agent-eval

name agent-eval description Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression. license MIT metadata {"origin":"ECC"} tools Read, Write, Edit, Bash, Grep, Glob Agent Eval Skill A lightweight CLI tool for comparing coding agents head-to-head on reproducible tasks. Every "which coding agent is best?" comparison runs on vibes — this tool systematizes it. When to Activate Comparing coding agents (Claude Code, Aider, Codex, etc.) on your own codebase Measuring agent performance before adopting a new tool or model Running regression checks when an agent updates its model or tooling Producing data-backed agent selection decisions for a team Installation Note: Install agent-eval from its repository after reviewing the source. Core Concepts YAML Task Definitions Define tasks declaratively. Each task specifies what to do, which files to touch, and how to judge success: name: add-retry-logic description: Add exponential backoff retry to the HTTP client repo: ./my-project files: - src/http_client.py prompt: | Add retry logic with exponential backoff to all HTTP requests. Max 3 retries. Initial delay 1s, max delay 30s. judge: - type: pytest command: pytest tests/test_http_client.py -v - type: grep pattern: "exponential_backoff|retry" files: src/http_client.py commit: "abc1234" # pin to specific commit for reproducibility Git Worktree Isolation Each agent run gets its own git worktree — no Docker required. This provides reproducibility isolation so agents cannot interfere with each other or corrupt the base repo. Metrics Collected Metric What It Measures Pass rate Did the agent produce code that passes the judge? Cost API spend per task (when available) Time Wall-clock seconds to completion Consistency Pass rate across repeated runs (e.g., 3/3 = 100%) Workflow 1. Define Tasks Create a tasks/ directory with YAML files, one per task: mkdir tasks # Write task definitions (see template above) 2. Run Agents Execute agents against your tasks: agent-eval run --task tasks/add-retry-logic.yaml --agent claude-code --agent aider --runs 3 Each run: Creates a fresh git worktree from the specified commit Hands the prompt to the agent Runs the judge criteria Records pass/fail, cost, and time 3. Compare Results Generate a comparison report: agent-eval report --format table Task: add-retry-logic (3 runs each) ┌──────────────┬───────────┬────────┬────────┬─────────────┐ │ Agent │ Pass Rate │ Cost │ Time │ Consistency │ ├──────────────┼───────────┼────────┼────────┼─────────────┤ │ claude-code │ 3/3 │ $0.12 │ 45s │ 100% │ │ aider │ 2/3 │ $0.08 │ 38s │ 67% │ └──────────────┴───────────┴────────┴────────┴─────────────┘ Judge Types Code-Based (deterministic) judge: - type: pytest command: pytest tests/ -v - type: command command: npm run build Pattern-Based judge: - type: grep pattern: "class.*Retry" files: src/**/*.py Model-Based (LLM-as-judge) judge: - type: llm prompt: | Does this implementation correctly handle exponential backoff? Check for: max retries, increasing delays, jitter. Best Practices Start with 3-5 tasks that represent your real workload, not toy examples Run at least 3 trials per agent to capture variance — agents are non-deterministic Pin the commit in your task YAML so results are reproducible across days/weeks Include at least one deterministic judge (tests, build) per task — LLM judges add noise Track cost alongside pass rate — a 95% agent at 10x the cost may not be the right choice Version your task definitions — they are test fixtures, treat them as code Links Repository: github.com/joaquinhuigomez/agent-eval