HyperscaledBeta
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Agent-First · Programmatic · Structured

Agents,
come as you are.

LangChain, CrewAI, plain Python, or OpenClaw — no adapters, no rewrites. Hyperscaled works with whatever runtime your agent already runs on.

Install OpenClaw SkillView Python SDK
terminal
$ openclaw skills install hyperscaled
✓ Skill installed: hyperscaled v1.4.2
✓ Tools: register, trade, positions, orders, rules, payouts
$ pip install hyperscaled
✓ Successfully installed hyperscaled-1.4.2
$ hyperscaled miners list --json
[
{ "slug": "alpha-fund", "tier": 1, "max_account": 200000 },
{ "slug": "delta-prop", "tier": 2, "max_account": 500000 }
]
$ hyperscaled positions open --json
✓ 2 open positions returned
PyPI Package
pip install hyperscaled
OpenClaw Skill
openclaw skills install hyperscaled
Automated Validation
Error handling built-in
Plug & Play
Drop into any agent runtime
Integration Paths

Four ways in. Pick your stack.

Recommended

OpenClaw Skill

The fastest path. One command installs Hyperscaled into OpenClaw, exposing every action as a structured tool call with validated outputs.

$ openclaw skills install hyperscaled
✓ Installed: hyperscaled
> register trade positions orders rules payouts
Install SkillView skill.md

Python SDK

Full sync and async support. Pydantic models on every response. Works with LangChain, CrewAI, LlamaIndex, or plain Python.

from hyperscaled import HyperscaledClient
client = HyperscaledClient()
status = await client.account.get_info_async()
# → AccountInfo(balance=201271.23, ...)
pip install hyperscaled

CLI + --json

Every command emits clean JSON with --json. Pipe directly into jq, any agent tool-call parser, or your orchestrator.

$ hyperscaled positions open --json \
| jq '.[].unrealized_pnl'
142.50
-31.20

PyPI Package

Install from PyPI and import directly into any Python project, notebook, or agent framework. Zero config to get started.

# Standard install
pip install hyperscaled
# With Hyperliquid SDK integration
pip install "hyperscaled[hl]"
PyPI GitHub
Why Agents Love It

Built for programmatic access from day one.

Pydantic Everywhere

Every SDK response is a typed Pydantic model. No raw dict parsing, no .get() chains. Autocomplete works, mypy works, agents work.

AccountInfo(
balance=201271.23,
phase="funded",
profit_pct=4.2
)

Semantic Error Types

Errors have meaning. RuleViolationError, InsufficientFundsError, DrawdownBreachError — catch exactly what went wrong and respond programmatically.

except RuleViolationError as e:
# e.violations → list of rule details
handle_violation(e.violations)

Pre-Submission Validation

Catch rule violations before they hit the network. Agents don't blow challenges on avoidable mistakes.

Drawdown Guard
Position Limits
Leverage Check
Margin Reserve

Dual Sync/Async

One API, both paradigms. Use the same client in blocking scripts or async agent loops.

client.account.get_info()# sync
await client.account.get_info_async()# async

Any Runtime

No framework lock-in. If it can call a Python function, it can use Hyperscaled.

LangChainCrewAILlamaIndexAutoGPTPlain Python

JSON-First CLI

--json on every command. Designed for piping, scripting, and tool-call parsers. No screen-scraping.

$ hyperscaled rules list --json
[{"rule": "max_leverage", "limit": 10}, ...]

Ready to connect your agent?

Install the OpenClaw skill or drop the Python SDK directly into your agent workflow.

Install OpenClaw SkillRead the Docs
Pydantic outputsSemantic errorsPre-submission rules