k-LLMs implements consensus on top of OpenAI SDK.

Built with 🩷 at retab

k-llms runs the same prompt across multiple LLM instances and merges their outputs into a single output using a consensus algorithm, giving you a single, reliable result with confidence scoring built-in.

pip install k-llms

How it works

1. Parallel execution

Send the same prompt to multiple LLM instances simultaneously.

2. Parse responses

Parse each response into structured data using your schema.

3. Consensus merge

Merge responses using majority voting and confidence scoring.

Basic usage

k-llms implements the K-LLMs methodology where multiple models evaluate the same prompt. Results are automatically reconciled into a single output with confidence scoring.

Works with any OpenAI-compatible models. Supports structured output validation with Pydantic models.

from k_llms import KLLMs
from pydantic import BaseModel

client = KLLMs()

class Event(BaseModel):
    name: str
    date: str
    participants: list[str]

response = client.chat.completions.parse(
    model="gpt-4.1-nano",
    messages=[{"role": "user", "content": "Extract event info: Alice and Bob are going to a science fair on Friday."}],
    response_format=Event,
    n=4,  # Run 4 parallel calls
)

print("Content:", response.choices[0].message.content)  # Event dict as a json string
print("Parsed:", response.choices[0].message.parsed)  # Event object
print("Likelihoods:", response.likelihoods)  # Confidence scores

for i in range(1, len(response.choices)):
    print(f"Choice {response.choices[i].index}: {response.choices[i].message.content}")

Why k-llms?

Enterprise-grade LLM consensus for everyone

k-llms gives you the state-of-the-art multi-model strategy popularized by Palantir's k-llm's approach with a single API call.

By using consensus voting across multiple models, you get a consistent result with higher accuracy, fewer hallucinations, and field-level confidence scores that help quantify uncertainty.

Palantir presentation explaining multi-LLM consensus

Watch: How k-llms unlocks reliability

A quick primer on the strategy that powers k-llms.