ExpertTrace Python SDK

ExpertTrace is an open-source toolkit for eliciting, structuring, and auditing expert judgment. It runs as a deterministic local workflow by default. An optional LLM layer can ask one targeted follow-up when an expert's answer needs more specificity.

Install

Core toolkit, with no runtime dependencies or model calls:

pip install elythera-experttrace

Toolkit plus the LiteLLM adapter:

pip install "elythera-experttrace[llm]"

For local development, clone the repository and use pip install -e . or pip install -e ".[llm]". The distribution is named elythera-experttrace; Python imports and the CLI use experttrace.

Deterministic quickstart

from experttrace import InterviewSession

session = InterviewSession(
    topic="Reviewing high-risk AI use cases",
    domain="AI governance",
    owner="AI Governance Council",
)

while not session.is_complete:
    prompt = session.current_prompt
    session.answer(input(f"{prompt.question}\n> "))

card = session.compile()
print(card.to_json())
print(session.audit().summary())

Adaptive quickstart

Install the llm extra, configure the environment variable required by your chosen provider, and pass a model:

from experttrace import InterviewSession, LiteLLMProvider

session = InterviewSession(
    topic="Reviewing high-risk AI use cases",
    llm=LiteLLMProvider("openai/gpt-5"),
    max_follow_ups_per_prompt=1,
)

while not session.is_complete:
    prompt = session.current_prompt
    session.answer(input(f"{prompt.label}\n{prompt.question}\n> "))

print(session.compile().to_json())

LiteLLM supports hosted providers and local runtimes. Model names and credentials follow the selected provider's LiteLLM configuration. Keep credentials in environment variables; never put them in source code or interview answers.

If you already have a model client, adapt it without installing LiteLLM:

from experttrace import CallableLLM, InterviewSession

def decide(system_prompt: str, user_prompt: str) -> dict:
    # Call your existing client and return the parsed JSON object.
    return {"should_ask": False, "question": "", "reason": "Sufficient detail."}

session = InterviewSession(
    topic="Vendor risk review",
    llm=CallableLLM(decide),
)

The callback must return should_ask as a boolean plus string question and reason fields.

CLI

Deterministic mode:

experttrace interview \
  --topic "Reviewing high-risk AI use cases" \
  --domain "AI governance" \
  --owner "AI Governance Council" \
  --output knowledge-card.json

Adaptive mode:

export OPENAI_API_KEY="..."
experttrace interview \
  --topic "Reviewing high-risk AI use cases" \
  --model openai/gpt-5 \
  --max-follow-ups 1 \
  --output knowledge-card.json

Audit a saved card:

experttrace audit knowledge-card.json

By default, model failures produce a warning and the deterministic interview continues. Add --strict-llm if a model failure should stop the run.

What the LLM does—and does not do

The optional model evaluates each base answer and may generate one concise follow-up about missing thresholds, exceptions, evidence, or escalation rules. The JSON compilation and quality audit remain deterministic. Expert approval remains explicit; the model never certifies that captured knowledge is true.

Privacy behavior is clear at the integration boundary:

• Deterministic mode sends nothing to a model provider.
• Adaptive mode sends the topic, domain, current question, and current answer to the provider chosen by the user.
• ExpertTrace does not require an Elythera service or account.

Design principles

• Offline and dependency-free by default.
• Portable, reviewable knowledge-card JSON.
• Replaceable interview protocols and model providers.
• Explainable audit findings instead of only a score.
• Model calls are opt-in and degrade gracefully unless strict mode is enabled.

Status

Version 0.1.1 is an alpha research release. It helps structure expert-provided knowledge; it does not replace professional review or establish factual truth.

Licensed under Apache-2.0.