Add temporal context memory to agent workflows with Zep
Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.
npx skills add agentskillexchange/skills --skill add-temporal-context-memory-to-agent-workflows-with-zep
Use this skill when an agent workflow needs durable, relationship-aware context instead of relying only on the model prompt, local chat history, or a generic vector store. The operator configures Zep Cloud or the Zep SDKs, sends chat messages, business events, and document context into Zep as the workflow runs, then retrieves pre-formatted context blocks for the agent before it responds or takes action.
What this skill actually does
Invoke this for production agent workflows where memory must survive sessions, reflect changing facts over time, or be shared across LangChain, LlamaIndex, AutoGen, or custom agent runtimes. The boundary is the agent memory loop: ingest context, retrieve assembled context, inspect what was used, and pass that context to the downstream agent. It is not a general Zep platform administration card or a generic SDK listing.
Inputs and prerequisites: Zep Cloud account; Python SDK zep-cloud or TypeScript SDK @getzep/zep-cloud; agent runtime such as LangChain, LlamaIndex, AutoGen, or a custom agent service.
Setup notes: Install the SDK for the target workflow, for example `pip install zep-cloud` or `npm install @getzep/zep-cloud`, configure a Zep API key, write agent messages and events to Zep, and retrieve assembled context before model calls.
Source and verification boundary: use https://help.getzep.com as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.