Multi-Agent AI Application (100% Offline)

A fully local, open-source agentic AI system with 45 specialized agents, 50 predefined workflows, and an MCP server — powered by Ollama. No API keys, no cloud services, no internet required after setup.

Complete Installation Guide
Local LLM Setup (Ollama)
Local MCP Server Setup
External Local MCP Servers
Quick Start
CLI Commands Reference
Agents (45)
Workflows (50)
Use Cases with Examples
MCP Client Integration
Advanced Features
Configuration Reference
Project Structure
Extending the System
Troubleshooting

Complete Installation Guide

Prerequisites

Python 3.12+
8GB+ RAM (16GB recommended for 13B models)
~5GB disk space (for model + dependencies)
No GPU required (but speeds up inference)

Step 1: Install Ollama (Local LLM Runtime)

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# macOS
brew install ollama

# Windows — download installer from https://ollama.com/download

# Verify installation
ollama --version

Step 2: Start Ollama Service

# Start the Ollama daemon
ollama serve

# It runs on http://localhost:11434 by default
# Keep this terminal open, or run as a system service:
# sudo systemctl enable ollama && sudo systemctl start ollama  (Linux)

Step 3: Pull a Local LLM Model

# Recommended: good balance of speed and quality
ollama pull llama3.1:8b

# Alternatives:
ollama pull mistral              # Fast, 7B params
ollama pull codellama:13b        # Best for code tasks
ollama pull qwen2.5:7b           # Good multilingual
ollama pull llama3.1:70b         # Best quality (needs 40GB+ RAM)
ollama pull deepseek-coder:6.7b  # Specialized for code
ollama pull phi3:mini             # Smallest, fastest

# Verify model is available
ollama list

Step 4: Set Up the Application

cd multi-agent-app

# Create Python virtual environment
python3 -m venv .venv

# Activate it
source .venv/bin/activate        # Linux/macOS
# .venv\Scripts\activate         # Windows PowerShell
# .venv\Scripts\activate.bat     # Windows CMD

# Install all dependencies
pip install -r requirements.txt

Step 5: Configure (Optional)

Edit config.py if you changed defaults:

OLLAMA_BASE_URL = "http://localhost:11434"  # Ollama address
MODEL_NAME = "llama3.1:8b"                  # Model you pulled
MAX_TOKENS = 2048                           # Max response length

Step 6: Run

# Interactive CLI mode
python main.py

# Or as MCP server
python main.py --mcp-server

Local LLM Setup (Ollama)

Managing Models

# List installed models
ollama list

# Pull a new model
ollama pull <model-name>

# Remove a model
ollama rm <model-name>

# Show model details
ollama show llama3.1:8b

# Test a model directly
ollama run llama3.1:8b "Hello, how are you?"

Recommended Models by Use Case

| Use Case | Model | RAM Needed | Speed | |----------|-------|-----------|-------| | General (default) | llama3.1:8b | 8GB | Fast | | Code-heavy work | codellama:13b | 16GB | Medium | | Fast responses | mistral or phi3:mini | 4-8GB | Very fast | | Complex reasoning | llama3.1:70b | 40GB+ | Slow | | Multilingual | qwen2.5:7b | 8GB | Fast | | Code + explanation | deepseek-coder:6.7b | 8GB | Fast |

Switch Model at Runtime

No restart needed — switch in the CLI:

/model codellama:13b
/model mistral

Ollama Configuration

# Change Ollama host/port (if needed)
export OLLAMA_HOST=0.0.0.0:11434

# Set GPU layers (for partial GPU offload)
export OLLAMA_NUM_GPU=999

# Set number of threads
export OLLAMA_NUM_THREAD=8

Local MCP Server Setup

This App as an MCP Server

Your multi-agent system IS an MCP server. Start it:

# stdio transport (for Claude Desktop, Cursor, etc.)
python main.py --mcp-server

# SSE/HTTP transport (for web clients or remote access on LAN)
python main.py --mcp-server --transport sse --host 0.0.0.0 --port 8080

What Gets Exposed via MCP

| Type | Count | Description | |------|-------|-------------| | Tools | 47 | run_multi_agent + 45 individual agent tools + list_agents | | Resources | 2 | agents://list, config://system |

MCP Server CLI Arguments

python main.py --mcp-server [OPTIONS]

Options:
  --transport {stdio,sse}   Transport protocol (default: stdio)
  --host HOST               Bind address for SSE (default: 0.0.0.0)
  --port PORT               Port for SSE (default: 8080)

External Local MCP Servers

Your agents can consume tools from OTHER local MCP servers running on your machine.

Install Local MCP Servers

# Install Node.js MCP servers (one-time, cached locally)
npx -y @modelcontextprotocol/server-filesystem /tmp
npx -y @modelcontextprotocol/server-sqlite mydb.sqlite
npx -y @modelcontextprotocol/server-memory

# Or install Python-based MCP servers
pip install mcp-server-fetch
pip install mcp-server-git

Configure External Servers

Edit config.py:

EXTERNAL_MCP_SERVERS = [
    # Filesystem access — agents can read/write local files
    {
        "name": "filesystem",
        "transport": "stdio",
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/jaspal/projects"]
    },

    # SQLite — agents can query local databases
    {
        "name": "sqlite",
        "transport": "stdio",
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-sqlite", "/home/jaspal/data/app.db"]
    },

    # Memory/Knowledge base — persistent agent memory
    {
        "name": "memory",
        "transport": "stdio",
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-memory"]
    },

    # Git — agents can interact with local repos
    {
        "name": "git",
        "transport": "stdio",
        "command": "python",
        "args": ["-m", "mcp_server_git", "--repo", "/home/jaspal/projects/myapp"]
    },

    # Custom local MCP server (running on localhost)
    {
        "name": "custom-tools",
        "transport": "sse",
        "url": "http://localhost:9090/sse"
    },
]

How It All Connects (100% Local)

┌─────────────────────────────────────────────────────────┐
│                    YOUR MACHINE                          │
│                                                         │
│  ┌─────────────┐     ┌──────────────────────────────┐  │
│  │   Ollama    │     │   Multi-Agent App            │  │
│  │ (Local LLM) │◄───►│   45 agents + supervisor     │  │
│  │ :11434      │     │   MCP server (stdio/SSE)     │  │
│  └─────────────┘     └──────────┬───────────────────┘  │
│                                  │                      │
│                    ┌─────────────┼─────────────┐        │
│                    ▼             ▼             ▼        │
│  ┌──────────────┐ ┌───────────┐ ┌───────────────┐     │
│  │ MCP Server:  │ │MCP Server:│ │ MCP Server:   │     │
│  │ filesystem   │ │ sqlite    │ │ memory        │     │
│  │ (local files)│ │ (local db)│ │ (local store) │     │
│  └──────────────┘ └───────────┘ └───────────────┘     │
│                                                         │
│  ┌──────────────────────────────────────────────────┐  │
│  │ MCP Clients: Claude Desktop / Cursor / VS Code   │  │
│  └──────────────────────────────────────────────────┘  │
│                                                         │
│  Network: ZERO external traffic                         │
└─────────────────────────────────────────────────────────┘

Quick Start

# Terminal 1: Start Ollama
ollama serve

# Terminal 2: Run the app
cd multi-agent-app
source .venv/bin/activate
python main.py

Then type:

🧑 You: write a Python REST API with Flask for a todo app

The supervisor automatically routes to the best agent(s) and returns the result.

CLI Commands Reference

Agent Execution

| Command | Description | Example | |---------|-------------|---------| | (just type) | Auto-route via supervisor | write a REST API for users | | /ask <agent> msg | Run specific agent | /ask coder write binary search | | /chain <a\|b\|c> msg | Chain agents sequentially | /chain coder\|reviewer\|tester build calculator | | /parallel <a,b> msg | Run concurrently | /parallel coder,security write login | | /compare <a,b> msg | Compare outputs | /compare coder,refactorer implement sort | | /workflow <name> msg | Predefined pipeline | /workflow full_dev build todo app | | /auto msg | Auto-select workflow | /auto fix the login bug | | /feedback <a> <r> msg | Iterative refinement | /feedback coder reviewer write parser | | /batch <agent> t1;;t2 | Batch process | /batch coder sort;;search;;hash | | /stream msg | Stream response | /stream explain monads |

File Context

| Command | Description | Example | |---------|-------------|---------| | /file <path> msg | Single file context | /file src/app.py review this | | /files <p1,p2> msg | Multiple files | /files api.py,db.py find bugs |

Session Management

| Command | Description | |---------|-------------| | /save [name] | Save session | | /load <name> | Load session | | /export | Export as markdown | | /sessions | List sessions | | /history | Show history | | /clear | Clear history |

Memory

| Command | Description | |---------|-------------| | /remember <key> note | Store persistent note | | /recall [key] | Recall notes | | /forget [key] | Clear memory |

System

| Command | Description | |---------|-------------| | /agents | List all 45 agents | | /workflows | List all 50 workflows | | /tokens | Token usage stats | | /model <name> | Switch model | | /health | Check Ollama | | /retry | Re-run last request | | /help | Show help | | quit / exit | Exit |

Agents (45)

| Agent | Description | Temp | |-------|-------------|------| | researcher | Gathers information and provides summaries | 0.7 | | coder | Writes clean, production-quality code | 0.3 | | reviewer | Reviews code/content for quality and correctness | 0.4 | | planner | Breaks down complex tasks into actionable steps | 0.5 | | debugger | Diagnoses errors and suggests targeted fixes | 0.2 | | writer | Writes documentation, emails, and reports | 0.6 | | tester | Writes test cases and testing strategies | 0.3 | | optimizer | Performance optimization and bottleneck analysis | 0.3 | | security | Security analysis and vulnerability detection | 0.2 | | data_analyst | Data analysis, SQL queries, and data modeling | 0.4 | | devops | CI/CD, Docker, Kubernetes, and infrastructure | 0.3 | | translator | Translation and localization between languages | 0.5 | | architect | System architecture design and trade-off analysis | 0.4 | | mentor | Explains concepts and guides learning | 0.6 | | summarizer | Condenses content into key points and summaries | 0.3 | | api_designer | API design, OpenAPI specs, and contracts | 0.3 | | database | Schema design, SQL optimization, and DB architecture | 0.3 | | ux_designer | UI/UX design, wireframes, and accessibility | 0.5 | | refactorer | Code restructuring and maintainability improvements | 0.2 | | explainer | Code walkthroughs and detailed explanations | 0.5 | | validator | Verifies implementations match requirements | 0.2 | | automator | Automation scripts, CLI tools, and workflows | 0.3 | | migrator | Code/database/infrastructure migrations | 0.3 | | prompt_engineer | Crafts and optimizes LLM prompts | 0.4 | | diagrammer | Creates Mermaid/PlantUML system diagrams | 0.3 | | estimator | Effort and time estimation for tasks | 0.4 | | compliance | Regulatory compliance and standards audits | 0.2 | | product_manager | Requirements, user stories, and prioritization | 0.5 | | interviewer | Interview questions and answer evaluation | 0.5 | | git_expert | Git workflows, branching, and conflict resolution | 0.3 | | accessibility | WCAG compliance and inclusive design | 0.3 | | performance_tester | Load testing and scalability analysis | 0.3 | | error_handler | Error handling patterns and resilience | 0.3 | | documentation | API docs, changelogs, and guides | 0.5 | | regex_expert | Crafts and explains regular expressions | 0.2 | | shell_expert | Shell scripting and Unix tools | 0.3 | | ml_engineer | ML pipelines, training, and evaluation | 0.4 | | concurrency | Async, threading, and parallel processing | 0.3 | | config_manager | Configuration, env vars, and feature flags | 0.3 | | code_generator | Boilerplate, scaffolding, and templates | 0.3 | | tech_lead | Technical decisions and team guidance | 0.4 | | seo_expert | SEO optimization and web performance | 0.4 | | monitoring | Observability, alerting, and SRE practices | 0.3 | | networking | DNS, load balancing, and network architecture | 0.3 | | contract_tester | API contract testing and compatibility | 0.2 |

Workflows (50)

Development

| Workflow | Pipeline | |----------|----------| | full_dev | planner → coder → reviewer → tester | | code_review | coder → reviewer → tester | | bug_fix | debugger → coder → tester | | refactor | explainer → refactorer → reviewer → tester | | scaffold | planner → code_generator → coder → tester | | optimize | coder → optimizer → reviewer | | error_resilience | error_handler → coder → tester → reviewer | | concurrent_system | architect → concurrency → coder → tester → reviewer |

API & Backend

| Workflow | Pipeline | |----------|----------| | api_build | api_designer → coder → tester → writer | | api_full | api_designer → code_generator → coder → tester → documentation → security | | api_contract | api_designer → contract_tester → tester → documentation | | db_design | planner → database → reviewer | | microservice | architect → api_designer → coder → contract_tester → devops | | full_stack | planner → architect → api_designer → database → coder → tester | | data_pipeline | data_analyst → coder → tester → devops |

DevOps & Infrastructure

| Workflow | Pipeline | |----------|----------| | deploy | devops → security → validator | | production_ready | coder → error_handler → security → performance_tester → devops | | release | tester → security → compliance → documentation → devops | | observability | monitoring → devops → shell_expert | | config_setup | config_manager → devops → validator | | network_setup | networking → security → devops → validator | | git_workflow | git_expert → devops → automator | | shell_automation | shell_expert → automator → tester |

Security & Quality

| Workflow | Pipeline | |----------|----------| | security_audit | coder → security → compliance | | compliance_check | security → compliance → validator | | full_review | explainer → reviewer → security → optimizer → accessibility | | perf_audit | performance_tester → optimizer → reviewer |

Frontend & UX

| Workflow | Pipeline | |----------|----------| | frontend | ux_designer → coder → accessibility → reviewer | | ux_audit | ux_designer → accessibility → reviewer | | seo_optimize | seo_expert → coder → performance_tester |

Documentation & Learning

| Workflow | Pipeline | |----------|----------| | docs | researcher → writer → reviewer | | learn | researcher → mentor → summarizer | | code_explain | explainer → diagrammer → summarizer | | team_onboard | documentation → diagrammer → mentor → explainer | | translate | translator → reviewer → writer |

Planning & Management

| Workflow | Pipeline | |----------|----------| | design | planner → architect → diagrammer | | estimate | planner → estimator → reviewer | | tech_spec | product_manager → architect → api_designer → estimator | | tech_decision | researcher → tech_lead → architect → estimator | | mvp | product_manager → planner → coder → tester | | startup_mvp | product_manager → planner → architect → code_generator → coder → tester → devops |

Migration & Modernization

| Workflow | Pipeline | |----------|----------| | migrate | planner → migrator → tester → reviewer | | legacy_modernize | explainer → architect → migrator → coder → tester |

Incident & Operations

| Workflow | Pipeline | |----------|----------| | incident | debugger → devops → summarizer | | incident_response | debugger → monitoring → devops → summarizer → documentation |

Specialized

| Workflow | Pipeline | |----------|----------| | ml_project | researcher → ml_engineer → coder → tester → documentation | | regex_build | regex_expert → tester → explainer | | prompt_craft | prompt_engineer → tester → optimizer | | interview_prep | researcher → interviewer → mentor | | onboarding | explainer → mentor → diagrammer |

Use Cases with Examples

🚀 Build a New Feature

/workflow full_dev implement user authentication with JWT and refresh tokens

🐛 Fix a Bug

/workflow bug_fix TypeError: Cannot read property 'map' of undefined in UserList.tsx

🏗️ Design a System

/workflow design design a real-time notification system for 100k users

📝 Write Documentation

/workflow docs document the payment processing module with API reference

🔒 Security Review

/files src/auth.py,src/middleware.py security audit these files

⚡ Optimize Performance

/workflow perf_audit our API response time is 2s, analyze and optimize

🎯 Direct Agent Call

/ask coder write a Python decorator for caching with TTL
/ask database design a schema for multi-tenant SaaS
/ask devops write a GitHub Actions CI/CD pipeline for a Node.js app
/ask shell_expert write a bash script to backup PostgreSQL daily

🔄 Iterative Refinement

/feedback coder reviewer write a thread-safe LRU cache in Python

(Coder writes → reviewer critiques → coder improves → until approved)

📊 Compare Approaches

/compare architect,optimizer design a caching strategy for product catalog

⚡ Parallel Execution

/parallel security,optimizer,accessibility audit this React component

📋 Batch Processing

/batch coder implement stack;;implement queue;;implement linked list;;implement BST

🤖 Auto-Routing

/auto our login endpoint is returning 500 errors in production

(Automatically selects bug_fix workflow)

📁 Multi-File Analysis

/files src/api.py,src/models.py,src/tests.py review for consistency issues

🎓 Learning

/workflow learn explain event-driven architecture with examples
/ask mentor explain the CAP theorem like I'm a junior developer

🚢 Production Release

/workflow release prepare v2.0 release for the payment service

🏢 Full Startup MVP

/workflow startup_mvp build a SaaS invoicing app with Stripe integration

MCP Client Integration

Claude Desktop

Add to ~/.config/claude/claude_desktop_config.json (Linux) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "multi-agent": {
      "command": "/home/jaspal/jscode/js-ai-apps-api/multi-agent-app/.venv/bin/python",
      "args": ["/home/jaspal/jscode/js-ai-apps-api/multi-agent-app/main.py", "--mcp-server"]
    }
  }
}

Cursor

Add to Cursor MCP settings:

{
  "multi-agent": {
    "command": "/home/jaspal/jscode/js-ai-apps-api/multi-agent-app/.venv/bin/python",
    "args": ["main.py", "--mcp-server"],
    "cwd": "/home/jaspal/jscode/js-ai-apps-api/multi-agent-app"
  }
}

VS Code (Copilot MCP)

{
  "mcp": {
    "servers": {
      "multi-agent": {
        "command": "python",
        "args": ["main.py", "--mcp-server"],
        "cwd": "/home/jaspal/jscode/js-ai-apps-api/multi-agent-app"
      }
    }
  }
}

Any MCP Client (SSE/HTTP)

# Start HTTP server
python main.py --mcp-server --transport sse --port 8080

# Connect from any MCP client to: http://localhost:8080/sse

Advanced Features

🔄 Feedback Loop

Iteratively refines output until approved:

/feedback coder reviewer write a production-ready connection pool

Agent writes → reviewer evaluates → agent improves → repeat (max 3 rounds).

🧠 Persistent Memory

Notes that survive across sessions:

/remember project Using PostgreSQL 15 with pgvector extension
/remember style snake_case, type hints, 4-space indent
/recall project
/forget project

⚡ Parallel Execution

Multiple agents simultaneously:

/parallel security,optimizer,reviewer analyze this module

📦 Batch Processing

Same agent, multiple tasks:

/batch tester write tests for login;;signup;;logout;;password-reset

🎯 Auto-Routing

Keyword-based workflow selection:

/auto deploy our app to kubernetes with monitoring

📁 Multi-File Context

Cross-file analysis:

/files src/api.py,src/models.py,tests/test_api.py find inconsistencies

🔀 Custom Chains

Build pipelines on the fly:

/chain planner|architect|coder|tester|documentation build a rate limiter

💾 Session Persistence

/save my-project
/load my-project
/export

📊 Token Tracking

/tokens
# Output: Tokens: ~12,450 total (4,200 in / 8,250 out) | Requests: 7

Configuration Reference

config.py

| Setting | Default | Description | |---------|---------|-------------| | OLLAMA_BASE_URL | http://localhost:11434 | Ollama API endpoint | | MODEL_NAME | llama3.1:8b | Default model | | TEMPERATURE | 0.7 | Default temperature | | MAX_TOKENS | 2048 | Max response tokens | | MCP_SERVER_NAME | MultiAgentSystem | MCP server name | | MCP_SERVER_TRANSPORT | stdio | Default transport | | MCP_SSE_HOST | 0.0.0.0 | SSE bind address | | MCP_SSE_PORT | 8080 | SSE port | | MCP_REQUEST_TIMEOUT | 300 | Request timeout (seconds) | | EXTERNAL_MCP_SERVERS | [] | External local MCP servers |

Environment Variables (Ollama)

export OLLAMA_HOST=0.0.0.0:11434   # Bind address
export OLLAMA_NUM_GPU=999           # GPU layers
export OLLAMA_NUM_THREAD=8          # CPU threads
export OLLAMA_KEEP_ALIVE=5m         # Model keep-alive time

Project Structure

multi-agent-app/
├── agent_registry.py   # 45 agent definitions (single source of truth)
├── config.py           # All settings + supervisor prompt
├── graph.py            # LangGraph supervisor orchestration
├── runners.py          # Execution modes + 50 workflows + advanced features
├── session.py          # History, tokens, save/load/export
├── main.py             # CLI dispatcher + entry point
├── mcp_server.py       # FastMCP server (dynamic tool registration)
├── tool_registry.py    # External MCP server consumption
├── requirements.txt    # Python dependencies
└── sessions/           # Saved sessions + agent memory
    ├── *.json          # Session files
    ├── *.md            # Exported conversations
    └── memory.json     # Persistent agent memory

Extending the System

Add a New Agent

Add one entry to agent_registry.py:

"my_agent": {
    "description": "What it does",
    "temperature": 0.3,
    "prompt": "You are a ... agent. Your job is to: 1) ... 2) ... 3) ...",
},

Automatically available in: supervisor routing, /ask, /chain, MCP tools.

Add a New Workflow

Add to WORKFLOWS in runners.py:

"my_workflow": ["planner", "my_agent", "reviewer", "tester"],

Add External MCP Server

Add to EXTERNAL_MCP_SERVERS in config.py:

{"name": "my-server", "transport": "stdio", "command": "python", "args": ["my_server.py"]}

Troubleshooting

Ollama not reachable

# Check if running
curl http://localhost:11434/api/tags

# Start it
ollama serve

Model not found

# List available models
ollama list

# Pull the model
ollama pull llama3.1:8b

Slow responses

Use a smaller model: /model mistral or /model phi3:mini
Reduce MAX_TOKENS in config.py
Use GPU: install CUDA/ROCm drivers

Out of memory

Use smaller model: llama3.1:8b instead of 13b/70b
Close other applications
Set OLLAMA_NUM_GPU=0 to use CPU only (slower but less RAM)

Import errors

# Make sure venv is activated
source .venv/bin/activate

# Reinstall dependencies
pip install -r requirements.txt

Requirements

Python 3.12+
Ollama (any version)
8GB+ RAM (16GB recommended)
No GPU required
No internet after initial setup

Python Dependencies

langchain>=0.3.0
langchain-ollama>=0.2.0
langgraph>=0.2.0
pydantic>=2.0.0
mcp>=1.0.0
requests>=2.28.0

License

MIT

Multi-Agent AI Application (100% Offline)

Table of Contents

Complete Installation Guide

Prerequisites

Step 1: Install Ollama (Local LLM Runtime)

Step 2: Start Ollama Service

Step 3: Pull a Local LLM Model

Step 4: Set Up the Application

Step 5: Configure (Optional)

Step 6: Run

Local LLM Setup (Ollama)

Managing Models

Recommended Models by Use Case

Switch Model at Runtime

Ollama Configuration

Local MCP Server Setup

This App as an MCP Server

What Gets Exposed via MCP

MCP Server CLI Arguments

External Local MCP Servers

Install Local MCP Servers

Configure External Servers

How It All Connects (100% Local)

Quick Start

CLI Commands Reference

Agent Execution

File Context

Session Management

Memory

System

Agents (45)

Workflows (50)

Development

API & Backend

DevOps & Infrastructure

Security & Quality

Frontend & UX

Documentation & Learning

Planning & Management

Migration & Modernization

Incident & Operations

Specialized

Use Cases with Examples

🚀 Build a New Feature

🐛 Fix a Bug

🏗️ Design a System

📝 Write Documentation

🔒 Security Review

⚡ Optimize Performance

🎯 Direct Agent Call

🔄 Iterative Refinement

📊 Compare Approaches

⚡ Parallel Execution

📋 Batch Processing

🤖 Auto-Routing

📁 Multi-File Analysis

🎓 Learning

🚢 Production Release

🏢 Full Startup MVP

MCP Client Integration

Claude Desktop

Cursor

VS Code (Copilot MCP)

Any MCP Client (SSE/HTTP)

Advanced Features

🔄 Feedback Loop

🧠 Persistent Memory

⚡ Parallel Execution

📦 Batch Processing

🎯 Auto-Routing

📁 Multi-File Context

🔀 Custom Chains

💾 Session Persistence

📊 Token Tracking

Configuration Reference

config.py

Environment Variables (Ollama)

Project Structure

Extending the System

Add a New Agent

安装包（如果需要）