capabilities nobody else ships.
| Feature |  |  Mem0 |  Zep |  |  |
|---|---|---|---|---|---|
| ●Typed beliefs (fact / belief / tenet) | ✓ | – | – | – | – |
| ●Confidence decay (Ebbinghaus curve) | ✓ | – | – | – | – |
| ●Decision reasoning traces | ✓ | – | – | – | – |
| ●Automatic contradiction detection | ✓ | – | – | – | – |
| ●Outcome feedback → belief adjustment | ✓ | – | – | – | – |
| Background consolidation (dreaming) | ✓ | – | – | – | ✓ |
| Multi-LLM support (BYOK) | ✓ | ✓ | ✓ | ✓ | – |
| Multi-tenant isolation | ✓ | ✓ | ✓ | – | – |
| Self-hostable | ✓ | ✓ | ✓ | ✓ | – |
| MCP server integration | ✓ | – | ✓ | – | – |
The first five rows are unique to Memgraph AI. No other memory system ships them.
See it work
One conversation in. Structured knowledge out.
Trust Score
89% on food. 44% on taxes. Now you know.
Trust scores from real outcomes. By category. Updated with every decision.
Accuracy over 847 decisionsLIVE
Risk Assessment92%
Product Recs78%
Tax Advice44%
Scheduling88%
89% on risk — 44% on tax adviceEXPLORE
By category
Food, finance, scheduling — see accuracy for each.
Trends
Declining before users notice. Improving as it learns.
Root cause
Wrong decision? Trace it to the exact belief.
Fix the source
Know which beliefs to update. Not symptoms. Source.
Minutes to set up. Gets sharper with every outcome.
SEE TRUST SCOREIntegration
Three calls. That's the whole integration.
Recall. Decide. Record. Works with any LLM.
01
client.recall()Right beliefs, scored by relevance. Clean context, not a token dump.
02
your_llm.chat()Any model. OpenAI, Anthropic, Ollama, yours. We stay out of the way.
03
client.record_outcome()Did it work? One call. Beliefs adjust automatically.
Frequently Asked Questions
Stop shipping AI that can't explain itself.
Free and open source. Three lines of Python. Your agent remembers its first conversation today.
pip install memgraph-sdk