Bring agents into your software.

A REST API for model calls, a Node.js client for invoking agents and an MCP server for working from your development tools. Choose the interface for your use case.

One call to integrate

An HTTP call with the task data included in the request. To access your systems, configure the agent’s tools and permissions.

calls reference

index.ts

const response = await fetch(
  "https://llm.zihin.ai/api/v3/llm/public/call",
  {
    method: "POST",
    headers: {
      "X-Api-Key": process.env.ZIHIN_API_KEY,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      query: "Summarize: order 4512 has 12 items and awaits approval.",
      model: "auto", // platform routing
      options: { temperature: 0.7, maxTokens: 4096 },
    }),
  }
);

if (!response.ok) throw new Error(`HTTP ${response.status}`);
const data = await response.json();
console.log(data.response);

What the API delivers

Check each feature’s contract before integrating.

Multi-provider

Models from different providers through one endpoint. Check the catalog for names, capabilities and availability.

Auto-routing

Use model: "auto" to delegate selection to platform routing, or specify an available model from the catalog.

Structured output

Force JSON responses with schema validation — for integrations that can't tolerate loose text.

Multimodal

Send images alongside text — automatically converted to each provider's native format.

Streaming

Real-time token-by-token responses via SSE.

Global cache

Reuse eligible responses according to cache rules. The effect on cost and latency depends on usage patterns.

Your editor talks to Zihin

The official MCP server connects MCP clients to the platform: manage agents, secrets and telemetry without leaving your editor.

MCP Server documentation

published on npm

npx @zihin/mcp-server

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