AnyTool: Universal Tool-Use Layer for AI Agents

✨ One Line of Code to Supercharge any Agent with
Fast, Scalable and Powerful Tool Use ✨

| ⚡ Fast - Lightning Tool Retrieval  |  📈 Self-Evolving Tool Orchestration  |  ⚡ Universal Tool Automation |

AnyTool is a Universal Tool-Use Layer that transforms how AI agents interact with tools. It solves three fundamental challenges that prevent reliable agent automation: overwhelming tool contexts, unreliable community tools, and limited capability coverage -- delivering the first truly intelligent tool orchestration system for production AI agents.

⚡ Fast - Lightning Tool Retrieval

• Smart Context Management: Progressive tool filtering delivers exact tools in milliseconds through multi-stage pipeline, eliminating context pollution while maintaining speed.

• Zero-Waste Processing: Pre-computed embeddings and lazy initialization eliminate redundant processing - tools are instantly ready across all executions.

📈 Scalable - Self-Evolving Tool Orchestration

• Adaptive MCP Tool Selection: Smart caching and selective re-indexing maintain constant performance from 10 to 10,000 tools with optimal resource usage.

• Self-Evolving Tool Optimization: System continuously improves through persistent memory, becoming more efficient as your tool ecosystem expands.

🌍 Powerful - Universal Tool Automation

• Quality-Aware Selection: Built-in reliability tracking and safety controls deliver production-ready automation through persistent learning and execution safeguards.

• Universal Tool-Use Capability: Multi-backend architecture seamlessly extends beyond web APIs to system operations, GUI automation, and deep research through unified interface.

One line to get intelligent tool orchestration. Zero-config setup transforms complex multi-tool workflows into a single API call.

from anytool import AnyTool

# One line to get intelligent tool orchestration
async with AnyTool() as tool_layer:
    result = await tool_layer.execute(
        "Research trending AI coding tools from GitHub and tech news, "
        "collect their features and user feedback, analyze adoption patterns, "
        "then create a comparison report with insights"
    )

• 🎯 Quick Start
• 🚀 Technical Innovation & Implementation
• 🔧 Configuration Guide
• 📖 Code Structure
• 🔗 Related Projects

# Clone repository
git clone https://github.com/HKUDS/AnyTool.git
cd AnyTool

# Create and activate conda environment (includes ffmpeg for video recording)
conda create -n anytool python=3.12 ffmpeg -c conda-forge -y
conda activate anytool

# Install dependencies
pip install -r requirements.txt

The local server is a lightweight Flask service that enables AnyTool to interact with your computer (GUI automation, Python/Bash execution, file operations, screen capture, etc.).

# Install macOS-specific dependencies
pip install pyobjc-core pyobjc-framework-cocoa pyobjc-framework-quartz atomacos

# Install Linux-specific dependencies
pip install python-xlib pyatspi numpy

# Install system packages
sudo apt install at-spi2-core python3-tk scrot

# Install Windows-specific dependencies
pip install pywinauto pywin32 PyGetWindow

After installing the platform-specific dependencies, start the local server:

python -m anytool.local_server.main

AnyTool is a plug-and-play Universal Tool-Use Layer for any AI agent. The task passed to execute() can come from your agent's planning module, user input, or any workflow system.

import asyncio
from anytool import AnyTool
from anytool.tool_layer import AnyToolConfig

async def main():
    config = AnyToolConfig(
        enable_recording=True,
        recording_backends=["gui", "shell", "mcp", "web"],
        enable_screenshot=True,
        enable_video=True,
    )
    
    async with AnyTool(config=config) as tool_layer:
        result = await tool_layer.execute(
            "Research trending AI coding tools from GitHub and tech news, "
            "collect their features and user feedback, analyze adoption patterns, "
            "then create a comparison report with insights"
        )
        print(result["response"])

asyncio.run(main())

The Problem. Current MCP agents suffer from a fundamental design flaw: they load ALL configured servers and tools at every execution step, creating an overwhelming action space, creates three critical issues:

• ⚡ Slow Performance with Massive Context Loading
  Complete tool set from all pre-configured servers loaded simultaneously at every step, degrading execution speed

• 🎯 Poor Accuracy from Blind Tool Setup
  Users cannot preview tools before connecting, leading to over-setup "just in case" and confusing tool selection

• 💸 Resource Waste with No Memory
  Same tools reloaded at every execution step with no caching, causing redundant loading

Motivation: "Load Everything" → "Retrieve What's Needed"
Improvement: Faster tool selection, cleaner context, and efficient resource usage through smart retrieval and memory.

🎯 Multi-Stage Tool Retrieval Pipeline

• Progressive MCP Tool Filtering: server selection → tool name matching → tool semantic search → LLM ranking
• Reduces MCP Tool Search Space: Each stage narrows down candidate tools for optimizing precision and speed

💾 Long-Term Tool Memory

• Save Once, Use Forever: Pre-compute tool embeddings once and save them to disk for instant reuse
• Zero Waste Processing: No more redundant processing - tools are ready to use immediately across all execution steps

🧠 Adaptive Tool Selection

• Adaptive MCP Tool Ranking: LLM-based tool selection refinement triggered only when MCP tool results are large or ambiguous
• Tool Selection Efficiency: Balances MCP tool accuracy with computational efficiency

🚀 On-Demand Resource Management

• Lazy MCP Server Startup: MCP server initialization triggered only when specific tools are needed
• Selective Tool Updates: Incremental re-indexing of only changed MCP tools, not the entire tool set

The Problem. Current MCP servers suffer from community contribution challenges that create three critical issues:

• 🔍 Poor Tool Descriptions
  Misleading claims, non-existent advertised tools, and vague capability specifications lead to wrong tool selection.

• 📊 No Reliability Signals
  Cannot assess MCP tool quality before use, causing blind selection decisions.

• ⚠️ Security and Safety Gaps
  Unvetted community tools may execute dangerous operations without proper safeguards.

Motivation: "Blind Tool Trust" → "Smart Quality Assessment"
Improvement: Reliable tool selection, safe execution, and autonomous recovery through quality tracking and safety controls.

🎯 Quality-Aware Tool Selection

• Description Quality Check: LLM-based evaluation of MCP tool description clarity and completeness.
• Performance-Based Ranking: Track call/success rates for each MCP tool in persistent memory to prioritize reliable options.

💾 Learning-Based Tool Memory

• Track Tool Performance: Remember which MCP tools work well and which fail over time.
• Smart Tool Prioritization: Automatically rank tools based on past success rates and description quality.

🛡️ Safety-First Execution

• Block Dangerous Operations: Prevent arbitrary code execution and require user approval for sensitive MCP tool operations.
• Execution Safeguards: Built-in safety controls for all MCP tool executions.

🚀 Self-Healing Tool Management

• Autonomous Tool Switching: Switch failed MCP tools locally without restarting expensive planning loops.
• Local Failure Recovery: Automatically switch to alternative MCP tools on failure without escalating to upper-level agents.

The Problem. Current MCP ecosystem focuses primarily on Web APIs and online services, creating significant automation gaps that prevent comprehensive task completion:

• 🖥️ Missing System Operations
  No native support for file manipulation, process management, or command execution on local systems.

• 🖱️ No Desktop Automation
  Cannot control GUI applications that lack APIs, limiting automation to web-only scenarios.

• 📊 Incomplete Tool Coverage
  Limited server categories in community and incomplete tool sets within existing servers create workflow bottlenecks.

Motivation: "Web-Only MCP" → "Universal Task Completion"
Improvement: Complete automation coverage through multi-backend architecture that seamlessly extends MCP capabilities beyond web APIs.

🏗️ Multi-Backend Architecture

• MCP Backend: Community servers for Web APIs and online services
• Shell Backend: Bash/Python execution for system-level operations and file management
• GUI Backend: Pixel-level automation for any visual application without API requirements
• Web Backend: Deep web research and data extraction capabilities

💡 Self-Evolving Capability Discovery

• Intelligent Gap Detection: Planning agent identifies when MCP tools are insufficient for task requirements
• Automatic Backend Selection: Shell/GUI backends automatically fill capability gaps without manual intervention
• Dynamic Capability Expansion: Previously impossible tasks become achievable through backend combination

🎭 Unified Tool Orchestration

• Uniform Tool Schema: All backends expose identical interface for seamless agent tool selection
• Transparent Backend Switching: Agents select optimal tools across backend types without knowing implementation details
• Intelligent Tool Routing: Automatic routing to the most appropriate backend based on task requirements

🚀 Seamless Integration Layer

• Single Tool Interface: Unified API that abstracts away backend complexity from AI agents.
• Cross-Backend Coordination: Enable complex workflows that span multiple backend capabilities.
• Consistent Safety Controls: Apply security and safety measures uniformly across all backend types.

AnyTool uses a layered configuration system:

• config_dev.json (highest priority): Local development overrides. Overrides all other configurations.
• config_agents.json: Agent definitions and backend access control
• config_mcp.json: MCP server registry
• config_grounding.json: Backend-specific settings and Smart Tool RAG configuration
• config_security.json: Security policies with runtime user confirmation for sensitive operations

Path: anytool/config/config_agents.json

Purpose: Define agent roles, control backend access scope, and set execution limits to prevent infinite loops.

Example configuration:

{
  "agents": [
    {
      "name": "GroundingAgent",
      "class_name": "GroundingAgent",
      "backend_scope": ["gui", "shell", "mcp", "system", "web"],
      "max_iterations": 20
    }
  ]
}

Key Fields:

Path: anytool/config/config_mcp.json (copy from config_mcp.json.example)

Purpose: Register MCP servers with connection details. AnyTool automatically discovers tools from all registered servers and makes them available through Smart Tool RAG.

Example configuration:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}"
      }
    }
  }
}

from anytool import AnyTool
from anytool.tool_layer import AnyToolConfig

config = AnyToolConfig(
    # LLM Configuration
    llm_model="anthropic/claude-sonnet-4-5",
    llm_enable_thinking=False,
    llm_timeout=120.0,
    llm_max_retries=3,
    llm_rate_limit_delay=0.0,
    llm_kwargs={},  # Additional LiteLLM parameters
    
    # Separate models for specific tasks (None = use llm_model)
    tool_retrieval_model=None,   # Model for tool retrieval LLM filter
    visual_analysis_model=None,  # Model for visual analysis
    
    # Grounding Configuration
    grounding_config_path=None,  # Path to custom config file
    grounding_max_iterations=20,
    grounding_system_prompt=None,  # Custom system prompt
    
    # Backend Configuration
    backend_scope=["gui", "shell", "mcp", "web", "system"],
    
    # Workspace Configuration
    workspace_dir=None,  # Auto-create temp dir if None
    
    # Recording Configuration
    enable_recording=True,
    recording_backends=["gui", "shell", "mcp"],
    recording_log_dir="./logs/recordings",
    enable_screenshot=True,
    enable_video=True,
    enable_conversation_log=True,  # Save LLM conversations to conversations.jsonl
    
    # Logging Configuration
    log_level="INFO",
    log_to_file=False,
    log_file_path=None,
)

async with AnyTool(config=config) as tool_layer:
    result = await tool_layer.execute("Your task here")
    # Or with external task_id for benchmark integration:
    # result = await tool_layer.execute("Your task", task_id="my-task-001")

Legend: ⚡ Core modules | 🔧 Supporting modules

AnyTool/
├── anytool/
│   ├── __init__.py                       # Package exports
│   ├── __main__.py                       # CLI entry point (python -m anytool)
│   ├── tool_layer.py                     # AnyTool main class
│   │
│   ├── ⚡ agents/                         # Agent System
│   ├── ⚡ grounding/                      # Unified Backend System
│   │   ├── core/                         # Core abstractions
│   │   └── backends/                     # Backend implementations
│   │       ├── shell/                    # Shell command execution
│   │       ├── gui/                      # Anthropic Computer Use
│   │       ├── mcp/                      # Model Context Protocol
│   │       └── web/                      # Web search & browsing
│   │
│   ├── 🔧 prompts/                       # Prompt Templates
│   ├── 🔧 llm/                           # LLM Integration
│   ├── 🔧 config/                        # Configuration System
│   ├── 🔧 local_server/                  # GUI Backend Server
│   ├── 🔧 recording/                     # Execution Recording
│   ├── 🔧 platform/                      # Platform Integration
│   └── 🔧 utils/                         # Utilities
│
├── .anytool/                             # Runtime cache
│   ├── embedding_cache/                  # Tool embeddings for Smart Tool RAG
│   └── tool_quality/                     # Persistent tool quality tracking data
│
├── logs/                                 # Execution logs
│
├── requirements.txt                      # Python dependencies
├── pyproject.toml                        # Package configuration
└── README.md

agents/
├── __init__.py
├── base.py                         # Base agent class with common functionality
└── grounding_agent.py              # Execution Agent (tool calling & iteration control)

grounding/core/
├── grounding_client.py             # Unified interface across all backends
├── provider.py                     # Abstract provider base class
├── session.py                      # Session lifecycle management
├── search_tools.py                 # Smart Tool RAG for semantic search
├── exceptions.py                   # Custom exception definitions
├── types.py                        # Shared type definitions
│
├── tool/                           # Tool abstraction layer
│   ├── base.py                     # Tool base class
│   ├── local_tool.py               # Local tool implementation
│   └── remote_tool.py              # Remote tool implementation
│
├── quality/                        # Self-evolving tool quality tracking
│   ├── manager.py                  # Quality manager with adaptive ranking
│   ├── store.py                    # Persistent quality data storage
│   └── types.py                    # Quality record data types
│
├── security/                       # Security & sandboxing 🔧
│   ├── policies.py                 # Security policy enforcement
│   ├── sandbox.py                  # Sandbox abstraction
│   └── e2b_sandbox.py              # E2B sandbox integration
│
├── system/                         # System-level provider
│   ├── provider.py
│   └── tool.py
│
└── transport/                      # Transport layer abstractions 🔧
    ├── connectors/
    │   ├── base.py
    │   └── aiohttp_connector.py
    └── task_managers/
        ├── base.py
        ├── async_ctx.py
        ├── aiohttp_connection_manager.py
        └── placeholder.py

backends/shell/
├── provider.py                     # Shell provider implementation
├── session.py                      # Shell session management
└── transport/
    └── connector.py                # HTTP connector to local server

backends/gui/
├── provider.py                     # GUI provider implementation
├── session.py                      # GUI session management
├── tool.py                         # GUI-specific tools
├── anthropic_client.py             # Anthropic API client wrapper
├── anthropic_utils.py              # Utility functions
├── config.py                       # GUI configuration
└── transport/
    ├── connector.py                # Computer Use API connector
    └── actions.py                  # Action execution logic

backends/mcp/
├── provider.py                     # MCP provider implementation
├── session.py                      # MCP session management
├── client.py                       # MCP client
├── config.py                       # MCP configuration loader
├── installer.py                    # MCP server installer
├── tool_converter.py               # Convert MCP tools to unified format
├── tool_cache.py                   # MCP tool cache for offline tool discovery
└── transport/
    ├── connectors/                 # Multiple transport types
    │   ├── base.py
    │   ├── stdio.py                # Standard I/O connector
    │   ├── http.py                 # HTTP connector
    │   ├── websocket.py            # WebSocket connector
    │   ├── sandbox.py              # Sandboxed connector
    │   └── utils.py
    └── task_managers/              # Protocol-specific managers
        ├── stdio.py
        ├── sse.py
        ├── streamable_http.py
        └── websocket.py

backends/web/
├── provider.py                     # Web provider implementation
└── session.py                      # Web session management

prompts/
├── __init__.py
└── grounding_agent_prompts.py     # Grounding agent system & tool selection prompts

llm/
├── __init__.py
└── client.py                       # LiteLLM wrapper with retry logic

config/
├── __init__.py
├── loader.py                       # Configuration file loader
├── constants.py                    # System constants
├── grounding.py                    # Grounding configuration dataclasses
├── utils.py                        # Configuration utilities
│
├── config_grounding.json           # Backend-specific settings
├── config_agents.json              # Agent configurations
├── config_mcp.json.example         # MCP server definitions (copy to config_mcp.json)
├── config_security.json            # Security policies
└── config_dev.json.example         # Development config template

local_server/
├── __init__.py
├── main.py                         # Flask application entry point
├── config.json                     # Server configuration
├── feature_checker.py              # Platform feature detection
├── health_checker.py               # Server health monitoring
├── platform_adapters/              # OS-specific implementations
│   ├── macos_adapter.py            # macOS automation (atomacos, pyobjc)
│   ├── linux_adapter.py            # Linux automation (pyatspi, xlib)
│   ├── windows_adapter.py          # Windows automation (pywinauto)
│   └── pyxcursor.py                # Custom cursor handling
├── utils/
│   ├── accessibility.py            # Accessibility tree utilities
│   └── screenshot.py               # Screenshot capture
└── README.md

recording/
├── __init__.py
├── recorder.py                     # Main recording manager
├── manager.py                      # Recording lifecycle management
├── action_recorder.py              # Action-level logging
├── video.py                        # Video capture integration
├── viewer.py                       # Trajectory viewer and analyzer
└── utils.py                        # Recording utilities

platform/
├── __init__.py
├── config.py                       # Platform-specific configuration
├── recording.py                    # Recording integration
├── screenshot.py                   # Screenshot utilities
└── system_info.py                  # System information gathering

utils/
├── logging.py                      # Structured logging system
├── ui.py                           # Terminal UI components
├── display.py                      # Display formatting utilities
├── cli_display.py                  # CLI-specific display
├── ui_integration.py               # UI integration helpers
└── telemetry/                      # Usage analytics (opt-in)
    ├── __init__.py
    ├── events.py
    ├── telemetry.py
    └── utils.py

logs/
├── <script_name>/                        # Main application logs
│   └── anytool_YYYY-MM-DD_HH-MM-SS.log   # Timestamped log files
│
└── recordings/                           # Execution recordings
    └── task_<id>/                        # Individual recording session
        ├── trajectory.json               # Complete execution trajectory
        ├── screenshots/                  # Visual execution record (GUI backend)
        │   ├── tool_<name>_<timestamp>.png
        │   ├── tool_<name>_<timestamp>.png
        │   └── ...                       # Sequential screenshots
        ├── workspace/                    # Task workspace
        │   └── [generated files]         # Files created during execution
        └── screen_recording.mp4          # Video recording (if enabled)

AnyTool builds upon excellent open-source projects, we sincerely thank their authors and contributors:

• OSWorld: Comprehensive benchmark for evaluating computer-use agents across diverse operating system tasks.
• mcp-use: Platform that simplifies MCP agent development with client SDKs.

❤️ Thanks for visiting ✨ AnyTool!