sandflare 2.1.19

Sandflare Python SDK — Firecracker microVM sandboxes for AI agents

Sandflare Python SDK

The official Python SDK for Sandflare — Firecracker microVM sandboxes for AI agents.

Install

pip install sandflare

For local development (editable):

pip install -e sdk/python/

Quick start

import os
from sandflare import Sandbox

sb = Sandbox.create("agent", size="nano")
result = sb.exec("echo hello")
print(result.stdout)   # "hello"
sb.delete()

Environment variables

Variable	Description
SANDFLARE_API_KEY	API key (required)
SANDFLARE_API_URL	Optional base URL override (default: https://api.sandflare.io)
PANDAAGENT_API_KEY	Legacy alias for SANDFLARE_API_KEY
PANDAAGENT_BASE_URL	Legacy alias for SANDFLARE_API_URL

All methods use X-API-Key header for authentication.

---

Sandbox

Create

# Basic
sb = Sandbox.create("my-agent")

# With size/tier
sb = Sandbox.create("my-agent", size="nano")   # 512 MB RAM
sb = Sandbox.create("my-agent", size="small")  # 1 GB RAM
sb = Sandbox.create("my-agent", size="medium") # 2 GB RAM

# With options
sb = Sandbox.create("my-agent",
    size="small",
    ttl_hours=2,
    ephemeral=True,          # auto-deleted on close
    metadata={"env": "prod", "version": "1.0"},
    label="production-run",
)

# From a snapshot
sb = Sandbox.create("restored", snapshot_id="<snapshot-uuid>")

sb.info is a SandboxInfo dataclass with these fields:

Field	Type	Description
name	str	Unique sandbox ID (e.g. sb-abc123)
status	str	ready, paused, stopped
tier	str | None	nano, small, medium, large, xl
label	str	Human-readable label
preview_url	str | None	https://3000-sb-abc123.sandflare.io
subdomain_url	str | None	https://sb-abc123.sandflare.io (port-less)
agent_url	str | None	Internal agent URL
metadata	dict	Custom key-value metadata
ephemeral	bool	True if auto-deleted on close
expires_at	str | None	ISO timestamp if TTL set
memory_mb	int	RAM in MB
vcpus	int	vCPU count

Get & List

# Get by name
sb = Sandbox.get("sb-abc123")

# List all
sandboxes = Sandbox.list()

# Filter by label
sandboxes = Sandbox.list(label="production-run")

# Filter by metadata
sandboxes = Sandbox.list(metadata={"env": "prod"})

Exec

# Run a shell command
result = sb.exec("echo hello")
print(result.stdout)   # "hello\n"
print(result.exit_code)  # 0
print(result.ok)         # True

# With env vars
result = sb.exec("echo $MY_VAR", env={"MY_VAR": "hello_world"})

# With working directory and timeout
result = sb.exec("ls", cwd="/tmp", timeout=10)

# Stream output in real-time
for event in sb.exec_stream("python3 train.py", timeout=120):
    if event.type in ("stdout", "stderr"):
        print(event.data, flush=True)
    elif event.type == "done":
        print(f"\nexited {event.exit_code}")

File I/O

# Write and read
sb.write_file("/tmp/hello.txt", "hello world")
content = sb.read_file("/tmp/hello.txt")   # str

# Binary files
sb.write_file("/tmp/data.bin", b"\x00\x01\x02")
raw = sb.read_file("/tmp/data.bin")

# List directory
entries = sb.ls("/tmp")
for e in entries:
    print(e.name, e.size, e.is_dir)

Run code

# Python
result = sb.run_python("print(2 + 2)")
print(result.stdout)   # "4\n"

# Node.js
result = sb.run_node("console.log(2 + 2)")
print(result.stdout)   # "4\n"

Lifecycle

sb.pause()         # snapshot memory to disk, free compute
sb.resume()        # restore from snapshot
sb.delete()        # destroy permanently
sb.close()         # alias for delete()

Snapshots

# Create snapshot (continues running after)
snap = sb.create_snapshot(
    name="after-data-load",
    description="Dataset v2 loaded",
    tags=["prod", "v2"],
)
print(snap.snapshot_id)   # UUID

# List snapshots for this sandbox
snaps = sb.list_snapshots()
for s in snaps:
    print(s.snapshot_id, s.name, s.status)

# Restore (creates new sandbox from snapshot)
restored = Sandbox.create("fork", snapshot_id=snap.snapshot_id)

Pause → auto-resume

When you call Sandbox.get(name) on a paused sandbox, it auto-resumes transparently:

sb.pause()
# ... later ...
sb2 = Sandbox.get(sb.name)  # auto-resumes, file state preserved

---

AI Memory (mem0-backed)

Sandbox-scoped memory: store learnings, preferences, and context that persist across runs and are semantically searchable.

# Sandbox-scoped memory
sb.memory.add("User prefers async patterns and strict typing")
sb.memory.add("Bug fix: always call .commit() after INSERT", category="bug_fix")
sb.memory.add("Project uses FastAPI + PostgreSQL", infer=True)  # LLM extraction (~30s async)

results = sb.memory.search("what database does this project use?")
for r in results:
    print(r["memory"])

memories = sb.memory.list()

# User-level memory (persists across ALL sandboxes)
from sandflare import UserMemory

mem = UserMemory()
mem.add("Prefers TypeScript over JavaScript")
results = mem.search("language preferences")
memories = mem.list()

---

Custom templates

from sandflare import Template

# Build from Dockerfile
job = Template.build(
    name="my-env",
    dockerfile="FROM ubuntu:22.04\nRUN apt-get update && apt-get install -y python3",
)

# Poll until ready
job = Template.wait_for_build(job.id, timeout_seconds=600)

# Use the template
sb = Sandbox.create("agent", template_id=job.id)

# Manage templates
jobs = Template.list()
job = Template.get_build_status("tmpl-abc123")
Template.delete("tmpl-abc123")

---

Auto-snapshots

# Enable: snapshot every 15 minutes
sb.enable_auto_snapshot(interval_mins=15)

# Disable
sb.disable_auto_snapshot()

---

Metrics & processes

m = sb.metrics()
print(f"CPU: {m.cpu_used_pct:.1f}%  RAM: {m.mem_used}/{m.mem_total}")

procs = sb.get_processes()
for p in procs:
    print(p.pid, p.command)

sb.kill_process(1234)

---

Git clone

result = sb.git_clone(
    "https://github.com/org/repo",
    path="/home/agent/repo",
    branch="main",
    depth=1,
)
print(result.output)

---

Build & publish

# Build wheel
cd sdk/python
python3 -m pip wheel . --no-deps -w dist/

# Publish to PyPI
TWINE_USERNAME=__token__ TWINE_PASSWORD=<token> python3 -m twine upload dist/*