nemo-evaluator 0.3.0

NeMo Evaluator — benchmark environments, pluggable solvers, interceptor proxy, and decision-grade scoring for LLMs

NeMo Evaluator

Documentation | GitHub | Issues

LLM evaluation framework with benchmark environments, pluggable solvers, composable interceptor proxy, and multi-format reporting.

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Install

pip install -e .                   # core
pip install -e ".[scoring]"        # + sympy for symbolic math
pip install -e ".[stats]"          # + scipy (regression analysis)
pip install -e ".[scoring,stats]"  # + sympy + scipy for confidence intervals
pip install -e ".[harbor]"         # + Harbor agents (OpenHands, Terminus-2)
pip install -e ".[inspect]"        # + Inspect AI log export
pip install -e ".[all]"            # common runtime integrations

Quick Start

export NVIDIA_API_KEY="your-api-key-here"

# Run a benchmark from the CLI
nel eval run --bench mmlu \
  --model-url https://integrate.api.nvidia.com/v1 \
  --model-id nvidia/nemotron-3-super-120b-a12b \
  --api-key $NVIDIA_API_KEY \
  --repeats 3 --max-problems 100

# Run from a YAML config
nel eval run config.yaml
nel eval run config.yaml --resume

# Generate a report
nel eval report ./eval_results/ -f markdown -o report.md

Benchmarks

17 built-in benchmarks plus external harness integrations:

Benchmark	Type	Scoring
mmlu, mmlu_pro, gpqa	Multichoice	multichoice_regex
gsm8k, math500, mgsm	Math	numeric_match / answer_line
drop, triviaqa	QA	fuzzy_match
humaneval	Code	code_sandbox (Docker)
simpleqa, healthbench	Judge	needs_judge
pinchbench	Agentic	code_sandbox / needs_judge
xstest	Safety	needs_judge
terminal-bench-hard, terminal-bench-v1	Terminal tasks	Task test harness
nmp_harbor	Agentic NMP	Harbor task tests

External environments via URI schemes: lm-eval://, skills://, vlmevalkit://, gym://, harbor://, container://.

Adapter Proxy

Built-in local interceptor proxy for LLM traffic. Intercepts all agent-to-model requests for caching, logging, payload modification, turn limiting, and custom transformations — no external dependencies required.

services:
  nemotron:
    type: api
    url: https://integrate.api.nvidia.com/v1/chat/completions
    protocol: chat_completions
    model: nvidia/nemotron-3-super-120b-a12b
    api_key: ${NVIDIA_API_KEY}
    proxy:
      request_timeout: 600
      interceptors:
        - name: turn_counter
          config:
            max_turns: 100
        - name: drop_params
          config:
            params: [max_tokens]
      verbose: true

Available interceptors:

Interceptor	Stage	Description
endpoint	request→response	Async HTTP forwarding with retry, backoff, connection pooling
caching	request→response	Disk-backed SQLite cache with canonical keys
turn_counter	request	Per-session turn counting with budget injection
drop_params	request	Strip named parameters from requests
modify_tools	request	Add/remove properties in tool schemas
system_message	request	Inject/replace/prepend system messages
payload_modifier	request	Recursive parameter add/remove/rename
raise_client_errors	response	Convert 4xx to exceptions
log_tokens	response	Log token usage per request
response_stats	response	Aggregate timing and token statistics
reasoning	response	Normalize <think> blocks to reasoning_content
progress_tracking	response	Progress counter with optional webhook
logging	request + response	Request/response logging with body preview

Solvers

Configured via solver.type in each benchmark:

Solver Type	Config type	Use Case
SimpleSolver	simple	Standard chat/completion/VLM (default)
HarborSolver	harbor	Harbor agents (OpenHands, Terminus-2, etc.)
ToolCallingSolver	tool_calling	Tool-use with Gym resource servers
GymDelegationSolver	gym_delegation	Delegate to nemo-gym server
OpenClawSolver	openclaw	OpenClaw CLI agent
ContainerSolver	container	Legacy container harness

Export

Evaluation results can be exported to experiment trackers and compatible formats:

output:
  export: [inspect, wandb, mlflow]

• inspect — Produces inspect_ai-compatible EvalLog JSON files. Install with pip install -e ".[inspect]".
• wandb / mlflow — Push scores and artifacts to experiment trackers. Install with pip install -e ".[export]".

BYOB (Bring Your Own Benchmark)

from nemo_evaluator import benchmark, scorer, ScorerInput, exact_match

@benchmark(name="my-bench", dataset="hf://my-org/data?split=test",
           prompt="Q: {question}\nA:", target_field="answer")
@scorer
def my_scorer(sample: ScorerInput) -> dict:
    return exact_match(sample)

Sandboxes

Per-problem Docker/SLURM sandboxes for code execution and agentic evaluation. Two modes: stateful (shared sandbox for solve + verify) and stateless (separate agent and verification containers with shared volume).

SLURM

Pyxis/Enroot-based execution with auto-selected container images per URI scheme. Uses node_pools topology for flexible resource allocation across model, agent, and sandbox nodes.

Tag suffix	Contents
:latest	Base + gym + vlmevalkit
:latest-lm-eval	+ lm-evaluation-harness
:latest-skills	+ NeMo Skills
:latest-full	All harnesses

CLI

Command	Purpose
nel eval run	Run evaluation (name or YAML)
nel eval merge <dir>	Merge sharded results
nel eval report <dir>	Generate reports
nel list	List benchmarks
nel serve -b <name>	Serve as HTTP endpoint
nel validate -b <name>	Sanity check
nel export <paths> --dest <exporter>	Export bundles
nel cache-sqsh <image>	Build a SLURM .sqsh cache image
nel report <dir>	Generate multi-benchmark reports
nel compare	Paired run comparison
nel gate	Multi-benchmark quality gate
nel config	Persistent user config
nel package	Containerize BYOB benchmark

Compare Results Between Runs

Use nel compare when you want to compare two runs of the same benchmark and inspect score deltas, flips, and statistical evidence.

nel compare ./results/baseline ./results/candidate --strict

Full tutorial: docs/tutorials/compare.md

Implement Quality Gates

Use nel gate when you want one GO / NO-GO / INCONCLUSIVE decision across multiple benchmarks from an explicit policy file.

nel gate ./results/baseline ./results/candidate \
  --policy gate_policy.yaml \
  --strict \
  --output gate_report.json

Full tutorial: docs/tutorials/quality-gate.md

Examples

See examples/configs/ for 25+ end-to-end configs covering all solver types, verification methods, and execution backends.

License

Apache 2.0