Skip to main content
Glama
Svarkovsky

ensemble-pro

by Svarkovsky
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Ensemble Pro: Multi-Model AI Council

Ensemble Pro is an open-source Model Context Protocol (MCP) server that allows multiple LLMs to debate, critique, and vote on any question to reach a consensus.

Built with a universal OpenAI-compatible API interface, it works with any model (OpenAI, NVIDIA NIM, Ollama, LM Studio, Groq, etc.) and can be integrated into any MCP-compatible client (Cursor, Claude Desktop, Chatbox, Zed).

Why Ensemble Pro?

Standard LLMs give you one probabilistic answer. Ensemble Pro gives you an argued consensus.

  • Resilient (Failover & Hot-Swap): If an API goes down mid-debate, the system automatically swaps in a backup model without interrupting the council.

  • Universal: Works with any OpenAI-compatible API. Mix local Ollama models with cloud GPT-4o.

  • Multilingual: Models are instructed to respond in the language of your prompt.

  • Self-Cleaning: Automatically deletes debate logs older than 72 hours.

  • Zero-Cost: Run it entirely on free tiers or local models.

Related MCP server: consensus-mcp

How It Works

When you ask a question, Ensemble Pro runs a 5-stage council:

 [ USER QUESTION ]
        │
        ▼
┌───────────────────────────────────────────────────────┐
│  STAGE 1: PROPOSAL (Parallel)                         │
│  ┌─────────┐  ┌─────────┐  ┌─────────┐                │
│  │ Model A │  │ Model B │  │ Model C │  (Anonymized)  │
│  └────┬────┘  └────┬────┘  └────┬────┘                │
│       │            │            │                     │
│       ▼            ▼            ▼                     │
│  STAGE 2: REVIEW (Parallel)                           │
│  Each model critiques the others' proposals.          │
│       │            │            │                     │
│       ▼            ▼            ▼                     │
│  STAGE 3: REBUTTAL (Parallel)                         │
│  Each model defends its proposal from criticism.      │
│       │            │            │                     │
│       ▼            ▼            ▼                     │
│  STAGE 4: VOTE (Parallel)                             │
│  Models rank the proposals (Borda Count).             │
│       │                                               │
│       ▼                                               │
│  [ WINNER DECLARED ]                                  │
│       │                                               │
│       ▼                                               │
│  STAGE 5: SYNTHESIS                                   │
│  The winning model synthesizes the final answer.      │
│       │                                               │
│       ▼                                               │
│ [ FINAL CONSENSUS ]                                   │
└───────────────────────────────────────────────────────┘

Failover & Hot-Swap Logic

 [ COUNCIL STARTS ]
       │
       ▼
 HEALTH CHECK ──> Model A: OK
                Model B: FAIL (Rate Limit)
                Model C: OK
       │
       ▼
 FAILOVER ─────> Replaces Model B with Backup Model D
       │
       ▼
 COUNCIL RUNS ─> Model A + Model C + Model D
       │
       ▼
 MID-DEBATE ───> Model C Crashes!
       │
       ▼
 HOT-SWAP ─────> Coordinator drops Model C, brings in Model E
       │
       ▼
 COUNCIL FINISHES (Uninterrupted)

Installation

Prerequisites

  • Python 3.10+

  • curl or git to download the repo

Quick Install

  1. Download the package.

  2. Run the installer:

bash install.sh

The installer will:

  1. Create an isolated Python virtual environment in ~/.ensemble-pro.

  2. Ask you for your API keys and model IDs (OpenAI, NVIDIA, Ollama, or Custom).

  3. Generate the JSON configuration block needed for your MCP client.

Integration

Ensemble Pro works with any MCP client. Use the JSON generated by install.sh.

Example for Cursor / Claude Desktop / Chatbox:

{
  "mcpServers": {
    "ensemble-pro": {
      "command": "/home/user/.ensemble-pro/.venv/bin/python",
      "args": ["-m", "src.mcp_server"],
      "cwd": "/home/user/.ensemble-pro",
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "NVIDIA_API_KEY": "nvapi-...",
        "OLLAMA_BASE_URL": "http://localhost:11434/v1"
      }
    }
  }
}

Usage in Chat

Once connected, simply ask your AI assistant to use the council:

"Use ensemble_pro to debate: Is Rust better than C++ for systems programming?"

The main model will trigger the MCP tool, run the council in the background, and return the synthesized consensus.

Uninstall

To completely remove Ensemble Pro and its sandbox:

bash uninstall.sh
# or manually:
rm -rf ~/.ensemble-pro

License

MIT License. Built upon the open-source ensemble framework.

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

View all related MCP servers

Related MCP Connectors

  • Reasoning Commons

    AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.

  • mcp-aichat

    MCP server for AI dialogue using various LLM models via AceDataCloud

  • microservices.sh

    MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Svarkovsky/ensemble-pro'

If you have feedback or need assistance with the MCP directory API, please join our Discord server