Cleanlab Trustworthy Language Model (TLM) - Trust Scores for every LLM output

In one line of code, Cleanlab TLM adds real-time evaluation of every response in LLM, RAG, and Agent systems.

Setup

TLM requires an API key. Get one here for free.

export CLEANLAB_TLM_API_KEY=<YOUR_API_KEY_HERE>

Install the package:

pip install cleanlab-tlm

Usage

TLM automatically scores the trustworthiness of responses generated from your own LLM in real-time:

from cleanlab_tlm import TLM

tlm = TLM(options={"log": ["explanation"]})
tlm.get_trustworthiness_score(
    prompt="What's the third month of the year alphabetically?",
    response="August"  # generated from any LLM model using the same prompt
)

This returns a dictionary with trustworthiness_score and optionally requested fields like explanation.

{
  "trustworthiness_score": 0.02993446111679077,
  "explanation": "Found alternate plausible response: December"
}

Alternatively, you generate responses and simultaneously score them with TLM:

tlm = TLM(options={"log": ["explanation"], "model": "gpt-4.1-mini"})  # GPT, Claude, etc.
tlm.prompt("What's the third month of the year alphabetically?")

This additionally returns a response.

{
  "response": "March.",
  "trustworthiness_score": 0.4590804375945598,
  "explanation": "Found alternate plausible response: December"
}

Why TLM?

• Trustworthiness Scores: Every LLM response is scored via state-of-the-art uncertainty estimation, helping you reliably gauge the likelihood of hallucinated/incorrect responses.
• Higher accuracy: Rigorous benchmarks show TLM consistently produces more accurate scores than other hallucination detectors and responses than other LLMs.
• Scalable API: TLM is suitable for all enterprise applications where correct LLM responses are vital, including data extraction, tagging/labeling, Q&A (RAG), Agents, and more.

Documentation

Comprehensive documentation and tutorials can be found here.

License

cleanlab-tlm is distributed under the terms of the MIT license.