bashlet

Python SDK for bashlet - a sandboxed bash execution environment. This SDK allows you to create bashlet instances and provide them as tools for AI agents.

Features

• Sandboxed Execution: Run shell commands in isolated environments
• Sync & Async: Both synchronous and asynchronous clients
• Multi-Framework Support: Generate tools for LangChain, OpenAI, Anthropic, and MCP
• Session Management: Create persistent sessions for stateful operations
• File Operations: Read, write, and list files in the sandbox
• Type-Safe: Full type hints with py.typed marker

Installation

pip install bashlet

Make sure you have bashlet installed:

cargo install bashlet

Optional Dependencies

Install with framework support:

# For LangChain
pip install bashlet[langchain]

# For OpenAI
pip install bashlet[openai]

# For Anthropic
pip install bashlet[anthropic]

# For MCP
pip install bashlet[mcp]

# For all frameworks
pip install bashlet[all]

Quick Start

Synchronous Client

from bashlet import Bashlet, Mount

bashlet = Bashlet(
    mounts=[Mount("./src", "/workspace")],
)

# Execute a command
result = bashlet.exec("ls -la /workspace")
print(result.stdout)
print(f"Exit code: {result.exit_code}")

Asynchronous Client

import asyncio
from bashlet import AsyncBashlet

async def main():
    bashlet = AsyncBashlet()
    result = await bashlet.exec("echo hello")
    print(result.stdout)

asyncio.run(main())

Usage with AI Frameworks

LangChain

from langchain_openai import ChatOpenAI
from bashlet import Bashlet

bashlet = Bashlet(
    mounts=[{"host_path": "./project", "guest_path": "/workspace"}],
)

# Get LangChain tools
tools = bashlet.to_langchain_tools()

# Bind tools to LLM
llm = ChatOpenAI(model="gpt-4-turbo")
llm_with_tools = llm.bind_tools(tools.all())

# Use in agent
response = llm_with_tools.invoke("List files in /workspace")

OpenAI Function Calling

from openai import OpenAI
from bashlet import Bashlet

client = OpenAI()
bashlet = Bashlet()

# Get OpenAI tools
handler = bashlet.to_openai_tools()

# Create completion with tools
response = client.chat.completions.create(
    model="gpt-4-turbo",
    tools=handler.definitions,
    messages=[{"role": "user", "content": "List files in current directory"}],
)

# Handle tool calls
for tool_call in response.choices[0].message.tool_calls or []:
    result = handler.handle(
        tool_call.function.name,
        tool_call.function.arguments  # JSON string or dict
    )
    print(result)

Anthropic Tool Use

from anthropic import Anthropic
from bashlet import Bashlet

client = Anthropic()
bashlet = Bashlet()

# Get Anthropic tools
handler = bashlet.to_anthropic_tools()

# Create message with tools
response = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    tools=handler.definitions,
    messages=[{"role": "user", "content": "List files in current directory"}],
)

# Handle tool use
for block in response.content:
    if block.type == "tool_use":
        result = handler.handle(block.name, block.input)
        print(result)

MCP (Model Context Protocol)

from mcp.server import Server
from mcp.server.stdio import stdio_server
from bashlet import Bashlet

bashlet = Bashlet()
handler = bashlet.to_mcp_tools()

server = Server("bashlet-server")

@server.list_tools()
async def list_tools():
    return handler.definitions

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    result = handler.handle(name, arguments)
    return result.content

async def main():
    async with stdio_server() as (read_stream, write_stream):
        await server.run(read_stream, write_stream)

Generic/Framework-Agnostic

from bashlet import Bashlet, create_generic_tools

bashlet = Bashlet()
tools = create_generic_tools(bashlet)

# Use with any framework
for tool in tools:
    print(f"Tool: {tool.name}")
    print(f"Description: {tool.description}")
    print(f"Parameters: {tool.parameters}")

# Execute a tool
exec_tool = next(t for t in tools if t.name == "bashlet_exec")
result = exec_tool.execute(command="echo hello")
print(result)

API Reference

Bashlet / AsyncBashlet

bashlet = Bashlet(
    binary_path="bashlet",      # Path to bashlet binary
    preset=None,                # Default preset name
    mounts=None,                # Default mounts
    env_vars=None,              # Default environment variables
    workdir=None,               # Default working directory
    timeout=300,                # Command timeout in seconds
    config_path=None,           # Path to config file
)

Methods

Tool Generators

Available Tools

Session Management

from bashlet import Bashlet

bashlet = Bashlet()

# Create a session
session_id = bashlet.create_session(
    name="my-session",
    ttl="1h",
    mounts=[{"host_path": "./data", "guest_path": "/data"}],
)

# Run commands in session (state persists)
bashlet.run_in_session(session_id, "cd /data && npm init -y")
bashlet.run_in_session(session_id, "npm install express")
result = bashlet.run_in_session(session_id, "cat package.json")
print(result.stdout)

# List sessions
sessions = bashlet.list_sessions()
for s in sessions:
    print(f"{s.id}: {s.name or 'unnamed'}")

# Terminate session
bashlet.terminate(session_id)

Error Handling

from bashlet import (
    Bashlet,
    BashletError,
    CommandExecutionError,
    BinaryNotFoundError,
    TimeoutError,
)

bashlet = Bashlet()

try:
    result = bashlet.exec("some-command")
except CommandExecutionError as e:
    print(f"Command failed with exit code {e.exit_code}")
    print(f"stderr: {e.stderr}")
except TimeoutError as e:
    print(f"Command timed out after {e.timeout_seconds}s")
except BinaryNotFoundError as e:
    print(f"Bashlet not found at: {e.binary_path}")
except BashletError as e:
    print(f"Bashlet error: {e}")

License

MIT