Developer Guide¶
Learn how to integrate memory into your AI applications. This guide covers integration patterns, memory types, extraction strategies, and production considerations.
Core Concepts¶
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🔄 Memory Integration Patterns
Three patterns for using memory: LLM-driven, code-driven, and background extraction
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📝 Working Memory
Session-scoped storage for active conversation state
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🧠 Long-term Memory
Persistent, cross-session storage for knowledge that should be retained
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🎯 Memory Extraction Strategies
Configure how memories are extracted: discrete, summary, preferences, or custom
Additional Topics¶
| Topic | Description |
|---|---|
| Memory Lifecycle | How memories are created, updated, and managed over time |
| Vector Store Backends | Configure Redis, Pinecone, Chroma, or other backends |
| AWS Bedrock | Using AWS Bedrock for embeddings and generation |
| Authentication | OAuth2/JWT and token-based authentication |
| Security | Security considerations for custom prompts |
Where to Start¶
Building a chatbot? Start with Memory Integration Patterns to understand your options.
Need to understand the data model? Read Working Memory and Long-term Memory.
Configuring extraction behavior? See Memory Extraction Strategies.