Build with Sugra API.

First call in under a minute.

Register, grab your API key from the Settings page, and make your first request. Every response is LLM-ready out of the box.

# GET US CPI - last 3 observations
curl -H "x-api-key: sugra_ao1_..." \
     "https://sugra.ai/api/fred/us/cpi?mode=last_n&n=3"

import httpx

r = httpx.get(
    "https://sugra.ai/api/fred/us/cpi",
    params={"mode": "last_n", "n": 3},
    headers={"x-api-key": "sugra_ao1_..."},
)
payload = r.json()
print(payload["data"], payload["meta"]["data_time"])

const res = await fetch(
  "https://sugra.ai/api/fred/us/cpi?mode=last_n&n=3",
  { headers: { "x-api-key": "sugra_ao1_..." } }
);
const { data, meta } = await res.json();
console.log(data, meta.data_time);

{ "data": [ ... ], "meta": { "source", "data_time", "unit_type", "cached", "fallback_used" } }

Hand an agent the spec. That's the integration.

Point an AI agent at sugra.ai/openapi.json, give it your API key, and it knows everything Sugra can do. Every endpoint, every parameter, every response schema - described in a typed OpenAPI 3.1 spec with consistent Envelope generics and semantic meta fields. No custom integration. No tool-writing. No documentation parsing.

When a user asks a question that spans domains - "what is the CPI trend, and how is weather in the US farm belt affecting grain prices?" - an agent reading the spec picks the right endpoints from 1476 available, calls them in parallel, and assembles one grounded answer from primary sources. Central bank data, weather observations, commodity prices - all in the same LLM-ready envelope.

No browsing the web. No sifting through blog posts, forum threads, and stale cached pages. No filtering out the noise. No hallucinated numbers. Just the source, the timestamp, the value - and a clean path from question to answer.

# Give an agent everything it needs to know:
export SUGRA_SPEC="https://sugra.ai/openapi.json"
export SUGRA_KEY="sugra_ao1_..."

# Ask a cross-domain question:
agent.ask("How is US inflation trending, and what is
           the weather doing to Midwest grain prices?")

# Agent picks endpoints automatically from 1476:
GET /api/fred/us/cpi                -> CPI series + data_time
GET /api/weather/forecast           -> Iowa, Illinois, Nebraska
GET /api/commodities/grains/corn    -> prices + source

# One grounded answer. Primary sources. Clean path
# from question to answer.

Native MCP server for Anthropic Claude, OpenAI GPT, Google Gemini, xAI, and any MCP-enabled IDE. Install with pip install sugra-api-mcp or connect to https://app.sugra.ai/mcp - same server, two transports. For non-MCP agents: OpenAI function-calling and Actions via OpenAPI 3.1.

The spec is production-enriched: typed Envelope[T] generics on 1476 endpoints, Markdown descriptions, global error schemas, APIKeyHeader security, externalDocs links.

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