Cleanlab Trustworthy Language Model

What is Cleanlab Trustworthy Language Model?

The Trustworthy Language Model, or TLM, is an API from Cleanlab that estimates how reliable an answer from a language model is. There are two ways to use it. You can keep your own LLM and call TLM's trust scoring method on each prompt and response pair, which works for any model and even for text written by people, or you can call TLM in place of your LLM so that one request returns both the answer and a score. Documented uses include LLM evaluation, data labeling and classification, as well as RAG, chatbots, agents, summarization, tool calls, structured outputs and data extraction, and it can explain why a score is low. Quality presets trade accuracy against latency and cost, and private deployment options exist. A Python client installs with pip, and the tutorials point to a free API key. The pricing page linked from the docs was not available, so plan prices are not confirmed; check Cleanlab for current pricing. Benchmark figures such as a 3x precision gain in RAG are Cleanlab's own.

Use cases

• Score LLM answers for reliability
• Detect incorrect RAG responses
• Gate agent tool calls on confidence
• Flag weak data extraction results
• Evaluate LLM outputs at scale
• Review auto-labeled data quality

Who it's for

• ML engineers
• AI developers
• Enterprise AI teams

Why use Cleanlab Trustworthy Language Model

• It needs no training on your data, so you can start without preparing or labeling a dataset.
• A score per answer gives you a number to threshold when deciding whether to show, retry or escalate.
• It works with any LLM, so you can keep your existing model and add scoring to it.
• Presets let you tune latency and cost for the task at hand.
• Explanations help engineers see why a response looked untrustworthy.

Cleanlab Trustworthy Language Model features

• Trustworthiness score for any prompt and response pair
• Scoring of outputs from any LLM or from humans
• Drop-in generation that returns an answer plus a score
• Five quality presets, from base to the highest setting
• Task settings for default, classification and code generation
• Explanations for why a response scored low
• TrustworthyRAG with evals for groundedness and context sufficiency
• TLM Lite for using different LLMs to generate and to score
• TLM Calibrated for aligning scores with your own ratings
• Python client installed with pip

Cleanlab Trustworthy Language Model integrations

• OpenAI Agents SDK
• OpenAI Chat Completions
• Google Colab

How to get started with Cleanlab Trustworthy Language Model

1. Install the cleanlab-tlm Python package and set your API key in the CLEANLAB_TLM_API_KEY environment variable.
2. Either generate a response with your own LLM and pass the prompt and response to the trust scoring method, or call TLM's prompt method to get both.
3. Pick a quality preset and task type to balance score reliability against latency and cost.
4. Read the trustworthiness score, and turn on the explanation option to see why a score is low.
5. Act on low scores with a fallback such as an abstention message, a retry or human approval.

Cleanlab Trustworthy Language Model limitations

• Scores estimate error likelihood and do not guarantee correctness
• Higher quality presets take longer and cost more
• Plan prices are not shown in the documentation reviewed
• Benchmark claims are published by Cleanlab itself

Cleanlab Trustworthy Language Model pricing

We couldn't confirm pricing on the official site. Check help.cleanlab.ai for current prices.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Cleanlab Trustworthy Language Model alternatives

The main Cleanlab Trustworthy Language Model alternatives listed on Debuly are Galileo, Arize Phoenix, Guardrails AI, TruLens, UQLM and Ragas. The table compares pricing and platforms; order is A–Z.

1. 

   Arize Phoenix

   Open-source tool for tracing, evaluating and experimenting on LLM apps and agents, free to self-host or run locally.

   Developer platformsAutomation and agents
   Open sourceVisit Arize Phoenix (official site)
2. 

   Galileo

   AI observability and eval platform that tunes evals to your data and turns them into guardrails for LLM apps and agents.

   Developer platformsAutomation and agents
   FreemiumVisit Galileo (official site)
3. 

   Guardrails AI

   Open-source framework that wraps LLM apps with validators for PII, hallucination and policy checks, plus Snowglobe simulation testing.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Guardrails AI (official site)
4. 

   Ragas

   Open-source Python library for evaluating LLM and RAG applications with metrics, experiments and synthetic test data generation.

   Developer platformsCodingResearch and search
   Open sourceVisit Ragas (official site)
5. 

   TruLens

   Open-source, OpenTelemetry-native Python library for tracing AI agents and RAG apps and scoring each step with LLM judges.

   Developer platformsCodingAutomation and agents
   Open sourceVisit TruLens (official site)
6. 

   UQLM

   Open-source Python library that scores LLM responses for hallucination risk using uncertainty quantification methods.

   Developer platformsCoding
   Open sourceVisit UQLM (official site)

Cleanlab Trustworthy Language Model FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

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