GraphCanon updated 5d · GitHub synced 5d
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
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (1100d since push)
- As of 5d
- Provenance
- Not a fork · Personal account
- As of 5d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install rellmSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
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
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 15, 2026)
```python import regexSource link
Tags
README
Installation
pip install rellm
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 example of parsing a context-free grammar (like JSON) with ReLLM, see r2d4/parserllm.
import regex
from transformers import AutoModelForCausalLM, AutoTokenizer
from rellm import complete_re
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
prompt = "ReLLM, the best way to get structured data out of LLMs, is an acronym for "
pattern = regex.compile(r'Re[a-z]+ L[a-z]+ L[a-z]+ M[a-z]+')
output = complete_re(tokenizer=tokenizer,
model=model,
prompt=prompt,
pattern=pattern,
do_sample=True,
max_new_tokens=80)
print(output)
> Realized Logistic Logistics Model
For agents
This page has a .md twin and JSON over the API.