🔀 MMCP — Multiple Model Context Protocol

Orchestrate AI models as a coordinated DAG. One CLI. Every model. Smart routing.

MCP standardizes tool use for a single model. MMCP standardizes context flow between models.

⚡ 30-Second Quick Start

pip install mmcp-core
mmcp login
mmcp run

That's it. Type a task, MMCP picks the best model + pattern automatically.

🧠 Smart Routing — The Right Model for Every Task

MMCP analyzes your task and auto-selects the optimal model from 8+ providers:

| Your Task | Model Selected | Why | |-----------|---------------|-----| | "Write a Python API with auth" | Gemini 2.5 Pro | Top-tier for coding | | "Debug this React component" | GPT-4o | Strong at code generation | | "Prove this calculus theorem" | DeepSeek R1 | Best for math & reasoning | | "Write a blog post about AI" | Claude Sonnet | Excellent creative writing | | "Analyze market strategy" | Claude Sonnet | Deep reasoning & analysis | | "Summarize this in one line" | Llama 4 Maverick | Fast & free |

You don't pick the model. You describe the task. MMCP figures out the rest.

🏗️ How It Works

User: "Build a REST API for a todo app"

MMCP Smart Router:
  ├── Task type:   Coding (2 keywords matched)
  ├── Complexity:  High
  ├── Pattern:     Deep dive (shard → merge)
  └── Model:       Gemini 2.5 Pro

DAG Execution:
  [root] orchestrator (gemini-2.5-pro)
    ├── [shard] analyst-1 ──┐
    ├── [shard] analyst-2 ──┤
    └── [shard] analyst-3 ──┤
                            ▼
                   [merge] editor
                       │
                   Final Output

Every node produces a Context Envelope — an inspectable, serializable record. The full DAG is your audit trail.

🔄 The 5 Protocol Operations

| Operation | Flow | Use Case | |-----------|------|----------| | Chain | A → B → C | Sequential review pipeline | | Fork/Merge | A → [B,C,D] → E | Parallel analysis, brainstorming | | Verify | Producer → Challenger → Judge | Adversarial fact-checking | | Shard | A → [A₁,A₂,A₃] → Merge | Long document processing | | Handoff | A → B | Transfer between specialists |

💰 MMCP Cloud — Use Without API Keys

Don't have API keys? Use MMCP Cloud — we handle the infrastructure:

mmcp login      # create free account
mmcp run        # just type your task
mmcp account    # check usage

| Plan | Price | Runs/mo | Best For | |------|-------|---------|----------| | Free | $0 | 50 | Try it out | | Pro | $19/mo | 500 | Daily use | | Team | $49/mo | Unlimited | Teams |

BYOK mode: Already have API keys? Use mmcp setup instead — it's free forever.

📦 Installation

Python (CLI + SDK)

pip install mmcp-core

# BYOK mode (bring your own key)
mmcp setup

# OR Cloud mode (no keys needed)
mmcp login

TypeScript/Node.js

npm install @mmcp/core

🛠️ CLI Commands

mmcp run                                    # Interactive smart mode
mmcp chain   "task" -r writer,reviewer      # Sequential pipeline
mmcp parallel "task" -f coder,analyst -m summarizer  # Parallel
mmcp verify  "task" -p expert -c critic -s judge     # Adversarial
mmcp shard   "task" -r analyst -n 3 -M editor        # Deep dive
mmcp audit   output.json                    # View audit trail
mmcp account                                # Usage & billing
mmcp version                                # Version info

🔧 Python SDK

from mmcp_core import MMCPOrchestrator, RoleBasedRouter

orc = MMCPOrchestrator(config={
    "router": RoleBasedRouter(),
    "adapter": call_openrouter,  # or call_anthropic
})

# Chain: writer → reviewer → editor
result = await orc.run_chain(
    "Write a blog post about quantum computing",
    ["writer", "reviewer", "editor"]
)

print(result.output)       # final text
print(result.total_tokens) # cost tracking
print(result.dag)          # full audit trail

📊 TypeScript SDK

import { MMCPOrchestrator, RoleBasedRouter } from "@mmcp/core";

const orc = new MMCPOrchestrator({
  router: new RoleBasedRouter({
    architect: { model_id: "claude-opus-4-20250514" },
    coder:     { model_id: "claude-sonnet-4-20250514" },
    verifier:  { model_id: "claude-sonnet-4-20250514" },
  }),
});

const result = await orc.runChain(
  "Build a REST API for a todo app",
  ["architect", "coder", "verifier"]
);

🆚 Why MMCP?

| Feature | MMCP | LangChain | CrewAI | AutoGen | |---------|------|-----------|--------|---------| | Multi-model DAG | ✅ | ❌ | ❌ | ⚠️ | | Smart model routing | ✅ | ❌ | ❌ | ❌ | | 8+ providers | ✅ | ✅ | ⚠️ | ⚠️ | | Audit trail | ✅ Built-in | ❌ | ❌ | ❌ | | CLI (no code needed) | ✅ | ❌ | ❌ | ❌ | | Cloud hosted | ✅ | ❌ | ❌ | ❌ | | Protocol-level spec | ✅ | ❌ | ❌ | ❌ | | Setup time | 30 sec | Hours | Hours | Hours |

🧪 Context Envelope (Protocol Primitive)

Every model invocation produces a Context Envelope:

{
  "mmcp_version": "0.1",
  "id": "ctx_a1b2c3",
  "parent_ids": ["ctx_root"],
  "task": "Review the code for security issues",
  "role": "security_auditor",
  "model": "anthropic/claude-sonnet-4",
  "branch_type": "fork",
  "status": "done",
  "output": "Found 3 potential vulnerabilities...",
  "tokens_used": 1847,
  "cost_usd": 0.003
}

🛣️ Roadmap

• [x] Core DAG schema + 5 protocol operations
• [x] TypeScript SDK + Python SDK
• [x] CLI with smart routing (8+ models)
• [x] MMCP Cloud (hosted proxy with billing)
• [x] Multi-provider: Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral
• [ ] Streaming outputs
• [ ] Web dashboard + playground
• [x] PyPI package (pip install mmcp-core) ✅
• [ ] Confidence scoring + auto-retry
• [ ] MMCP Registry (share pipeline configs)
• [ ] Enterprise: SSO, audit export, compliance

🤝 Contributing

git clone https://github.com/RagavRida/mmcp.git
cd mmcp/python
pip install -e ".[all]"
pytest

Or install from PyPI to use:

pip install mmcp-core

📄 License

MIT — use it for anything.

⭐ Star this repo if MMCP saves you from model selection headaches

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