Step-by-step guide to integrating MemHouse with LangGraph and Claude to create support bots that remember customer preferences while adhering to strict privacy scoping.
Learn how to wire MemHouse into your existing Python agent pipeline using our official Python SDK or Model Context Protocol (MCP) server.
In this tutorial, we construct a support bot that automatically remembers customer preferences while keeping tenant data strictly separated.
How high-concurrency process architecture enables extreme throughput, zero locks, and fault-tolerant memory pipelines.
Why simple vector storage fails in enterprise agent deployments. A deep dive into Gate A / Gate B governance, fact lifecycle state machines, and zero cross-tenant bleeding.
MemHouse v0.4.0 introduces native support for short-lived worker swarms (LangGraph / CrewAI), Model Context Protocol (MCP) server endpoints, and zero-loss personal agent connectors for OpenClaw and Hermes Agent.