Use Cases

See how Tensorheart Memory powers real-world AI applications.

Why Memory Matters

Every AI application benefits from remembering context. Here's what memory enables:

Application	Without Memory	With Memory
Customer Support	"What's your order number?" (every time)	"I see your order #12345 from last week"
Personal Assistant	Generic suggestions	Personalized recommendations
Sales AI	Cold, scripted responses	Context-aware conversations
Code Assistant	Searches entire codebase	Finds relevant files instantly

Build support agents that remember customer history, past issues, and preferences.

Create assistants that learn user preferences, habits, and context over time.

Build coding assistants that understand your codebase and remember patterns.

Power sales conversations with CRM data, meeting notes, and customer insights.

These patterns work across all use cases:

# Before each response, fetch relevant user context
memories = memory.query(
    context=user_message,
    space_id=f"user_{user_id}"
)

# After each conversation, extract and save new information
memory.extract(
    content=conversation_transcript,
    content_type="conversation"
)

# Prioritize recent information
memories = memory.query(
    context=user_message,
    temporal_boost=True
)

Pick a use case above, or jump straight into building: