How To Configure AI Agent Memory In Dify: A Complete Guide

Dify’s memory system addresses the stateless nature of language models by providing conversational context and options for persistent user information. Its built-in short-term memory uses TokenBufferMemory to include recent messages in prompts, with configurable window sizes and backend limits of 2,000 tokens and 500 messages, while larger windows improve context at increased token cost. Long-term memory can be implemented through Knowledge Bases and vector databases for semantic retrieval across sessions, or through Conversation Variables for structured data such as preferences and accumulated facts. Configuration involves enabling memory on Agent or LLM nodes, selecting an appropriate context window, connecting user input and outputs in a Chatflow, and preserving the conversation_id across API calls to maintain continuity. The article also describes API-based testing and conversation-history retrieval, recommends moderate memory windows for support agents, and presents Mem0 as an external plugin-based alternative that simplifies persistent memory, storage, and semantic search for more complex applications.

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