Conversational Agent Memory

Your chatbot forgets users between sessions. Mnemoverse fixes that.

The Problem

Every conversation starts from zero. Your agent asks "What's your name?" for the 10th time. Users hate it.

The Solution

Store user insights during conversation. Load them at the start of the next one. Per-user isolation via domains.

How It Works

from mnemoverse import MnemoClient

client = MnemoClient(api_key="mk_live_YOUR_KEY")

# During conversation — agent learns something about the user
client.write(
    "Alice prefers email notifications over Slack",
    concepts=["alice", "notifications", "email"],
    domain="user:alice"
)

client.write(
    "Alice is on the Pro plan, working on a trading bot",
    concepts=["alice", "plan", "trading"],
    domain="user:alice"
)

# Next session — agent loads context before responding
memories = client.read(
    "What do I know about Alice?",
    domain="user:alice",
    top_k=10
)

# Feed memories into LLM prompt as context
context = "\n".join([m.content for m in memories.items])

Architecture

User message → Agent
                ↓
         memory_read("user context", domain="user:{id}")
                ↓
         LLM generates response (with memory context)
                ↓
         memory_write(insights learned, domain="user:{id}")
                ↓
         Response → User

Per-User Isolation

Each user gets their own memory domain. Alice's memories never leak to Bob.

# Alice's conversation
client.write("Prefers dark mode", domain="user:alice")

# Bob's conversation  
client.write("Uses light mode, large fonts", domain="user:bob")

# Reading Alice's context — only gets Alice's memories
client.read("user preferences", domain="user:alice")
# → "Prefers dark mode"

Feedback Loop

When a memory helps the agent give a better answer, reinforce it:

memories = client.read("Alice's notification preferences")

# Agent used this memory and user was happy
client.feedback(
    atom_ids=[memories.items[0].atom_id],
    outcome=1.0  # Very helpful
)

Over time, useful memories surface first. Stale ones fade.

Use Cases

Scenario	What to Remember
Customer support	User's plan, past issues, preferred contact method
Personal assistant	Schedule preferences, dietary restrictions, travel habits
Tutoring bot	Student's level, topics covered, learning pace
Sales agent	Prospect's company, pain points, decision timeline
Health advisor	Conditions, medications, goals, doctor preferences

Compared to RAG

RAG retrieves from a static knowledge base. Mnemoverse remembers from conversations. RAG answers "what does the docs say?" — Mnemoverse answers "what did we discuss last time?"

They complement each other:

• RAG = product knowledge (docs, FAQ)
• Mnemoverse = user knowledge (preferences, history, context)

Get Started

1. Get an API key (free, 30 seconds)
2. pip install mnemoverse
3. Add write() after conversations, read() before them
4. That's it — your agent now remembers users