AI & machine learning

Agents: launch agents, resolve approvals and questions, manage configs, secrets and machines

Agent canvas: build, run and schedule graphs, with MCP tools, webhooks and connected integrations

AI assistant workspace: manage agents, files, skills, members, approvals and billing (36 of 843 ops shown)

Memory API: store and search typed facts, entities and history for AI agents

AI model gateway: chat, embeddings, images, audio, video, batches and files via OpenAI- and Anthropic-style endpoints

Generative AI: queue image and video generation jobs, check status, fetch results, and call chat models

LLM gateway: chat, embeddings, images and audio, plus API keys, batches and credits

AI agents: configure conversational agents, channels, conversations, prompts and tools for voice and SMS

Orchestration: distribute tasks across agent CLIs, manage sessions, MCP servers, schedules and budgets

LLM observability: trace requests, manage prompts and evaluators, track costs, and query metrics

AI models: chat, embed and rerank text, plus fine-tuning, datasets, batches and connectors

Integrations: create AI consciousnesses, upload memories, run chat, voice interviews and chatrooms

AI agent sessions: run agents, manage their configs, budgets, billing and API keys

Hosted AI agents and sandboxes: deploy, run and wire agents to messaging, email and billing

AI generation: create images, video, 3D models and audio, plus enhance and upscale media

Agents and skills platform: manage agent identities, mindsets, skill catalogs, folder syncs and integration access

Agents and swarms: run multi-agent completions, batch workflows, chat-compatible calls, logs and rate limits

AI model proxies (OpenAI, Anthropic), file uploads, email auth and run tracking via one brain gateway

Agent hosting: create long-lived agents, sessions, files, memory, vaults and webhooks over HTTP

AI agents: manage agent workspaces, tools, scheduled tasks and conversation invocations

Voice AI agents: simulate, test and analyse calls, chats, metrics and customer flows

Agent and swarm orchestration: run multi-agent completions, batch and graph workflows, models, logs and usage metrics

LLM platform: manage agents, knowledge bases, datasets, evals, prompts, traces and logs

LLM observability: trace spans, datasets, experiments, prompts, annotations and projects