Generates production-grade synthetic datasets from scratch or from seed data using dependency-aware samplers, LLM-backed text columns, built-in validators, previewing, and LLM-as-judge scoring.
A stateful agent harness, shipped as a CLI, whose agents keep memory, skills, and prompts across sessions instead of resetting each run. Context is git-versioned via MemFS, and agents can rewrite their own prompts and skills over time.
Turns content briefs into SEO-optimized long-form articles via chained Claude Code commands and specialized subagents. Pulls live GA4, Search Console, and DataForSEO data to ground keyword research, drafting, and on-page optimization in real rankings.
Automates multi-step web tasks by perceiving webpages as pixels and issuing low-level mouse, keyboard and scroll actions. A 7B-parameter multimodal agent trained on 145K synthetic trajectories (FaraGen), designed for on-device deployment and efficient task completion (~16 steps/task).
Agent memory that learns over time instead of just recalling past chats: retain/recall/reflect primitives turn interactions into facts, experiences, and mental models. Reports top LongMemEval scores; self-hostable with Python and Node SDKs.
Visual workspace for managing multiple LLM-powered coding agent sessions and iterating on code, docs, and mockups. Combines WYSIWYG editors, session kanban, task tracking, and git tooling so developers can review, approve, and integrate agent-generated changes.
Builds knowledge-grounded AI agents by combining hybrid RAG retrieval with a visual, block-based workflow editor, keeping question-answering tied to your own data. Supports document import, reranking, MCP, and self-hosted deployment.
Automatically generates complete short-form videos from a single topic: drafts script with an LLM, produces AI images/video, synthesizes multilingual TTS (including voice cloning), adds background music, and composes the final video. Supports local ComfyUI/RunningHub or direct model APIs and customizable templates.
Aggregates SEC EDGAR filings into raw files, parsed plaintext, and rich filing metadata for LLM training and retrieval. Includes ~8.05M filings (~590 GB, ~43B tokens), per-filing token counts, and parsed outputs; Apache-2.0.
Provides 2 million synthetic, expert-verified coding examples with step-by-step reasoning and executable solutions for fine-tuning instruction-following and code-generation models. Curated through multi-stage filtering and automated test validation to prioritize correctness and reasoning.
Agent-first development platform: spawn autonomous agents that plan, edit, run terminals, and drive a browser to verify their own work, returning reviewable artifacts like plans and screenshots. Defaults to Gemini 3 Pro; also runs Claude and GPT-OSS.
Wraps LangGraph in an opinionated harness giving an agent planning, file read/write, sub-agent delegation, and persistent memory out of the box. Aimed at long-horizon work where plain ReAct loops exhaust their context window.