SDKs

Official SDKs

Typed Python client for the Claix API, plus drop-in tools for LangChain, LangGraph, CrewAI, and LlamaIndex.

LangChain / LangGraph

Claix tools for LangChain and LangGraph

Endpoint

SDKpip install 'claix-ai[langchain]'

Three LangChain BaseTools wrap the same ClaixClient: extract a file, ask a persisted document, or ask a knowledge space. Use them with LangGraph create_react_agent or any LangChain agent that accepts tools.

2. Tools

ClassTool nameWhat it does
ClaixExtractToolclaix_extractExtract schema-typed JSON from PDF, Excel/CSV, Word/text, or image. Optional space_id and is_agent_mode.
ClaixDocumentContextToolclaix_document_contextAsk 1–5 questions about a persisted document_id (max 400 chars each).
ClaixSpaceContextToolclaix_space_contextCross-document Q&A over a knowledge space_id — compare, sum, reconcile.

3. LangGraph ReAct

from claix import ClaixClient
from claix.integrations.langchain import (
    ClaixDocumentContextTool,
    ClaixExtractTool,
    ClaixSpaceContextTool,
)
from langgraph.prebuilt import create_react_agent

client = ClaixClient()
tools = [
    ClaixExtractTool(client=client),
    ClaixDocumentContextTool(client=client),
    ClaixSpaceContextTool(client=client),
]
agent = create_react_agent("openai:gpt-4.1", tools)
agent.invoke({
    "messages": [{
        "role": "user",
        "content": "Extract invoice.pdf with schema 3c7a9f21-4b8e-4d1a-9c6f-2e0d8a5b7c4f, then ask the document_id for the VAT total.",
    }]
})

create_react_agent builds a StateGraph that loops until the model stops calling tools. Prefer ClaixSpaceContextTool for multi-file nodes.

Request examples

from claix import ClaixClient
from claix.integrations.langchain import (
    ClaixDocumentContextTool,
    ClaixExtractTool,
    ClaixSpaceContextTool,
)
from langgraph.prebuilt import create_react_agent

client = ClaixClient()  # CLAIX_API_KEY
tools = [
    ClaixExtractTool(client=client),
    ClaixDocumentContextTool(client=client),
    ClaixSpaceContextTool(client=client),
]
agent = create_react_agent("openai:gpt-4.1", tools)
agent.invoke({
    "messages": [{
        "role": "user",
        "content": "Extract invoice.pdf with schema 3c7a9f21-4b8e-4d1a-9c6f-2e0d8a5b7c4f, then ask the document_id for the VAT total.",
    }]
})