getzep/zep is a repository of examples, framework integrations, ingestion tools, and evaluation utilities for building agent memory with Zep Cloud, a managed service for storing and retrieving information used by AI agents. It is for developers integrating Zep Cloud into agent applications and workflows. The catalogue skills and MCP entry are add-ons for working with that ecosystem.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add getzep/zep --skill zep-eval-harnessgit clone --depth 1 https://github.com/getzep/zepWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/getzep/zep/zep-eval-harness)<a href="https://agentmods.dev/skills/getzep/zep/zep-eval-harness"><img src="https://agentmods.dev/badge/skills/getzep/zep/zep-eval-harness/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/getzep/zep/zep-eval-harness"><img src="https://agentmods.dev/badge/skills/getzep/zep/zep-eval-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00139 | $0.03105 |
| Opus 5.5 | $0.00056 | $0.01242 |
| Sonnet 5.5 | $0.00028 | $0.00621 |
| Haiku 4.5 | $0.00014 | $0.00311 |
Grade A, and why
zep-eval-harness scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Zep Eval Harness
An end-to-end evaluation framework for testing Zep's memory retrieval and question-answering capabilities. The pipeline ingests user conversations, telemetry, and documents into Zep knowledge graphs, then evaluates retrieval quality by searching those graphs with test questions and grading the results against golden answers.
The pipeline has four steps:
- Chunk documents — split documents and generate LLM-based summaries + contextualizations
- Ingest users — create Zep users, add conversations and telemetry to user graphs
- Ingest documents — send pre-chunked documents to a standalone Zep document graph
- Evaluate — for each test case: search graphs → assess context completeness → generate LLM response → grade answer accuracy
Scope: Single-Shot Retrieval
The harness evaluates single-shot retrieval only. Every test case issues one retrieval from the raw test question through the context endpoint (config/evaluation_config/retrieval_strategy.py), then hands the resulting context block to the response model in a single turn — no second retrieval round, no query reformulation. This mirrors deterministic/programmatic retrieval, not the tool-based pattern where an agent is handed Zep search tools (e.g. search_graph from the Zep MCP server) and decides when and what to search.
That makes the harness a clean instrument for the ingestion and search configuration, but it says nothing about agent tool-use behavior. A tool-based agent may do better (several targeted searches, reformulating after a weak result) or worse (never searching, poorly phrased queries, running out of turns). When reporting results, scope conclusions to the config under test and never present them as a prediction of production agent performance.
Evaluation Metrics
Context Completeness (PRIMARY) — Did Zep retrieve sufficient information to answer the question?
- COMPLETE: All necessary information present in the retrieved context
- PARTIAL: Some relevant information, but incomplete
- INSUFFICIENT: Missing critical information
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago Changed 59bb9070704a
- 1mo ago First seen · 228 lines · 139 tokens per session scan A c79db57eff47
zep-eval-harness is a skill published in the GitHub repository getzep/zep (4,974 stars, last pushed 6d ago), licensed Apache-2.0. It adds 139 tokens to every session and 3,105 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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