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Keywords: agent memory, graph database, GenAI, LangGraph, Neo4j
Overview:
This article explores how graph databases, specifically Neo4j, can be used to model different types of agent memory in AI systems. It draws inspiration from Harrison Chase’s work on memory management in LangGraph, outlining how to implement and expand upon these ideas using a graph database. The article discusses short-term and long-term memory, focusing on semantic, episodic, procedural, and temporal memory types. It provides conceptual data models and examples of how these memory types can be implemented and updated in a graph database to improve agent performance and knowledge retention. The author emphasizes the importance of thoughtful memory management as AI systems become more reliant on long-term memory.
Section Summaries:
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Short-Term Memory: Short-term memory is temporary and exists within a single conversation. It typically contains conversation history and doesn’t require an external database. Long conversations can cause performance issues due to high token counts, which can be mitigated by maintaining a message window or summarizing previous messages.
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Long-Term Memory: Long-term memory allows agents to access information from previous conversations, improving system performance. It is based on human memory and can be implemented in a graph database. The following memory types are based on our understanding of human memory and contain examples of how to implement it in a graph database.
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Writing Memory: Memory can be written in the hot path during runtime for immediate updates and transparency, or in the background for less frequent updates and de-duplication of work. Both methods are viable in managing agent memory, and their appropriate usage will be detailed for each memory type below.
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Semantic Memory: Semantic memory contains facts about the world, such as user information or documents used in a RAG pipeline. It requires proper maintenance and can change frequently, leading to complexity in managing updates. Semantic memory lends itself well to hot-path updating to prevent the agent from communicating out-of-date information.
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Episodic Memory: Episodic memory stores remembered experiences, such as event details and past agent actions, and is commonly used in few-shot prompting. Previous question-answer pairs are stored in the graph to provide examples in the prompt. Episodic memory is better written in the background with receipt of user feedback to prevent the inclusion of bad or unhelpful memories.
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Procedural Memory: Procedural memory stores how to do something, such as system prompts, tool descriptions, and instructions. It can be updated in a feedback loop from the end user or another system. Procedural memory also lends itself well to being written in the background once feedback is received.
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Temporal Memory: Temporal memory stores how data changes over time and can apply to any previously discussed type. It allows the agent to be aware of how things have changed. Temporal memory can be implemented by including timestamps on relationships or implementing a PREVIOUS relationship between nodes to maintain versions.
Related Tools:
- Neo4j: https://neo4j.com/
- LangGraph: https://langchain-ai.github.io/langgraph/
References:
- LangGraph memory documentation: https://langchain-ai.github.io/langgraph/concepts/memory/
- GitHub repository with data models: https://github.com/a-s-g93/agentic-memory-article
- Original article on Medium: https://medium.com/neo4j/modeling-agent-memory-d3b6bc3bb9c4
Original Article Link: https://neo4j.com/blog/developer/modeling-agent-memory/
source: https://neo4j.com/blog/developer/modeling-agent-memory/