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Embeds a GUI agent in your web page as client-side JavaScript, letting users drive the interface with natural language — it reads the DOM as text (no screenshots) and performs clicks and form fills. Bring your own LLM; no extension or backend required.
Lets AI agents describe interactive UIs as declarative JSON instead of executable code; client apps render the components with native widgets from a pre-approved catalog, keeping agent-generated UI safe across trust boundaries.
Turns papers, repositories, or natural-language research goals into local-first executable 'quests' that automate reproducible experiments, branching, and result-to-paper workflows. Preserves experiment history, supports web/TUI/connectors, and keeps human takeover and inspection simple.
Provides Gymnasium-style APIs and tooling to run isolated, networked execution environments for agentic reinforcement learning. Offers async/sync EnvClients, Docker/Kubernetes container providers, a web UI and CLI for scaffolding and deploying environments (Hugging Face Spaces); experimental and evolving.
Provides a Gymnasium-style API and tooling to create, deploy, and interact with isolated execution environments for agentic RL training. Includes async/sync clients, a web interface, CLI, Docker-based deployment, and Hugging Face Spaces integration.
Packages Every's 'compound engineering' workflow into 26 slash commands for AI coding agents like Claude Code — brainstorm, plan, work, review, debug. Skews effort toward planning and review, and saves each run's lessons so the next task is easier.
Autonomously performs end-to-end data science tasks — from cleaning and exploration to modeling, visualization, and analyst-grade reports — via an agentic LLM. Open-source model, code, datasets and demos; supports vLLM deployment, Jupyter/CLI/Web UIs, and OpenAI-style APIs.
Provides a persistent, dependency-aware structured memory for coding agents — replacing scattered markdown plans with a versioned task/issue graph backed by Dolt. Agent-optimized features include JSON output, dependency tracking, zero-conflict IDs, and semantic compaction for long-horizon workflows.
A dependency-aware issue tracker for AI coding agents that stores tasks as a version-controlled graph in a Dolt database, so agents keep context across long-horizon work. Content-hash IDs prevent merge conflicts when multiple agents edit in parallel.
Provides a set of Claude Code skills that let an LLM-driven agent control Browserbase via browser automation and the official bb CLI — includes browser automation with anti-bot/solver support, cookie sync, fetch/tracing, site-debugging, and serverless function workflows.
A library of 232 ready-made AI agent personas across 16 divisions — engineering, design, marketing, sales, security, finance, and more. Each defines a role, workflow, and concrete deliverables rather than just a prompt template.
Decomposes a financial question into a research plan, then autonomously pulls live market data — income statements, balance sheets, cash flows — and self-checks until confident. Logs every tool call and reasoning step to JSONL scratchpads.