Cloud sandbox for AI agents.
Fork in 100 ms. Persistent REPL.
Firecracker microVMs with copy-on-write snapshot forking. Your agents get a fresh Linux box in 7 ms, branch it N ways for tree-search, and keep Python state across run_code calls.
from podflare import Sandbox
sbx = Sandbox(template="python-datasci")
print(sbx.run_code("print(sum(range(100)))").stdout) # 4950
# Fork the running sandbox for agent tree-search
children = sbx.fork(n=5) # 101 ms for 5 copies
children[0].run_code("x = 1") # isolated per branchpip install podflare · also available on npm as podflare and podflare-mcp for Claude Desktop / Cursor / Cline.
fork(n)
Diff-snapshot 5 copies of a running sandbox. CoW memory + reflinked rootfs, so branches diverge for free.
Persistent REPL
Variables, imports, open files survive across run_code calls. python-datasci template preloads pandas, numpy, scipy, matplotlib.
Every framework
OpenAI Agents SDK, Vercel AI SDK, Anthropic code_execution, MCP. Drop-in replacement for any tool-use pattern.
Why agents need this
Tree search without state hell
Classic sandboxes give you one shell per agent. Branching a long-running computation means re-running setup from scratch. Forking lets an agent explore N hypotheses from a shared ancestor state — each branch gets the parent's memory, files, environment.
Secure by construction
Every sandbox is a Firecracker microVM with its own KVM boundary. No egress by default — your agents can't exfiltrate data or call arbitrary APIs without opt-in. See the egress model.
Five minutes from sign-up to first sandbox.
Mint a key, pip install podflare, you're running.