Gemini API

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Install

pip install "decimalai[google-genai]"

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Use

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
support = agent_config("support-agent", fallback="You are a support agent.")


def lookup_order(order_id: str) -> str:
    """Look up the status of an order."""
    return f"Order {order_id} shipped yesterday."


question = "Where is order A1001?"
with support.run(question) as run:      # one user message = one run = one trace
    chat = client.chats.create(model="gemini-3.5-flash", config=types.GenerateContentConfig(
        system_instruction=run.system,     # the saved prompt, then the skill list
        tools=[lookup_order, *run.tools],  # your functions, then load_skill
    ))
    answer = chat.send_message(run.message)
print(answer.text)

• agent_config("support-agent") reads the agent’s saved prompt when it is called. It initializes the SDK from DECIMAL_API_KEY when decimalai.init() has not been called, and a name your workspace has no agent by is created, so a misspelled name makes a new agent rather than an error. fallback= is what runs when the agent has no saved prompt yet, when no DecimalAI key is set, or when DecimalAI cannot be reached at startup. With no key at all, the agent runs without a DecimalAI key: the skills your project lists in .decimal/skills.toml, with nothing sent.
• support.run(question) routes the agent’s skill list on question and opens the run’s trace. It calls no model: make the model calls inside the with block.
• run.system is the system instruction: the saved prompt, then the agent’s skill list with one sentence telling the model to load a skill before using it.
• run.message is the user turn, as text. Send it instead of the bare question: in a chat it can carry more.
• run.tools holds load_skill as a Python function, which automatic function calling declares and calls like your own. The model calls it with a skill’s name and gets the skill’s full text back, which counts as delivered and activated on the usage ladder. Its parameter is a plain string: when the agent’s whole list fits, a name not on it is answered with the list and loads nothing.

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Your own loop

with support.run(question) as run:
    config = types.GenerateContentConfig(system_instruction=run.system,
                                         tools=[*TOOLS, *run.tool_params])
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(model="gemini-3.5-flash",
                                                  contents=contents, config=config)
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        contents.append({"role": "user", "parts": [
            run.tool_result(call) or answer_my_function(call) for call in response.function_calls]})
print(response.text)

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Async

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Chats

conversation = support.conversation(variables={"customer": {"name": "Sam"}})   # one per chat

with conversation.run(question) as run:
    contents.append({"role": "user", "parts": [{"text": run.message}]})
    ...                                          # the loop above, on this chat's contents
saved = conversation.state                      # JSON: support.conversation(state=saved) resumes it

• run.message is the user turn to send: the question, plus, for an agent whose skill list is cut to fit, the skills routed for this message that the chat has not shown yet.
• conversation.state is the conversation as JSON-ready data. A server that keeps no chat in memory stores it beside the chat’s history, and support.conversation(state=saved) resumes it in another process.
• Variables are the conversation’s: they are filled once, at its first message.

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Change the prompt without redeploying

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When an agent’s skills are switched off

• The tool’s kill switch. With DECIMALAI_LOAD_SKILL_TOOL=0 or decimalai.init(load_skill_tool=False), every run is routed and the text of the skills picked for it goes into run.system instead, and run.tools and run.tool_params are empty.
• decimalai.init(enabled=False) after agent_config() stops routing and skills for that config; the prompt it read keeps running.
• on_unavailable="fail". By default a run whose skill list never came back answers without skills, and its trace says so. With agent_config(..., on_unavailable="fail") opening the run raises instead.

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Tracing

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What’s next