AI Memory for Healthcare Agents
Healthcare AI that remembers patient context, medical history, and care preferences across every interaction.
The challenge
Repeated intake questions
Patients describe their history, medications, and symptoms every time they interact with the AI assistant.
No care continuity
AI health assistants don't track conversations over time — missing patterns in symptoms, mood, or behavior.
Generic health advice
Without patient context, AI gives generic recommendations instead of personalized guidance based on history.
Data sovereignty concerns
Healthcare data must stay within controlled environments. Cloud-only solutions don't meet compliance needs.
How Mengram solves it
Patient context
Semantic memory stores patient preferences, conditions, and care notes. Always available for personalized interactions.
Interaction history
Episodic memory tracks symptom reports, mood changes, and care interactions over time — surfacing patterns.
Care workflows
Procedural memory captures proven care pathways and follow-up procedures that improve with each patient interaction.
Self-hostable
Deploy Mengram on your own infrastructure. All memory stays within your data boundary. MIT licensed.
Quick implementation
from mengram import Mengram
# Self-hosted for data sovereignty
m = Mengram(base_url="https://your-mengram.internal.com")
def patient_interaction(patient_id: str, message: str):
# Full patient context in one call
profile = m.profile(user_id=patient_id)
# "Patient is managing Type 2 diabetes. Prefers morning check-ins.
# Last reported A1C: 7.2%. Current medications: metformin.
# Last visit: discussed increasing exercise routine."
# Search for relevant history
history = m.search(message, user_id=patient_id)
# After interaction, store for continuity
m.add(f"Patient reported: {message}", user_id=patient_id)
Results
100%
Context retention
Self-host
Data sovereignty
HIPAA
Ready architecture
Build healthcare agents with memory
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