> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bravadotrade.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Agents Trading Prediction Markets

> Give AI agents programmatic access to prediction market trading with the Bravado APIs and MCP server.

## The problem

Autonomous agents (LLM-driven or otherwise) that trade prediction markets need three things at once: a clean market data feed, a safe execution surface with strong idempotency guarantees, and a way to reason about wallet state without ambiguity. Wiring an agent to Polymarket's raw contracts requires custom code to smooth all three.

## What Bravado provides

* **Semantic endpoints.** Every action an agent might want (place order, get positions, look up a market, compute PnL) is a single REST call with typed request and response.
* **Idempotency.** Every mutating call requires an `Idempotency-Key` header. Agents that retry on failure cannot double-execute. See [Idempotency](/reference/idempotency).
* **Machine-readable docs.** `llms.txt` and `llms-full.txt` are published so any code-generation agent can consume the full API surface without scraping.
* **Optional MCP server.** For agent frameworks that support the Model Context Protocol, Bravado exposes a hosted MCP endpoint.

## APIs used

| API                              | What the agent uses it for                                                                        |
| -------------------------------- | ------------------------------------------------------------------------------------------------- |
| [Trade API](/products/trade-api) | Placing and cancelling orders, reading positions and balances, so the agent can act on a decision |
| [Data API](/products/data-api)   | Wallet history, PnL, and leaderboards that give the agent grounded context before it trades       |
| [UMA API](/products/uma-api)     | Resolution state, so an agent does not treat a concluded event as a settled, redeemable position  |

<Note>
  The UMA API endpoint reference is still being written. Until it lands, resolution state has to be inferred from position data. See [UMA API](/products/uma-api).
</Note>

## Worked example

An LLM tool-calling function that lets an agent evaluate a market and place a scaled position:

```python theme={null}
import os, uuid, requests

API = "https://bravado-api-k7kaq.ondigitalocean.app"
H = {"Authorization": f"Bearer {os.environ['BRAVADO_API_KEY']}", "Content-Type": "application/json"}

def place_scaled_order(symbol: str, side: str, confidence: float, max_notional_usd: float):
    """Tool an agent can call.
    confidence: 0.0 to 1.0. Higher confidence -> larger position, up to max_notional_usd.
    """
    notional = str(round(max_notional_usd * confidence, 2))

    r = requests.post(
        f"{API}/v2/trade/order",
        headers={**H, "Idempotency-Key": str(uuid.uuid4())},
        json={
            "symbol": symbol,
            "side": side,
            "type": "MARKET",
            "quote_amount": notional,
        },
    )
    r.raise_for_status()
    return r.json()

def get_position(symbol: str):
    """Agent can inspect its own state."""
    r = requests.get(f"{API}/v2/trade/positions", headers=H, params={"symbol": symbol})
    r.raise_for_status()
    return r.json()
```

Register these as tools with the agent framework (LangChain, OpenAI function-calling, Anthropic tool use, etc.) and constrain `max_notional_usd` at the tool boundary so the agent cannot exceed a budget per action.

## Safety patterns

* **Per-tool notional caps.** Enforce dollar limits at the tool layer, not in the agent's prompt.
* **Dry-run mode.** Route to a test wallet or a preview endpoint during development.
* **Cooldowns and daily caps.** Reject tool calls that exceed a rolling limit.
* **Full audit trail.** Every mutating call has an idempotency key. Log them per agent session for post-hoc review.

## Related

* [Build with AI](/build-with-ai)
* [Idempotency](/reference/idempotency)
* [Trade API reference](/api/trade/overview)