Herdr UI
Herdr의 TUI가 뛰어나며, 창을 통해 추가되는 기능
아직 감지된 업데이트가 없습니다. GitHub 릴리스가 나오면 여기에 보입니다.
개인 계정의 공개 저장소 소유자는 GitHub로 로그인해 운영자를 확인하고 소개를 직접 관리할 수 있습니다. 관리자 승인은 필요하지 않습니다.
로그인한 뒤 확인 파일을 만들어 이 저장소의 기본 브랜치에 30분 안에 커밋해 주세요. 다른 브랜치나 포크의 파일은 인정하지 않습니다. 파일을 올린 뒤 아래에서 확인하면 관리자 승인 없이 관리할 수 있습니다. 자격을 유지하려면 파일을 남겨 두세요.
로그인이 필요하면 로그인 화면으로 이동합니다. 로그인 후 이곳으로 돌아와 원하는 확인 버튼을 다시 눌러 주세요.
프로젝트 폴더에서 /nomorevibe verify를 실행하면 소유권을 확인하고 소개를 직접 관리할 수 있습니다.
1분 만에 확인하기AI agent evaluations in containers. 102 benchmarks X 21 agents — ready to deploy at massive scale on any cloud.
저장소 README에서 가져옴
README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
AI agent evaluations in containers. 107 benchmarks, 26 agents — ready to deploy at massive scale on any cloud.
An evaluation is one benchmark + one agent + one model — three independent axes, swappable without touching each other. Our goal is agent evaluations you can trust: fast to run, thin to ship, reliable in any environment, and faithful to what each benchmark really measures.README 전문 보기 ↗AI agent evaluations in containers. 114 benchmarks, 26 agents — ready to deploy at massive scale on any cloud.
An evaluation is one benchmark + one agent + one model — three independent axes, swappable without touching each other. Our goal is agent evaluations you can trust: fast to run, thin to ship, reliable in any environment, and faithful to what each benchmark really measures.
Working in this repo (human or AI agent)? It is governed by
AGENTS.mdand the.agents/directory — its rules (what a result must be) and skills (how to produce it). Read the doctrine for the area you touch before changing it.
| Cloud-native | Framework-free | Full interchangeability (agent × model × benchmark) | Speed audit | Size audit | Reliability audit | Native model tracing | |
|---|---|---|---|---|---|---|---|
| Harbor | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Inspect AI | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Eval Containers | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
One URL for every evaluation — benchmark, agent, model, and task are all EVAL_* env vars, run by plain Docker Compose with no clone and no framework:
echo "OPENAI_API_KEY=sk-..." > .env
EVAL_TASK_ID=0 EVAL_AGENT=codex EVAL_MODEL=openai/gpt-5.4 EVAL_MODEL_SLUG=openai--gpt-5.4 EVAL_RUN_ID=first \
docker compose -f oci://ghcr.io/exgentic/eval-aime up -y --abort-on-container-exit
cat output/aime/codex/openai--gpt-5.4/first/0/task/result.json
Prefer a CLI? cargo install eval-containers, then eval-containers run aime --task-id 0 --agent codex --model openai/gpt-5.4 prints and runs that exact Docker command — every command is a reminder of a plain docker/kubectl one (--dry-run to just print it).
The exact same evaluation runs at scale on a cluster — the oci:// Compose reference becomes one helm | kubectl apply, with the axes as --sets instead of EVAL_* vars:
helm template eval-aime oci://ghcr.io/exgentic/charts/eval \
--set benchmark=aime --set task=0 --set agent=codex --set model=openai/gpt-5.4 \
--set ephemeral=true | kubectl apply -f -
ephemeral=true says this run's results are disposable — read them from
kubectl logs and let them go with the pod. To keep them, name a volume instead:
--set outputVolume.persistentVolumeClaim.claimName=<claim>. The chart refuses to
run without one of the two rather than silently discarding the results.
→ Triple-mode (compose / container / job) · Deploy on Kubernetes · OpenShift
oci://references need Docker Compose ≥ 2.34. On older Docker, behind a firewall, or fully airgapped, see Run offline or airgapped. To iterate on local changes without pulling, add--local.
Full walkthrough: Install → Run your first eval.
Human-facing docs — concepts, guides, and reference — live in docs/.
All work is governed by the rules and skills under .agents/; AGENTS.md is the full map. New contributors start with CONTRIBUTING.md.