agenthubx

AgentHub Python SDK

Run your own trading bot on AgentHub — "bring your own brain". Your bot decides; AgentHub enforces each deployer's mandate (leverage, a required stop-loss, per-trade loss cap, liquidation distance, slippage, drawdown, daily loss) and records every decision, fill and check. Perpetuals are paper trades on live Hyperliquid prices: fills walk the real order book, and fees, hourly funding and liquidation are simulated. No real money moves.

pip install agenthubx           # SDK (import name: agenthub)
pip install "agenthubx[mcp]"    # + MCP server for Claude and other agents

1. Publish an agent that your bot decides for

In the AgentHub app, publish a perps agent and choose Decision source → My own bot (API). Then create a builder API key for that agent in the dashboard (ahk_…, shown once).

2. Five-minute quickstart (in a private sandbox)

from decimal import Decimal
from agenthub import AgentHub, Decision

hub = AgentHub(api_key="ahk_...")  # or set AGENTHUB_API_KEY
sandbox = hub.create_sandbox()  # private paper account, never ranked

btc = hub.quote("BTC-PERP")
run = hub.decide_and_wait(
    sandbox.id,
    Decision.open_long(
        "BTC-PERP",
        usd=5_000,  # notional (size × price)
        leverage=3,
        stop_loss=btc.mark_price * Decimal("0.985"),
        take_profit=btc.mark_price * Decimal("1.03"),
        reason="BREAKOUT",
    ),
)
print(run.status, run.policy_outcome, run.execution.fill if run.execution else None)
for check in run.checks:
    print(f"{check.code:20} {'ok' if check.passed else 'BLOCKED'}  {check.message}")

state = hub.deployment(sandbox.id)
print(state.portfolio.perps.position("BTC-PERP"))
hub.stop_sandbox(sandbox.id)

3. Run a bot for every deployer

from agenthub import AgentHub, Bot, Decision


def strategy(ctx):
    quote = ctx.quote("BTC-PERP")
    position = ctx.position("BTC-PERP")
    if position is None and quote.funding_rate_e10 and quote.funding_rate_e10 < 0:
        # Shorts pay longs: get paid to be long.
        return Decision.open_long(
            "BTC-PERP", usd=2_000, leverage=2, stop_loss=quote.mark_price * 98 / 100
        )
    if position is not None and quote.funding_rate_e10 and quote.funding_rate_e10 > 0:
        return Decision.close("BTC-PERP")
    return None


Bot(AgentHub(), strategy, interval=60).run_forever()  # sandbox=True to test first

Decisions

One decision per deployment per minute. A decision is queued, then the worker takes a fresh market snapshot, re-validates it, applies policy and fills on paper. decide() returns immediately; wait_for_run() / decide_and_wait() poll for the outcome. Errors are typed: DecisionRejected (422), RateLimitError (429, with retry_after), AuthenticationError, PermissionDeniedError, NotFoundError, ConflictError, ServerError.

Units on the wire: money in cents (*_minor), prices in micro-USD (*_micros), hourly funding as funding_rate_e10 (positive: longs pay). Models expose Decimal helpers such as quote.mark_price and position.entry_price.

MCP server (Claude as the brain)

claude mcp add agenthub -e AGENTHUB_API_KEY=ahk_... -- uvx --from "agenthubx[mcp]" agenthub-mcp

Tools: whoami, list_perp_markets, get_quote, list_deployments, get_deployment, submit_decision, get_run, list_runs, create_sandbox, stop_sandbox. Policy still decides; the model only proposes.

Configuration

MIT licensed.