AGI-MCP
An advanced MCP server implementing a cognitive architecture through the GOTCHA framework and ATLAS process for sophisticated task management and reasoning. It provides persistent SQLite memory, lifecycle hooks, and a subagent system to enable complex, agentic AI workflows.
README
AGI-MCP: Advanced General Intelligence Model Context Protocol Server
A comprehensive Model Context Protocol (MCP) server implementing AGI-like capabilities through the GOTCHA Framework, ATLAS Process, Thinking Mechanism, Hook System, and Subagent Architecture with integrated database memory for persistent state management.
🌟 Features
🎯 GOTCHA Framework (6-Layer Architecture)
A sophisticated cognitive architecture for agentic systems:
- Goals (G) - Define and manage objectives with priorities
- Observations (O) - Perceive and record environmental state
- Thoughts (T) - Reason and plan based on observations
- Commands (C) - Select and execute actions systematically
- Hypotheses (H) - Form and validate predictions
- Assessments (A) - Evaluate performance and capture learnings
🗺️ ATLAS Process (5-Step Methodology)
Structured task execution methodology:
- Analyze (A) - Understand task context and complexity
- Task Breakdown (T) - Decompose into manageable subtasks
- Learn (L) - Gather necessary knowledge and resources
- Act (A) - Execute planned actions systematically
- Synthesize (S) - Integrate results and extract insights
🧠 Thinking Mechanism
Intelligent reasoning and filtering layer:
- Prompt Evaluation - Assesses relevance and safety of user inputs
- Tool Use Validation - Evaluates appropriateness of tool execution
- Completion Assessment - Determines when work is truly complete
- Purpose-Based Filtering - Aligns all actions with agent purpose
🔗 Hook System
Claude Code-style lifecycle hooks for customization:
- 11 Hook Events - SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop, SubagentStart/Stop, and more
- Command & Prompt Hooks - Both shell command and LLM-based evaluation
- Decision Control - Allow, deny, or modify operations dynamically
- Context Injection - Add information at key lifecycle points
🤖 Subagent System
Specialized AI assistants for focused tasks:
- 4 Built-in Subagents - Explore, General-Purpose, Task-Executor, Code-Reviewer
- Custom Subagents - Create project or user-level specialists
- Isolated Contexts - Each subagent has its own memory and permissions
- Tool Restrictions - Fine-grained control over subagent capabilities
- Resumable Sessions - Continue previous subagent work
💾 Database Integration
Persistent memory as source of truth:
- SQLite Database - All operations persisted
- Session Tracking - Complete history and analytics
- ATLAS History - Full execution traces
- Query Optimization - Indexed for performance
🏗️ Memory Infrastructure
Automatic initialization and management:
- Auto-Detection - Checks for existing infrastructure
- Directory Creation -
memory/logsanddatastructures - Schema Initialization - Database setup on first run
- Logging System - Comprehensive session logs
📦 Installation
Standard Installation
# Clone the repository
git clone https://github.com/muah1987/AGI-MCP.git
cd AGI-MCP
# Install dependencies
npm install
# Build the project
npm run build
# Run tests
npm test
Docker Installation
# Option 1: Pull from Docker Hub (recommended)
docker pull muah1987/agi-mcp:latest
# Option 2: Build locally
docker build -t agi-mcp:latest .
# Option 3: Use docker-compose
docker-compose build
# Run the test script to validate the build
./test-docker.sh
Publishing to Docker Hub
Manual Publishing
# 1. Create .env file with your credentials
cp .env.example .env
# Edit .env and add your DOCKER_USERNAME and DOCKER_TOKEN
# 2. Build and push to Docker Hub
./push-docker.sh
Automated Publishing with GitHub Actions
The repository includes a GitHub Actions workflow that automatically builds and pushes Docker images to DockerHub on every push to the main branch or when a tag is created.
Setup:
-
Add the following secrets to your GitHub repository settings:
DOCKER_LOGIN- Your DockerHub usernameDOCKER_PASSWORD- Your DockerHub password or access token
-
The workflow will automatically:
- Build the Docker image using the Dockerfile
- Tag it with
latestand the version frompackage.json - Push it to DockerHub under
$DOCKER_LOGIN/agi-mcp(where$DOCKER_LOGINis your DockerHub username)
Manual Trigger:
You can also trigger the workflow manually from the Actions tab in GitHub.
🚀 Quick Start
As MCP Server (Native)
Add to your MCP client configuration (e.g., Claude Desktop, Cline):
{
"mcpServers": {
"agi-mcp": {
"command": "node",
"args": ["/absolute/path/to/AGI-MCP/dist/index.js"]
}
}
}
As MCP Server (Docker)
Using Docker for isolated deployment:
{
"mcpServers": {
"agi-mcp": {
"command": "docker",
"args": ["run", "-i", "--rm", "muah1987/agi-mcp:latest"]
}
}
}
Or using locally built image:
{
"mcpServers": {
"agi-mcp": {
"command": "docker",
"args": ["run", "-i", "--rm", "agi-mcp:latest"]
}
}
}
Direct Execution
# Native
npm start
# Docker
docker run -i agi-mcp:latest
# Docker Compose
docker-compose up
First Session
On first run, AGI-MCP automatically:
- Creates
memory/MEMORY.mddocumentation - Sets up
memory/logs/directory - Creates
data/directory - Initializes SQLite database
- Loads all subagents
- Configures hook system
🛠️ Available Tools
GOTCHA Framework (7 tools)
set_goal- Define system objectivesobserve- Record environmental observationsthink- Capture reasoning processesexecute_command- Execute and log commandsform_hypothesis- Create predictionsassess_performance- Evaluate and learnprocess_goal_with_gotcha- Full cycle processing
ATLAS Process (2 tools)
execute_atlas_task- Run complete 5-step processget_atlas_history- View task execution history
Memory Management (3 tools)
get_active_goals- List active objectivesget_memory- Query memory by layerget_session_summary- Session overview
Subagent Management (3 tools)
execute_subagent- Delegate to specialistresume_subagent- Continue previous worklist_subagents- View available subagents
📚 Documentation
Core Documentation
- README.md - This file, project overview
- CONTRIBUTING.md - Contribution guidelines
- CODE_OF_CONDUCT.md - Community standards
- SECURITY.md - Security policy and reporting
- CHANGELOG.md - Version history
- LICENSE - MIT License
Technical Documentation (docs/)
- docs/ARCHITECTURE.md - System architecture and design
- docs/ADVANCED.md - Thinking Mechanism, Hooks, and Subagents
- docs/USAGE.md - Comprehensive usage guide with examples
- docs/SKILLS.md - Skill system and orchestration
- docs/QUICKREF.md - Quick reference for developers
- docs/AGENTS.md - Subagent documentation
- docs/GETTING_STARTED.md - Quickstart guide
- docs/DEPLOYMENT.md - Deployment guide
- docs/API.md - Complete API documentation
- docs/MEMORY_SYSTEM.md - Memory architecture
- docs/PROJECT_SUMMARY.md - Project statistics
🏗️ Architecture
AGI-MCP/
├── src/
│ ├── index.ts # Main MCP server
│ ├── database/
│ │ ├── memory-db.ts # SQLite operations
│ │ └── infrastructure.ts # Auto-initialization
│ ├── gotcha/
│ │ ├── framework.ts # GOTCHA 6-layer system
│ │ └── thinking.ts # Thinking mechanism
│ ├── atlas/
│ │ └── process.ts # ATLAS 5-step process
│ ├── hooks/
│ │ └── hook-system.ts # Lifecycle hooks
│ ├── subagents/
│ │ └── subagent-system.ts # Subagent management
│ └── tools/
│ └── mcp-tools.ts # MCP tool definitions
├── memory/ # Memory system
│ ├── MEMORY.md # Documentation
│ └── logs/ # Session logs
├── data/ # Database storage
│ └── agi-mcp.db # SQLite database
└── .agi-mcp/ # Configuration
├── hooks/ # Hook scripts
├── subagents/ # Custom subagents
└── hooks-config.json # Hook configuration
💡 Examples
Execute ATLAS Task
await tools.handleToolCall('execute_atlas_task', {
task_id: 'research-001',
description: 'Research quantum computing applications'
});
Use Subagent
await tools.handleToolCall('execute_subagent', {
subagent: 'code-reviewer',
task: 'Review authentication module for security'
});
Configure Hook
{
"hooks": {
"PreToolUse": [{
"matcher": "execute_command",
"hooks": [{
"type": "command",
"command": "./validate-command.sh"
}]
}]
}
}
🧪 Testing
# Run all tests
npm test
# Build project
npm run build
🤝 Contributing
Contributions are welcome! See CONTRIBUTING.md for guidelines.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🔒 Security
See SECURITY.md for security policies and reporting vulnerabilities.
📝 Changelog
See CHANGELOG.md for version history and changes.
🙏 Acknowledgments
- Model Context Protocol by Anthropic
- Inspired by AGI principles and cognitive architectures
- Built with TypeScript and SQLite
📧 Support
- Create an Issue
- Read the Documentation
- Check Discussions
AGI-MCP - Building towards Artificial General Intelligence through Model Context Protocol
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