Protocol Comparison

How MoltSpeak compares to existing protocols and standards for AI agent communication.

Executive Summary

MoltSpeak is designed specifically for agent-to-agent (A2A) communication with a focus on efficiency, security, and privacy. Existing protocols were designed for different purposes:

Protocol	Primary Purpose	A2A Fit
MoltSpeak	A2A communication	⭐⭐⭐⭐⭐ Native
MCP	Tool/resource access	⭐⭐⭐ Good for tools
OpenAI Functions	LLM tool calls	⭐⭐ Single-agent focus
LangChain	Agent orchestration	⭐⭐⭐ Orchestration bias
REST/GraphQL	API communication	⭐⭐ Too low-level
gRPC	Service mesh	⭐⭐ No agent semantics
ActivityPub	Social federation	⭐ Wrong domain

Key Differentiators

🤖

Native A2A Semantics

Operations like task, handoff, consent are first-class citizens.

🛡️

Privacy Built-in

PII detection and consent are protocol-level, not app-level.

🔄

Capability Negotiation

Agents discover and verify each other's abilities.

🏷️

Classification Tags

Every message explicitly marked for sensitivity.

Feature Comparison Matrix

Feature	MoltSpeak	MCP	OpenAI Functions	LangChain	gRPC
A2A Communication	✅ Native	⚠️ Partial	❌ No	⚠️ Partial	❌ No
Tool Invocation	✅ Yes	✅ Native	✅ Native	✅ Yes	⚠️ Manual
Task Delegation	✅ Native	❌ No	❌ No	⚠️ Custom	❌ No
Streaming	✅ Yes	✅ Yes	⚠️ Limited	✅ Yes	✅ Yes
E2E Encryption	✅ Native	❌ No	❌ No	❌ No	⚠️ TLS only
Identity Verification	✅ Native	❌ No	❌ No	❌ No	⚠️ mTLS
PII Detection	✅ Native	❌ No	❌ No	❌ No	❌ No
Consent Tracking	✅ Native	❌ No	❌ No	❌ No	❌ No
Data Classification	✅ Native	❌ No	❌ No	❌ No	❌ No
Multi-Agent Coordination	✅ Native	❌ No	❌ No	⚠️ Custom	❌ No
Human Auditable	✅ JSON	✅ JSON-RPC	✅ JSON	⚠️ Varies	❌ Binary
Model Agnostic	✅ Yes	✅ Yes	❌ OpenAI	⚠️ Focus	✅ Yes

Message Size Comparison

Same operation expressed in different formats:

Natural Language

"Hi, I need you to search the web 
for information about quantum 
computing breakthroughs in 2024."

Size: 167 bytes

MoltSpeak

{
  "v": "0.1", "id": "q1",
  "op": "tool",
  "p": {
    "action": "invoke",
    "tool": "web_search",
    "input": {
      "query": "quantum computing 2024",
      "max_results": 5
    }
  },
  "cls": "int"
}

Size: 177 bytes

Size Analysis

For simple tool calls, sizes are comparable. MoltSpeak's overhead pays off when you need sender/recipient identification, classification, signatures, and consent proofs—features that would require custom fields in other protocols.

MoltSpeak vs MCP

MCP (Model Context Protocol) was developed by Anthropic for Claude, focusing on tool/resource access using JSON-RPC 2.0 in a client-server model.

Aspect	MoltSpeak	MCP
Architecture	Peer-to-peer	Client-server
Identity	Agent IDs + signatures	Connection-based
Sessions	Explicit handshake	Transport-level
Privacy	Built-in PII handling	App-level
Classification	Protocol-level	None

MCP Request

JSON

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "read_file",
    "arguments": {"path": "/data/report.csv"}
  }
}

Equivalent MoltSpeak

JSON

{
  "v": "0.1",
  "id": "t-001",
  "op": "tool",
  "from": {"agent": "analyst-a1", "org": "acme"},
  "to": {"agent": "file-server-f1", "org": "acme"},
  "p": {
    "action": "invoke",
    "tool": "read_file",
    "input": {"path": "/data/report.csv"}
  },
  "cls": "conf",
  "sig": "ed25519:..."
}

When to use which?

MCP: Single agent accessing tools/resources, local tool integration, simple request-response patterns.

MoltSpeak: Multiple agents coordinating, cross-organization communication, privacy-sensitive data, audit requirements.

MoltSpeak vs OpenAI Functions

OpenAI Function Calling provides structured output from GPT models with schema-based tool definitions, tightly coupled to the OpenAI API.

Aspect	MoltSpeak	OpenAI Functions
Scope	Agent-to-agent	Model-to-app
Direction	Bidirectional	Model → App
Authentication	Native	API key only
Multi-model	Yes	OpenAI only
Task Management	Native	None

OpenAI Function Definition

JSON

{
  "name": "get_weather",
  "description": "Get weather for a location",
  "parameters": {
    "type": "object",
    "properties": {
      "location": {"type": "string"},
      "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
    },
    "required": ["location"]
  }
}

Bridge Pattern: An MoltSpeak agent can internally use OpenAI function calling while communicating externally via MoltSpeak.

MoltSpeak vs LangChain/LangGraph

LangChain is a Python framework for LLM apps with agent and chain abstractions, tool and memory systems. LangGraph handles complex flows.

Aspect	MoltSpeak	LangChain
Type	Protocol	Framework
Language	Any	Python/JS
Scope	Wire format	Full stack
Agents	Peer-to-peer	Orchestrated
Standard	Specification	Implementation

Complementary Use

LangChain/LangGraph orchestrates agents; MoltSpeak defines how they talk. LangChain agents can use MoltSpeak for external communication while using native LangChain patterns internally.

MoltSpeak vs gRPC

gRPC is a high-performance RPC framework with Protocol Buffers for serialization, strongly typed contracts, and native streaming support.

Aspect	MoltSpeak	gRPC
Serialization	JSON	Protobuf (binary)
Human Readable	Yes	No
Agent Semantics	Native	None
Privacy Features	Native	None
Learning Curve	Low	Medium

Layer Model

┌─────────────────────────────────────────┐ │ Application Layer │ │ (MoltSpeak messages) │ ├─────────────────────────────────────────┤ │ Transport Layer │ │ (gRPC / HTTP / WebSocket) │ └─────────────────────────────────────────┘

gRPC is a transport; MoltSpeak is a semantic protocol. They can work together.

MoltSpeak vs ActivityPub

ActivityPub is a W3C standard for federated social networks using an actor model with inbox/outbox pattern, used by Mastodon and others.

Aspect	MoltSpeak	ActivityPub
Domain	AI Agents	Social Networks
Actor Type	Agents	People/Bots
Content	Structured ops	Social objects
Privacy	Native PII	Public default
Real-time	Yes	Polling-based

ActivityPub is for social federation; MoltSpeak is for agent coordination. Different domains with some conceptual overlap (actors, activities).

Use Case Analysis

Use Case	Best Choice	Why
Simple Tool Call	OpenAI Functions or MCP	No privacy concerns, no multi-agent coordination
Multi-Agent Pipeline	MoltSpeak	Native task delegation, progress tracking, handoffs
Privacy-Sensitive Data	MoltSpeak	Built-in consent, PII detection, classification
Cross-Org Agent Mesh	MoltSpeak	Strong identity, capability attestation
High-Performance Internal	gRPC + MoltSpeak	gRPC transport, MoltSpeak semantics

Migration Paths

From OpenAI Functions → MoltSpeak

Python

def openai_to_moltspeak(function_call, context):
    return {
        "v": "0.1",
        "id": str(uuid4()),
        "op": "tool",
        "from": context.current_agent,
        "to": context.tool_provider,
        "p": {
            "action": "invoke",
            "tool": function_call["name"],
            "input": json.loads(function_call["arguments"])
        },
        "cls": classify_data(function_call["arguments"])
    }

From MCP → MoltSpeak

Python

def mcp_to_moltspeak(mcp_request, sender, recipient):
    return {
        "v": "0.1",
        "id": str(mcp_request.get("id")),
        "op": "tool",
        "from": sender,
        "to": recipient,
        "p": {
            "action": "invoke" if mcp_request["method"] == "tools/call" else "list",
            "tool": mcp_request["params"].get("name"),
            "input": mcp_request["params"].get("arguments", {})
        },
        "cls": "int"
    }

From LangChain → MoltSpeak

Python

class MoltSpeakAgent:
    def __init__(self, langchain_agent, identity):
        self.agent = langchain_agent
        self.identity = identity
    
    def receive(self, moltspeak_msg):
        # Convert MoltSpeak to LangChain input
        result = self.agent.run(moltspeak_msg["p"])
        
        # Convert result back to MoltSpeak
        return {
            "v": "0.1",
            "op": "respond",
            "re": moltspeak_msg["id"],
            "from": self.identity,
            "p": {"data": result}
        }

Hybrid Approaches

MoltSpeak + MCP

Pattern: MoltSpeak for A2A, MCP for tool access

┌─────────────┐ MoltSpeak ┌─────────────┐ │ Agent A │◄──────────────►│ Agent B │ └──────┬──────┘ └──────┬──────┘ │ MCP │ MCP ▼ ▼ ┌─────────────┐ ┌─────────────┐ │ Tools A │ │ Tools B │ └─────────────┘ └─────────────┘

MoltSpeak + gRPC

Pattern: MoltSpeak semantics over gRPC transport

Protobuf

service MoltSpeakService {
  rpc Exchange(MoltSpeakMessage) returns (MoltSpeakMessage);
  rpc Session(stream MoltSpeakMessage) returns (stream MoltSpeakMessage);
}

message MoltSpeakEnvelope {
  string version = 1;
  bytes message_json = 2;  // MoltSpeak JSON
}

MoltSpeak + OpenAI Functions

Pattern: Internal function calling, external MoltSpeak

Python

class HybridAgent:
    def process(self, user_input):
        # Internal: Use OpenAI function calling
        response = openai.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": user_input}],
            functions=self.internal_functions
        )
        
        # External: Use MoltSpeak for delegation
        if needs_external_agent(response):
            moltspeak_msg = self.to_moltspeak(response)
            external_result = self.send_moltspeak(moltspeak_msg)
            return self.integrate_result(response, external_result)

Recommendation Summary

Scenario	Recommended Approach
Single agent + tools	MCP or OpenAI Functions
Multi-agent + privacy	MoltSpeak
High performance internal	gRPC with MoltSpeak semantics
Cross-org federation	MoltSpeak
Rapid prototyping	LangChain (add MoltSpeak later)
Existing OpenAI app	Keep Functions, add MoltSpeak for A2A
Full stack new build	MoltSpeak + MCP for tools

Conclusion

MoltSpeak isn't meant to replace existing protocols—it fills a gap they don't address: secure, privacy-preserving, semantically rich agent-to-agent communication.

The ideal architecture often combines multiple protocols: MoltSpeak for agent coordination, MCP for tool access, OpenAI Functions for LLM structure, gRPC for performance-critical paths.