Introduction

Memorer is a knowledge graph memory layer for AI applications. It lets your AI remember users, learn from conversations, and build structured knowledge over time.

What is Memorer?

Most AI applications are stateless — they forget everything between sessions. Memorer changes that by providing:

Persistent memory — Store and recall information across conversations
Knowledge graphs — Automatically extract entities and relationships from text
Semantic search — Find relevant memories using natural language queries
Conversation tracking — Maintain context within and across sessions
Memory consolidation — Automatically organize and deduplicate knowledge

How it works


from memorer import Memorer
 
client = Memorer(api_key="mem_sk_...")
user = client.for_user("user-123")
 
# Store a memory — returns IngestResponse with extraction counts
user.remember("The user prefers dark mode and uses VS Code")
 
# Recall relevant memories — returns QueryResponse with context string
results = user.recall("What editor does the user prefer?")
print(results.context)
# "The user prefers dark mode and uses VS Code"

The SDK is synchronous — no await needed.

Key concepts

Concept	Description
Memories	Individual pieces of stored knowledge (direct, derived, or inferred)
Entities	Named things extracted from memories (people, places, preferences)
Relationships	Connections between entities in the knowledge graph
Conversations	Sessions that combine short-term context with long-term memory
Consolidation	Automatic cleanup that merges duplicates and removes stale data

Next steps

Getting started — Install the SDK and store your first memory
Concepts — Understand the architecture
SDK Reference — Full API documentation
Integrations — Use with OpenAI, Anthropic, or LangChain