Build LLM apps by chaining nodes on a visual canvas — prompts, branching, RAG, agents, tools — and ship the same graph as an API or hosted app. Bundles a plugin marketplace, model routing across hosted and local providers, and built-in observability.
Enterprise-grade multi-agent orchestration framework that builds, runs, and scales autonomous agent swarms for production. Offers modular swarm architectures, protocol support (MCP, AOP), a marketplace, multi-model provider integrations and observability.
Self-hostable chat client that unifies many LLM providers (OpenAI, Claude, Gemini, Ollama, DeepSeek) behind one UI. Adds file-upload knowledge-base RAG, vision/TTS, an MCP plugin system, and an agent marketplace, with one-click Vercel or Docker deployment.
Runs LLM-generated Python in a Rust sandbox that starts in tens of microseconds (~60µs), with no container overhead. Filesystem, network, and environment access are blocked, and state serializes for pause/resume with per-run resource limits.
Turns local documents into a private, self-hosted ChatGPT-style assistant with no-code agents for web browsing and workflow automation. Runs across LLM providers — OpenAI, Anthropic, Ollama — and routes tools smartly to cut token use.
Adds agent-native UI patterns to apps through chat, generative UI, shared state, human-in-the-loop flows, and AG-UI-based frontend integrations.
Orchestrates LLM-based roles (product managers, architects, engineers) to turn a one-line requirement into user stories, APIs and a starter code repo. SOP-driven multi-agent workflows with CLI and library APIs for prototype generation and agentic development.
Lets LLMs run code and control a user’s computer via natural language (Python, JavaScript, Shell, etc.) with interactive approval. Supports local or hosted models, terminal and Colab/Codespaces integrations, streaming output, and configurable safety/auto-run options.
Gives AI agents persistent long-term memory: ingests documents in any format and continuously builds a self-hosted knowledge graph fusing vector embeddings, graph reasoning, and ontology grounding, so agents recall and reason over connected facts.
Coordinates multiple LLM agents that converse to solve a task, splitting work across customizable roles that call tools, run code, and loop in humans. The v0.4 redesign adds async messaging and Python/.NET interoperability across distributed networks.
Self-hostable platform for building enterprise GenAI apps with visual workflow orchestration — loops, parallelism, human-in-the-loop — plus RAG, agents, unified model management, and in-house OCR for handwriting and rare characters.