Build Siesta AI into anything
Wherever your products, scripts, and other AI tools live, Siesta AI meets them there. Reach its agents, chat, and knowledge through a REST API, an embeddable web widget, a command-line tool, an MCP server, an agent-to-agent protocol, and a realtime streaming API.
# Ask an agent, straight from your stack $ curl https://api.siesta.ai/v1/chat \ -H "Authorization: Bearer $SIESTA_KEY" \ -d '{ "agent_id": "sales-assistant", "message": "Summarize the new leads from today" }' # → streams a grounded answer from your data
Build on Siesta AI, your way
Whether you’re shipping a product feature, wiring up an internal script, or connecting another AI system, there’s a clean interface waiting for it.
The API everything runs on
Send messages, spin up and configure agents, react to events with webhooks, and manage the knowledge your agents can reach, all through one clean, well-documented REST API that any language can call.
// Send a message to one of your agents const res = await fetch("https://api.siesta.ai/v1/chat", { method: "POST", headers: { Authorization: `Bearer ${process.env.SIESTA_KEY}`, "Content-Type": "application/json" }, body: JSON.stringify({ agent_id: "support-bot", message: "How do I reset my password?" }) }); const { answer } = await res.json();
Add AI to any site in one line
Paste a single script tag and a Siesta AI chat bubble appears on your public site, help center, or client portal, answering from your content, matching your brand, and handing off to your agents when it matters.
<!-- Drop this before </body> --> <script src="https://widget.siesta.ai/v1.js" data-agent="support-bot" data-theme="brand" defer></script> <!-- Done. The chat bubble is now live. -->
Siesta AI in your terminal
Install once and drive your whole workspace from the command line. Run agents, upload knowledge, and pipe results into scripts. Perfect for automation, CI pipelines, and scheduled jobs.
# Install and sign in $ npm i -g @siesta-ai/cli $ siesta login # Run an agent and capture the result $ siesta agents run sales-assistant \ --input "Draft a follow-up for Acme Corp" \ --json > reply.json # Add a document to your knowledge base $ siesta knowledge add ./handbook.pdf
Plug your workspace into any AI tool
Siesta AI speaks the Model Context Protocol. Point Claude, Cursor, or any MCP-compatible client at your workspace and your agents, knowledge, and tasks instantly become tools they can call, with no glue code to maintain.
// Add Siesta AI to your MCP client { "mcpServers": { "siesta": { "url": "https://mcp.siesta.ai", "headers": { "Authorization": "Bearer ${SIESTA_KEY}" } } } }
Your agents, in the wider network
Every Siesta AI agent can publish an open agent-to-agent (A2A) profile, so other systems can discover its skills and delegate real work to it, and your agents can call out to external ones the same way. Multi-agent workflows without a proprietary lock-in.
# Any agent exposes a discoverable profile GET /.well-known/agent.json { "name": "Siesta Sales Agent", "protocol": "a2a/v1", "skills": ["research", "draft", "schedule"], "endpoint": "https://a2a.siesta.ai/agents/sales" }
Live agents, streamed as they think
Open a short-lived agent session and stream audio, transcripts, tool calls, and approval states over a single WebSocket. Built for voice experiences, live agent interfaces, and public widgets that need to react the moment things happen.
// 1. Create a short-lived realtime session const res = await fetch( `${API}/Agent/${agentId}/realtime-session`, { method: "POST", headers: { "X-Api-Key": KEY } } ); const { sessionId, conversationId } = await res.json(); // 2. Stream transcripts, audio, and tool events const ws = new WebSocket( `${WS}/Agent/${agentId}/realtime` + `?sessionId=${sessionId}` ); ws.onmessage = (e) => render(JSON.parse(e.data));
Start building today
Grab an API key, read the docs, and ship your first Siesta AI integration in an afternoon.