coder_eval — evaluate AI coding agents & their skills

A framework for evaluating AI coding agents and their skills — built for CLI and skill builders — with sandboxing, reproducibility, and data-driven analysis. Not an "agentic coding" benchmark: it measures how effective your CLI and skills are when used by coding agents.

The Coding Agents Gym. A sandboxed, reproducible framework to evaluate, benchmark, and A/B-test AI coding agents — Claude Code, Codex, and Google Antigravity (Gemini) today, any agent via a plugin SPI — with declarative YAML tasks and weighted scoring.

• Declarative YAML tasks with pinned dependencies and clear success criteria
• Sandboxed execution in isolated environments with resource limits
• Weighted, continuous scoring (0.0–1.0) with fractional credit and thresholds
• Many criterion types — from file checks to code similarity and LLM-graded rubrics
• Agent abstraction — Claude Code, Codex, and Antigravity (Gemini) today, extensible via a plugin SPI
• Experiment layer — A/B agent configs (models, tools, prompts) side-by-side
• Full telemetry — every tool call, token counts, and cost, with real-time streaming

What you can do with it

• Benchmark coding agents — score an agent across a suite of tasks with weighted, pass/fail thresholds
• Compare models & configs — A/B-test Claude vs. Codex vs. Gemini, model vs. model, tool-on vs. tool-off, prompt vs. prompt
• Evaluate skills — verify an agent actually engages a target skill (skill_triggered) and score skill-driven suites (SkillsBench-style)
• Keep skills up to date in CI — re-validate your skills on every change or on a schedule; catch silent regressions when models, prompts, or the skills themselves drift
• Gate CI on agent quality — run the suite in GitHub Actions and fail the build on regressions
• Bring your own dataset — fan one task out over many rows for larger benchmark suites

Keeping skills fresh? Run coder_eval as a scheduled GitHub Actions job so your skills are continuously re-evaluated against the latest model — a skill that quietly stops triggering surfaces as a failing criterion before your users hit it. See Tutorial 02 — Running coder_eval in CI.

Quick Start

Prerequisites: Python 3.13+, uv 0.8+, and the Claude CLI (brew install claude). Developed on macOS; CI runs on Linux.

git clone https://github.com/UiPath/coder_eval.git
cd coder_eval

uv sync --extra dev          # install core + dev tools
cp .env.example .env         # then set ANTHROPIC_API_KEY

uv run coder-eval plan tasks/hello_date.yaml   # validate (no tokens spent)
uv run coder-eval run  tasks/hello_date.yaml   # run your first evaluation
uv run coder-eval report runs/latest           # view the result

New here? Follow Tutorial 01 — Your First Evaluation.

The optional [uipath] extra (uv sync --extra dev --extra uipath) adds the in-host uipath SDK for local sandbox parity; it installs from public PyPI (no credentials required). Without it the framework runs end-to-end; uipath-dependent features fail at dispatch with a clear hint.

Using coder_eval in CI or another project? Install the published package: pip install coder-eval (or uv add coder-eval; extras install the same way — pip install "coder-eval[codex,antigravity]"). In a real CI gate, pin to a specific released version so a harness upgrade can't silently move your results. See Tutorial 02 — Running coder_eval in CI for the full setup.

Telemetry

📊 Usage telemetry is on by default. coder-eval sends anonymous usage telemetry (command names, outcomes, counts, durations, an anonymous install id, platform info) to help improve the tool. It never captures prompts, file contents, or repo paths, and prints a one-time notice on first run. To disable it, set TELEMETRY_ENABLED=false in your .env or environment. See Usage Telemetry for details and how to route it to your own resource.

Documentation

How it compares

• vs. SWE-bench and fixed benchmarks — SWE-bench is a fixed dataset of GitHub issues; coder_eval is a framework for authoring your own tasks in declarative YAML, so you evaluate the skills and workflows you care about (and can still wrap a fixed dataset via Bring Your Own Dataset).
• vs. LLM-output eval harnesses (e.g. OpenAI Evals) — those grade a model's text; coder_eval runs a full agent in a sandbox with real tool use and multi-turn dialog, then scores the files and commands it actually produced (continuous 0.0–1.0) — not just a judge over a string.
• vs. hand-rolled scripts — reproducible sandboxes, weighted criteria, cost/token telemetry, A/B experiments, and CI-ready pass/fail gates out of the box.

Task Definition

A task is a YAML file: a prompt, the agent config, a sandbox, and success criteria.

task_id: "hello_world"
description: "Create a Python script that prints Hello, World!"
initial_prompt: "Create hello.py that prints 'Hello, World!'"

agent:
  type: "claude-code"
  permission_mode: "acceptEdits"
  allowed_tools: ["Read", "Write", "Bash"]

sandbox:
  driver: "tempdir"
  python: {}

success_criteria:
  - type: "file_exists"
    path: "hello.py"
    description: "hello.py must be created"
  - type: "run_command"
    command: "python hello.py"
    timeout: 10
    description: "Script must execute successfully"

Tasks can omit the agent section entirely — defaults resolve from the experiment layer (experiments/default.yaml). For the full schema and every criterion type, see the Task Definition Guide.

Tip: In Claude Code, use /coder-eval-task-create to scaffold a task from a natural-language description, and /coder-eval-run-analysis runs/latest to get improvement suggestions from a completed run.

Development

make install    # package + dev + [uipath] deps + pre-commit hooks
make verify     # format + lint + typecheck + test + coverage (CI equivalent)

Run make verify before pushing — it mirrors CI (80% coverage threshold). See CONTRIBUTING.md for the full workflow, commit conventions, and extension points (new criteria, new agents).

Known limits & non-goals

• Not a fixed benchmark or leaderboard — coder_eval scores your tasks and ships example tasks, not a canonical scored dataset.
• Tasks execute real code — run untrusted tasks only under the container driver (see Docker Isolation); the tempdir driver is not a security boundary.
• Bring your own model credentials — Anthropic, Bedrock, or Gemini keys; coder_eval does not proxy or supply model access.
• Python 3.13+ only.

Support & security

• Security vulnerabilities — report privately via SECURITY.md; never open a public issue.
• Bugs & questions — open a GitHub issue.
• Everything else — reach the maintainers privately at coder-eval@uipath.com.

License

© 2026 UiPath. Licensed under the Apache License, Version 2.0 — see LICENSE and NOTICE.

Acknowledgments

Built with the Claude Agent SDK, Pydantic, Typer, and Rich.