One API for every domain
that drives a decision.

What makes it different.

Most data APIs are narrow by design - one domain, one region, one asset class. Sugra API is the opposite: a single, unified data layer across every domain that drives a real-world decision.

25+ data domains. One integration.

Organized into 11 top-level API categories in Swagger, but the actual coverage runs much deeper. Financial markets, macro, government, climate, physical world, blockchain networks, internet infrastructure, global news - through one hosted API. Add a new domain to your product and your integration does not change.

Built for teams that ship against the real world.

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 469+ 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 469+:
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 GPT Store Actions via OpenAPI 3.1.

The spec is production-enriched: typed Envelope[T] generics on 469+ endpoints, Markdown descriptions, global error schemas, APIKeyHeader security, externalDocs links.

Three patterns. One category per problem.

Data in 2026 comes in three shapes. Most APIs cover one domain. A few platforms give you tooling to integrate many sources yourself. Sugra is the only hosted API that aggregates across every domain that drives a decision - through one key, one envelope, and one bill.

One plan - all data. Volume is the only difference.

All 469+ endpoints, all 64+ primary sources, LLM-ready output - on every plan. Pick by volume. No feature gating. Upgrade or downgrade anytime.

Before you start.