AgenticX MCP工具

System Requirements

• Python: 3.10+
• Memory: 4GB+ RAM recommended
• System: Windows / Linux / macOS
• Core Dependencies: ~27 lightweight packages, installs in seconds (see pyproject.toml)
• Optional Dependencies: 15 feature groups available via pip install "agenticx[xxx]"

Installation

Option 1: Install from PyPI (Recommended)

```bash

Core install (lightweight, no torch, installs in seconds)

pip install agenticx

Install optional features as needed

pip install "agenticx[memory]" # Memory: mem0, chromadb, qdrant, redis, milvus pip install "agenticx[document]" # Document processing: PDF, Word, PPT parsing pip install "agenticx[graph]" # Knowledge graph: networkx, neo4j, community detection pip install "agenticx[llm]" # Extra LLMs: anthropic, ollama pip install "agenticx[monitoring]" # Observability: prometheus, opentelemetry pip install "agenticx[mcp]" # MCP protocol pip install "agenticx[database]" # Database backends: postgres, SQLAlchemy pip install "agenticx[data]" # Data analysis: pandas, scikit-learn, matplotlib pip install "agenticx[ocr]" # OCR (pulls in torch ~2GB): easyocr pip install "agenticx[volcengine]" # Volcengine AgentKit pip install "agenticx[all]" # Everything

> **Tip**: The core package includes only ~27 lightweight dependencies and installs in seconds. Heavy dependencies (torch, pandas, etc.) are optional extras - install only what you need.

> **Browser automation**: To run [browser-use](https://github.com/browser-use/browser-use) as an MCP server from AgenticX (`mcp_connect` / `mcp_call`), see [examples/browser-use-mcp.md](examples/browser-use-mcp.md).

> **Desktop MCP upgrades (2026-04)**: Machi Settings now supports MCP brand auto-discovery (Cursor / Trae / Claude / OpenClaw / Hermes / Codex), built-in Monaco JSON editor with schema validation, and one-click install from ModelScope MCP marketplace.

#### Option 2: Install from Source (Development)

Quick Start

CLI Quick Start

After installation, the agx command-line tool is available:

```bash

Tool Usage Example

```python from agenticx.tools import tool

@tool def calculate_sum(x: int, y: int) -> int: """Calculate the sum of two numbers""" return x + y

@tool def search_web(query: str) -> str: """Search web information""" return f"Search results: {query}"

Complete Examples

We provide rich examples demonstrating various framework capabilities:

Basic agent usage

python examples/m5_agent_demo.py

- Demonstrates basic agent creation and execution
- Tool invocation and error handling
- Event-driven execution flow

**Multi-Agent Collaboration**

Multi-agent collaboration example

python examples/m5_multi_agent_demo.py ``` - Multi-agent collaboration patterns - Task distribution and result aggregation - Inter-agent communication

Memory system example

python examples/memory_example.py

- Long-term memory storage and retrieval
- Context memory management

**Healthcare Scenario**

Human-in-the-loop example

python examples/human_in_the_loop_example.py ``` - Human approval workflows - Human-machine collaboration patterns - Risk control mechanisms

Detailed documentation: examples/README_HITL.md

LLM chat example

python examples/llm_chat_example.py ``` - Multi-model support demonstration - Streaming response handling - Cost control and monitoring

Micro-sandbox example

python examples/microsandbox_example.py ``` - Secure code execution environment - Resource limits and isolation

Technical blog: examples/microsandbox_blog.md

Intent recognition service example

python examples/agenticx-for-intent-recognition/main.py ```

A production-grade, layered intent recognition service built entirely on the AgenticX framework, demonstrating real-world usage of Agents, Workflows, Tools, and Storage systems.

Architecture: - Agent Layer: Hierarchical agent design — a base IntentRecognitionAgent (LLM-powered) with specialized agents (GeneralIntentAgent, SearchIntentAgent, FunctionIntentAgent) for fine-grained classification - Workflow Engine: Pipeline-based orchestration — preprocessing → intent classification → entity extraction → rule matching → post-processing; plus dedicated workflows for each intent type - Tool System: Hybrid entity extraction (UIE + LLM + Rule extractors with confidence-weighted fusion), regex/full-text matching, and a full post-processing suite (confidence adjustment, conflict resolution, entity optimization, intent refinement) - API Gateway: Async service layer with rate limiting, concurrent control, batch processing, health checks, and performance metrics - Storage: SQLite-backed data persistence for training data management via UnifiedStorageManager - Data Models: Pydantic-based type-safe data contracts for API requests/responses and domain objects

Key capabilities: - Three-tier Intent Classification: General dialogue (greetings, chitchat), information search (factual/how-to/comparison queries), and function/tool invocation - Hybrid Entity Extraction: Combines UIE models, LLM, and rule-based extractors with intelligent fusion strategies - Full Post-processing Pipeline: Confidence adjustment, conflict resolution, entity optimization, and intent refinement - Extensible Design: Add new intent types by simply creating a new agent and workflow — zero changes to existing code

See: examples/agenticx-for-intent-recognition/

GUI Agent example

python examples/agenticx-for-guiagent/AgenticX-GUIAgent/main.py ``` - Complete GUI automation framework with human-aligned learning - Action reflection (A/B/C classification) and stuck detection - Action caching system for performance optimization - REACT output parsing and compact action schema - Device-Cloud routing for intelligent model selection - DAG-based task verification

Key capabilities: - Action Reflection: Automatic action result classification (success/wrong_state/no_change) - Stuck Detection: Continuous failure detection and recovery strategy recommendation - Action Caching: Trajectory caching with exact and fuzzy matching (up to 9x speedup) - REACT Parsing: Standardized REACT format output parsing - Smart Routing: Dynamic device-cloud model selection based on task complexity and sensitivity - DAG Verification: Multi-path task verification with dual-semantic dependencies

See: examples/agenticx-for-guiagent/

More Application Examples

Set environment variables

export OPENAI_API_KEY="your-api-key" export ANTHROPIC_API_KEY="your-api-key" # Optional ```

Complete Installation Guide: For system dependencies (antiword, tesseract) and advanced document processing features, see INSTALL.md

Configure LLM

llm = OpenAIProvider(model="gpt-4")

Start the API server

agx serve --port 8000

Basic workflow orchestration

python examples/m6_m7_simple_demo.py

- Workflow creation and execution
- Task output parsing and validation
- Conditional routing and error handling

**Complex Workflow**

Complex workflow orchestration

python examples/m6_m7_comprehensive_demo.py ``` - Complex workflow graph structures - Parallel execution and conditional branching - Complete lifecycle management

Observability module demo

python examples/m9_observability_demo.py ``` - Real-time performance monitoring - Execution trajectory analysis - Failure analysis and recovery recommendations - Data export and report generation

LLM Integration

Chatbot ```bash

✅ Completed Modules (M1-M11, M13-M17)

🚧 Planned Modules