Python SDK

Structured predictions with lr.predict()

Minimal example

import lightningrod as lr

client = lr.LightningRod(api_key="your-api-key")

result = client.predict("Will the Fed cut interest rates in 2026?", model="foresight-v4")

print(result.content)

Response fields:

Field	Type	Description
content	str	Full response, including any <answer> tags.
thinking	str | None	Reasoning, when returned.
sources	list[Source]	URL citations from research. Each source has url and title.
usage	Usage	Token counts and cost fields.
model	str	Model that served the request.
id	str	Response ID.
binary	BinaryPrediction | None	Populated when answer_type="binary".
continuous	ContinuousPrediction | None	Populated when answer_type="continuous".
multiple_choice	MultiChoicePrediction | None	Populated when answer_type="multiple_choice".
free_response	FreeResponsePrediction | None	Populated when answer_type="free_response".

Answer formats

When answer_type is set, predict() parses the response tags into typed fields on PredictionResult.

import lightningrod as lr

client = lr.LightningRod(api_key="your-api-key")

result = client.predict(
    "Will the Fed cut interest rates in 2026?",
    answer_type="binary",
)

print(result.binary.probability)
print(result.content)

answer_type	PredictionResult field
"binary"	result.binary.probability
"continuous"	result.continuous.mean, result.continuous.standard_deviation
"multiple_choice"	result.multiple_choice.probabilities
"free_response"	result.free_response.text
"auto"	One of the above fields, inferred from the server-classified answer type

answer_type="auto" classifies the question server-side and populates the matching prediction field. result.usage.classification_cost_usd is set when classification runs (cost is negligible).

See our API reference for more response examples.

Research

Pass research=True to query all default sources, or pass a list to restrict providers:

result = client.predict(
    "Will the Fed cut interest rates in 2026?",
    research=["perplexity", "google_news"],
)

for source in result.sources:
    print(source.title, source.url)
print(result.usage.research_cost_usd)

See our API reference for an up-to-date list of supported sources.

Reasoning effort

result = client.predict(
    "Will the Fed cut interest rates in 2026?",
    reasoning_effort="low",
)

print(result.content)
print(result.usage.total_tokens)

Recommendation: use low reasoning effort if cost and latency outweigh marginal improvements in accuracy.

See Recipes for more forecasting guidelines.