Experiments on whether language models can learn to detect read-only activation probes
FOREST is a research agent for long-horizon science, where evidence gets lost, errors compound, and goals drift. It explores ideas, reads literature, forms hypotheses, runs real experiments, analyzes results, revises paths, and builds a full paper—all in an editable graph you can inspect, branch, redirect, with claims and figures linked to evidence
Recursive self-improvement for robot manipulation, with real-robot experiments and shared executable skills.
:hedgehog: PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all from Slack, web, desktop, or the MCP.
Semi-automated research assistant for academic research and software development. Supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication.
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
Open-source AI marketing skills — growth experiments, sales pipeline, content ops, outbound, SEO, and finance automation
For running psychology and neuroscience experiments
Video masking tools for ComfyUI: process a masked subject at high resolution inside a moving crop, then paste it back without jitter or visible seams
Calculator that computes statistical power, MDE and required sample size for switchback experiments; based on paper Pankratev (2026) 'Powerful Switchback Experiments -- Or Not?'.
Tool for real-time visualization, monitoring and collaborative analysis of AI/ML experiments and live data. Supports Python, PyTorch/Torch, NumPy, TensorFlow/Keras https://visdom.dev
Evaluation and Tracking for LLM Experiments and AI Agents
A blueprint-driven AutoResearch runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review.
Gin provides a lightweight configuration framework for Python
Hyperparameter Experiments with TensorFlow and Keras
bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent
A Python package for causal inference in quasi-experimental settings
A platform for managing machine learning experiments
Scientific measurement library for instruments, experiments, and live-plotting
EEG Experiments in Python
Python framework for multiplayer decision games, behavioral experiments, and surveys
Bayesian Optimization and Design of Experiments
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.
An open-source Python library that automates the analysis and reporting of machine learning experiments, generating structured and comprehensive reports that make model performance, results, and key insights easier to understand and evaluate by data professionals, with optional Groq-powered AI explanations and recommendations for deeper insights.
A library of experiments using frontier LLMs/VLMs for robotics tasks, currently focusing on GPT-6 Astra.
A collection of real-time ASCII rendering experiments built with Python, mathematics, and terminal graphics.
Auditable LLM multi-agent town simulation with AgentScope, Mesa, replay, policy experiments, and Streamlit
Reproducible Microduck RL experiments, policies, simulation assets, and hardware add-ons
Code, data and findings behind researchchamber.fun: experiments on open AI models with a pain pattern switched on
A self-evolving skill framework for evaluable agent tasks, with reproducible multi-domain validation experiments.
An agent skill to discover TypeSafe Jev opportunities, design typed questions, and learn from recent community experiments.
面向 AI/CS 科研的多 Skill 实验智能体:助力全自动科研实验,以 Claim 驱动、骨干实验优先、独立结果审计与 Evidence Freeze,把发散的模型能力收束为可追溯、可复现、可收敛的论文证据。
Experimental Linux bring-up research for the M4 MacBook Air (T8132/J713): device-tree patches, kernel experiments, and documented findings. Not an installer.
An open-source tool for running controlled behavioral experiments on AI systems at scale.
PromptLedger - version prompt templates, run deterministic comparison snapshots, score candidate responses, and ship diffable experiment reports. Python core plus a Go companion CLI.
🦉 Data Versioning and ML Experiments