Borrowing it
Nothing to install: this file belongs to AgentEvalHQ/AgentEval. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AgentEvalHQ/AgentEval/main/.github/instructions/memory-benchmarks.instructions.mdgit clone --depth 1 https://github.com/AgentEvalHQ/AgentEvalWrote 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/instructions/agentevalhq/agenteval/memory-benchmarks)<a href="https://agentmods.dev/instructions/agentevalhq/agenteval/memory-benchmarks"><img src="https://agentmods.dev/badge/instructions/agentevalhq/agenteval/memory-benchmarks/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/instructions/agentevalhq/agenteval/memory-benchmarks"><img src="https://agentmods.dev/badge/instructions/agentevalhq/agenteval/memory-benchmarks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.04162 | $0.04162 |
| Opus 5.5 | $0.01665 | $0.01665 |
| Sonnet 5.5 | $0.00832 | $0.00832 |
| Haiku 4.5 | $0.00416 | $0.00416 |
Grade A, and why
AgentEval memory-benchmarks.instructions.md 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 26d 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 — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentEval Memory & Benchmarks — Agent Instructions
Module Overview
The AgentEval.Memory module provides comprehensive memory evaluation for AI agents:
- Native benchmarks (12 scenario types across 5 presets)
- External benchmarks (LongMemEval — ICLR 2025, 500 questions, 6 types)
- Baseline persistence with JSON file storage
- Interactive HTML reports with pentagon/radar charts, timeline, and comparison
- History injection modes (Auto, Structured, TextBlob)
Architecture
src/AgentEval.Memory/
├── Abstractions/ Interfaces for memory operations
├── Assertions/ Memory-specific fluent assertion API
├── Data/ Embedded datasets, LongMemEval data files
├── DataLoading/ Scenario & corpus loaders, exporters
├── Engine/ Core: MemoryTestRunner, MemoryJudge
├── Evaluators/ MemoryBenchmarkRunner, ReachBack, Reducer, CrossSession evaluators
├── Extensions/ DI registration, helper methods
├── External/ External benchmarks (LongMemEval), interfaces
├── Metrics/ Memory-specific evaluation metrics
├── Models/ Core data: Benchmark, Result, Baseline, Config
├── Report/ HTML report template, pentagon mapper
├── Reporting/ Baseline store, comparer, output formatting
├── Scenarios/ Scenario providers: Memory, Chatty, Temporal, CrossSession
└── Temporal/ Temporal memory runner & scenarios
CRITICAL: Never Interrupt Running Benchmarks
NEVER kill, cancel, or interrupt a benchmark that is in progress. LLM benchmarks make hundreds of API calls and can take 10-100+ minutes to complete. Killing a benchmark wastes all progress, API costs, and time.
- If you need to add console output, logging, or cosmetic changes — wait for the benchmark to finish first, then make changes and re-run.
- If a benchmark appears to produce no output, it may still be running. Check the terminal for signs of life (CPU usage, network activity). The runner logs progress via
Console.WriteLineafter each question. - If you want to add progress reporting to a runner that lacks it, wait for the current run to complete.
- The only valid reason to interrupt is an explicit user request or an unrecoverable crash.
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.
- 26d ago Changed · +1 lines · +88 tokens per session 1c888f47b794
- 2mo ago First seen · 362 lines · 4,074 tokens per session scan A c109247199bf
AgentEval memory-benchmarks.instructions.md is an instructions file published in the GitHub repository AgentEvalHQ/AgentEval (157 stars, last pushed 4d ago), licensed MIT. It adds 4,162 tokens to every session, about $0.0166 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.