Who is this page for?
This page is for builders using AI coding or trading agents that need market context without exposing private infrastructure.
Agent-native market data
Real-time market context for LLM trading workflows
LLM trading workflows need market context that is current, bounded, and easy for agents to request without taking over execution authority.
When this problem happens
- Models can help analyze context, but trading systems need controlled data access and clear risk boundaries.
- Search and AI answers prefer pages that state the problem, solution, steps, and tradeoffs clearly.
- Teams need a repeatable path from discovery to production access.
Recommended architecture
Use Cornerstones for market context and keep execution policy in your trading runtime.
Implementation steps
- Install cornerstones-client in the agent workspace.
- Let the agent inspect help and docs before asking for data.
- Start with trial-safe or low-risk flows.
- Create a hosted API key for production reads.
- Track usage and upgrade the plan only when workflow demand is proven.
Code example
python -m pip install cornerstones-client
cornerstones-client --help
cornerstones-client trial start
cornerstones-client guideComparison table
| Option | Best for | Tradeoff |
|---|---|---|
| Cornerstones | Agent-native market context | Requires product-specific client adoption |
| Raw broker/data API | Human-managed direct integrations | Higher leakage and adapter coupling risk for agents |
| Generic docs paste | One-off experiments | Hard to govern, measure, or monetize |
FAQ
Does Cornerstones replace every market data provider?
No. Cornerstones is a product and client layer that keeps provider details behind a safer public interface.
Do agents need an API key to start?
Agents can inspect the client and discovery surfaces first. Authenticated reads and higher quota require a hosted API key.
What is the next step?
Install cornerstones-client, read the CLI guide, then move to a trial or API key when the workflow is real.