Comparisons

MPL occupies a unique position in the ecosystem of developer tools and protocols. This page explains how MPL relates to and differs from existing solutions, and where it provides complementary value.

Comparison Matrix

The following table provides a high-level feature comparison across tools and protocols:

Feature	MPL	Schema Registries	OpenAPI	gRPC/Protobuf	AG-UI/ACP
Typed schemas
Quality metrics (QoM)
Handshake negotiation
Tamper detection
Policy engine
Audit trails
Transport independence
Runtime enforcement
Agent-native design
Semantic governance

---

MPL vs Schema Registries

Examples: Confluent Schema Registry, AWS Glue Schema Registry, Apicurio Registry

Schema registries are well-established tools for managing data schemas in event-driven architectures. MPL shares the concept of centralized schema management but extends it significantly for agent communication.

Key Differences

Aspect	Schema Registries	MPL
Primary focus	Data pipeline schemas	Agent message governance
Validation scope	Structural correctness	Structure + quality + compliance
Enforcement timing	Typically build-time/deploy-time	Runtime enforcement via proxy
Quality metrics	Not provided	Schema Fidelity, Instruction Compliance, Groundedness, Determinism
Negotiation	Not provided	Handshake protocol for capability agreement
Policy	Basic compatibility rules	Full policy engine with declarative rules
Provenance	Schema versioning	Envelope signing + tamper detection + audit trails
Target domain	Data engineering	AI agent systems

When to Use What

Use a Schema Registry when...

• You need schema evolution for Kafka/event streams
• Your primary concern is serialization compatibility (Avro, Protobuf, JSON Schema)
• You are building traditional data pipelines
• You need integration with existing data infrastructure

Use MPL when...

• You are building multi-agent systems that exchange semantic messages
• You need runtime quality guarantees beyond structural validation
• You require tamper detection and provenance tracking
• You need policy-based governance of agent communications
• You want handshake negotiation between agents

Complementary Usage

MPL does not replace schema registries. In architectures where agents produce events consumed by data pipelines, you might use:

• MPL for governing agent-to-agent communication
• Schema Registry for governing agent-to-pipeline data formats

---

MPL vs OpenAPI / AsyncAPI

OpenAPI defines HTTP API contracts. AsyncAPI extends this to event-driven APIs. Both provide excellent tooling for traditional service-to-service communication.

Key Differences

Aspect	OpenAPI/AsyncAPI	MPL
Contract model	Request/response or pub/sub	Multi-hop agent workflows
Validation	Structural schema validation	Structural + semantic quality
Negotiation	Not provided (static contracts)	Dynamic handshake per interaction
Quality of Message	Not addressed	Core feature (multiple metrics)
Tamper detection	Not provided	Envelope signing and verification
Transport	HTTP (OpenAPI), multiple (AsyncAPI)	Transport-agnostic
Design philosophy	API-first design	Governance-first design
Target interaction	Client-server / publisher-subscriber	Agent-to-agent

How They Relate

graph LR
    A[Agent A] -->|MPL Envelope| B[Agent B]
    B -->|OpenAPI Call| C[External Service]
    A -->|OpenAPI Call| D[External Service]

    style A fill:#e1f5fe
    style B fill:#e1f5fe
    style C fill:#fff3e0
    style D fill:#fff3e0

MPL works alongside OpenAPI, not as a replacement:

• An agent might use OpenAPI to call external services (weather APIs, databases)
• The same agent uses MPL to communicate governed messages to other agents
• MPL envelopes can be transported over OpenAPI-defined endpoints

What MPL Adds

Beyond what OpenAPI provides, MPL introduces:

1. Quality of Message (QoM): Metrics like Schema Fidelity and Groundedness that measure not just structural correctness but semantic quality
2. Handshake negotiation: Agents dynamically agree on STypes, metrics thresholds, and policies before exchanging data
3. Tamper detection: Cryptographic signing ensures envelope integrity across multi-hop workflows
4. Multi-hop governance: Policies and metrics are maintained as messages flow through chains of agents

---

MPL vs gRPC / Protocol Buffers

gRPC provides high-performance typed RPC with Protocol Buffer serialization. It is excellent for service-to-service communication with strong typing guarantees.

Key Differences

Aspect	gRPC/Protobuf	MPL
Primary value	Typed RPC with code generation	Semantic governance and compliance
Schema language	Protocol Buffers (.proto)	JSON Schema (STypes)
Transport	HTTP/2 (primarily)	Transport-agnostic (HTTP, gRPC, WebSocket, etc.)
Quality metrics	Not provided	Schema Fidelity, Instruction Compliance, etc.
Policy enforcement	Not provided	Declarative policy engine
Negotiation	Not provided (static contracts)	Dynamic handshake protocol
Code generation	Strong (multi-language)	SDK-based (Rust, Python, TypeScript)
Performance focus	Serialization speed, network efficiency	Governance with minimal overhead

Layered Architecture

MPL can operate on top of gRPC, adding governance capabilities that gRPC does not provide:

graph TB
    subgraph "MPL Layer"
        E[Envelope] --> Q[QoM Metrics]
        E --> P[Policy Check]
        E --> S[Signature Verification]
    end

    subgraph "Transport Layer"
        G[gRPC]
        H[HTTP/REST]
        W[WebSocket]
    end

    Q --> G
    P --> G
    S --> G
    Q --> H
    P --> H
    S --> H
    Q --> W
    P --> W
    S --> W

When to Use Together

A common pattern is:

1. gRPC handles the transport: serialization, streaming, load balancing
2. MPL handles the governance: quality metrics, policy enforcement, audit trails
3. The MPL sidecar proxy can intercept gRPC traffic transparently

Example: gRPC + MPL

Agent A --[gRPC]--> MPL Proxy --[validate envelope]--> Agent B
                        |
                        v
                   Policy Engine
                   QoM Metrics
                   Audit Log

---

MPL vs Emerging Agent Protocols

Examples: AG-UI (Agent-User Interaction), ACP (Agent Communication Protocol), and similar emerging standards.

The agent protocol ecosystem is rapidly evolving. Several new protocols address different aspects of agent communication and coordination.

Key Differences

Aspect	AG-UI / ACP	MPL
Primary focus	Agent UX and coordination	Semantic governance and compliance
Interaction model	Agent lifecycle management	Message quality and policy
Schema governance	Minimal or not addressed	Core feature
Quality metrics	Not provided	Multiple QoM dimensions
Policy enforcement	Not provided	Declarative policy engine
Tamper detection	Not provided	Cryptographic envelope signing
Transport	Protocol-specific	Transport-agnostic overlay

Complementary, Not Competitive

MPL is designed to be complementary to these protocols, not a replacement:

graph TB
    subgraph "Governance Layer (MPL)"
        QoM[Quality Metrics]
        POL[Policy Engine]
        AUD[Audit Trails]
        SIG[Tamper Detection]
    end

    subgraph "Communication Layer"
        A2A[Google A2A]
        ACP[ACP]
        AGUI[AG-UI]
        MCP[Anthropic MCP]
    end

    subgraph "Transport Layer"
        HTTP[HTTP/REST]
        WS[WebSocket]
        GRPC[gRPC]
    end

    QoM --> A2A
    QoM --> ACP
    QoM --> AGUI
    QoM --> MCP
    POL --> A2A
    POL --> ACP
    POL --> AGUI
    POL --> MCP

    A2A --> HTTP
    ACP --> WS
    AGUI --> HTTP
    MCP --> HTTP

How MPL Overlays Agent Protocols

MPL can wrap messages from any agent protocol in governed envelopes:

1. Agent A sends an A2A TaskMessage
2. MPL proxy intercepts and wraps it in an MPL envelope
3. Envelope includes SType reference, QoM metrics, and signature
4. Policy engine evaluates the envelope against declared policies
5. Agent B receives the governed message, can verify integrity and quality

This means organizations can adopt MPL incrementally without replacing their existing agent communication infrastructure.

Integration Status

Protocol	MPL Integration	Status
Google A2A	Native bridge in mpl-core	Complete
Anthropic MCP	Planned native integration	Future
AG-UI	Overlay via proxy	Future
ACP	Overlay via proxy	Future

---

Summary

Use Case	Recommended Approach
Schema validation for data pipelines	Schema Registry
HTTP API contracts	OpenAPI
High-performance typed RPC	gRPC
Agent UX and lifecycle	AG-UI / ACP
Agent message governance	MPL
Semantic quality assurance	MPL
Multi-agent compliance	MPL
Agent communication + governance	Agent protocol + MPL overlay
Typed transport + governance	gRPC + MPL sidecar

MPL's Unique Value

MPL is the only solution that combines typed schemas, quality metrics, handshake negotiation, tamper detection, policy enforcement, and audit trails in a single, transport-agnostic protocol designed specifically for AI agent communication.