---
date: 2026-04-10
title: Free-Form Text Scores
description: Capture open-ended feedback and qualitative annotations with the new TEXT score type.
author: tobiaswochinger
ogImage: /images/changelog/2026-04-10-text-scores.png
canonical: /docs/evaluation/scores/overview
---

> **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 Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated.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/scores/overview) and the API/SDK reference (https://api.reference.langfuse.com).

Langfuse now supports `TEXT` scores for open-ended feedback and qualitative annotations. Use them to capture reviewer notes, user feedback, or other qualitative input directly as scores on traces and observations.

This is especially useful for open coding workflows: collect qualitative labels and notes first, then turn recurring patterns into categorical, boolean, or numeric scores once your taxonomy is clear.

Text scores are stored as strings between 1 and 500 characters and are available via the API and SDKs. They work alongside numeric, categorical, and boolean scores, but are intentionally excluded from score analytics because free-form text cannot be meaningfully aggregated.

## What's New

- Create `TEXT` scores for qualitative feedback and annotations
- Ingest text score values via UI, API, and SDKs
- Use text scores for open coding before formalizing recurring patterns into structured scores

## Get started

- [Score Types](/docs/evaluation/scores/overview#score-types)
- [Scores via SDK](/docs/evaluation/evaluation-methods/scores-via-sdk)

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---

## Agent Instructions

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  ```bash
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  ```

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### 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 <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.