Coder Eval — evaluate and benchmark AI coding agents and their skills
Coder Eval (pip install coder-eval / uv tool install coder-eval) is an
open-source, agent-agnostic framework for evaluating and benchmarking AI coding
agents and their skills — built for benchmark authors, CLI builders, and skill
builders — with sandboxing, reproducibility, and data-driven analysis. It runs a real
agent — Claude Code, OpenAI Codex, Google Antigravity (Gemini),
OpenCode, or Pi — in a sandbox against declarative YAML tasks, then scores the files and
commands it actually produced. Changing harness is one field (agent.type); the
tasks, criteria, scoring, telemetry, and reports stay the same.
Reach for it when you want to benchmark agents on your own domain tasks,
test whether a skill triggers in the agent you ship for, A/B-test Claude Code
vs. Codex vs. Gemini vs. OpenCode vs. Pi (or model vs. model, prompt vs. prompt), or
gate CI on coding-agent quality. It is not a fixed leaderboard: unlike
SWE-bench or SkillsBench, which rank models on a shared task set, you bring the tasks
and you bring the scoring — weighted 0.0–1.0 criteria, a skill_triggered activation
check, an A/B experiment layer, and per-tool cost telemetry, over whatever work you
care about. See How it compares.
📚 Full docs: coder-eval.com/docs.
- 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-agnostic by design — Claude Code, OpenAI Codex, Antigravity (Gemini), OpenCode, and Pi today; add your own harness through the 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
Watch the intro
🔊 Turn the sound on — GitHub's inline player always starts muted.
▶ Also on YouTube: Coder Eval: UiPath open-source framework to test AI Coding Agents — what the framework does, and how a run works end to end.
What you can do with it
- Benchmark coding agents — score an agent across a suite of tasks with weighted scoring and pass/fail thresholds
- Compare models & configs — A/B-test Claude vs. Codex vs. Gemini vs. OpenCode vs. Pi, 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), on whichever harness your users run - 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 runtime of at least one coding agent — plus that agent's own model credentials. Pick the agent you want to evaluate; two of the four ship with a Coder Eval extra, the other two are separate CLIs you install yourself:
The examples below use the default claude-code agent. Developed on macOS; CI runs on
Linux.
git clone https://github.com/UiPath/coder_eval.git
cd coder_eval
uv sync # install the framework (add --extra codex /
# --extra antigravity for those runtimes)
cp .env.example .env # then set ANTHROPIC_API_KEY — or skip that: an
# existing Claude Code login (`claude login`) is
# picked up automatically
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.
Two more extras you only need on purpose: --extra dev adds the contributor
toolchain (pytest, ruff, pyright, pre-commit — see
CONTRIBUTING.md), and --extra uipath adds the in-host uipath
SDK for local sandbox parity (public PyPI, no credentials). Without either, the
framework still runs end-to-end.
Just want the CLI, without cloning? Install the published package — this is also what a CI job or another repo does:
uv tool install coder-eval # puts the `coder-eval` CLI on your PATH,
# in its own isolated environment
uv tool install "coder-eval[codex,antigravity]" # same, with agent extras
coder-eval --version # verify the install
To add it as a project dependency instead: uv add coder-eval or
pip install coder-eval. In a real CI gate, pin to a specific released version
so a harness upgrade can't silently move your results. (The example tasks/
live in this repo — clone it or point the CLI at your own task files.) See
Tutorial 02 — Running Coder Eval in CI for
the full setup.
Use inside Claude Code
Coder Eval evaluates any of the supported agents, and it also ships an authoring front-end for one of them: this repo is a Claude Code plugin marketplace, so the whole loop — scaffold a suite, author a task, check whether a skill triggers, read the results — runs inside Claude Code. The suites you author this way run on every harness:
/plugin marketplace add UiPath/coder_eval
/plugin install coder-eval@coder-eval
That adds six slash commands: /coder-eval:init, /coder-eval:check-skill,
/coder-eval:task, /coder-eval:lint-tasks, /coder-eval:analyze and
/coder-eval:ci. They drive the coder-eval CLI, so install it too
(uv tool install coder-eval). See Claude Code Plugin.
Use as a GitHub Action
A composite action — on the Marketplace as
coder_eval — runs
coder-eval as a CI gate. It installs the pinned CLI, runs your tasks, writes a
JUnit XML report, reports where its artifacts landed, and fails the step on any
task failure:
- uses: actions/setup-node@v4 # the claude-code agent needs the Claude CLI…
with: { node-version: '20' }
- run: npm install -g @anthropic-ai/claude-code
- uses: UiPath/coder_eval@v0 # …then run the gate (@v1 once 1.0.0 ships; @vX.Y.Z pins exactly)
id: eval
with:
args: |
tests/tasks/**/*.yaml
--model
claude-sonnet-5
env: |
ANTHROPIC_API_KEY=${{ secrets.ANTHROPIC_API_KEY }}
Eight inputs, and none of them is a coder-eval run flag. The CLI has 21;
GitHub silently ignores an input the referenced tag does not define, so a
forwarding input that is mistyped or newer than your pin yields a run that
measured something else and still exits 0. A wrong CLI flag is a hard error. So
flags and task globs all go through args, and an input exists only where the
action does something with the value besides pass it along.
Outputs: run-dir, junit-path (<run-dir>/junit.xml) and run-md-path
(<run-dir>/run.md). The action writes nothing to the job summary — a consumer
that has to redact the report first cannot undo a write that already happened:
- if: always()
run: cat "${{ steps.eval.outputs.run-md-path }}" >> "$GITHUB_STEP_SUMMARY"
- uses: mikepenz/action-junit-report@v5
if: always()
with:
report_paths: ${{ steps.eval.outputs.junit-path }}
Credentials and backend config are the sole responsibility of env — a
passthrough exported for the run step only (never written to $GITHUB_ENV, so
it can't leak into later steps). Set whatever the run needs, Anthropic or not:
- uses: UiPath/coder_eval@v0
with:
args: tests/tasks/**/*.yaml
env: |
API_BACKEND=bedrock
AWS_BEARER_TOKEN_BEDROCK=${{ secrets.BEDROCK_TOKEN }}
The step's exit code is coder-eval's own: non-zero on any failed task.
Agent runtime is the caller's responsibility. The action is agent-agnostic — it installs
coder-evalbut no coding-agent runtime, which is why the example above starts withactions/setup-node+npm install -g @anthropic-ai/claude-code: the defaultclaude-codeagent needs theclaudeCLI onPATHbefore the action runs. Swap those steps for your own agent's runtime as needed.
Security. Evaluated tasks execute agent-generated code. Do not run this action under
pull_request_targetwith secrets exposed to untrusted fork PRs — usepull_requestand gate on the same-repo condition, as this repo's own dogfood job does.
Telemetry
📊 Usage telemetry is on by default.
coder-evalsends 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, setTELEMETRY_ENABLED=falsein your.envor environment. See Usage Telemetry for details and how to route it to your own resource.
Documentation
How it compares
- vs. fixed benchmarks (SWE-bench, SkillsBench) — they score a canonical dataset; Coder Eval scores your tasks with continuous 0.0–1.0 weighted criteria (and can still wrap a fixed dataset via Bring Your Own Dataset).
- vs. large-scale / RL harnesses (Harbor) — Harbor targets scale and RL rollouts; Coder Eval targets weighted, skill-aware suites gated in CI.
- vs. model-output eval tools (OpenAI Evals) — they grade model text; Coder Eval runs a full agent in a sandbox and scores the files and commands it produced.
- vs. hand-rolled scripts — reproducible sandboxes, weighted criteria, cost/token telemetry, A/B experiments, and CI-ready pass/fail gates out of the box.
See the full comparison — with sources.
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"
agent.type is the only harness-specific line: swap it for codex, antigravity, or
opencode — or override it per run with coder-eval run … -D agent.type=opencode — and
the same criteria score the same way. 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; for what each run_limits field
means on each harness, see Run-Limit Parity.
Tip: With the Claude Code plugin installed, use
/coder-eval:taskto scaffold a task from a natural-language description, and/coder-eval:analyze runs/latestto 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
tempdirdriver is not a security boundary. - Bring your own agent runtime and model credentials — Coder Eval never supplies
model access, and it only ships an agent runtime where an extra says so (
codex,antigravity); Claude Code and OpenCode are separate CLIs. Supply the runtime (see Quick Start) and the keys it needs — Anthropic, Bedrock, OpenAI, Gemini, or an OpenRouter key for open-weight models via OpenCode. - 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 [email protected].
License
© 2026 UiPath. Licensed under the Apache License, Version 2.0 — see LICENSE and NOTICE.
Acknowledgments
Built with Pydantic, Typer, and Rich, on top of the harnesses it drives — the Claude Agent SDK, the Codex SDK, Google Antigravity, and OpenCode.
