UQLM

What is UQLM?

UQLM is a Python library for detecting hallucinations in the outputs of large language models. After pip install uqlm, you generate responses through a LangChain chat model and UQLM returns a confidence score between 0 and 1 for each one, where higher means a lower chance of error. It groups its scorers into black-box methods that measure consistency across several samples and work with any model, white-box methods that use token probabilities and add little cost, LLM-as-a-judge scorers including a panel of judges, and ensembles that combine scorers and can be tuned on ground-truth answers. Long-text scorers work at the claim level and can refine a response by dropping low-confidence claims, and a CodeGenUQ interface estimates whether generated code is functionally correct without running it. It is free to use and documented with example notebooks.

Use cases

• Score LLM answers for hallucination risk
• Select the most confident response
• Flag low-confidence claims in long text
• Estimate if generated code is correct
• Tune an ensemble on labeled data
• Compare judge models on reliability

Who it's for

• ML engineers
• AI developers
• Researchers
• LLM application teams

Why use UQLM

• Each response gets a numeric confidence score, so you can route low-confidence answers to review.
• Scorer families let you trade cost against compatibility, from cheap token probabilities to multi-sample consistency.
• Claim-level scoring can remove unsupported statements from long outputs.
• It works with whatever LangChain-compatible model you already use.

UQLM features

• Black-box consistency scorers such as semantic entropy
• White-box token-probability scorers
• LLM-as-a-judge scorers and judge panels
• Tunable ensemble scorers
• Claim-level long-text scoring with response refinement
• CodeGenUQ for generated code
• Works with any LangChain chat model
• Results returned as a dataframe
• Example notebooks and documentation site

UQLM integrations

• LangChain
• OpenAI
• Azure OpenAI
• Google Vertex AI
• Ollama

How to get started with UQLM

1. Install the package from PyPI with pip install uqlm.
2. Wrap your model as a LangChain chat model.
3. Choose a scorer class such as BlackBoxUQ, WhiteBoxUQ, LLMPanel, UQEnsemble or LongTextUQ.
4. Call generate_and_score with your prompts, and the number of sampled responses where needed.
5. Read the scores from results.to_df() and set a threshold for review or refinement.

UQLM limitations

• Black-box scorers need multiple generations, which adds latency and cost
• White-box scorers need access to token probabilities
• Scores estimate uncertainty and do not prove an answer is correct

UQLM pricing

UQLM is open source, so you can use and self-host it for free.

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

UQLM alternatives

The main UQLM alternatives listed on Debuly are Ragas, TruLens, Guardrails AI, Arize Phoenix, Cleanlab Trustworthy Language Model and Cleanlab. 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. 

   Cleanlab

   Scores the trustworthiness of AI answers to catch hallucinations, and cleans messy datasets, for RAG, agent and ML teams.

   Developer platformsAutomation and agents
   Contact salesVisit Cleanlab (official site)
3. 

   Cleanlab Trustworthy Language Model

   API that gives any LLM response a trustworthiness score in real time, so you can catch likely errors before users see them.

   Developer platformsAutomation and agents
   Visit Cleanlab Trustworthy Language Model (official site)
4. 

   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)
5. 

   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)
6. 

   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)

UQLM 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

• Logo: from avatars.githubusercontent.com · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

Spot an error? Report an issue with this listing. We also remove a logo or image on the owner's request.