snipara-sandbox 2.2.2

Snipara Sandbox runtime for stateful, sandboxed AI workflow execution

Snipara Sandbox

Stateful execution runtime for persistent AI workflows

Modern AI agents can reason, write code, call tools, and solve complex tasks.

But most agent workflows are still transient. They lose execution state, restart from partial context, repeat failed work, and struggle to resume reliably across sessions, tools, users, and model changes.

Snipara Sandbox is a state-aware execution runtime for persistent AI workflows. It adds a durable execution layer around AI agents so they can execute code safely, preserve trajectories, validate intermediate results, and resume long-running workflows with continuity.

Context
  -> Planning
  -> Execution
  -> Validation
  -> Persistent State
  -> Resume Later

Snipara Sandbox is part of the broader Snipara ecosystem. Snipara provides persistent project memory and shared context. Snipara Sandbox adds persistent execution, resumable workflows, and sandboxed task continuity.

The Problem

Large context windows are useful, but they do not solve execution continuity.

AI systems also need infrastructure for:

• persistent execution state
• resumable workflows
• sandboxed code execution
• validation-aware task loops
• trajectory persistence
• recoverable orchestration

Without that layer, even strong agents often rediscover prior results, lose intermediate state, and restart work instead of continuing it.

Snipara Sandbox exists to make execution durable.

Core Concepts

Persistent execution state

Snipara Sandbox gives workflows a stateful execution environment instead of treating every task as a one-shot prompt. Agents can store intermediate results, inspect prior execution, and continue from known state.

Sandboxed execution

Agents can execute Python in controlled environments:

• local RestrictedPython execution for fast trusted development
• Docker isolation for stronger process and filesystem boundaries
• optional WebAssembly support for portable sandboxing

Trajectory persistence

Executions can be logged as structured trajectories. This makes workflows easier to inspect, debug, audit, replay, and improve.

Validation-aware orchestration

Snipara Sandbox is designed for workflows that do not just generate outputs, but also check them. Code execution, tool calls, and validation steps become part of the same recoverable workflow.

Features

• Persistent workflow state
• Sandboxed Python execution
• Docker isolation with resource limits
• Resumable task orchestration
• Trajectory logging
• Validation-aware execution loops
• Multi-step execution continuity
• MCP-compatible runtime tools
• Optional Snipara context and memory integration
• LiteLLM backend support for OpenAI, Anthropic, and 100+ providers

Relationship With Snipara

Snipara Sandbox is designed to work naturally with Snipara.

LLM / Agent
  -> Snipara Context and Memory
  -> Snipara Sandbox
  -> Sandboxed Execution

In this architecture:

• Snipara provides persistent shared context, project memory, and retrieval.
• Snipara Sandbox provides persistent execution continuity and sandboxed runtime state.
• The LLM handles reasoning, generation, and tool selection.

The runtime is optional. Snipara can function independently as a shared memory and context layer for AI systems.

Snipara Sandbox extends the stack when a workflow needs:

• long-running task execution
• state continuity across sessions
• validation through real code
• auditable execution traces
• recoverable orchestration

In short: Snipara keeps the context alive. Snipara Sandbox keeps the execution alive.

Installation

# Basic install
pip install snipara-sandbox

# With Docker sandbox support
pip install snipara-sandbox[docker]

# With MCP server support
pip install snipara-sandbox[mcp]

# With Snipara integration
pip install snipara-sandbox[snipara]

# Full install
pip install snipara-sandbox[all]

Package version in this repo: 2.2.2

See CHANGELOG.md for release history.

Quick Start

CLI

snipara-sandbox init

snipara-sandbox run "Analyze this dataset and validate the result with Python"

snipara-sandbox run --env docker "Process this untrusted input in an isolated runtime"

snipara-sandbox logs

Inspect the effective configuration:

snipara-sandbox config show
snipara-sandbox config show --json

The legacy rlm command remains available for existing users.

Python API

import asyncio

from snipara_sandbox import SniparaSandbox


async def main():
    runtime = SniparaSandbox(
        model="gpt-4o-mini",
        environment="docker",
        max_depth=4,
        token_budget=8000,
    )

    result = await runtime.completion(
        "Find the data quality issues in ./data and validate the findings."
    )

    print(result.response)
    print(result.trajectory_id)


asyncio.run(main())

MCP Runtime Tools

Snipara Sandbox includes an MCP server that gives AI coding agents a sandboxed Python runtime without requiring separate LLM API keys for the runtime itself.

pip install snipara-sandbox[mcp]
snipara-sandbox mcp-serve

Example MCP configuration:

{
  "mcpServers": {
    "SniparaSandbox": {
      "command": "snipara-sandbox",
      "args": ["mcp-serve"]
    }
  }
}

Core MCP tools:

Tool	Purpose
execute_python	Execute Python in a sandboxed session
get_repl_context	Read persistent session variables
set_repl_context	Store persistent session variables
clear_repl_context	Reset session state
list_sessions	Inspect active runtime sessions
destroy_session	Destroy a runtime session
snipara_agent_run	Start an autonomous agent task
snipara_agent_status	Check an agent run
snipara_agent_cancel	Cancel an agent run

Using Snipara Sandbox With Snipara

Snipara credentials are detected automatically when available. The native HTTP client is preferred, with snipara-mcp kept as a compatibility fallback.

OAuth:

snipara-mcp-login
snipara-mcp-status

API key:

export SNIPARA_API_KEY=snp-...
export SNIPARA_PROJECT_SLUG=my-project

Python:

from snipara_sandbox import SniparaSandbox

runtime = SniparaSandbox(
    model="gpt-4o-mini",
    environment="docker",
    snipara_api_key="snp-...",
    snipara_project_slug="my-project",
)

result = await runtime.completion(
    "Use project context to explain the authentication flow and validate examples."
)

When configured, Snipara tools provide semantic context retrieval, shared project context, and optional durable memory. Snipara Sandbox uses that context during execution while preserving the workflow trajectory.

Configuration

Create snipara-sandbox.toml in your project:

[snipara_sandbox]
backend = "litellm"
model = "gpt-4o-mini"
environment = "docker"
max_depth = 4
max_subcalls = 12
token_budget = 8000
verbose = false

docker_image = "python:3.11-slim"
docker_cpus = 1.0
docker_memory = "512m"
docker_network_disabled = true
docker_mount_workspace = true
# docker_workspace_path = "."
docker_workspace_setup = "none"  # set to "dev" to preinstall workspace test deps
# docker_workspace_install_command = "python -m pip install -e \".[dev]\""

snipara_project_slug = "your-project"

Environment variables are also supported:

export SNIPARA_SANDBOX_MODEL=gpt-4o-mini
export SNIPARA_SANDBOX_ENVIRONMENT=docker
export SNIPARA_PROJECT_SLUG=my-project

Runtime Environments

Environment	Use case	Isolation
local	Fast trusted development	RestrictedPython in process
docker	Production and untrusted inputs	Container isolation
wasm	Portable sandboxing	WebAssembly runtime

Docker mode is recommended for production and untrusted execution. By default it mounts the current workspace read-only at /workspace, so the runtime can inspect and test the repo while keeping process isolation and network disabled.

For actual repo-backed test runs, enable workspace setup:

[snipara_sandbox]
environment = "docker"
docker_mount_workspace = true
docker_workspace_setup = "dev"

That prepares a cached local Docker image from the current workspace and installs its dependencies at image-build time. Runtime execution still uses a read-only mount and no network. If your project does not use .[dev], set docker_workspace_install_command explicitly.

snipara-sandbox run --env docker "Validate this user-submitted transformation"

Trajectory Logging

Snipara Sandbox records execution trajectories as structured JSONL logs.

snipara-sandbox logs
snipara-sandbox logs <trajectory-id>

Trajectory events include:

• prompts and responses
• tool calls and tool results
• parent and child call relationships
• token usage
• duration and execution metadata

For visual inspection:

pip install snipara-sandbox[visualizer]
snipara-sandbox visualize

Development

git clone https://github.com/Snipara/snipara-sandbox
cd snipara-sandbox

pip install -e ".[dev]"

pytest
ruff check src/
mypy src/
python -m build

Documentation

• Quickstart Guide
• Architecture Guide
• MCP Integration
• Configuration
• Tool Development
• Snipara Integration

Repositories

• Snipara Server
• Snipara MCP
• Snipara Memory
• Snipara Sandbox

License

Apache 2.0. See LICENSE.