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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.
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.
Self-hostable chat UI that connects to any LLM and adds Agents, Web Search, RAG, connectors, code execution and image generation. Ships connectors to 40+ sources and deployment guides for Docker/K8s. Best for teams needing private, extensible chat platforms.
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.
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.
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.
Role-playing LLM agents — CEO, CTO, programmer, tester — collaborate through staged dialogues to turn a one-line prompt into a working software project. Now generalized into a zero-code platform for building custom multi-agent workflows beyond coding.
Build and deploy enterprise-grade conversational agents with integrated RAG pipelines, workflow orchestration, multi-modal IO, and model-agnostic integrations (private and public LLMs). Designed for self-hosted production with vector stores and tooling integrations.