DeepChamp for Agents
Guides for building sports research agents
How to choose a sports data or intelligence API, connect an MCP server, measure a signal against the closing line and build a research agent that never claims more than it knows.
By DeepChamp research team · Updated
All guides
Try the DeepChamp for Agents free tier in your browser
Send the free DeepChamp for Agents slate request from your browser, read the response envelope, and copy the same call as curl or Python.
How to evaluate a sports betting data or intelligence API before you build an agent on it
Eight checks for choosing a sports betting data or intelligence API for an AI agent: scope, licence, freshness, audit trail, closing line value and cost.
Sports MCP servers for AI agents: the types and how to choose
Sports MCP servers fall into documentation, data and research-intelligence types: what each returns, what needs an API key, and how to connect DeepChamp.
Sports data and intelligence APIs for AI agents, compared by type
Sports odds, statistics, MCP and research-intelligence APIs compared by type for AI agents: what each returns, what you still build, and how they fit together.
How an AI agent measures closing line value
Closing line value for AI agents: log each signal and entry price, read the close from your own feed, remove the margin and report the mean with an interval.
What each DeepChamp for Agents product line returns
Reference for every DeepChamp for Agents product line: tools, endpoints, inputs, response fields, and which lines are live today and which are not.
Cookbook: sports research agents in four frameworks
Short, runnable recipes for building sports research agents on the DeepChamp MCP server and REST API with four agent frameworks.
Build a daily slate briefing agent with the Claude Agent SDK
Build a daily sports slate briefing agent with the Claude Agent SDK and the DeepChamp MCP server in one short Python file.
Build a matchup research agent with the OpenAI Agents SDK
Connect the OpenAI Agents SDK to the DeepChamp MCP server and answer slate and matchup research questions in one short Python file.
Build a sports research agent with LangChain and one REST tool
Build a sports research agent with LangChain using a single REST tool for the DeepChamp daily slate and a plain tool-calling loop.
Build a sports research agent with the Vercel AI SDK and MCP
Use the Vercel AI SDK MCP client to give generateText the DeepChamp slate and matchup tools, in one short TypeScript file.
DeepChamp for Agents: frequently asked questions
Answers about DeepChamp for Agents: what it returns, whether it is free, how agents connect and pay, which frameworks work, and what it does not claim.
Slate coverage: how many games DeepChamp lists per sport per day
A reproducible count of scheduled games on the DeepChamp daily slates by sport and day for the last seven days, with method, citation and a JSON file.
This page is published by DeepChamp AI, so read it as one vendor’s view. Examples use hypothetical numbers and are not live markets, picks or claims about performance.
21+ for U.S. sports betting. Follow the laws and age requirements where you are. DeepChamp AI provides independent sports betting research and analysis. It does not accept or place wagers, hold customer funds for betting, or operate a sportsbook. No wins or profits are guaranteed. If you or someone you know has a gambling problem, call or text 1-800-MY-RESET or visit the National Council on Problem Gambling.