EXboys/langchain-skilllite

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# langchain-skilllite [![PyPI version](https://badge.fury.io/py/langchain-skilllite.svg)](https://badge.fury.io/py/langchain-skilllite) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/) LangChain integration for [SkillLite](https://github.com/EXboys/skilllite) - a lightweight sandboxed Python skill execution engine. > **Note:** langchain-skilllite 0.1.7+ requires [skilllite 0.1.8+](https://github.com/EXboys/skilllite) which provides `run_skill`, `scan_code`, and `execute_code` APIs. ## Table of Contents - [Features](#features) - [Installation](#installation) - [Quick Start](#quick-start) - [Project Structure](#project-structure) - [Environment Configuration](#environment-configuration) - [Examples](#examples) - [Example 1: Basic Usage](#example-1-basic-usage) - [Example 2: Callback Handler](#example-2-callback-handler) - [Example 3: Security Scanning](#example-3-security-scanning) - [Example 4: Direct Execution](#example-4-direct-execution) - [Security Levels](#security-levels) - [API Reference](#api-reference) - [Troubleshooting](#troubleshooting) - [Links](#links) --- ## Features | Feature | Description | |---------|-------------| | 🔒 **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 | | 🔗 **LangChain Native** | Seamless integration with LangChain agents and LangGraph | ### Advantages over Standard SkillLite | Aspect | Standard SkillLite | langchain-skilllite | |--------|-------------------|---------------------| | Integration | Manual execution | Native LangChain tools | | Agent Support | Manual loop implementation | Works with any LangChain agent | | Async | Basic support | Full async support for LangGraph | | Monitoring | Custom implementation | Built-in callback handlers | --- ## Installation ```bash pip install langchain-skilllite ``` This will also install the required dependencies: - `langchain-core>=0.3.0` - `skilllite>=0.1.8` ### Optional Dependencies ```bash # For LangGraph support pip install langchain-skilllite[langgraph] # For OpenAI integration pip install langchain-openai # For development pip install langchain-skilllite[dev] ``` --- ## Quick Start ```python 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", "Convert 'hello world' to uppercase")] }) ``` --- ## Project Structure ``` langchain-skilllite/ ├── langchain_skilllite/ # Main package │ ├── __init__.py # Package exports │ ├── core.py # SkillManager, SkillInfo, SecurityScanResult │ ├── tools.py # SkillLiteTool & SkillLiteToolkit │ ├── callbacks.py # SkillLiteCallbackHandler │ └── _version.py # Version info ├── examples/ # Example scripts │ ├── 01_basic.py # Basic usage example │ ├── 02_with_callback.py # Callback handler example │ ├── 03_security_scan.py # Security scanning example │ └── 04_direct_execute.py # Direct execution example ├── .skills/ # Sample skills for examples │ ├── echo/ # Echo skill │ ├── greeter/ # Greeter skill │ └── text-upper/ # Text uppercase skill ├── images/ # Execution result screenshots ├── tests/ # Test suite ├── .env # Environment configuration └── pyproject.toml # Package configuration ``` --- ## Environment Configuration Create a `.env` file in your project root: ```bash # OpenAI Compatible API Configuration BASE_URL=https://api.openai.com/v1 API_KEY=your-api-key-here MODEL=gpt-4o-mini # Skills Configuration SKILLS_DIR=./.skills # Network Configuration (Optional) ALLOW_NETWORK=True NETWORK_TIMEOUT=30 # Sandbox Configuration ENABLE_SANDBOX=true SKILLBOX_SANDBOX_LEVEL=3 # Security Settings # SKILLBOX_AUTO_APPROVE=1 # Auto-approve all security warnings SKILLBOX_AUTO_APPROVE=0 # Require manual confirmation # Resource Limits EXECUTION_TIMEOUT=120 # Skill execution timeout (seconds) MAX_MEMORY_MB=256 # Maximum memory limit (MB) ``` ### Supported API Providers | Provider | BASE_URL | |----------|----------| | OpenAI | `https://api.openai.com/v1` | | DeepSeek | `https://api.deepseek.com/v1` | | Alibaba DashScope | `https://dashscope.aliyuncs.com/compatible-mode/v1` | | Azure OpenAI | `https://{resource}.openai.azure.com/openai/deployments/{deployment}` | --- ## Examples ### Example 1: Basic Usage Load skills and use with a LangChain agent. ```python """ Basic usage: Load skills and use with LangChain agent. """ import os from pathlib import Path from dotenv import load_dotenv from langchain_openai import ChatOpenAI from langgraph.prebuilt import create_react_agent from langchain_skilllite import SkillLiteToolkit # Load .env (from current working directory) load_dotenv() # Setup skills_dir = Path(".skills") llm = ChatOpenAI( base_url=os.getenv("BASE_URL"), api_key=os.getenv("API_KEY"), model=os.getenv("MODEL", "gpt-4o-mini"), ) # Load skills as LangChain tools tools = SkillLiteToolkit.from_directory(skills_dir) print(f"Loaded {len(tools)} skills: {[t.name for t in tools]}") # Create agent and run agent = create_react_agent(llm, tools) result = agent.invoke({"messages": [("user", "Convert 'hello world' to uppercase")]}) # Print result for msg in result["messages"]: print(f"[{msg.type}]: {msg.content[:200] if msg.content else ''}") ``` **Run the example:** ```bash cd langchain-skilllite python examples/01_basic.py ``` **Execution Result:** ![Basic Demo](images/01_basic_demo.png) --- ### Example 2: Callback Handler Use callback handlers to track and monitor tool execution. ```python """ Using callbacks to track tool execution. """ import os from pathlib import Path from dotenv import load_dotenv from langchain_openai import ChatOpenAI from langgraph.prebuilt import create_react_agent from langchain_skilllite import SkillLiteToolkit, SkillLiteCallbackHandler # Load .env (from current working directory) load_dotenv() skills_dir = Path(".skills") llm = ChatOpenAI( base_url=os.getenv("BASE_URL"), api_key=os.getenv("API_KEY"), model=os.getenv("MODEL", "gpt-4o-mini"), ) tools = SkillLiteToolkit.from_directory(skills_dir) # Create callback handler callback = SkillLiteCallbackHandler() agent = create_react_agent(llm, tools) result = agent.invoke( {"messages": [("user", "Greet Alice")]}, config={"callbacks": [callback]} ) # Show execution stats summary = callback.get_execution_summary() print(f"\nExecutions: {summary['tool_executions']}") print(f"Successful: {summary['successful']}") ``` **Run the example:** ```bash cd langchain-skilllite python examples/02_with_callback.py ``` **Execution Result:** ![Callback Demo](images/02_with_callback.png) --- ### Example 3: Security Scanning Enable security scanning with user confirmation for sandbox level 3. ```python """ Using security scanning with confirmation callback (sandbox level 3). """ import os from pathlib import Path from dotenv import load_dotenv from langchain_openai import ChatOpenAI from langgraph.prebuilt import create_react_agent from langchain_skilllite import SkillLiteToolkit # Load .env (from current working directory) load_dotenv() skills_dir = Path(".skills") llm = ChatOpenAI( base_url=os.getenv("BASE_URL"), api_key=os.getenv("API_KEY"), model=os.getenv("MODEL", "gpt-4o-mini"), ) # Define confirmation callback for user approval def confirm_execution(report: str, scan_id: str) -> bool: """Ask user to confirm skill execution after security scan.""" print("\n" + "=" * 60) print("🔐 SECURITY SCAN REPORT") print("=" * 60) print(report) print("=" * 60) response = input("\nProceed with execution? [y/N]: ").strip().lower() return response == 'y' # Enable security scanning with confirmation callback tools = SkillLiteToolkit.from_directory( skills_dir, sandbox_level=3, # Enable security scanning confirmation_callback=confirm_execution, ) agent = create_react_agent(llm, tools) result = agent.invoke({"messages": [("user", "Echo 'test message'")]}) for msg in result["messages"]: print(f"[{msg.type}]: {msg.content[:200] if msg.content else ''}") ``` **Run the example:** ```bash cd langchain-skilllite python examples/03_security_scan.py ``` **Execution Result:** ![Security Scan Demo](images/03_security_scan.png) --- ### Example 4: Direct Execution Execute skills directly without an LLM agent. ```python """ Direct skill execution without LLM. """ from pathlib import Path from langchain_skilllite import SkillManager skills_dir = Path(".skills") # Create manager manager = SkillManager(skills_dir=str(skills_dir)) # List skills print("Available skills:") for skill in manager.list_skills(): print(f" - {skill.name}: {(skill.description or '')[:50]}...") # Execute directly result = manager.execute("text-upper", {"text": "hello world"}) print(f"\nResult: {result['output']}") ``` **Run the example:** ```bash cd langchain-skilllite python examples/04_direct_execute.py ``` **Execution Result:** ![Direct Execute Demo](images/04_direct_execute.png) --- ## Security Levels SkillLite supports three sandbox security levels: | Level | Description | Use Case | |-------|-------------|----------| | **1** | No sandbox - direct execution | Development & trusted skills | | **2** | Sandbox isolation only | Standard protection | | **3** | Sandbox + security scanning (default) | Production & untrusted skills | ### Level 3: Security Scanning When `sandbox_level=3`, skills are scanned before execution: ```python 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) ```python 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 ) ``` --- ## API Reference ### SkillManager Manage skills from a directory. Parse SKILL.md and execute skills. ```python from langchain_skilllite import SkillManager manager = SkillManager(skills_dir="./skills") # List all skills for skill in manager.list_skills(): print(f"{skill.name}: {skill.description}") # Execute a skill result = manager.execute("text-upper", {"text": "hello"}) # result: {"success": True, "output": "...", "exit_code": 0} ``` ### SkillLiteTool LangChain `BaseTool` adapter for a single SkillLite skill. ```python from langchain_skilllite import SkillLiteTool tool = SkillLiteTool( name="skill_name", description="Skill description", manager=manager, skill_name="skill_name", allow_network=False, timeout=60, sandbox_level=3, confirmation_callback=confirm_execution, ) ``` ### SkillLiteToolkit Factory class for creating multiple `SkillLiteTool` instances. ```python from langchain_skilllite import SkillLiteToolkit # From a skills directory (recommended) tools = SkillLiteToolkit.from_directory( skills_dir="./skills", skill_names=["calculator", "web_search"], # Optional filter allow_network=True, timeout=60, sandbox_level=3, confirmation_callback=confirm_execution, ) # From an existing SkillManager from langchain_skilllite import SkillManager manager = SkillManager(skills_dir="./skills") tools = SkillLiteToolkit.from_manager(manager) ``` **Parameters:** | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `skills_dir` / `manager` | str / SkillManager | - | Skills directory path or SkillManager instance | | `skill_names` | List[str] | None | Filter to specific skills (default: all) | | `allow_network` | bool | False | Allow network access for skills | | `timeout` | int | None | Execution timeout in seconds | | `sandbox_level` | int | 3 | Sandbox security level (1/2/3) | | `confirmation_callback` | Callable | None | Sync callback for security confirmation (sandbox_level=3) | | `async_confirmation_callback` | Callable | None | Async confirmation callback | ### SkillLiteCallbackHandler LangChain callback handler for monitoring skill execution. ```python 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 summary = handler.get_execution_summary() print(f"Total executions: {summary['tool_executions']}") print(f"Successful: {summary['successful']}") print(f"Errors: {summary['errors']}") print(f"Success rate: {summary['success_rate']:.2%}") ``` --- ## Troubleshooting ### Common Issues #### 1. Skills Not Found ``` Error: No skills found in directory ``` **Solution:** Ensure your skills directory contains valid skill folders with `SKILL.md` files. ```bash .skills/ ├── my-skill/ │ ├── SKILL.md # Required │ └── scripts/ │ └── main.py # Entry point ``` #### 2. API Key Not Set ``` Error: OpenAI API key not found ``` **Solution:** Create a `.env` file with your API configuration: ```bash API_KEY=your-api-key-here BASE_URL=https://api.openai.com/v1 MODEL=gpt-4o-mini ``` #### 3. SkillLite Binary Not Found ``` Error: skilllite binary not found. Run: pip install skilllite ``` **Solution:** Install the skilllite package (includes the sandbox binary): ```bash pip install skilllite ``` #### 4. Security Scan Blocking Execution ``` 🔐 Security Review Required ``` **Solution:** Either provide a `confirmation_callback` or set `sandbox_level=2`: ```python # Option 1: Add confirmation callback tools = SkillLiteToolkit.from_directory( "./skills", confirmation_callback=lambda r, s: True # Auto-approve ) # Option 2: Disable security scanning tools = SkillLiteToolkit.from_directory( "./skills", sandbox_level=2 ) ``` #### 5. Timeout Errors ``` Error: Skill execution timed out ``` **Solution:** Increase the timeout value: ```python tools = SkillLiteToolkit.from_directory( "./skills", timeout=120 # 2 minutes ) ``` ### Environment Variables | Variable | Description | Default | |----------|-------------|---------| | `SKILLBOX_SANDBOX_LEVEL` | Default sandbox level | 3 | | `SKILLBOX_AUTO_APPROVE` | Auto-approve security warnings | 0 | | `EXECUTION_TIMEOUT` | Default execution timeout | 120 | | `MAX_MEMORY_MB` | Maximum memory for skill execution | 512 | --- ## Requirements - Python >= 3.9 - langchain-core >= 0.3.0 - skilllite >= 0.1.8 - python-dotenv (for examples) - langchain-openai (for LLM integration) - langgraph (for agent examples) --- ## License MIT License - see [LICENSE](LICENSE) for details. --- ## Links - [SkillLite Repository](https://github.com/EXboys/skilllite) - [langchain-skilllite on PyPI](https://pypi.org/project/langchain-skilllite/) - [LangChain Documentation](https://python.langchain.com/) - [LangGraph Documentation](https://langchain-ai.github.io/langgraph/) - [LangChain Integration Tutorial](../tutorials/04_langchain_integration/README.md)

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Skills في هذا المستودع

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skill

المهنة

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آخر تحديث

echo

مطوّرو البرمجيات

Echo back the input message. Use when the user wants to test or echo something.

لغة النص الأصلي: غير محددة

2026-02-04

greeter

مطوّرو البرمجيات

Generate a personalized greeting. Use when the user wants to greet someone by name.

لغة النص الأصلي: غير محددة

2026-02-04

text-upper

مطوّرو البرمجيات

Convert text to uppercase. Use when the user wants to convert text to all capital letters.

لغة النص الأصلي: الإنجليزية

2026-02-04

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