Lumify
Lumify is a real-time sports data and odds API purpose-built for AI agents and autonomous trading/betting systems, rather than human dashboards.
🔗 Visit Lumify
Description
Imagine you're building a robot assistant that needs to know, instantly and precisely, what's happening across every major sports league — scores, odds, and how likely a team is to win — without a human ever having to read a webpage or interpret a messy spreadsheet. That's the gap Lumify fills. It's a data service built specifically for software "agents" — AI programs that make decisions on their own — rather than for people scrolling a sports app. Instead of dumping loosely formatted text, Lumify hands machines clean, structured facts they can act on immediately, plus an explanation of how each number was calculated.
Under the hood, Lumify is a REST API (with a TypeScript SDK and an MCP server for direct use by AI agent frameworks) covering 8 sports and 17 leagues, including the NFL, NBA, MLB, NHL, NCAA, tennis and soccer. It aggregates odds from nine sportsbooks — including FanDuel, DraftKings, and Pinnacle — into a single canonical format, strips the bookmakers' built-in margin ("vig") to compute fair win probabilities, tracks line movement over time with timestamps, and attaches a rationale to each signal so an agent (or its developer) can see why a number was produced. It's aimed at autonomous betting and quantitative trading systems, as well as agentic media tools that need to reason about sports outcomes programmatically.
💬 Our review
The short version: if you're building an AI agent or trading bot that needs clean, explainable sports and odds data, Lumify gives you a sensible starting point with a genuinely usable free tier.
Compared to established sports-data providers like Sportradar or SportsDataIO, and lighter-weight odds aggregators like The Odds API, Lumify's real differentiator is that it was designed from the ground up for machine consumers — MCP server support, an OpenAPI spec, and embedded rationale for every prediction are not common in older, human-dashboard-first competitors. The free tier (1,000 non-expiring credits, no card required) is generous enough to actually prototype with, and pay-as-you-go pricing at $5 per 1,000 credits is straightforward. The catch: this is a young product with no public case studies yet, coverage tops out at 17 leagues (narrower than incumbents with decades of global sports data), and the Growth tier's $199/month jump is steep if you outgrow pay-as-you-go. Worth trying for agent-specific use cases; not yet a proven replacement for established data vendors at scale.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Free: 1,000 non-expiring credits, 20 req/min, 2 API keys, no card required. Pay As You Go: $5 per 1,000 credits, 60 req/min, 5 keys. Growth: $199/mo for 50,000 credits, 120 req/min, 20 keys, overage $4/1,000 credits. Enterprise: custom pricing, unlimited credits/keys, 99.9% SLA.
Pros
Purpose-built for AI agents: MCP server, OpenAPI docs, machine-readable structured data
Free tier is genuinely usable: 1,000 non-expiring credits, no card required
Vig-stripped fair probability calculations with explainable rationale per signal
Aggregates 9 sportsbooks into one canonical schema
Cons
Very young product with no public case studies or track record yet
Coverage limited to 8 sports / 17 leagues, narrower than long-established data vendors
Growth tier jumps to $199/month, a steep step up from pay-as-you-go
No named integrations or ecosystem beyond the API/SDK itself