Connect LLMs to major chat platforms so teams can build, deploy, and operate multi-platform AI chatbots and agents. Provides multi-platform adapters, a plugin marketplace, an MCP server and built-in RAG plus production features like access control, rate limiting and monitoring.
Builds no-code automations with TypeScript-based integrations, AI pieces, human-in-the-loop steps, and MCP exposure for community and product workflows.
Runs an agentic RAG loop over scientific papers: searches literature, gathers and re-ranks evidence chunks, then answers with in-text citations. Adds metadata-aware embeddings, retraction checks, and contradiction detection across full PDFs.
Provides a minimal, Zig-written headless browser tailored for AI agents and automation — runs JavaScript, supports key Web APIs, exposes the Chrome DevTools Protocol for Puppeteer/Playwright, and targets low memory usage and fast startup for large-scale scraping and agent workflows.
Visual canvas for composing, testing, and deploying LLM-based pipelines and multi-agent workflows. Supports major LLMs and vector databases, exports flows as APIs or MCP servers, and offers a desktop bundle for local experimentation and iteration.
Maps your existing C#, Python, or Java functions into a form AI models can invoke, then translates model requests into real function calls and feeds results back. Model-agnostic middleware: swap in newer models without rewriting your app.
Drives autonomous penetration testing and CTF solving via cooperating LLM sessions that track a pentest task tree. Scored 86.5% on the XBOW benchmark suite at ~$1.11 per solved task, and works with OpenAI, Claude, Gemini, and local Ollama models.
Framework for building multi-agent systems where LLM agents take roles and converse to complete tasks via inception prompting, with no human in the loop after the initial brief. Used to auto-generate instruction data and run large-scale agent simulations.
Runs AI-generated code in secure, isolated cloud sandboxes you control via Python or JavaScript SDKs; supports self-hosting (Terraform) and AWS/GCP, enabling agents and code-interpreting workflows to execute real-world tools safely.
Builds production RAG systems around deep document understanding, explainable chunking, hybrid retrieval, citations, and agent workflows for messy enterprise documents.
Puts OpenAI-, Anthropic- and Ollama-compatible endpoints in front of 60+ inference backends, so existing client code runs unchanged against local models for text, vision, audio, image and embeddings. Runs CPU-only or accelerated, data stays local.