langchain-skilllite 0.1.3

LangChain integration for SkillLite - Lightweight sandboxed Python skill execution engine

langchain-skilllite

LangChain integration for SkillLite - a lightweight sandboxed Python skill execution engine.

Features

• 🔒 Sandboxed Execution - All skills run in a Rust-based sandbox (skillbox)
• 📝 Declarative Skills - Define skills via SKILL.md, no Python wrappers needed
• 🔍 Security Scanning - Pre-execution code analysis for dangerous operations
• ✅ Confirmation Callbacks - User approval for high-severity security issues
• ⚡ Async Support - Full async support for LangGraph agents

Installation

pip install langchain-skilllite

This will also install the required dependencies:

• langchain-core>=0.3.0
• skilllite>=0.1.1

Quick Start

from langchain_skilllite import SkillLiteToolkit
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

# Load all skills from a directory as LangChain tools
tools = SkillLiteToolkit.from_directory("./skills")

# Create a LangGraph agent
agent = create_react_agent(ChatOpenAI(model="gpt-4"), tools)

# Run the agent
result = agent.invoke({
    "messages": [("user", "Calculate 15 + 27 using the calculator skill")]
})

Usage

Basic Usage with SkillManager

from skilllite import SkillManager
from langchain_skilllite import SkillLiteToolkit

# Create a SkillManager
manager = SkillManager(skills_dir="./skills")

# Convert all skills to LangChain tools
tools = SkillLiteToolkit.from_manager(manager)

# Or select specific skills
tools = SkillLiteToolkit.from_manager(
    manager,
    skill_names=["calculator", "web_search"],
    allow_network=True,
    timeout=60
)

Security Levels

SkillLite supports three sandbox security levels:

Level	Description
1	No sandbox - direct execution (fastest, least secure)
2	Sandbox isolation only
3	Sandbox + security scanning (default, most secure)

# Level 3 with confirmation callback for high-severity issues
def confirm_execution(report: str, scan_id: str) -> bool:
    print(report)
    return input("Proceed? [y/N]: ").lower() == 'y'

tools = SkillLiteToolkit.from_directory(
    "./skills",
    sandbox_level=3,
    confirmation_callback=confirm_execution
)

Async Confirmation (for LangGraph)

import asyncio

async def async_confirm(report: str, scan_id: str) -> bool:
    print(report)
    # In a real app, this might be a UI prompt
    return True

tools = SkillLiteToolkit.from_directory(
    "./skills",
    sandbox_level=3,
    async_confirmation_callback=async_confirm
)

Callback Handler for Monitoring

from langchain_skilllite import SkillLiteCallbackHandler

handler = SkillLiteCallbackHandler(verbose=True)

# Use with agent
result = agent.invoke(
    {"messages": [("user", "Run my skill")]},
    config={"callbacks": [handler]}
)

# Get execution summary
print(handler.get_execution_summary())

API Reference

SkillLiteTool

LangChain BaseTool adapter for a single SkillLite skill.

SkillLiteToolkit

Factory class for creating multiple SkillLiteTool instances.

• from_manager(manager, ...) - Create tools from a SkillManager
• from_directory(skills_dir, ...) - Create tools from a skills directory

SkillLiteCallbackHandler

LangChain callback handler for monitoring skill execution.

Requirements

• Python >= 3.9
• langchain-core >= 0.3.0
• skilllite >= 0.1.1

License

MIT License - see LICENSE for details.

Links

• SkillLite Repository
• LangChain Documentation
• LangGraph Documentation