Agentic LLM evaluation pipeline with cache-aware graph execution
Project description
neXa-gauge
Cache-aware LLM evaluation CLI with topology-based execution and branch-level cost estimation.
Install
PyPI (recommended)
pip install nexa-gauge
With Hugging Face adapter support:
pip install "nexa-gauge[huggingface]"
From source (development)
git clone git@github.com:Sardhendu/nexa-gauge.git
cd nexa-gauge
pip install -e .
Quick Start
# set your provider key
export OPENAI_API_KEY="<your-key>"
# inspect CLI
nexagauge --help
# estimate first
nexagauge estimate grounding --input sample.json --limit 5
# run and write reports
nexagauge run eval --input sample.json --limit 5 --output-dir ./report
CLI Overview
nexagauge run <target_node> --input <source> [flags]nexagauge estimate <target_node> --input <source> [flags]
Most-used flags:
- data:
--input,--adapter,--split,--start,--end,--limit - model routing:
--model,--llm-model,--llm-fallback - cache:
--force,--no-cache,--cache-dir - execution:
--max-workers,--max-in-flight,--continue-on-error - debug:
--debug(enables node logs; hides progress bar) - output (
run):--output-dir
Node Topology
Canonical nodes:
scanchunkclaimsdedupgeval_stepsrelevancegroundingredteamgevalreferenceevalreport
Typical paths:
grounding:scan -> chunk -> claims -> dedup -> groundingrelevance:scan -> chunk -> claims -> dedup -> relevancegeval:scan -> geval_steps -> gevaleval: full graph execution and aggregation
Configuration
See .env.example for environment settings.
Minimum for LLM-backed runs:
OPENAI_API_KEY(or alternative provider key)LLM_MODEL(default available)
Per-node overrides are supported:
LLM_{NODE}_MODELLLM_{NODE}_FALLBACK_MODELLLM_{NODE}_TEMPERATURE
For Maintainers
uv sync
make lint
make test
make ci
Build distributions:
uv build
Expected artifacts:
dist/nexa_gauge-<version>-py3-none-any.whldist/nexa_gauge-<version>.tar.gz
Project Standards
- License: MIT
- Security policy: SECURITY.md
- Contributing guide: CONTRIBUTING.md
- Code of conduct: CODE_OF_CONDUCT.md
Documentation
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