CrewAI

pip install "crewai>=1.15.27"

export DEEPINFRA_API_KEY="<your DeepInfra API key here>"

from crewai import LLM

llm = LLM(model="deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731", temperature=0.2)

researcher:
  role: Researcher
  goal: Find and summarise the facts the task asks for
  backstory: A careful analyst who cites what the tools return.
  llm: deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731

from crewai import Agent, Crew, LLM, Task
from crewai.tools import tool

llm = LLM(model="deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731", temperature=0.2)


@tool("Exchange rate")
def exchange_rate(pair: str) -> str:
    """Return the current exchange rate for a currency pair such as 'EUR/USD'."""
    rates = {"EUR/USD": 1.0842, "GBP/USD": 1.2650, "USD/JPY": 156.30}
    return f"{pair}: {rates.get(pair.upper(), 'unknown pair')}"


analyst = Agent(
    role="FX analyst",
    goal="Answer currency questions with numbers from the exchange rate tool",
    backstory="You never guess a rate; you always call the tool first.",
    llm=llm,
    tools=[exchange_rate],
)

task = Task(
    description="How many US dollars is 250 euros? Use the tool for the rate and show the arithmetic.",
    expected_output="One sentence with the rate used and the converted amount.",
    agent=analyst,
)

result = Crew(agents=[analyst], tasks=[task]).kickoff()
print(result.raw)
print(result.token_usage)

250 euros × 1.0842 = 271.05 US dollars, using the EUR/USD rate of 1.0842.
total_tokens=906 prompt_tokens=830 cached_prompt_tokens=256 completion_tokens=76 reasoning_tokens=0 cache_creation_tokens=0 successful_requests=2

import os

from crewai import Agent, Crew, LLM, Task
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource

llm = LLM(model="deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731")

facts = StringKnowledgeSource(
    content=(
        "Orbital Coffee opened in 2019 in Sofia. Its house blend is called Perihelion. "
        "The roastery runs on a 15 kg Loring roaster and ships wholesale on Tuesdays."
    )
)

embedder = {
    "provider": "openai",
    "config": {
        "api_key": os.environ["DEEPINFRA_API_KEY"],
        "api_base": "https://api.deepinfra.com/v1/openai",
        "model_name": "BAAI/bge-m3",
    },
}

agent = Agent(
    role="Support agent",
    goal="Answer questions about Orbital Coffee from the knowledge base",
    backstory="You answer only from what the knowledge base says.",
    llm=llm,
)

task = Task(
    description="What is Orbital Coffee's house blend called, and on which day does wholesale ship?",
    expected_output="A one-sentence answer.",
    agent=agent,
)

crew = Crew(agents=[agent], tasks=[task], knowledge_sources=[facts], embedder=embedder)
print(crew.kickoff().raw)

Orbital Coffee's house blend is called Perihelion, and wholesale ships on Tuesdays.

pip install deepinfra

from crewai.tools import BaseTool
from deepinfra import Sandbox
from pydantic import BaseModel, Field


class RunPythonInput(BaseModel):
    code: str = Field(..., description="Python source to execute.")


class DeepInfraSandboxPython(BaseTool):
    name: str = "Run Python in a DeepInfra sandbox"
    description: str = (
        "Executes Python code in an isolated DeepInfra Sandbox and returns "
        "stdout, stderr and the exit code."
    )
    args_schema: type[BaseModel] = RunPythonInput
    plan: str = "nano"

    def _run(self, code: str) -> str:
        with Sandbox.create(plan=self.plan, timeout="10m", tags={"framework": "crewai"}) as sb:
            result = sb.run_python(code)
        return f"exit={result.returncode}\nstdout:\n{result.stdout}\nstderr:\n{result.stderr}"