Architecting Agent Memory: Principles, Patterns, and Best Practices
summary
Architecture advice and concrete components for agent memory: a modular lifecycle of generation, storage, retrieval, integration, updating, forgetting implemented as distinct stores — persona, conversation, workflow, episodic, long‑term, toolbox and agent registry — backed by MongoDB as the memory provider that supports vector, text, graph and geospatial queries in one place. Practical patterns include storing JSON tool schemas in the DB and fetching relevant ones (OpenAI guidance: 10 to 21 tools in context), using embeddings plus re‑rankers to cut hallucination, and an experimental Memoriz library for patterns; prototypes are working but still experimental.
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chapters (12)
- 0:00Introduction and promise
- 1:51Evolution of AI systems
- 2:56What is an AI agent
- 3:57Why memory matters
- 6:03Agent memory fundamentals
- 7:29Memory management components
- 8:35MongoDB as memory provider
- 9:38Types of agent memory
- 13:32Memory tools and solutions
- 14:39MongoDB’s AI vision
- 15:41Neuroscience and AI inspiration
- 17:06Closing and Q&A
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