{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Cleanlab TLM scores the trustworthiness of LLM responses in real-time, detecting hallucinations and incorrect outputs by evaluating every response against the original prompt.","skillfed_tags":["llm-safety","api-client","hallucination-detection"],"use_cases":["Score responses from your own LLM pipeline before returning them to users, filtering out low-confidence outputs.","Build a RAG system that flags uncertain retrieval-augmented answers and triggers fallback logic or human review.","Evaluate data-extraction or tagging tasks where hallucinated field values would corrupt downstream processes.","Monitor agent outputs in real-time to detect when an agent has generated an unreliable response.","Benchmark the quality of different LLM models or prompts by comparing their trustworthiness score distributions."],"what_it_does":"Cleanlab TLM is a Python client for a remote trustworthiness-scoring service that evaluates LLM outputs in real-time. It wraps HTTP calls to Cleanlab's API to assign a confidence score (0\u20131) to any LLM response, flagging likely hallucinations or incorrect answers by comparing the response against the prompt. The package can either score responses you've already generated from another LLM, or generate and score responses itself by calling a model (GPT, Claude, etc.) on your behalf.\n\nThe service is designed for production use in RAG systems, agents, and data-extraction pipelines where incorrect LLM outputs carry real cost. It depends on aiohttp, requests, pandas, tqdm, and a few utility libraries\u2014all standard, low-friction dependencies. You must obtain and configure a free API key before use, and all scoring happens remotely on Cleanlab's servers, not locally.","worth_installing":"Yes, if you need real-time hallucination detection for production LLM systems and can accept a remote API dependency. The package is stable, has low install friction, and covers a genuine gap in local hallucination detection. However, the aging maintenance signal (only 24 stars, no recent activity beyond the last commit date) and reliance on an external API key and service mean you should verify the API's stability and cost structure for your use case before committing to it at scale."},"id":"cleanlab-tlm","links":{"html":"https://skillfed.io/packages/cleanlab-tlm","md":"https://skillfed.io/packages/cleanlab-tlm.md","pypi":"https://pypi.org/project/cleanlab-tlm/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-11-21","license_spdx":"MIT","license_treatment":"permissive","name":"cleanlab-tlm","python_support":"supports_current","summary":"Python client library for Cleanlab Trustworthy Language Model"},"popularity":{"monthly_downloads":163721,"position":10564,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.39"}