podflare 0.0.1

Podflare Python SDK — cloud AI-native sandbox for agents with 100ms fork() and persistent REPL.

Podflare — Python SDK

Cloud sandbox for AI agents. Fork in 100 ms, persistent Python REPL, run_code, upload/download, merge_into.

• Docs: https://docs.podflare.ai
• Dashboard: https://dashboard.podflare.ai — mint an API key in seconds
• GitHub: https://github.com/PodFlare-ai/podflare

Install

pip install podflare

Quickstart

from podflare import Sandbox

# Create a sandbox (persistent Python REPL, preloaded libs on python-datasci)
sbx = Sandbox(template="python-datasci")

# Execute code — state persists across calls
sbx.run_code("x = 42")
print(sbx.run_code("print(x * 2)").stdout)  # "84"

# Fork — spawn N copies of the running sandbox
children = sbx.fork(n=5)
children[0].run_code("x = 100")   # isolated per branch

# Merge a branch back into the parent
sbx.merge_into(children[0])

# Upload + download files
sbx.upload("data.csv", open("local.csv", "rb").read())
sbx.run_code("import pandas as pd; df = pd.read_csv('data.csv')")

# Destroy
sbx.close()

Configuration

Env var	Default	What it does
PODFLARE_API_KEY	(none — required)	Bearer token from the dashboard (pk_live_...).
PODFLARE_HOSTD_URL	https://api.podflare.ai	Override for self-hosted or staging deployments.

You can also pass them as constructor arguments:

from podflare import Sandbox

sbx = Sandbox(
    host="https://api.podflare.ai",
    api_key="pk_live_...",
    template="default",
)

Streaming

with Sandbox() as s:
    for ev in s.run_code_stream("for i in range(3): print(i)"):
        if ev.type == "stdout":
            print(ev.data, flush=True)

Framework adapters

First-class integrations:

• OpenAI Agents SDK — podflare.adapters.openai_agents
• Anthropic code_execution tool — podflare.adapters.anthropic
• MCP server (podflare-mcp npm package) for Claude Desktop / Cursor / Cline / Zed

See the integrations docs for copy-paste examples.

Links

• Documentation
• API reference
• Status
• Issues

License

Proprietary.