LangChain

export ZEROGPU_API_KEY="zgpu-api-..."
export ZEROGPU_PROJECT_ID="your-project-id"

pip install langchain-zerogpu

from langchain_zerogpu import ZeroGPUClassifyZeroShotTool

tool = ZeroGPUClassifyZeroShotTool()  # reads creds from the environment

print(tool.invoke({
    "text": "The new GPU smashes every benchmark we threw at it.",
    "labels": ["tech", "politics", "sports"],
}))

{ "label": "tech", "scores": { "tech": 0.95, "politics": 0.03, "sports": 0.02 } }

from langchain_zerogpu import ZeroGPUChatTool

tool = ZeroGPUChatTool()
print(tool.invoke({
    "text": "Explain WebSockets in two sentences.",
    "system": "You are a concise technical writer.",
}))

WebSockets provide a persistent, full-duplex connection between a client and a
server over a single TCP socket. Unlike HTTP request/response cycles, both sides
can push messages at any time, making them ideal for chat, live dashboards, and
multiplayer state sync.

from langchain_zerogpu import ZeroGPUChatThinkingTool

tool = ZeroGPUChatThinkingTool()
print(tool.invoke({
    "text": "If a train leaves at 3 PM going 60 mph, when does it cover 150 miles?"
}))

<thinking>
The train travels at 60 mph and needs to cover 150 miles.
Time = distance / speed = 150 / 60 = 2.5 hours.
Starting at 3:00 PM, adding 2 hours and 30 minutes lands at 5:30 PM.
</thinking>

The train covers 150 miles at 5:30 PM.

from langchain_zerogpu import ZeroGPUSummarizeTool

tool = ZeroGPUSummarizeTool()
print(tool.invoke({
    "text": (
        "The board met Thursday to review Q3 results. Revenue rose 18% "
        "year-over-year to $42M, driven mainly by enterprise renewals and a "
        "strong launch in the EU market. Operating margin slipped to 11% from "
        "14% as headcount grew 30% ahead of the new data-center buildout."
    )
}))

Q3 revenue grew 18% YoY to $42M on enterprise renewals and EU growth, but
operating margin fell to 11% due to a 30% headcount increase for the
data-center buildout.

from langchain_zerogpu import ZeroGPUClassifyIABTool

tool = ZeroGPUClassifyIABTool()
print(tool.invoke({"text": "The Lakers signed a new point guard ahead of the playoffs."}))

{
  "categories": [
    { "id": "IAB17-44", "name": "Basketball", "confidence": 0.97 }
  ]
}

from langchain_zerogpu import ZeroGPUClassifyIABEnrichedTool

tool = ZeroGPUClassifyIABEnrichedTool()
print(tool.invoke({
    "text": "Compare the Tesla Model Y and the Hyundai Ioniq 5 for a family of four."
}))

{
  "categories": [{ "id": "IAB2-1", "name": "Auto Buyers", "confidence": 0.92 }],
  "topics": ["electric vehicles", "family cars"],
  "keywords": ["Tesla Model Y", "Hyundai Ioniq 5"],
  "intent": "comparison-shopping"
}

from langchain_zerogpu import ZeroGPUClassifyZeroShotTool

tool = ZeroGPUClassifyZeroShotTool()
print(tool.invoke({
    "text": "I love how fast this laptop boots up.",
    "labels": ["positive", "negative", "neutral"],
}))

{ "label": "positive", "scores": { "positive": 0.94, "neutral": 0.04, "negative": 0.02 } }

from langchain_zerogpu import ZeroGPUClassifyStructuredTool

tool = ZeroGPUClassifyStructuredTool()
print(tool.invoke({
    "text": "Support replied quickly but the fix didn't work.",
    "schema": {
        "sentiment": ["positive", "negative", "neutral"],
        "topic": ["support", "billing", "product"],
    },
}))

{ "sentiment": "negative", "topic": "support" }

from langchain_zerogpu import ZeroGPUExtractEntitiesTool

tool = ZeroGPUExtractEntitiesTool()
print(tool.invoke({
    "text": "Apple CEO Tim Cook met with Sundar Pichai in Cupertino on Monday.",
    "labels": ["person", "organization", "location"],
    "threshold": 0.4,
}))

[
  { "label": "organization", "text": "Apple",         "score": 0.98 },
  { "label": "person",       "text": "Tim Cook",      "score": 0.97 },
  { "label": "person",       "text": "Sundar Pichai", "score": 0.96 },
  { "label": "location",     "text": "Cupertino",     "score": 0.91 }
]

from langchain_zerogpu import ZeroGPUExtractPIITool

tool = ZeroGPUExtractPIITool()
print(tool.invoke({
    "text": "Contact Jane Doe at [email protected] or +1 (415) 555-1212.",
    "categories": ["identity", "contact"],
}))

[
  { "category": "identity", "label": "person", "text": "Jane Doe",          "score": 0.96 },
  { "category": "contact",  "label": "email",  "text": "[email protected]",  "score": 0.99 },
  { "category": "contact",  "label": "phone",  "text": "+1 (415) 555-1212", "score": 0.95 }
]

from langchain_zerogpu import ZeroGPURedactPIITool

tool = ZeroGPURedactPIITool()
print(tool.invoke({"text": "Email John Smith at [email protected] about invoice 12345."}))

Email [PERSON] at [EMAIL] about invoice 12345.

from langchain_zerogpu import ZeroGPUExtractJSONTool

tool = ZeroGPUExtractJSONTool()
print(tool.invoke({
    "text": "Reach Maria Lopez at [email protected] or 415-555-0188.",
    "schema": {
        "contact": [
            "name::str::Full name",
            "email::str::Email address",
            "phone::str::Phone number",
        ]
    },
}))

{
  "contact": {
    "name": "Maria Lopez",
    "email": "[email protected]",
    "phone": "415-555-0188"
  }
}

from langchain.agents import create_agent
from langchain_zerogpu import ZeroGPUToolkit

toolkit = ZeroGPUToolkit()  # reads ZEROGPU_API_KEY / ZEROGPU_PROJECT_ID
tools = toolkit.get_tools()  # all eleven tools, one shared client

agent = create_agent("anthropic:claude-sonnet-4-6", tools=tools)

result = agent.invoke({
    "messages": [
        {"role": "user", "content": (
            "Triage this support ticket: summarize it in one line and tell me "
            "whether it's a bug, feature request, or question. "
            "'The CSV export on the billing page returns a 500 error whenever I "
            "select more than 90 days of data. Smaller ranges work fine.'"
        )}
    ]
})
print(result["messages"][-1].content)

Summary: Exporting more than 90 days of billing data as CSV returns a 500 error,
while smaller date ranges work fine.
Category: bug.

from langchain.chat_models import init_chat_model
from langchain_zerogpu import ZeroGPUExtractPIITool

llm = init_chat_model("anthropic:claude-sonnet-4-6")
llm_with_tools = llm.bind_tools([ZeroGPUExtractPIITool()])

from langchain_zerogpu import ZeroGPUSummarizeTool

summary = await ZeroGPUSummarizeTool().ainvoke({
    "text": (
        "The board met Thursday to review Q3 results. Revenue rose 18% "
        "year-over-year to $42M, driven by enterprise renewals and a strong EU "
        "launch, while operating margin slipped to 11% from 14% as headcount "
        "grew ahead of the new data-center buildout."
    )
})
print(summary)

Q3 revenue grew 18% year-over-year to $42M on enterprise renewals and EU
expansion, though operating margin fell to 11% as headcount outpaced the new
data-center buildout.

redacted = ZeroGPURedactPIITool().invoke({"text": user_input})
audit = ZeroGPUExtractPIITool().invoke({"text": user_input})
agent.invoke({"messages": [{"role": "user", "content": redacted}]})