LLM Evaluation System is an agent-guided platform for evaluating language models and agents, generating datasets and configuring multiple judges from natural-language requests before producing an analysis report. It is for comparing model responses, testing agents, and creating document-grounded evaluation data.
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 agentmods add instructions/awslabs/llm-evaluation-system/claude-mdgit clone --depth 1 https://github.com/awslabs/llm-evaluation-systemWrote 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/awslabs/llm-evaluation-system/claude-md)<a href="https://agentmods.dev/instructions/awslabs/llm-evaluation-system/claude-md"><img src="https://agentmods.dev/badge/instructions/awslabs/llm-evaluation-system/claude-md.svg" alt="Measured on agentmods" 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 | $0.07449 | $0.07449 |
| Opus 5 | $0.03725 | $0.03725 |
| Sonnet 5 | $0.01490 | $0.01490 |
| Haiku 4.5 | $0.00745 | $0.00745 |
Grade A, and why
llm-evaluation-system CLAUDE.md scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**Testing the web app for real** — pytest greens don't prove the web app works; exercise it against the live `make dev` stack in layers: (1) `pytest` for pure logic; (2) `curl`/`urllib` against `:4001` for `/api/*` route How it starts
The opening of the file, as written. The whole thing — 403 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This is the canonical agent-facing doc for the repo — Claude Code reads it directly, and AGENTS.md is a thin pointer back here so other tools that follow the agents.md convention (Codex, Cursor) land in the same place.
What's in this repo
Two deployables that share some code:
eval_mcp/— the MCP package published to PyPI asllm-evaluation-system(entry pointeval-mcp). Self-contained, no database, no web app. This is what 99% of users install.backend/+frontend/— the optional EKS web app (FastAPI chat + Vite/React UI + Cognito auth)../deploy.shis its entry point;make devruns it locally via Docker Compose.
frontend/ is a single Vite + React SPA (client-side routing via react-router). vite build produces a static bundle served two ways: bundled into eval_mcp/viewer_static/ for the MCP's local results viewer (npm run build:viewer), and served from a private S3 bucket via CloudFront OAC for the EKS web deployment (no frontend pod — see ARCHITECTURE.md). Locally, nginx serves the bundle and proxies the gated paths (/api, /inspect) to the backend, mirroring that CloudFront/S3 split. Changing frontend code therefore affects the PyPI wheel — the viewer static is package data per pyproject.toml.
Key files
| File | Purpose |
|---|---|
eval_mcp/server.py |
Unified MCP server — every tool is registered here |
eval_mcp/tools/ |
Tool handlers (QA gen, judge, config, run, …) |
eval_mcp/core/bedrock_client.py |
Bedrock client + cross-region inference + API-key auth |
eval_mcp/core/judge_config.py |
Default judge models and criteria |
eval_mcp/core/pricing.py |
Live model pricing from LiteLLM (24h cache → vendored snapshot fallback); no hand-maintained price table |
eval_mcp/core/litellm_pricing_snapshot.json |
Vendored offline fallback for pricing; refresh with make sync-pricing |
backend/core/agent.py |
EKS web app's agent system prompt + loop (the MCP itself doesn't host an agent) |
Makefile |
Local dev commands (make dev, make logs, make restart, make stop, make release) |
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.
- 6d ago First seen · 403 lines · 7,449 tokens per session scan A aa2bc8a37df5
llm-evaluation-system CLAUDE.md is an instructions file published in the GitHub repository awslabs/llm-evaluation-system (23 stars, last pushed 4d ago), licensed Apache-2.0. It adds 7,449 tokens to every session, about $0.0372 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). 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).
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
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.
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.