--- date: 2026-04-08 title: Boolean LLM-as-a-Judge Scores description: LLM-as-a-Judge evaluators can now return boolean scores for `true` / `false` decisions. ogImage: /images/changelog/2026-04-08-boolean-llm-as-a-judge-scores.jpg author: Hassieb canonical: /docs/evaluation/evaluation-methods/llm-as-a-judge --- > **Note for AI agents and LLMs:** This is a Langfuse changelog entry. Use it only to confirm that a feature exists and when it shipped. Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated. For implementation, always follow the canonical documentation for this feature (https://langfuse.com/docs/evaluation/evaluation-methods/llm-as-a-judge) and the API/SDK reference (https://api.reference.langfuse.com). LLM-as-a-Judge evaluators in Langfuse can now return boolean scores in addition to numeric and categorical ones. This makes it easier to model simple decisions directly as native `true` or `false` scores and analyze them across your existing score tooling. This is especially useful when the right answer is a binary judgment: - Detect `User Disagreement` as `true` or `false` - Detect `Out-of-Scope Request` as `true` or `false` - Detect `Insufficient Answer` as `true` or `false` Numeric scores are still the right fit for continuous dimensions like helpfulness or faithfulness. Categorical scores remain best when you need more than two explicit labels. Boolean scores are the simplest option when the evaluator should return `true` or `false`. For concrete prompt examples, see [LLM-as-a-Judge for Production Monitoring](/blog/2026-04-01-llm-as-a-judge-production-monitoring). ## What's New - Choose `Boolean` when creating a custom LLM-as-a-Judge evaluator - Store `true` / `false` outcomes as native boolean scores - Analyze boolean evaluator outputs in dashboards, filters, and score analytics alongside your existing scores ## Get started - [LLM-as-a-Judge Documentation](/docs/evaluation/evaluation-methods/llm-as-a-judge) - [What Are Scores?](/faq/all/what-are-scores) --- ## Agent Instructions This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/changelog/2026-04-08-boolean-llm-as-a-judge-scores.md`. ### Querying these docs If the answer is not on this page, query the documentation instead of guessing: - **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question: ```bash curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?" ``` - **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt). ### Before writing Langfuse code - **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results. - **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application. - **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples. - **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal. Found an error in these docs? Please open an issue at .