명령줄에서 OmniRoute의 A2A 서버로 작업을 보냅니다. 실행 이력과 로그를 조회하고 작업을 감시하거나 취소하며 에이전트 간 통신을 테스트합니다.
- 서버 상태와 사용 가능한 스킬 확인
- JSON 입력으로 스킬 호출
- 작업 실행 이력과 로그 조회
Create and run automated tests that score LLM answers against expected outputs or judging criteria. Monitor progress, inspect individual results, compare models on the same tests, and use score thresholds in CI, an automated checking workflow.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Create and run evaluation suites, watch live benchmark progress, view scorecards, compare model performance, and integrate eval runs with CI workflows from the CLI.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Create and run evaluation suites, watch live benchmark progress, view scorecards, compare model performance, and integrate eval runs with CI workflows from the CLI.
npm install -g omniroute # or: npx omniroute
omniroute --version
evalExample:
omniroute eval
eval suitesExample:
omniroute eval suites
eval listExample:
omniroute eval list
eval get <suiteId>Example:
omniroute eval get <suiteId>
eval createFlags:
--file <path>Example:
omniroute eval create
eval run <suiteId>Flags:
-m, --model <id>--combo <name>--concurrency <n>--tag <tag>--watchExample:
omniroute eval run <suiteId>
eval listFlags:
--suite <id>--status <s>--since <ts>--limit <n>Example:
omniroute eval list
eval get <runId>Example:
omniroute eval get <runId>
eval results <runId>Flags:
--failedExample:
omniroute eval results <runId>
eval cancel <runId>Flags:
--yesExample:
omniroute eval cancel <runId>
eval scorecard <runId>Example:
omniroute eval scorecard <runId>
simulate [prompt]Flags:
--file <path>-m, --model <id>--combo <name>--reasoning-effort <level>--thinking-budget <n>--explainExample:
omniroute simulate [prompt]
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Requires the omniroute CLI. See CLI entry-point skill for install + global flags.
Evals are automated test suites that score LLM outputs against expected answers or rubrics. OmniRoute stores suites and run results in its local database.
omniroute eval suites list # List all eval suites
omniroute eval suites list --json # JSON output
omniroute eval suites get <suiteId> # Full suite definition
omniroute eval suites create \
--name "code-quality" \
--rubric "exact-match" \
--samples-file ./samples.jsonl # JSONL: {input, expected_output}
Rubric options: exact-match, contains, llm-judge, regex.
--samples-file format (one JSON object per line):
{"input": "What is 2+2?", "expected_output": "4"}
{"input": "Translate 'hello' to Spanish", "expected_output": "hola"}
omniroute eval suites run <suiteId> \
--model claude-sonnet-4-6 # Run suite against a specific model
omniroute eval suites run <suiteId> \
--model gpt-4o \
--watch # Live TUI progress (EvalWatch)
The run is asynchronous. Use --watch for a live terminal dashboard or poll manually:
RUN_ID=$(omniroute eval suites run <suiteId> --model claude-sonnet-4-6 --output json | jq -r '.id')
omniroute eval get $RUN_ID
omniroute eval list # List all eval runs
omniroute eval list --json
omniroute eval get <runId> # Run details (status, model, score)
omniroute eval results <runId> # Per-sample results
omniroute eval scorecard <runId> # Full scorecard with pass/fail per sample
omniroute eval cancel <runId> # Cancel a running eval
omniroute eval scorecard <runId> --output json
Response fields per sample:
{
"id": "sample-1",
"score": 0.95,
"passed": true,
"input": "What is 2+2?",
"output": "4",
"expected": "4"
}
Run the same suite against multiple models and compare:
for MODEL in claude-sonnet-4-6 gpt-4o gemini-2.0-flash; do
omniroute eval suites run $SUITE_ID --model $MODEL --output json | jq '{model: .model, score: .score}'
done
# Run and fail CI if score drops below threshold
SCORE=$(omniroute eval suites run $SUITE_ID --model claude-sonnet-4-6 --output json | jq -r '.score')
python3 -c "import sys; score=float('$SCORE'); sys.exit(0 if score >= 0.90 else 1)"
suites create fails with invalid rubric → use one of: exact-match, contains, llm-judge, regexsuites run returns model not found → verify model ID with omniroute models --search <name>eval get shows status: failed → check omniroute logs --search eval for error detailsscorecard returns empty results → the run may still be running; poll omniroute eval get <runId> until status is completed아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
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명령줄에서 OmniRoute의 A2A 서버로 작업을 보냅니다. 실행 이력과 로그를 조회하고 작업을 감시하거나 취소하며 에이전트 간 통신을 테스트합니다.
Back up and restore OmniRoute data from the command line. Covers incremental snapshots, encryption, cloud synchronization, scheduled backups, and archive import and export, with options for retention and previewing an import.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Submit groups of AI requests as batch jobs with the OmniRoute command line. Manage input files, monitor or cancel jobs, save results and errors, and incorporate batch processing into automated build and deployment workflows.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Send prompts to LLMs from a terminal, display answers as they arrive, or hold an ongoing conversation. Use files or standard input for prompts, choose models, and set system instructions through OmniRoute.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Configure prompt compression, which shortens text sent to AI models, through the OmniRoute command line. Adjust RTK filters, Caveman rules, and combined compression modes, then preview the results using real prompts.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Manage OmniRoute context settings from the command line, including token budgets and RTK filters for processing context. Switch saved server connections, import or export configurations, and inspect or expire conversation sessions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.