# google-deepmind/funsearch

Repository: https://github.com/google-deepmind/funsearch
Canonical: https://ross.abutalabs.com/products/funsearch
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2024-02-05T10:32:18+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 72
- inputs: {"age_days": 1014, "days_push": 940, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1110, forks 183 (observed 2026-08-28T04:03:37.583494+00:00)

## What it is
FunSearch is the official repository accompanying DeepMind's Nature 2023 paper on discovering new mathematical results via program search with large language models. It provides discovered results (cap sets, admissible sets, bin packing heuristics, cyclic graph independent sets, corner-free sets) plus a single-threaded implementation of the evolutionary search pipeline.

## Use cases
- reproduce FunSearch results from the Nature paper
- understand how LLM-driven evolutionary program search works
- adapt FunSearch pipeline to custom language models and sandboxes
- explore discovered cap set and admissible set constructions
- evaluate bin packing heuristics discovered by FunSearch
- learn about mathematical discovery with large language models

## When to choose
- you want to study or reproduce the FunSearch method or paper results
- you want to adapt LLM-based program search to your own problems
- you need the discovered combinatorial constructions in numerical format

## When to avoid
- you need a production-ready distributed FunSearch system with LLMs and sandbox included
- you want a turnkey library with pip installation and APIs
- you need support or frequent updates

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: llm-inference, agent-framework, data-generation, benchmarking
- domain: artificial-intelligence, large-language-models, mathematics
- platform: python
- tags: program-search, evolutionary-algorithm, combinatorics, research-code, llm-driven-discovery, jupyter-notebooks, algorithms, research, web-server

## Member repositories
- google-deepmind/funsearch (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:37.583494+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 2026-08-30T06:43:27.123905+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/funsearch (fetched 2026-08-28T04:03:37.583494+00:00, sha 547083ffe205)
- Data as of 2026-08-30T08:39:29.467469+00:00.