An agentic evaluation framework

Platform for stateful agents: AI with advanced memory that can learn and self-improve over time.

Official Code of Memento: Fine-tuning LLM Agents without Fine-tuning LLMs

Readymade evaluators for agent trajectories

A Docker sandbox template for running GitHub Copilot CLI in an isolated environment, similar to how Docker supports Claude Code and Gemini CLI via docker sandbox run

Zotero MCP: Connects your Zotero research library with Claude and other AI assistants via the Model Context Protocol to discuss papers, get summaries, analyze citations, and more.

DSPy: The framework for programming—not prompting—language models

LLM Wiki is a cross-platform desktop application that turns your documents into an organized, interlinked knowledge base — automatically. Instead of traditional RAG (retrieve-and-answer from scratc…

AI agents running research on single-GPU nanochat training automatically

From a goal to a task DAG, automatically. TypeScript-native multi-agent orchestration.

Open-source Claude Code skills and Codex skills for AI-first work. Audit, re-engineer, and bootstrap projects with AI-first design principles.

The Multilingual Entity Linking of Occupations (MELO) Benchmark

WorkRB: Work Research Benchmark

SKILLSPAN: Competences as Spans for Skill Extraction from Job Postings

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Late Interaction Models Training & Retrieval

GitHub Mirror of RecPack: Experimentation Toolkit for Top-N Recommendation (see https://gitlab.com/recpack-maintainers/recpack)

State-of-the-Art Embeddings, Retrieval, and Reranking

The code used to evaluate embedding models on the Massive Legal Embedding Benchmark (MLEB).

MTEB: Massive Text Embedding Benchmark

🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL

In this codebase we establish a benchmark for egocentric user adaptation based on Ego4d.First, we start from a population model which has data from many users to learn user-agnostic representations…

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

[Spotlight ICLR 2023 paper] Continual evaluation for lifelong learning with neural networks, identifying the stability gap.

arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

HiPlot makes understanding high dimensional data easy

CVPR 2022 Continual Learning in Computer Vision Workshop Challenge

MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.

Multi-Joint dynamics with Contact. A general purpose physics simulator.