The Aipolabs ACI Python SDK offers a convenient way to access the Aipolabs ACI REST API from any Python 3.10+ application.

In most cases, you should use the SDK to interact with our system programmatically unless you have a specific reason to call the API directly.

Both the SDK and API are currently in beta, so breaking changes may occur.

SDKs for additional languages are coming soon. If you’re interested in contributing to our open-source SDKs, please reach out!

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Usage

Aipolabs platform is built with agent-first principles. Although you can call each of the APIs below any way you prefer in your application, we strongly recommend trying the Agent-centric features and taking a look at the examples to get the most out of the platform and to enable the full potential and vision of future agentic applications.

For complete and up-to-date documentation, please check out the SDK repository.

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Client

from aipolabs import Aipolabs

client = Aipolabs(
    # it reads from environment variable by default so you can omit it if you set it in your environment
    api_key=os.environ.get("AIPOLABS_API_KEY")
)

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Apps

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Types

from aipolabs.types.apps import App, AppDetails

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Methods

# search for apps, returns list of basic app data, sorted by relevance to the intent
# all parameters are optional
apps: list[App] = client.apps.search(
    intent="I want to search the web",
    configured_only=True,
    categories=["search"],
    limit=10,
    offset=0
)

# get detailed information about an app, including functions supported by the app
app_details: AppDetails = client.apps.get(app_name="BRAVE_SEARCH")

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Functions

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Types

from aipolabs.types.functions import Function, FunctionExecutionResult, InferenceProvider

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Methods

# search for functions, returns list of basic function data, sorted by relevance to the intent
# all parameters are optional
functions: list[Function] = client.functions.search(
    app_names=["BRAVE_SEARCH", "TAVILY"],
    intent="I want to search the web",
    configured_only=True,
    limit=10,
    offset=0
)

# get function definition of a specific function, this is the schema you can feed into LLM
# the actual format is defined by the inference provider
function_definition: dict = client.functions.get_definition(
    function_name="BRAVE_SEARCH__WEB_SEARCH",
    inference_provider=InferenceProvider.OPENAI
)

# execute a function with the provided parameters
result: FunctionExecutionResult = client.functions.execute(
    function_name="BRAVE_SEARCH__WEB_SEARCH",
    function_parameters={"query": {"q": "what is the weather in barcelona"}},
    linked_account_owner_id="john_doe"
)

if result.success:
    print(result.data)
else:
    print(result.error)
QuickstartMeta Functions