MCP Apache Thrift Example: Polyglot AI Orchestration Platform

Combining A2A (Agent-to-Agent) and MCP (Model Context Protocol) with Spring Boot and Apache Thrift creates a powerful, polyglot AI orchestration platform. This architecture allows you to expose intelligent agent actions uniformly using A2A/MCP annotations, while leveraging Spring Boot's robust ecosystem for scalability, security, and configuration.

By integrating Apache Thrift, your agents gain high-performance, cross-language interoperability—enabling seamless communication with Python, C++, Go, or other non-Java services through strongly typed, binary RPC calls. This approach ensures consistent agent interfaces across protocols and languages while unlocking native advantages of each runtime—for example, executing Python's rich ML logic or Scala's Spark pipelines directly from a Java agent.

The result is a distributed, language-agnostic Agentic Mesh that supports real-time decisioning, extensibility, and lightning-fast service-to-service collaboration.

🔁 REST vs Thrift in Agentic Systems

Feature	Thrift	REST
Protocol Efficiency	Binary protocol, faster and smaller payloads	Text-based (JSON/XML), larger payloads
Cross-Language RPC	Designed for this — native code generation for Python, Java, Go, C++, etc.	Language-agnostic but manual — client has to know endpoints, payloads
Strict Contracts	Strongly typed interface via .thrift file	Loose contract, usually documented via OpenAPI/Swagger
Latency	Lower latency due to binary transport	Higher latency due to JSON parsing, HTTP overhead
Streaming Support	Supports multiplexed/bidirectional streaming (via Framed transport, etc.)	REST is stateless; streaming requires WebSockets or gRPC
AI Agent Integration	Works well for tightly orchestrated agent-to-agent calls	Better suited for tool-like integrations, not fine-grained agent chaining

🔬 Use Cases and Examples

Example Implementation

This is how you will create a service which can work with A2A, MCP, as well as Thrift:

package org.example;

import com.t4a.annotations.Action;
import com.t4a.annotations.Agent;
import org.apache.thrift.TException;
import org.apache.thrift.protocol.TBinaryProtocol;
import org.apache.thrift.protocol.TProtocol;
import org.apache.thrift.transport.TSocket;
import org.apache.thrift.transport.TTransport;

@Agent(groupName = "Weather Services")
public class WeatherServiceImpl implements WeatherService.Iface {

    /**
     * Assume:     *
     * You have a Python Thrift server exposing a method like getForecast(city)     *
     * You generated Thrift stubs for both Java and Python from the same .thrift file
     * @param city
     * @return
     */
    @Override
    @Action(description = "Get forecast delegated to Python via Thrift")
    public String getForecast(String city) {
        try {
            TTransport transport = new TSocket("localhost", 9091); // Python Thrift server
            transport.open();
            TProtocol protocol = new TBinaryProtocol(transport);

            // PythonWeatherService is the client interface generated from your .thrift file
            //PythonWeatherService.Client client = new PythonWeatherService.Client(protocol);
            // String response = client.getForecast(city); // Remote call to Python
            String response = "Mocked response for city: " + city; // Replace with actual client call
            transport.close();
            return "Delegated forecast (Python): " + response;
        } catch (TException e) {
            return "Error delegating to Python: " + e.getMessage();
        }
    }

    @Override
    @Action(description = "Get temperature")
    public double getTemperature(String city) {
        return 23.7;
    }
}

Note how we are calling another Thrift agent written in Python from the getForecast method.

1. Cross-Language AI Model Orchestration

Use Case: A Java-based A2A agent receives a prompt and delegates deep learning inference to a Python agent running a PyTorch or TensorFlow model over Thrift.

Why Thrift? Avoids REST overhead and allows type-safe, low-latency calls to Python AI code.

Example:

// Java agent calls Python Thrift agent for image captioning
String caption = pythonAIClient.captionImage(imageBytes);

2. Robot Control: Java UI + Python Motor Driver

Use Case: A Java agentic control panel exposes MCP tools for robotic actions, but delegates actual hardware control to Python-based ROS nodes using Thrift.

Why Thrift? Real-time coordination and binary payloads (e.g., for sensor data or commands) make Thrift ideal for robot communication.

3. Financial Microservices in Multiple Languages

Use Case: Kotlin-based pricing engine delegates volatility computation to a C++ service via Thrift; exposes the result via MCP as an intelligent agent.

Why Thrift? C++ gives performance, Kotlin gives orchestration. Thrift bridges them seamlessly, keeping A2A-style interop across JVM and native.

4. Game AI Agents with Real-Time Scripting

Use Case: A Groovy-based MCP agent delegates pathfinding logic or NPC behavior to a Python script engine exposed over Thrift.

Why Thrift? Dynamic game behavior (e.g., scriptable events or AI decisions) can be routed in real-time without converting data formats between REST clients.

5. Edge AI AgenticMesh with Mixed Languages

Use Case: A Scala agent on an edge device uses Spark to aggregate IoT sensor data, then calls a Go agent (via Thrift) for anomaly detection logic.

Why Thrift? Thrift enables type-safe communication between edge analytics (Scala) and optimized Go microservices, preserving AI agent structure.

🧠 Summary of Benefits

Feature	Benefit
Binary, low-latency	Ideal for AI model chaining or robotics
Cross-language support	True polyglot agentic mesh
Strong typing	Safer RPC across systems
Works with A2A/MCP seamlessly	Keeps the agent input/output format consistent, logic flexible

Implementation Notes

You can pass control to another language via Apache Thrift from your Java-based Thrift agent like WeatherServiceImpl. The key is to use a Thrift client inside the Java agent to call a Thrift server implemented in another language (e.g., Python, Go, C++).

Thrift Advantages:

• Need very low latency and overhead (binary vs JSON)
• Are calling from systems already using Thrift-based microservices
• Want strongly-typed RPC interfaces
• Prefer a compiled client interface (instead of dynamic JSON payloads)

Architecture Benefits

Standardized Agent Communication:

• A2A enables agents to discover and interact with each other using a standardized format
• MCP provides a structured way for AI models to call external tools and services, with strong input/output schemas, error handling, and security

Spring Boot Integration:

• Spring Boot simplifies the development and deployment of microservices, offering robust features for configuration, security, and dependency management
• You can easily annotate services with @Agent and @Action to expose them to AI agents, and integrate security using Spring Security

Apache Thrift for Cross-Language RPC:

• Thrift provides high-performance, binary RPC calls between services written in different languages (e.g., Java, Python, C++, Go)
• This allows Java-based A2A/MCP agents to delegate tasks to non-Java services (such as Python-based ML models or C++ analytics engines) efficiently and with strong typing

Secure, Scalable, and Extensible:

• Combining these technologies enables you to build a distributed, language-agnostic agent mesh that supports real-time decision-making, extensibility, and seamless service-to-service collaboration
• Security is maintained across protocols using Spring Security, with consistent authentication and authorization controls

📊 Thrift vs gRPC: Key Differences

Feature	Thrift	gRPC
Serialization	Supports multiple formats (Binary, Compact, JSON)	Uses Protocol Buffers (Protobuf)
Transport Protocol	Supports TCP, HTTP, Kafka	Primarily uses HTTP/2
Language Support	Broader range (Java, C++, Python, Ruby, Go, etc.)	Focuses on C++, Java, Python, Go, C#
Performance	Good, but may have slightly higher overhead due to multiple serialization formats	High performance due to Protobuf and HTTP/2 multiplexing
Community & Ecosystem	Active community, but smaller than gRPC	Strong backing from Google and CNCF, widely adopted in cloud-native environments
Flexibility	More flexible in defining message types and service contracts	More opinionated, designed for simplicity

💡 Which One Should You Use?

• Use Thrift if you need cross-language support, flexible serialization, and multiple transport options
• Use gRPC if you want high performance, strong cloud-native integrations, and efficient streaming with HTTP/2

Code is here