# maitrix-org/llm-reasoners

A library for advanced large language model reasoning

Repository: https://github.com/maitrix-org/llm-reasoners
Canonical: https://ross.abutalabs.com/products/llm-reasoners
Homepage: https://www.llm-reasoners.net/
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-06-10T02:19:40+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 25, release rhythm 8, longevity 84
- inputs: {"age_days": 1189, "days_push": 450, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2343, forks 204 (observed 2026-08-28T04:06:39.442302+00:00)

## What it is
LLM Reasoners is a Python library that provides a standardized modular framework for advanced LLM reasoning, implementing algorithms like MCTS (RAP), Tree-of-Thoughts, Guided Search, and GRACE with pluggable search algorithms, reward functions, and world models. It includes an interactive reasoning-tree visualizer, AutoRace reasoning-chain evaluation, and integrates with high-performance backends such as SGLang, HuggingFace Transformers, and OpenAI API.

## Use cases
- implement tree-of-thoughts reasoning with LLMs
- run MCTS-based planning for LLM problem solving
- compare different LLM reasoning algorithms on benchmarks
- visualize and debug LLM reasoning trees
- evaluate reasoning chains automatically with AutoRace
- scale inference-time compute with process reward models
- build custom reward functions and world models for LLM search

## When to choose
- you need state-of-the-art reasoning/search algorithms (ToT, RAP, MCTS) for LLMs
- you want a modular framework to prototype new reasoning methods
- you need to visualize or evaluate multi-step reasoning chains
- you want efficient reasoning with SGLang-backed structured generation

## When to avoid
- you only need simple single-shot prompting or chat completions
- you need a production chatbot or RAG pipeline rather than reasoning research
- you work outside Python or need a no-code tool
- you require fine-tuning or training of LLMs rather than inference-time reasoning

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-inference, agent-framework, prompt-engineering, data-visualization
- domain: large-language-models, artificial-intelligence, machine-learning
- platform: python, cross-platform
- tags: llm-reasoning, tree-of-thoughts, mcts, chain-of-thought, search-algorithms, reward-functions, world-model, sglang, reasoning-visualization, auto-race, natural-language-processing, research

## Member repositories
- maitrix-org/llm-reasoners (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:39.442302+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:37:14.989190+00:00, confidence not recorded.
  - readme: https://github.com/maitrix-org/llm-reasoners (fetched 2026-08-28T04:06:39.442302+00:00, sha b75f07e1e419)
  - homepage: https://www.llm-reasoners.net/ (fetched 2026-08-29T10:17:45.558052+00:00, sha dcef84d7b61a)
- Data as of 2026-08-30T08:39:29.467469+00:00.