An open-source, free all-in-one workspace for AI Prompt and Skill management, featuring versioned Prompt editing, multi-platform Skill distribution, multi-model parallel testing, and local-first data synchronization.
A local proxy that automatically routes each LLM request to the cheapest still-capable model
The first experimental fully peer-to-peer distributed AGI system where intelligence compounds continuously through autonomous agent networks, supporting decentralized training across heterogeneous devices, P2P inference routing, and a built-in blockchain micropayment economy.
A local-first personal AI agent framework from Stanford that enables offline agent orchestration, skill import, and trace-driven continuous learning through five composable primitives, supporting 10+ inference backends and four interaction modes.
A Docker Compose-based CLI orchestrator for local LLM stacks — spin up pre-wired inference backends, frontend UIs, RAG, voice, image generation, and more with a single command
A Rust-based cross-platform CLI tool that right-sizes LLM models to your system's RAM, CPU, and GPU by detecting specs and recommending optimal models and quantization strategies. Covers 206 models from 57 providers.
An open-source framework for large language model evaluations from the UK AI Safety Institute, featuring a modular Datasets/Solvers/Scorers architecture, multi-model/tool support, sandboxed execution, and 100+ pre-built benchmarks.
An open-source framework for building, evaluating, and training general multi-agent systems. Features natural language agent creation, distributed reinforcement learning training pipeline, and complex environment interactions. Ranks top on authoritative benchmarks including GAIA, OSWorld, and VisualWebArena.
An AI-powered data science team of agents that automates data loading, cleaning, feature engineering, EDA, visualization, and machine learning modeling (H2O + MLflow) through specialized agent collaboration, featuring a Streamlit visual pipeline studio to perform common data science tasks 10X faster.
A generative agent framework inspired by human dual-process theory, combining fast and slow thinking mechanisms with in-context reinforcement learning to efficiently solve complex interactive reasoning tasks.
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