Portfolio concentration
76%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
28 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (24), Lean (1), C++ (1)
Average size
75
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
76%
of the visible star count comes from this organization's top three repositories.
75
stars per repository in this same snapshot.
Python
is the most common language here, with 28 repositories updated in the last 90 days.
Why this rank
This organization stands out because its public portfolio is relatively balanced across 30 repositories.
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 76% 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 (24), Lean (1), C++ (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.
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | facebookresearch/brain2qwerty Non-invasive decoding of typed sentences from MEG and EEG brain recordings using a convolutional encoder, transformer, and character-level language model. | Python | 912 |
| 2 | facebookresearch/meshflow Repository for the CVPR 2026 paper MeshFlow Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer by Weiyu Li, Antoine Toisoul, Tom Monnier, Roman Shapovalov, Rakesh Ranjan, Ping Tan and Andrea Vedaldi. | Python | 510 |
| 3 | facebookresearch/atlas-lean ATLAS Autoformalized Textbook Library At Scale | Lean | 285 |
| 4 | facebookresearch/autoform-bot Autoform Bot | Python | 100 |
| 5 | facebookresearch/Flow-World-Models FlowWM stochastic world modeling via flow matching in DINOv3 feature space, with the FuturePerception (Waymo) benchmark. | Python | 76 |
| 6 | facebookresearch/project_superdex SuperDex brings together a purpose-built physics engine, robotics authoring tools, and a scalable reinforcement learning interface in a unified simulation platform, with VR-based teleoperation and additional capabilities planned for future releases. | C++ | 69 |
| 7 | facebookresearch/AutoPartGen [NeurIPS 2025] AutoPartGen: Autoregressive 3D Part Generation and Discovery. This is a re-implementation of original model. | Python | 64 |
| 8 | facebookresearch/TUA-Bench A benchmark for general-purpose terminal-use agents. | Python | 46 |
| 9 | facebookresearch/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. | Python | 42 |
| 10 | facebookresearch/sparse-delta-memory This repositories contains the reference implementation for the Sparse Delta Memory paper.More precisely, it contains the model definition as well as triton and cuda kernels for the Sparse Delta Memory layer. | Python | 36 |
| 11 | facebookresearch/moe_vie Official code release for "MoE-ViE Mixture of Experts Vision Encoder for Efficient Image and Video Understanding" (ECCV 2026) | Python | 18 |
| 12 | facebookresearch/proxymate Diagnose and correct proxy metrics against a primary outcome across four levels (representativity, unit, estimate, domain). It diagnoses failures, then apply adequate corrections when needed. Supporting code for the paper "proxymate: Diagnosing and Correcting Proxy Metrics for Reliable Inference." | Python | 16 |
| 13 | facebookresearch/kernel_bench_verified Welcome to KernelBench-Verified. This repository provides a robust, realistic evaluation framework for assessing custom CUDA kernels generated by Large Language Models (LLMs). | Python | 14 |
| 14 | facebookresearch/r3d Official source code release for R3D Quantitative 3D Spatial Reasoning for Egocentric Wearables | Python | 13 |
| 15 | facebookresearch/LSD Code for “Speculative Sampling For Faster Molecular Dynamics”. | Python | 11 |
| 16 | facebookresearch/S-EMBER Official codebase for S-EMBER A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval | Python | 7 |
| 17 | facebookresearch/MJEPA Training and evaluation code for our paper "MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning" | Python | 4 |
| 18 | facebookresearch/multicalibration_for_matching Companion repository to the paper "Multicalibration yields better matchings".https//arxiv.org/abs/2511.11413 | Jupyter Notebook | 3 |
| 19 | facebookresearch/digiworld A benchmark for evaluating Computer Use Agents (CUAs) on 15 sandboxed Android apps. DigiWorld has 3.2M+ unique configurations across data, themes, and UI states. | TypeScript | 2 |
| 20 | facebookresearch/GAMUT GAMUT a Two-Level Meta-Rubric Benchmark for Long-Form Factuality | Python | 2 |
| 21 | facebookresearch/reliable-cua Statistical evaluation for PRISM-compliant Computer-Using Agent (CUA) benchmarks. | Python | 1 |
| 22 | facebookresearch/projectaria_timecode_bridge Reference firmware and 3D-printable enclosure for a SubGHz LTC beacon (STEVAL-FKI868V2) that time-aligns Project Aria Gen2 captures with any SMPTE LTC source. | C | 1 |
| 23 | facebookresearch/hst-bench HST-Bench evaluation dataset contains 753 agentic tasks along with the time taken by human annotators to solve each task. This dataset was collected as part of our ICML 2026 paper on Scaling Small Agents Through Strategy Auctions https//arxiv.org/pdf/2602.02751 | Python | 1 |
| 24 | facebookresearch/projectaria_unity_tools Unity toolkit for interacting with Aria devices. | C# | 1 |
| 25 | facebookresearch/outlier_impact This is for a research project on detecting outliers by their impact | Python | 1 |
| 26 | facebookresearch/Sharp_Linear-Minimax_Constants_for_Causal_Smoothing This repo contains the code supporting the paper "Sharp Linear-Minimax Constants for Causal Smoothing" submitted to IEEE TSP | Python | 0 |
| 27 | facebookresearch/abtest_opportunity_cost Code for the paper "A/B testing with opportunity costs: Trading off reward and error control" by Ben Chugg, Artem Vorozhtsov, and Houssam Nassif | Python | 0 |
| 28 | facebookresearch/REAP-pipeline-for-coding-agent-benchmarks Artifact for REAP paper "REAP Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage" | Python | 0 |
| 29 | facebookresearch/SaliMory This is the source implementation for SaliMory Orchestrating Cognitive Memory forConversational Agents. | Python | 0 |
| 30 | facebookresearch/Learning_Regularization_Structure_for_Biosignal_Template_Estimation A repository supporting the paper "Learning Regularization Structure for Biosignal Template Estimation". | Python | 0 |
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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