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开始使用agent-eval
编码代理（Claude Code、Aider、Codex等）在自定义任务上的直接比较，包含通过率、成本、时间和一致性指标
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编码代理（Claude Code、Aider、Codex等）在自定义任务上的直接比较，包含通过率、成本、时间和一致性指标
| name | agent-eval |
| description | 编码代理（Claude Code、Aider、Codex等）在自定义任务上的直接比较，包含通过率、成本、时间和一致性指标 |
| origin | ECC |
| tools | Read, Write, Edit, Bash, Grep, Glob |
一个轻量级 CLI 工具，用于在可复现的任务上对编码代理进行头对头比较。每个“哪个编码代理最好？”的比较都基于感觉——本工具将其系统化。
# pinned to v0.1.0 — latest stable commit
pip install git+https://github.com/joaquinhuigomez/agent-eval.git@6d062a2f5cda6ea443bf5d458d361892c04e749b
以声明方式定义任务。每个任务指定要做什么、要修改哪些文件以及如何判断成功：
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
每个代理运行都获得自己的 git 工作树——无需 Docker。这提供了可复现的隔离，使得代理之间不会相互干扰或损坏基础仓库。
| 指标 | 衡量内容 |
|---|---|
| 通过率 | 代理生成的代码是否通过了判断？ |
| 成本 | 每个任务的 API 花费（如果可用） |
| 时间 | 完成所需的挂钟秒数 |
| 一致性 | 跨重复运行的通过率（例如，3/3 = 100%） |
创建一个 tasks/ 目录，其中包含 YAML 文件，每个任务一个文件：
mkdir tasks
# Write task definitions (see template above)
针对你的任务执行代理：
agent-eval run --task tasks/add-retry-logic.yaml --agent claude-code --agent aider --runs 3
每次运行：
生成比较报告：
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:
- type: pytest
command: pytest tests/ -v
- type: command
command: npm run build
judge:
- type: grep
pattern: "class.*Retry"
files: src/**/*.py
judge:
- type: llm
prompt: |
Does this implementation correctly handle exponential backoff?
Check for: max retries, increasing delays, jitter.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Orchestrate building a brand-new feature end to end — research, plan, TDD implementation, review, and gated commit — by delegating each phase to the matching ECC agent. Use when adding a capability that does not exist yet.
Orchestrate bootstrapping a working MVP from a design or spec document — ingest the doc, plan thin vertical slices, scaffold the first end-to-end slice, then TDD-implement, review, and gated commit. Use to turn an SDD/PRD into a running starting point.
Orchestrate altering an existing, working feature to new desired behavior — update its tests to the new spec, change the implementation to match, review, and gated commit. Use when behavior is not broken but should be different.
Orchestrate fixing a bug — reproduce it as a failing regression test, fix to green, review, and gated commit — by delegating each phase to the matching ECC agent. Use when existing behavior is broken or wrong.
Shared orchestration engine for the orch-* skill family. Defines the gated Research-Plan-TDD-Review-Commit pipeline, the size classifier, the agent map, and the two human gates that the orch-* operation skills delegate to. Not usually invoked directly.