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rellm

r2d4/rellm

Exact structure out of any language model completion

GitHub

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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511 stars24 forksLast push Aug 10, 2023 Python MIT

Decision brief

rellm is a Python tool that guarantees structured outputs from language model completions by leveraging the Hugging Face Transformers library.

Good fit when

- When you require precise and exact structure in output data generated from any language model, utilizing rellm can ensure consistency.
- If your application needs to process text through Hugging Face's models and demands that outputs strictly adhere to specific formats, rellm is ideal.

Avoid when

- Avoid using rellm if you are not working with the Hugging Face Transformers library or do not need structured output formats.
- If your project can tolerate some level of unstructured or less rigidly formatted outputs from language models, other solutions might be more appropriate.

Observed Jul 12, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report

Maintenance: Dormant (1100d since push); As of Aug 15, 2026
Provenance: Not a fork · Personal account; As of Aug 15, 2026
Security (OSV): No lockfile; As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install rellm

PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

rellm is a tool that ensures structured outputs from language model completions using the Hugging Face Transformers library.

Capability facts

Languages: python
Source: github.language+pyproject.toml · Aug 15, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 15, 2026)

```python import regex

Source link

README

Installation The preliminary results are interesting even for small models, constraining the token space with ReLLM can improve the quality of the completions. Not to mention the ability to more easily parse the output programmatically. Take a look at some of the examples. For an...

For agents

This page has a .md twin and JSON over the API.

/tools/r2d4-rellm.md GET /api/graphcanon/tools/r2d4-rellm GET /api/graphcanon/graph?tool=r2d4-rellm