Topic
AI Agent Memory Systems
13 posts on ai agent memory systems — part of ai agents & automation on the n4n AI blog.
Working memory vs context window: what's the difference
A practical comparison of working memory vs context window for LLM agents: capabilities, cost, latency, ergonomics, and limits, with a clear verdict for engineers.
Why AI agents forget context in long conversations
Analyzes why an AI agent forgets context in long conversations: truncation, summarization loss, retrieval gaps, and cost tradeoffs, with engineering fixes.
What is episodic memory in AI agents
Episodic memory in AI agents stores timestamped interaction records, enabling context retrieval and learning from past sessions. Technical explainer.
Vector databases vs knowledge graphs for agent memory
A practical engineer's comparison of vector database vs knowledge graph memory for AI agents: capabilities, cost, latency, ergonomics, and verdict.
Memory summarization strategies for long-running agents
A practical guide to agent memory summarization for long-running agents: strategies, code, tradeoffs, and pitfalls for engineers building LLM systems.
Memory retrieval latency: what it costs your agent
Agent memory retrieval latency silently taxes multi-step LLM agents. This analysis breaks down where milliseconds go and how to budget for them.
How to prevent memory poisoning in AI agents
Practical steps to stop AI agent memory poisoning: isolate writes, validate provenance, use signed entries, and monitor drift in production agent systems.
How to evaluate AI agent memory systems
Practical steps to evaluate AI agent memory systems with reproducible tests, contrastive datasets, and metrics for recall, leakage, and cost.
How to build agent memory with pgvector
Step-by-step tutorial for building durable agent memory pgvector in Postgres: schema, embeddings, similarity search, and a minimal agent loop with code.
How to add persistent memory to a chatbot agent
Build a persistent memory chatbot agent with external storage and retrieval. Step-by-step Python implementation using embeddings and Postgres.
How AI agents decide what to remember
A technical analysis of AI agent memory selection: how systems choose what to persist, retrieval strategies, tradeoffs, and practical implementation patterns.
Short-term vs long-term memory in AI agents
Compare short-term vs long-term agent memory across cost, latency, ergonomics, and limits, with a table and verdict for engineers building LLM agents.
How to design a memory system for AI agents
A practical guide for engineers to design AI agent memory system with tiered storage, explicit write paths, retrieval, and summarization loops for production agents.
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