Meta Research
64%
of the visible star count comes from this organization's top three repositories.
250
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 64% 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), Jupyter Notebook (3), HTML (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.
Top Repositories
| # | Repository | Language | ⭐ Stars | 🍴 Forks | Updated |
|---|---|---|---|---|---|
| 1 | facebookresearch/HyperAgents Self-referential self-improving agents that can optimize for any computable task | Python | 2.2K | 282 | 1 weeks ago |
| 2 | facebookresearch/tribev2 This repository contains the code to train and evaluate TRIBE v2, a multimodal model for brain response prediction | Jupyter Notebook | 1.8K | 399 | 1 weeks ago |
| 3 | facebookresearch/ShapeR Code for the ShapeR research paper | Python | 750 | 53 | 1 months ago |
| 4 | facebookresearch/EUPE Efficient Universal Perception Encoder: a single on-device vision encoder with versatile representations that match or exceed specialized experts across multiple task domains. | Python | 471 | 28 | 6 days ago |
| 5 | facebookresearch/Action100M A Large-scale Video Action Dataset | Python | 445 | 12 | 2 months ago |
| 6 | facebookresearch/actionmesh 🎬ActionMesh: A fast video to animated mesh model with unprecedented quality. Generate animated mesh seamlessly importable into any 3D software in less than a minute. | Python | 328 | 26 | 2 days ago |
| 7 | facebookresearch/boxer Code for the Boxer research paper | Python | 324 | 29 | 3 days ago |
| 8 | facebookresearch/ai4animationpy A Python framework for AI-driven character animation using neural networks. | Python | 308 | 27 | 5 days ago |
| 9 | facebookresearch/lagernvs Official code for "LagerNVS Latent Geometry for Fully Neural Real-time Novel View Synthesis" (CVPR 2026) | Python | 290 | 10 | 3 days ago |
| 10 | facebookresearch/tensor-layouts A pure-Python implementation of the Nvidia CuTe layout algebra intended to be approachable and easy to learn. | Python | 164 | 10 | 3 days ago |
| 11 | facebookresearch/repoprover Research code base for Automatic Textbook Formalization | Python | 128 | 6 | 1 weeks ago |
| 12 | facebookresearch/airs-bench AIRS-Bench: an AI Research Science benchmark for quantifying the end-to-end AI research abilities of LLM agents | Python | 77 | 6 | 3 weeks ago |
| 13 | facebookresearch/dexwm Official code and data from DexWM ("World Models Can Leverage Human Videos for Dexterous Manipulation"). | Python | 39 | 1 | 2 days ago |
| 14 | facebookresearch/sphere-encoder PyTorch Implementation of Image Generation with a Sphere Encoder | Python | 32 | 4 | 1 months ago |
| 15 | facebookresearch/egagent Code for "Agentic Very Long Video Understanding" (EGAgent) [ACL 2026 Main] | Python | 29 | 3 | Yesterday |
| 16 | facebookresearch/lst Code for Latent Speech-Text Transformer (LST) | Python | 14 | 2 | 1 months ago |
| 17 | facebookresearch/algebraic-combinatorics Automatic textbook formalization of Grinberg Algebraic Combinatorics | HTML | 10 | 5 | 1 weeks ago |
| 18 | facebookresearch/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. | Python | 9 | 1 | 1 weeks ago |
| 19 | facebookresearch/flowception Authors implementation of "Flowception Temporally Expansive Flow Matching for Video Generation". | Jupyter Notebook | 9 | 0 | 1 months ago |
| 20 | facebookresearch/projectaria_gen2_depth_from_stereo A tutorial and a set of tools to compute depth-from-stereo with Project Aria Gen2 devices. This includes stereo image rectification as well as disparity estimation | Jupyter Notebook | 9 | 2 | 1 months ago |
| 21 | facebookresearch/wybecoder WybeCoder Verified Generation of Imperative Code with LLMs | Python | 6 | 1 | 1 weeks ago |
| 22 | facebookresearch/bites Bayesian Inference & Tooling for Experimentation Systems | Python | 5 | 1 | Yesterday |
| 23 | facebookresearch/MRSQ MRS.Q is a model-based reinforcement learning algorithm that selects actions with search. | Python | 5 | 0 | 1 months ago |
| 24 | facebookresearch/physkin Physics-based bone-driven neural garment animation | Python | 4 | 1 | 1 weeks ago |
| 25 | facebookresearch/VoxelCodeBench Code for VoxelCodeBench. | Python | 4 | 0 | 5 days ago |
| 26 | facebookresearch/MuLoCo MuLoCo Muon is a practical inner optimizer for DiLoCo | Python | 4 | 1 | 1 months ago |
| 27 | facebookresearch/ads_model_kernel_library High-performance GPU kernels for Ads and Recsys model training, independently implemented and optimized for real-world workloads and model-specific input characteristics. | Python | 4 | 2 | 4 days ago |
| 28 | facebookresearch/GISTBench GISTBench Dataset and Evaluation Code | 3 | 0 | 2 weeks ago | |
| 29 | facebookresearch/adaptive_exploration_latent_state_bandits Implementation of the proposed algorithms and simulation studies in Adaptive Exploration for Latent-State Bandits | Python | 2 | 0 | 1 months ago |
| 30 | facebookresearch/spatio_directional_hash_encoding We propose a new spatio-directional neural encoding for Neural Rendering applications that is compact and efficient, and supports all-frequency signals in both space and direction. | Python | 1 | 0 | 1 months ago |
How to Read This 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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