Drops Claude Code into GitHub Actions so it responds to @claude mentions in PRs and issues — answering questions, reviewing diffs, and committing fixes or features on a branch. Runs on your runners via the Anthropic API, Bedrock, or Vertex AI.
Framework for building an organization's internal coding agents — runs tasks in isolated cloud sandboxes, integrates with Slack/Linear/GitHub, orchestrates subagents, and automates commits/PRs. Built on LangGraph and Deep Agents for easy customization.
Wraps Claude Code as an MCP server that orchestrates 100+ specialized agents into self-organizing swarms — hierarchical, mesh, or adaptive consensus — backed by persistent vector memory, coordination hooks, and secure cross-machine federation.
Extends RAG beyond text: parses PDFs and Office files containing images, tables, equations, and charts, then queries them through one multimodal knowledge graph. Built on LightRAG, it replaces separate parsing and retrieval tools.
Gives AI coding assistants a queryable index of n8n's 2,000+ workflow nodes — their real properties, operations, and 2,300+ templates — so generated workflow JSON validates instead of hallucinating node names and connections.
GPU-accelerated, non-destructive RAW photo editor designed for fast, low-footprint desktop workflows (<20MB). Built with Rust/WGPU/Tauri, it offers real-time 32-bit GPU processing, AI-assisted masking and optional ComfyUI integration for generative edits, plus presets and batch export.
Transforms Claude Code into a structured development platform by injecting behavioral instructions and orchestrating workflows via 30 slash commands. Provides 20 specialized agents and optional MCP server integrations for faster, token‑efficient research and agent-driven dev workflows.
Offline-first knowledge server that bundles local AI chat (Ollama + vector RAG), offline Wikipedia/education/maps, and utility tools behind a Dockerized management UI — designed to keep searchable knowledge available without cloud access.
Browser-based AI development platform that runs tasks inside isolated cloud development environments: natural-language agents read code, run commands, modify files, and integrate results back into Git. Key features include per-task sandboxes, multi-model selection, and an enterprise private-deploy option.
Provides a visual, low-code environment to build, debug, and deploy AI agents—integrates model services (OpenAI, Volcengine), RAG, plugins, workflows, and a Chat SDK for embedding agents into apps.
Bundles Langflow, Docling, and OpenSearch into one installable package so you can ingest messy documents, run agentic retrieval with re-ranking, and chat over your own knowledge base. Ships Python/TS SDKs and a built-in MCP server at /mcp.
Spec-driven agentic dev platform that turns a prompt into requirements, a design doc, and sequenced tasks before any code is written, then implements from the spec. Runs across IDE, CLI, web, and mobile; validates output with property-based tests.