Panofy Python SDK

Python SDK for the Panofy Agent Platform.

Installation

pip install panofy

Quick Start

from panofy import Panofy

panofy = Panofy(
    agent_id="your-agent-id",
    api_key="da_your_api_key",
)

result = panofy.predict(input="hello")
print(result)

# Inspect token usage and point cost from the last predict run.
print(panofy.last_usage())       # cache_read, cache_write, output_token
print(panofy.last_point_usage()) # points_consumed

# Pass multiple Agent input fields directly as keyword arguments.
summary = panofy.predict(
    title="AI 入门",
    language="zh-CN",
    max_length=200,
)

# String values that point to existing local files are uploaded automatically
# and rewritten to task-visible filenames in FUNC_INPUT.json.
report = panofy.predict(
    claim_application_file="./Claims_application-C2104.json",
)

# Disable output-side file downloads while still returning parsed FUNC_OUTPUT.json.
raw_report = panofy.predict(
    claim_application_file="./Claims_application-C2104.json",
    output_dir=None,
)

# Bound the wait time and best-effort abort the task server-side on timeout.
bounded = panofy.predict(
    input="hello",
    timeout=600.0,
)

predict() uploads FUNC_INPUT.json, starts the BFF plan→execute pipeline via /api/sdk/predict-async, polls /api/sdk/runs/{run_id} until the SDK run reaches a terminal status, then downloads and returns parsed FUNC_OUTPUT.json. After a terminal run, panofy.last_usage() returns the latest token usage as cache_read, cache_write, and output_token when returned by the API; panofy.last_point_usage() returns points_consumed. Use predict_with_metadata(...) when you want the parsed output and run metadata (run_id, task_id, points, and usage) in one return value.

result = panofy.predict_with_metadata(input="hello")
print(result.output)
print(result.run.run_id, result.run.points_consumed, result.run.usage)

The client defaults to https://panofy.ai. Pass base_url="http://localhost:3000" only for local development or a custom/private deployment.

If an Agent input field name conflicts with SDK controls such as timeout, output_dir, resolve_files, or files, pass one complete dict instead: panofy.predict({"timeout": 30, "input": "hello"}).

One-shot training

from panofy import train

result = await train(
    api_key="da_your_api_key",
    name="sales analyst",
    model_id="PANOFY_AIR",
    instruction="You are a sales data analyst.",
    training_data=[
        "./training/function_definition.md",
        "./training/FUNC_INPUT.json",
        "./training/FUNC_OUTPUT.json",
    ],
)

print(result.agent_id, result.task_id)

License

MIT