GitStar
Meta ResearchOrganization

Meta Research

@facebookresearch • Open source projects from facebookresearch. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.

Portfolio concentration

82%

Top three share

Shows whether the organization is driven by one breakout repo or several visible projects.

Breadth

30 repos

Visible snapshot

30 repositories updated in the last 90 days.

Leading language

Python

Portfolio mix

Python (22), Jupyter Notebook (3), Rust (1)

Average size

219

Stars per repository

Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.

Updated: 2026-05-27(10h ago)GitHub API fallback30 repositories

Portfolio Shape

82%

of the visible star count comes from this organization's top three repositories.

Average Repository Size

219

stars per repository in this same snapshot.

Current Mix

Python

is the most common language here, with 30 repositories updated in the last 90 days.

Why this rank

This organization stands out because its public portfolio is relatively balanced across 30 repositories.

Balanced portfolio across 30 reposTop 3 share 82%

Organization pages work best when you separate portfolio breadth from flagship concentration. In Meta Research's case, the visible top three repositories account for about 82% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.

The dominant language mix here is Python (22), Jupyter Notebook (3), Rust (1). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.

Source: GitHub API fallback. This is the same cache-first snapshot used by the organization ranking list, so the summary view and the detail view should stay aligned.

Top Repositories

#RepositoryLanguage⭐ Stars
1facebookresearch/tribev2

This repository contains the code to train and evaluate TRIBE v2, a multimodal model for brain response prediction

Jupyter Notebook2.7K
2facebookresearch/vggt-omega

[CVPR 2026 Oral] VGGT Omega

Python2K
3facebookresearch/tuna-2

Official implementation of Tuna-2: Pixel Embeddings Beat Vision Encoders for Unified Understanding and Generation

Python689
4facebookresearch/ProgramBench

Can Language Models Rebuild Programs From Scratch?

Python660
5facebookresearch/neuroai

Python suite for neuroscience research across all modalities.

Python227
6facebookresearch/WavFlow

MultiModal Audio Generation in Raw Waveform Space.

Python106
7facebookresearch/sira

Superintelligent Retrieval Agent (SIRA)

Rust72
8facebookresearch/wybecoder

WybeCoder Verified Generation of Imperative Code with LLMs

Python34
9facebookresearch/dance

Dance is an end-to-end framework that detects and classifies events in EEG signals. In a single forward pass, it extracts a set of events directly from the raw, unaligned recording.

Python16
10facebookresearch/reasoning-memory

Procedural Knowledge at Scale Improves ReasoningThis repository contains the minimal, end-to-end pipeline for reproducing the paper results generate a procedural knowledge datastore, build retrieval indices, run retrieval, perform model rollouts with retrieved subroutines, and filter the samples to output the final metrics.

Python13
11facebookresearch/LAMP

[CVPR 26'] Code for the LAMP research paper.

13
12facebookresearch/lilo

Official Code Repository for the paper "LILO Bayesian Optimization with Natural Language Feedback".

Python8
13facebookresearch/fresco

Official Code Repository for the paper FRESCO Benchmarking and Optimizing Re-rankers for Evolving Semantic Conflict in Retrieval-Augmented Generation

Python5
14facebookresearch/egobabyvlm

Repository for the EgoBabyVLM Challenge

Python4
15facebookresearch/gim

Grounded Integration Measure

Python4
16facebookresearch/secpriv-skill

Unified LLM-agent skill for code review covering both security (CWE) and privacy (GDPR) through one detector-validator methodology. Ships the SKILL.md, a 128-case Python benchmark across 30 canonical categories with a held-out true-negative subset, and the evaluation harness from the SecPriv paper.

Python3
17facebookresearch/Brittlebench

Brittlebench

Python3
18facebookresearch/multiview_hair_capture

Official implementation of "Strand-accurate Multi-view Hair Capture" (CVPR 2019)

C++3
19facebookresearch/multicalibrated_llm_measurement

This repository contains replication materials for the paper "Unbiased Prevalence Estimation with Multicalibrated LLMs"

Python3
20facebookresearch/sondos_fpga

This repo contains the source code for Sondos shell as well as example designs demonstrating RTL interfaces, hardware abstraction layer (HAL) and example SW applications

SystemVerilog3
21facebookresearch/outlier_impact

This is for a research project on detecting outliers by their impact

Python2
22facebookresearch/unity

Models for the work in Fully Self-Supervised Pretraining with Transformers for Recommendation

Python2
23facebookresearch/benchmarking-with-surrogates-paper

This repository contains the code for generating the benchmark results in the paper "How Surrogate Fidelity and Dataset Size Shape Benchmarking Conclusions A Case Study in Ads Ranking", to be submitted to AutoML Conference, 2026

Python2
24facebookresearch/SCRuB-code-open

Code implements analysis for SCRuB (Social Concept Reasoning under Rubric-Based Evaluation) framework for assessing model reasoning capabilities within the domain of social concepts abstract ideas and categories that shape human social life, relationships, and institutions.

Python2
25facebookresearch/simex_deconvolution

This is a research project for robustly estimating underlying effect size distributions when there is measurement noise.

Python2
26facebookresearch/multicalibration_for_matching

Companion repository to the paper "Multicalibration yields better matchings".https//arxiv.org/abs/2511.11413

Jupyter Notebook1
27facebookresearch/compute-optimal-tokenization

The repository contains raw data results and code for scaling laws fitting and visualization used in "Compute Optimal Tokenization" paper.

Python1
28facebookresearch/estimate-level-adjustment

Supporting code material to the paper "Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts".

Jupyter Notebook1
29facebookresearch/shapcpm

Efficient Calculation of Shapley Values in Critical Path Method (CPM) Networks for Concurrent Delay Analysis

Python1
30facebookresearch/projectaria-plugins

Official Claude Code marketplace for Project Aria — install ARK plugins for Aria SDK development, VRS data processing, MPS workflows, and device operations.

HTML0

Next step after the organization read

Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.

How to read this organization snapshot

Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.

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