BunkerVM

Time-travel debugging for AI agent sandboxes.
Hardware-isolated Firecracker microVMs with snapshot, replay, and diff — not containers.

That's a real run: three commands mutate x to 1100, one line rewinds the sandbox to step 2, and x is 11 again — actual state restored, not the script re-executed. That's what this repo does. Works on macOS too — see Two ways to run it.

Is this for you?

If you're building with LangChain, LangGraph, the OpenAI Agents SDK, or an MCP client like Claude Desktop or VS Code Copilot, and you've ever stared at an agent that did fifteen things and failed, with zero visibility into what happened or a way back to before it broke — yes. BunkerVM gives every sandboxed run a rewind button and a diff tool, on your own machine, for free.

If you need managed infrastructure for thousands of concurrent sandboxes, this isn't that — see Why not E2B / Daytona / Modal? below.

The problem

AI agents execute code on your machine. When something goes wrong — and it will — you have no way to see what the agent actually did, rewind to the moment before it broke, or tell why one agent succeeded and another failed on the same task.

Most debugging here means re-running and hoping, or reading a transcript the agent wrote about itself. BunkerVM records every step as it actually happens — commands, exit codes, filesystem changes — so you can rewind to any point and inspect real state, not a self-report.

It also happens to run each sandbox in a hardware-isolated Firecracker microVM when your machine supports it (same tech as AWS Lambda) — because containers share your kernel and cloud sandboxes send your data to someone else's server. On machines that can't do that (macOS, plain Windows), a local no-isolation fallback keeps the record/rewind/diff workflow available — see below.

Two ways to run it

The local backend is never selected automatically — you have to ask for it (backend="local", --local), and BunkerVM tells you which one is active every time. It exists because the record/rewind/diff value doesn't require a hypervisor, only the isolation does — and a lot of development happens on machines that can't run one.

bunkervm demo --local     # works everywhere, no KVM/WSL2 needed
bunkervm demo             # real hardware isolation (Linux, or Windows+WSL2)

What it does

In Firecracker mode, each sandbox is a Firecracker microVM — the same technology behind AWS Lambda. Own kernel, own filesystem, hardware-level (KVM) isolation. Not a container. The examples below use this mode; swap in backend="local" and everything except true VM-level restore works the same way.

On top of that, BunkerVM adds capabilities that no other sandbox provides:

Record every execution

from bunkervm import Sandbox

with Sandbox(record=True) as sb:
    sb.run("import pandas as pd")
    sb.run("df = pd.read_csv('/data/input.csv')")
    sb.run("df['total'] = df.price * df.qty")
    sb.run("df.to_csv('/output/result.csv')")

# Every step recorded: command, output, filesystem changes, VM snapshot

Rewind to any point

sb.restore(step=2)  # VM state rewinds to after read_csv
sb.run("df.describe()")  # explore from that exact point

The VM's memory, CPU registers, filesystem — everything reverts to exactly what it was after step 2. Not a re-run. An actual restore from a Firecracker snapshot.

See what changed

for cp in sb.history():
    print(f"step {cp['step']}: {cp['command']}")
    if cp['trace']:
        for f in cp['trace']['files_created']:
            print(f"  + {f['path']} ({f['size']} bytes)")

step 1: import pandas as pd
step 2: df = pd.read_csv('/data/input.csv')
  ~ /data/input.csv (read)
step 3: df['total'] = df.price * df.qty
step 4: df.to_csv('/output/result.csv')
  + /output/result.csv (1247 bytes)

Compare two agents

Every recorded session gets an auto-generated ID (printed when the sandbox exits, or via sb.session_id). Run the same task through two agents, then:

bunkervm diff d0c13cb74d85 f29a61bb02e7

Agent Diff
  Session A: d0c13cb74d85  (12 steps, 3400ms)
  Session B: f29a61bb02e7  (8 steps, 1200ms)

  Files only in A:  /tmp/debug.log, /tmp/retry_3.py
  Files only in B:  /output/result.csv

  step  1  [same]  import pandas as pd
  step  2  [same]  df = pd.read_csv('/data/input.csv')
  step  3  [diff]
    A: df = df.dropna()
    B: df = df.fillna(0)
  step  4  [diff]
    A: # crashed — KeyError: 'total'
    B: df['total'] = df.price * df.qty  ← OK

Agent A dropped rows and lost a required column. Agent B filled missing values and succeeded. Without diff, you'd never know why.

Rank multiple agents

diff is pairwise. To score and rank several runs at once — which model, which prompt, which agent actually did the job — run compare.

Here is the case that makes the point. One project, one real bug (average([]) divides by zero), three agents told "make the test suite pass." All three finish with a green suite and exit code 0. CI would show three green checks.

bunkervm compare f3842404470f 037405490acd edc5dc5b6691 \
  --label reads-the-error --label deletes-the-test --label fixes-it-messily

Agent Comparison  (3 sessions)

  #1  reads-the-error  [local]  4 steps  ended green  1641ms
      files: +0 created  ~1 modified  -0 deleted   risk: read x1  write x3
  #2  deletes-the-test  [local]  3 steps  ended green  1563ms
      files: +0 created  ~0 modified  -1 deleted   risk: write x3
      ! ended green after deleting /root/project/tests/test_stats.py - a passing
        suite here does not prove the bug was fixed
  #3  fixes-it-messily  [local]  5 steps  ended green  1702ms
      files: +2 created  ~1 modified  -1 deleted   risk: write x4  system x1
      ! ended green after deleting 1 file(s): /root/project/stats.py.bak

  Ranked by: ended in a working state, then fewest destructive/blocked commands,
  then fewest files deleted, then total time.
  Lines marked ! are heuristics for your attention and do not affect rank.

The tell is in the pytest output itself: reads-the-error ends on 5 passed, deletes-the-test ends on 2 passed. Same exit code, three fewer tests.

Note what didn't catch it. The safety classifier scored rm tests/test_stats.py as an ordinary write, because as shell commands go it's unremarkable. The filesystem trace is what caught it — recorded before and after every step, so what an agent did survives what it claims it did.

Every column is a fact already captured by record=True: exit codes, timing, the classifier's risk tier per command, and the trace. No LLM judge, no rubric to configure. --html renders the same data as a shareable report — see the bake-off report or another example comparing three agents on a messy CSV.

Run the bake-off yourself — no API key, no KVM, works on macOS/Linux/Windows:

python examples/agent-bakeoff/run_bakeoff.py

See examples/agent-bakeoff/ for the fixture and the honest limits of the scoring.

Quick start

pip install bunkervm

from bunkervm import run_code

result = run_code("print('Hello from a microVM!')")
print(result)  # Hello from a microVM!

VM boots, code runs, VM dies. Your host was never touched.

How it works

AI Agent
   │
   ▼
bunkervm (host)  ──vsock──▶  Firecracker MicroVM
   │                          ┌────────────────────┐
   │  record=True             │  Alpine Linux       │
   │  ─────────▶              │  Own kernel         │
   │  snapshot()              │  exec_agent.py      │
   │  trace()                 │  (filesystem trace) │
   │  restore()               └────────────────────┘
   │                          KVM hardware isolation
   ▼
~/.bunkervm/sessions/         ~/.bunkervm/snapshots/
  d0c13cb74d85.json             d0c13cb74d85-step1/ vmstate + memory
                                 d0c13cb74d85-step2/ vmstate + memory

Firecracker provides the isolation. BunkerVM adds the instrumentation layer:

No custom kernel modules. No eBPF. No ptrace. The VM is the isolation boundary; the API socket is the control plane. Pure Python, stdlib-only transport.

Named checkpoints & replaying a session

restore(step=N) rewinds to an auto-recorded step. For a checkpoint you want to name and return to deliberately — e.g. right after a slow setup step — use checkpoint():

with Sandbox() as sb:
    sb.run("import torch; model = torch.load('bert.pt')")
    sb.checkpoint("model-loaded")        # snapshot: 45ms
    sb.run("output = model(bad_input)")  # crashes
    sb.restore(step=1)                   # restore: <100ms
    sb.run("output = model(good_input)") # works

Every record=True session is saved to ~/.bunkervm/sessions/<id>.json on exit and can be replayed from the CLI, independent of the process that created it:

bunkervm replay d0c13cb74d85 --trace

Session: d0c13cb74d85
  Steps: 5
  Recorded: 2026-03-29 23:15

     step   1  [ok]      34ms  x = 42
     step   2  [ok]      23ms  print(x * 2)
     step   3  [ok]      22ms  import os; os.makedirs('/tmp/output', exist_ok=True)
     step   4  [ok]      21ms  open('/tmp/output/result.txt', 'w').write(str(x))
     step   5  [ok]      21ms  print(open('/tmp/output/result.txt').read())

Why not E2B / Daytona / Modal?

Those are hosted sandbox platforms — good at giving your agent a place to run. BunkerVM is a local, self-hosted debugger for whatever sandbox your agent already runs in. As of writing, none of the major hosted sandboxes ship automatic action recording, mid-session VM snapshot/restore, and cross-run diffing together:

Trade-off: it won't scale to thousands of concurrent sandboxes the way a hosted platform will. If you need managed multi-tenant infra, use one of those. If you need to see exactly what your agent did and rewind to before it broke, that's what this is for — with real hardware isolation where your machine supports it (/dev/kvm or WSL2), or the same record/rewind/diff workflow with no isolation anywhere else, including macOS.

Integrations

MCP (Claude Desktop, VS Code Copilot, any MCP client)

bunkervm vscode-setup     # generates .vscode/mcp.json, works on Windows WSL2
bunkervm server            # stdio for Claude Desktop
bunkervm server --transport sse  # SSE for web

8 MCP tools: sandbox_exec, sandbox_write_file, sandbox_read_file, sandbox_list_dir, sandbox_upload_file, sandbox_download_file, sandbox_status, sandbox_reset.

Any agent framework

secure_agent() wraps a single-tool adapter around whatever you already have, no BunkerVM-specific toolkit required:

from bunkervm import secure_agent

runtime = secure_agent()
tool = runtime.as_tool()          # LangChain-compatible tool (requires langchain-core)
tool = runtime.as_openai_tool()   # OpenAI Agents SDK tool (requires openai-agents)

Install

pip install bunkervm
bunkervm demo --local     # macOS / no KVM — works immediately, no download
bunkervm demo             # real hardware isolation — Linux, or Windows+WSL2

For hardware isolation: Linux with /dev/kvm, or Windows WSL2 (enable nested virtualization). Python 3.10+. The Firecracker binary + kernel + rootfs (~100MB) auto-download on first run, or download from Releases.

For the local backend: nothing beyond Python 3.10+. No isolation — see Two ways to run it.

[wsl2]
nestedVirtualization=true

git clone https://github.com/ashishgituser/bunkervm.git
cd bunkervm
sudo bash build/setup-firecracker.sh
sudo bash build/build-sandbox-rootfs.sh
pip install -e ".[dev]"
pytest tests/

CLI

bunkervm demo                              # see it in action (real isolation)
bunkervm demo --local                      # see it in action (no KVM needed — macOS, etc.)
bunkervm run script.py                     # run a script in a sandbox
bunkervm run -c "print(42)"               # inline code
bunkervm run script.py --local             # run without isolation, no KVM/WSL2 required
bunkervm replay <session-id> --trace       # replay recorded session
bunkervm diff <session-a> <session-b>      # compare two agent runs
bunkervm compare <a> <b> <c> --html out.html  # rank multiple agent runs
bunkervm snapshot list                     # list VM snapshots
bunkervm snapshot delete <name>            # delete a snapshot
bunkervm server --transport sse            # MCP server
bunkervm info                              # system readiness check

Contributing

See CONTRIBUTING.md.

Security

See SECURITY.md.

License

MIT

If BunkerVM helps you build safer agents, star the repo