Portfolio concentration
87%
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 (25), C++ (1), TypeScript (1)
Average size
55
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
87%
of the visible star count comes from this organization's top three repositories.
55
stars per repository in this same snapshot.
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.
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 87% 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 (25), C++ (1), TypeScript (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/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++ | 721 |
| 2 | facebookresearch/context-language-models Official repository for "Context Language Models" | Python | 664 |
| 3 | facebookresearch/swe-sweep How many bugs can LMs find & fix in large codebases? | Python | 59 |
| 4 | facebookresearch/EmoRES-TTS Residual emotion steering for emotional TTS generation. EmoRES-TTS decomposes a mixed-emotion steering vector into a shared axis and an emotion-carrying residual and weight the two independently. | Python | 37 |
| 5 | facebookresearch/moe_vie Official code release for "MoE-ViE Mixture of Experts Vision Encoder for Efficient Image and Video Understanding" (ECCV 2026) | Python | 31 |
| 6 | facebookresearch/WearableQA WearableQA A Benchmark for Health Reasoningover Real-World Wearable Data | Python | 24 |
| 7 | facebookresearch/GenIA Generative Reconstruction with Test-Time Input Alignment | Python | 20 |
| 8 | 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 | 17 |
| 9 | facebookresearch/MJEPA Training and evaluation code for our paper "MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning" | Python | 15 |
| 10 | facebookresearch/UnStep Official implementation of the paper UnStep Training-Free Acceleration of Causal Video Diffusion with Fewer Steps Than Distillation | Python | 14 |
| 11 | facebookresearch/midtraining-distillation Code for our paper: "Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall" | Python | 12 |
| 12 | facebookresearch/robo_jepa OSS of roboJEPA | Python | 11 |
| 13 | 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 | 8 |
| 14 | facebookresearch/E2E-SWE E2E-SWE Benchmarking LLMs on Building Working Codebases from Scratch | Python | 3 |
| 15 | 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 | 3 |
| 16 | facebookresearch/hear_voice_bias_benchmark HEAR (Human-recorded Evaluation of Audio-LLM bias by Real speakers), a large-scale, ecologically valid benchmark comprising real human audio samples from demographically diverse participants. | Python | 2 |
| 17 | facebookresearch/adepts Repo for 'ADeptS-Bench Measuring the Trustworthiness of Computer Use Agents across Devices' | Python | 2 |
| 18 | facebookresearch/schlepp schlepp is the Python package for the Schlepp synthetic dataset, which features multi-camera, multi-actor, multi-modal object manipulation sequences. It provides dense ground-truth optical flow, depth, segmentation, point tracks, oriented bounding boxes, object animation, and MHR body parameters. | Python | 2 |
| 19 | facebookresearch/reliable-cua Statistical evaluation for PRISM-compliant Computer-Using Agent (CUA) benchmarks. | Python | 2 |
| 20 | facebookresearch/GAMUT GAMUT a Two-Level Meta-Rubric Benchmark for Long-Form Factuality | Python | 2 |
| 21 | facebookresearch/logbook Source for Logbook: Extremely Long-form Audio Event Understanding | Python | 1 |
| 22 | facebookresearch/show3d Python API for the SHOW3D dataset (CVPR 2026) https://huggingface.co/datasets/facebook/show3d-dataset | Python | 1 |
| 23 | facebookresearch/Tabula-Rasa Official source code for "Tabula Rasa Monte Carlo estimation of unit-variance noise with controlled spatio-temporal correlation" | Jupyter Notebook | 1 |
| 24 | 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 | 1 |
| 25 | facebookresearch/SaliMory This is the source implementation for SaliMory Orchestrating Cognitive Memory forConversational Agents. | Python | 1 |
| 26 | facebookresearch/best-n-selection-video-reasoning Code for the paper "Selecting or Solving? What Best-of-$N$ Verification Delivers in Video Reasoning" | Python | 0 |
| 27 | facebookresearch/logact Hardening Agentic Systems via a Shared Log Design | Rust | 0 |
| 28 | 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 |
| 29 | facebookresearch/REAP-pipeline-for-coding-agent-benchmarks Artifact for REAP paper "REAP Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage" | 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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