Conversation Memory
Build AI assistants that remember context and maintain coherent, multi-turn conversations.
The Challenge
❌ Without Conversation Memory:
- • Users repeat themselves every message
- • AI forgets context mid-conversation
- • Follow-up questions fail completely
- • Frustrating, robotic interactions
✓ With RAG Engine Memory:
- • Full conversation history retained
- • Natural follow-up questions work
- • Context builds across turns
- • Human-like conversation flow
See It In Action
Notice how the AI understands context from previous messages
Example 1
Example 2
How It Works
Intelligent context management for natural dialogue
Context Retention
AI remembers the full conversation history, enabling natural follow-up questions without repetition.
Multi-Turn Dialogue
Support complex, multi-step conversations that build on previous exchanges.
Session Management
Automatically manage conversation sessions with configurable memory windows.
Persistent Memory
Optionally persist conversations across sessions for returning users.
Use Cases
Customer Support
Handle complex support tickets with context-aware conversations.
User Onboarding
Guide users through setup processes step by step.
Research Assistant
Support iterative research with building context.
Sales Conversations
Maintain context across sales qualification flows.
Comparison
True conversation memory sets us apart
| Feature | RAG Engine | LangChain | LlamaIndex | Pinecone |
|---|---|---|---|---|
| Multi-turn context | ✓ | ~ | ~ | ✗ |
| Automatic summarization | ✓ | ✗ | ✗ | ✗ |
| Cross-session memory | ✓ | ✗ | ✗ | ✗ |
| Token-efficient context | ✓ | ~ | ~ | ✗ |
| Memory analytics | ✓ | ✗ | ✗ | ✗ |
Works Great With
Ready to Get Started?
Build smarter AI assistants with conversation memory today.
Get Started Free