Agentic Team MCP
Enables agent execution through the Gemini Antigravity CLI and Google Gemini direct API providers.
Enables agent execution through the OpenAI direct API and supports heavy code generation via the Codex CLI.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Agentic Team MCPSpin up a three-tier team to refactor the auth module and have the CEO propose a plan first."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Agentic Team MCP — Persistent Multi-Agent Orchestration for Model Context Protocol
Agentic Team MCP is an enterprise-grade, local-first multi-agent orchestration platform designed around the Model Context Protocol (MCP). It establishes a persistent, hierarchical agent workforce (CEO Strategy → Manager Execution → Specialist Workers) that bridges native CLI coding environments (Claude Code, Gemini Antigravity, Codex) with unified direct API providers (DeepSeek, Z.ai/GLM, Google Gemini, OpenAI, and OpenRouter).
Author's Note
Abdulaziz Komilov (@menma4ever), student researcher in local model fine-tuning and quantization, building persistent, cost-effective multi-agent teams across native CLIs (Claude Code, Gemini Antigravity, Codex) and Model Context Protocol.
Modern agent frameworks often suffer from three fatal flaws: fragile ephemeral execution contexts, proprietary cloud lock-in, and ballooning API token costs. Agentic Team MCP was engineered to solve these problems by coupling persistent SQLite event sourcing with native CLI adapters (leveraging existing subscription authorizations like Claude Code, Gemini Antigravity, and Codex CLI) alongside high-efficiency open-weights models (DeepSeek-V3/R1 and GLM-5). The result is an autonomous, self-healing team architecture capable of executing complex engineering milestones locally, deterministically, and cost-effectively.
Related MCP server: MEMGRAPH-MCP
Visual Architecture
flowchart TD
subgraph ClientLayer["User & Client Layer"]
User["Developer / User"]
ClaudeDesktop["Claude Desktop"]
CursorIDE["Cursor IDE"]
WebBrowser["Web Browser (Studio GUI)"]
end
subgraph GatewayLayer["MCP & Gateway Layer"]
MCPServer["FastMCP Stdio Server<br/>(mcp_server/server.py)"]
WebStudio["Web Studio & REST Gateway<br/>(FastAPI / Uvicorn)"]
end
subgraph CoreLayer["Orchestrator Core"]
Engine["Orchestrator Engine<br/>(engine/orchestrator.py)"]
SQLiteStore["SQLite Event Sourcing<br/>(team.sqlite3)"]
Queues["Task Queues & Loop Monitor"]
Heartbeat["Heartbeat & Liveness Tracker"]
end
subgraph TeamHierarchy["Hierarchical Agent Team"]
CEO["CEO Agent<br/>(Strategic Planning & Architecture)"]
Manager["Manager Agent<br/>(Milestone Breakdown & Task Dispatch)"]
Worker1["Specialist Worker 1<br/>(Packaging / Implementation)"]
Worker2["Specialist Worker 2<br/>(Documentation / QA)"]
end
subgraph ExecutionLayer["Execution Harnesses & Providers"]
subgraph CLIAdapters["Native CLI Harnesses"]
ClaudeCode["Claude Code CLI"]
AntigravityCLI["Gemini Antigravity CLI"]
CodexCLI["Codex CLI"]
HermesCLI["Hermes / OpenClaw"]
end
subgraph DirectAPI["Direct API Providers"]
DeepSeekAPI["DeepSeek (V3 / R1)"]
ZaiAPI["Z.ai / GLM-5"]
GeminiAPI["Google Gemini"]
OpenAIAPI["OpenAI"]
OpenRouterAPI["OpenRouter / SiliconFlow / Groq"]
end
end
User --> ClaudeDesktop
User --> CursorIDE
User --> WebBrowser
ClaudeDesktop -->|"stdio MCP"| MCPServer
CursorIDE -->|"stdio MCP"| MCPServer
WebBrowser -->|"HTTP / WebSocket"| WebStudio
MCPServer -->|"Engine Actions"| Engine
WebStudio -->|"REST / Event Streams"| Engine
Engine <--> SQLiteStore
Engine <--> Queues
Engine <--> Heartbeat
Engine --> CEO
CEO -->|"Dispatches Roadmap"| Manager
Manager -->|"Assigns Task"| Worker1
Manager -->|"Assigns Task"| Worker2
CEO -.->|"Executes via"| CLIAdapters
CEO -.->|"Executes via"| DirectAPI
Manager -.->|"Executes via"| CLIAdapters
Manager -.->|"Executes via"| DirectAPI
Worker1 -.->|"Executes via"| CLIAdapters
Worker1 -.->|"Executes via"| DirectAPI
Worker2 -.->|"Executes via"| CLIAdapters
Worker2 -.->|"Executes via"| DirectAPICore Features Matrix
Feature | Agentic Team MCP | Traditional Multi-Agent Frameworks | Standard MCP Servers |
Persistence Model | Resilient SQLite Event Sourcing (resumes after restart/crash) | In-memory or ephemeral sessions | Ephemeral (lifetime of stdio pipe) |
Team Hierarchy | Strict 3-Tier (CEO → Manager → Specialists) | Flat peer-to-peer or unstructured swarm | Single-agent tool provider |
Execution Harness | Dual Harness (Native CLI Subprocesses + Direct API) | API-only (HTTP calls) | External tool execution only |
Cost Optimization | Subscribed CLI Auth Pools (Claude Code, Antigravity, Codex) | Per-token commercial billing only | Host application pays per call |
Local-First Security | Air-gapped local storage, zero telemetry, auto key-redaction | Cloud dashboard telemetry & logs | Depends on client implementation |
Real-time Web Studio | Full visual canvas, live terminal streams, process monitors | Static CLI output or paid SaaS dashboard | None (headless) |
Tool Protocol | Full Model Context Protocol (MCP) specification support | Custom proprietary tool schemes | MCP Standard |
Highlights
Autonomous Hierarchical Task Decomposition
The CEO defines strategy, breaks roadmaps into phases, and delegates to the Manager.
The Manager spawns and supervises dedicated Specialist Workers (e.g., Packaging Specialist, Documentation Specialist, Test Engineer).
Workers report real results with artifact paths, automatically waking the supervisor upon completion.
Multi-Harness Runtime Execution
Seamlessly mix and match execution environments: run high-level planning on Gemini Antigravity or Claude Code, run heavy code generation on Codex CLI, and run background bulk analysis on DeepSeek-V3 or Z.ai GLM-5.
Built-in token and credential pool management rotation for seamless multi-account load balancing.
Resilient SQLite Event Sourcing & Session Persistence
Every message, status change, tool execution, and artifact generation is immutably recorded in
team.sqlite3.Complete machine restarts or process crashes are instantly recoverable without loss of agent state or conversation context.
Real-time Web Studio GUI
Interactive visual agent graph with live status indicators (
idle,working,queued,blocked).Integrated terminal monitors streaming subprocess stdout/stderr in real time.
Comprehensive telemetry dashboards for tracking turn count, token consumption, and response times.
Granular Security Boundary
Strict workspace sandboxing: each specialist worker operates within its designated project directory (
workers/<name>/).Automatic regex-based redaction of all sensitive API keys and tokens across console outputs and log files.
Separate scoped authentication tokens for agent subprocesses versus the owner dashboard.
2-Minute Quickstart Guide
Prerequisites
Python 3.11+ installed and available on your system
PATH.Git installed.
(Optional) Installed CLI tools:
claude(Claude Code),agy(Antigravity CLI), orcodex(OpenAI Codex).
Step 1: Installation & Setup
Windows (One-Click Setup)
Clone the repository and run the automated PowerShell setup script:
git clone https://github.com/menma4ever/agentic-team-mcp.git
cd agentic-team-mcp
.\Setup.ps1Manual Virtual Environment Setup (Cross-Platform)
# 1. Clone the repository
git clone https://github.com/menma4ever/agentic-team-mcp.git
cd agentic-team-mcp
# 2. Create and activate a Python virtual environment
python -m venv .venv
# On Windows (PowerShell):
.venv\Scripts\Activate.ps1
# On Linux / macOS:
source .venv/bin/activate
# 3. Install core dependencies
pip install -r requirements.txtStep 2: Configuration
Copy the clean example settings template to settings.json:
cp settings.example.json settings.jsonEdit settings.json with your preferred API keys or enable local CLI harnesses:
{
"api_keys": {
"deepseek": "sk-your-deepseek-key",
"zai": "your-zai-api-key",
"gemini": "your-gemini-api-key",
"openai": "",
"anthropic": ""
},
"cli_auth_enabled": {
"claude": true,
"agy": true,
"codex": false
}
}Step 3: Launching the Platform
Launch Web Studio & Orchestrator Engine
On Windows, simply double-click Launch.cmd or run:
Launch.cmdAlternatively, from an activated virtual environment:
python main.pyThis automatically boots the background orchestrator service, launches the Web Studio GUI, and opens your default browser at http://127.0.0.1:8765/#token=<token>.
Available Command-Line Arguments
python main.py [OPTIONS]
Options:
--port INTEGER Port for web studio & engine (default: 8765)
--no-browser Start engine and studio without opening browser
--mcp Run as stdio Model Context Protocol (MCP) server
--console TEXT Open human-in-the-loop interactive console for agentStep 4: Connecting to MCP Clients
Agentic Team MCP operates as a high-performance stdio MCP server that connects directly to your background engine.
Claude Desktop Configuration
Add the server definition to your claude_desktop_config.json:
Windows:
%APPDATA%\Claude\claude_desktop_config.jsonmacOS:
~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"agentic-team": {
"command": "C:\\path\\to\\agentic-team-mcp\\.venv\\Scripts\\python.exe",
"args": [
"C:\\path\\to\\agentic-team-mcp\\main.py",
"--mcp"
]
}
}
}Cursor IDE Configuration
Add the configuration to .cursor/mcp.json in your workspace or global Cursor settings:
{
"mcpServers": {
"agentic-team": {
"command": "C:\\path\\to\\agentic-team-mcp\\.venv\\Scripts\\python.exe",
"args": [
"C:\\path\\to\\agentic-team-mcp\\main.py",
"--mcp"
]
}
}
}Available MCP Tools Reference
When connected via MCP, Agentic Team exposes a comprehensive set of orchestration tools:
Tool Name | Scope | Description |
| Workspace | Enumerate all active and completed multi-agent team projects. |
| Workspace | Initialize a new project and provision the root CEO agent. |
| Inspection | Retrieve the full hierarchical agent tree with live statuses and telemetry. |
| Owner | Inspect real-time execution event logs and command outputs. |
| Owner | Read authenticated conversation messages and handoff records. |
| Orchestration | Dispatch an operational Manager under the CEO for milestone management. |
| Orchestration | Provision specialized workers with assigned task descriptions and harnesses. |
| Messaging | Dispatch targeted, authenticated peer or hierarchy messages. |
| Management | Dynamically switch models or harnesses with saved state handoff. |
| Status | Query worker lifecycle stage, current activity, and recent outputs. |
| Cleanup | Safely decommission worker processes and clean up or archive workspaces. |
| Hierarchy | Bubble up blocking architectural or security issues to the CEO. |
| Action Bus | Unified action channel ( |
Directory Structure
agentic-team-mcp/
├── Launch.cmd # Fast Windows launcher
├── Setup.ps1 # Automated PowerShell virtualenv & dependency setup
├── LICENSE # MIT License
├── README.md # Project documentation & guides
├── requirements.txt # Core dependencies
├── settings.example.json # Example configuration template
├── main.py # Main entry point (Web Studio, Engine & MCP Server)
├── core/ # Configuration, auth pool, and credentials
│ ├── auth_pool.py # Multi-account rotation & CLI auth slots
│ ├── catalog.py # Dynamic model & harness discovery
│ ├── config.py # Pydantic schema validation & redaction
│ ├── credential_store.py # Secure local credential storage
│ └── service.py # Engine lifecycle & process locking
├── engine/ # Orchestration core & persistence
│ ├── actions.py # Agent action handlers & dispatching
│ ├── loop_monitor.py # Stuck-loop detection & runaway turn prevention
│ ├── orchestrator.py # Central event loop & agent scheduler
│ └── store.py # SQLite event-sourcing database layer
├── harness/ # Subprocess & provider execution harnesses
│ ├── cli_runner.py # PTY/pipe adapters for Claude, Antigravity, Codex
│ └── direct_api.py # Direct async streaming HTTP API client
├── mcp_server/ # Model Context Protocol stdio server
│ └── server.py # FastMCP tool declarations & engine proxy
└── web/ # Web Studio dashboard & REST API
├── app.py # FastAPI server & WebSocket endpoints
└── static/ # Interactive graph, terminal streams, and UICommunity & Feedback
We welcome contributions, feedback, and questions from researchers and builders working on autonomous multi-agent systems and MCP tooling.
Telegram: @zwyci
Discord:
77terminator77GitHub Issues: menma4ever/agentic-team-mcp/issues
GitHub Discussions: menma4ever/agentic-team-mcp/discussions
License
Distributed under the MIT License. See LICENSE for complete terms.
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