ega 1.0.0

Runtime enforcement layer for LLM outputs.

EGA

EGA is not an eval tool. It is a runtime enforcement layer.

What problem it solves

LLM outputs are candidates, not answers. EGA verifies each claim against source evidence at runtime before allowing output downstream. Eval tools score after the fact. EGA enforces before emit.

Install

pip install ega

Usage

from ega import verify_answer
from ega.config import PipelineConfig, VerifierConfig
from ega.contract import PolicyConfig

config = PipelineConfig(
    policy=PolicyConfig(),
    verifier=VerifierConfig(use_oss_nli=True),
)

result = verify_answer(
    llm_output="The company was founded in 2001 and has 500 employees.",
    source_text="The company was founded in 2001. It currently employs 500 people.",
    config=config,
    return_pipeline_output=True,
)

print(result["payload_status"])
# ACCEPT or REJECT

What you get back

Every response includes payload_status, per-unit audit records with authority and decision, and tracking_id. Distribution drift signal is available when calibration data is present. Use summarize_result() to extract operational signals for logging.

Output modes

Mode	Behavior
strict	Full payload or rejection metadata — nothing partial
adapter	Validation envelope with accepted and rejected fields separated

Set via output_mode in PipelineConfig.

Current limitations

• Sentence-level segmentation, not semantic claim decomposition
• Calibration bootstrapped on pilot data, not production-grade
• Clean inputs may show authority=conformal_oor until recalibrated on production data
• Structured output BM25 routing untested on real pipelines
• Not benchmarked against RAGAS or TruLens

CLI

ega run --answer-file answer.txt --evidence-file evidence.json

answer.txt — plain text LLM output. evidence.json — JSON array of {id, text, metadata} objects.

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