Tako VM
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Tako VM 0.1.4 is out: typed Python SDK, reliability layer, and end-to-end hardening.<br>See what's new
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Tako-Research/TakoVM
Getting Started
Guide
API Reference
Architecture
Security
Deployment
About
Tako VM¶
A secure file system for your agents to execute code.
File system and Python execution for your agents: run untrusted code in gVisor-isolated containers, with the queue, workers, execution history, retries, and replay you'd otherwise build yourself.
Get started<br>View on GitHub
Run your first job in two minutes¶
pip install "tako-vm[server]"<br>tako-vm setup # pull the executor Docker image<br>tako-vm server # start server (auto-starts PostgreSQL via Docker)
Python SDKREST APIAsync jobs
from tako_vm import Sandbox
with Sandbox() as sb:<br>result = sb.run("print(1 + 1)")<br>print(result.stdout) # "2"
curl -X POST http://localhost:8000/execute \<br>-H "Content-Type: application/json" \<br>-d '{"code": "print(1 + 1)"}'
import tako_vm
tako_vm.configure("http://localhost:8000")
job_id = tako_vm.submit_code("print(sum(i * i for i in range(10**6)))")<br>result = tako_vm.get_result(job_id, timeout=60)<br>print(result["stdout"])
Quick start
Install, run the server, and execute your first sandboxed job.
Getting started
Security model
gVisor isolation, seccomp, the threat model, and how to harden for production.
Security
Agent integration
Wire Tako VM into LangChain or OpenAI tool-calling as a code-execution tool.
Agent guide
API reference
Every endpoint and SDK method: sync execution, async jobs, replay, artifacts.
REST API · Python SDK
Why Tako VM?¶
Sandbox-only tools (e2b, microsandbox) give you isolated execution. You still need to build the job system yourself.
Feature<br>Sandbox-only<br>Tako VM
Job queue<br>Build with Redis/Celery<br>Built-in
Execution history<br>Build with Postgres<br>PostgreSQL included
Retries<br>Write retry logic<br>Automatic
Replay/debugging<br>Build custom tooling<br>Rerun/fork API
Idempotency<br>Implement deduplication<br>idempotency_key
Job queue + workers : no Redis/Celery setup needed
Execution history : every job persisted with timing and artifacts
Replay to debug : rerun past jobs with exact same inputs
gVisor isolation : userspace-kernel syscall interception (runsc), seccomp filtering, no network by default
Self-hosted : zero per-execution cost, works offline
Security model¶
Tako VM exists to run untrusted, often AI-generated, code, so isolation is layered: each job runs in its own ephemeral Docker container behind gVisor's userspace kernel, with a default-deny seccomp profile, no network, dropped capabilities, and a non-root user. Even a kernel exploit stays inside the sandbox.
For production with untrusted code, set security_mode: strict so execution fails rather than silently falling back to standard runc when gVisor is unavailable.
Read the threat model
How it works¶
flowchart LR<br>C["Client(SDK / REST)"] --> S["Tako VM Server(queue + workers)"]<br>S --> G["gVisor sandbox(per-job container)"]<br>S --> P[("PostgreSQLexecution history")]<br>Three core concepts:
Sandbox : a gVisor-backed, single-use container that executes one job and is destroyed.
Job : an asynchronous unit of work with a persisted ExecutionRecord lifecycle (queued → running → succeeded/failed/timeout/oom/cancelled).
Executor image : the container image jobs run in; runtime requirements can be installed per-job via uv (opt-in: allow_runtime_requirements), with an optional shared cache (enable_runtime_dependency_cache) to speed up repeat installs.
Where Tako VM is headed
The direction is a serverless filesystem for agents : durable, per-agent workspaces that persist and rehydrate across runs, spinning up on demand and scaling to zero with state preserved. Today each container is single-use; persistent workspaces are on the roadmap. gVisor remains the sole isolation boundary.
Next steps¶
Installation: set up Tako VM
Quick Start: run your first code
Agent Integration: use Tako VM as an agent tool
Architecture: how Tako VM works
Changelog: what's new in each release
2026-06-23
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