Introduction

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What is MemContext?

MemContext is memory infrastructure for AI agents and applications. It provides a REST API and MCP server that allow AI systems to:

• Save durable user and project knowledge across sessions
• Isolate app users and tenants with hard memory containers via scope
• Search memories with natural-language questions and exact terms
• Evolve memories over time with automatic version chains
• Profile users with pre-aggregated context from their memory
• Ingest workspace documents through Context Vault for RAG-style AI context

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Who is it for?

MemContext serves three primary use cases:

1. Coding assistants that need stable preferences, decisions, and project context across sessions
2. Product applications that need evolving memory for content generation, support, onboarding, or personalization
3. Workspace AI tools that need to retrieve source passages and extracted facts from files, docs, and URLs

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The Three Retrieval Modes

MemContext supports three ways to retrieve context: This gives your app a clear choice. Use memories when you need concise facts. Use document chunks when you need source text. Use hybrid when you want the AI layer to receive both precise passages and clean extracted knowledge.

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Core primitives

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Why it exists

Simple retrieval is not enough for memory. Memory needs:

• Updates when reality changes
• History when strategy evolves
• Exact-match retrieval for product names, org names, and decisions
• Lightweight APIs other tools can build on

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Next steps