Lightning Rod Python SDK
Foresight returns a calibrated probability for any question about the future through an OpenAI-compatible API.
Trusted for high-stakes predictions by Numinous, Shore Capital Partners, Awardable, ERIS Marketplace, and others. Foresight processes billions of tokens and serves 100k+ inference requests every day.
Documentation · Get an API key · Research paper
⚡ Better AI Predictions
Foresight is served behind an OpenAI-compatible endpoint, so any OpenAI client works — just point base_url at Lightning Rod.
from openai import OpenAI
client = OpenAI(
api_key="your-api-key",
base_url="https://api.lightningrod.ai/v1/openai",
)
completion = client.chat.completions.create(
model="foresight-v4",
messages=[
{"role": "user", "content": "Will the Fed cut rates at its next meeting?"},
],
extra_body={"research": True}, # Auto research the most relevant prediction context
)
print(completion.choices[0].message.content)
See the forecasting guides for how to write good forecasting prompts.
Prefer an SDK helper?
lr.predict() wraps the same API and parses the structured answer for you:
pip install lightningrod-ai
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",
research=True,
)
print(result.binary.probability)
🏗️ Train an expert on your domain
Need a model tuned to your domain? Our platform turns raw sources into labeled datasets and fine-tuned models. Read the Future-as-Label paper or view public models and datasets on Hugging Face.
📚 Learn more
- Documentation & API reference — quickstart, guides, recipes, and the REST API reference
- Quickstart notebook — OpenAI-compatible client and
lr.predict(), side by side (runnable in Colab)
3 maintainers
Metadata
3 maintainers
Release files for lightningrod-ai 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lightningrod_ai-1.1.0.tar.gz | 20.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lightningrod_ai-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 38.1 kB
Release files / lightningrod_ai-1.1.0.tar.gz
| Download URL | lightningrod_ai-1.1.0.tar.gz |
|---|---|
| Size | 20.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
02932b3247c4b46a47548686729740d83db6d2ebe70044056b05eabcfa89a102
|
|
BLAKE2b-256 checksum How to use checksums |
5abddd653ae61aeb9b8bbbacfc486cdab8492604903d777548f500c08ec7a6e4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.11
|
Release files / lightningrod_ai-1.1.0-py3-none-any.whl
| Download URL | lightningrod_ai-1.1.0-py3-none-any.whl |
|---|---|
| Size | 18.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
81f0a80fcf164f2a2dd91e9919452ea747fc923f246fe1e20c38c7b8f408ed5a
|
|
BLAKE2b-256 checksum How to use checksums |
3a02d1569da4b61b10219518e28105955df1511e4747bbc1361706af08a44ca5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.11
|
