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Documentation Index

Fetch the complete documentation index at: https://docs.podflare.ai/llms.txt

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Install

pip install podflare openai-agents

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Use

from agents import Agent, Runner
from podflare.integrations.openai_agents import podflare_code_interpreter

tool = podflare_code_interpreter()

agent = Agent(
    name="assistant",
    instructions="Use the run_code tool to answer computational questions.",
    tools=[tool],
)

result = await Runner.run(agent, "What is the sum of squares from 1 to 100?")
print(result.final_output)

# Cleanup the lazily-created Podflare sandbox
tool.close_podflare_sandbox()

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What the tool does

  • On the first call, lazily creates a Podflare sandbox.
  • On each invocation, calls sandbox.run_code(code, language).
  • Returns a dict with keys stdout, stderr, exit_code.
  • The same sandbox is reused across invocations — Python REPL state persists, so the agent can build up state over a conversation.

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Options

tool = podflare_code_interpreter(
    host="https://api.podflare.ai",   # default: PODFLARE_HOSTD_URL env
    template="python-datasci",          # default: the primary pool
)

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Cleanup

The returned tool exposes a close_podflare_sandbox() method. Call it after the agent run completes to destroy the VM.
For many short-lived agent runs, creating a new sandbox per run is fine (pool hits are ~10 ms). For long-running conversations, reuse the same tool across turns to keep REPL state alive.