memory-integration

To maintain context across AI agent sessions, you need an integration that stores episodic memory for conversation recall and a knowledge graph for persistent facts. This ensures session continuity by capturing decisions and lessons learned for future retrieval.

A knowledge graph for persistent facts is a structured memory mechanism that stores entities like Projects, Issues, Decisions, Patterns, Problems, and Persons. It records their relationships to enable accurate factual recall during future AI agent interactions.

You can retrieve past debugging steps by searching the episodic memory using a relevant identifier, such as an issue number. The agent recalls previous decisions, lessons learned, and relevant code snippets from past sessions to avoid redundant effort.

You should update the knowledge base with mid-work learning continuously as new facts emerge. This involves storing new entities and relationships during the session, followed by a final knowledge base update when the session ends to ensure all context is preserved.

No, this memory integration has no external dependencies. It operates independently to manage session start context gathering, mid-work learning storage, and session end knowledge base updates without requiring additional packages or external libraries.

Episodic memory maintains conversation history and specific lessons learned for sequential recall, whereas a knowledge graph stores persistent factual entities and their structural relationships. Both work together to provide comprehensive context continuity across sessions.