Guide developers in building Zep apps with Context Graphs for agent memory.
npx skills add https://github.com/getzep/zep --skill building-with-zep
Please help me install this Agent Skill. Skill: building-with-zep Source: https://github.com/getzep/zep/tree/main/plugins/building-with-zep/skills/building-with-zep Command: npx skills add https://github.com/getzep/zep --skill building-with-zep
This guide helps software engineers design and configure applications that use Zep for agent memory, enabling low-latency retrieval, scalable graphs, and governance.
Start by creating a user, opening a thread, ingesting sample data, and retrieving a Context Block to validate the integration.
High-intent search queries and answers about installing and using this skill.
Agent memory context graphs support ingesting multi-source data by integrating chat, business, and document inputs into a unified graph structure. You configure cross-source ingestion pipelines to populate the graph for downstream retrieval.
Ontology customization for agent memory involves defining specific entity and edge types within your context graph. You establish custom instructions and user summaries to tailor how the system extracts and retrieves relevant data.
Agent memory retrieval supports low-latency requirements across multi-user graphs. The architecture is designed to scale while maintaining fast retrieval speeds for context blocks needed in prompts.
Benchmarking agent memory retrieval requires measuring latency and recall metrics using evaluation harnesses. You start with a simple configuration, measure baseline performance, and iterate based on evaluation results.
Custom instructions enable governance over agent memory extraction and retrieval processes. You define rules that control how entities, edges, and user summaries are processed within the context graph framework.
Integrating Zep for agent memory requires creating a user, opening a thread, ingesting sample data, and retrieving a context block. This validates the end-to-end integration before full deployment.
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