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LangGraph Currency Agent with A2A Protocol

Overview

The LangGraph Currency Agent is a demonstration of how to build an AI agent for currency conversion using LangGraph framework that's exposed through Google's Agent-to-Agent (A2A) protocol. This implementation showcases how AI agents can communicate in a standardized way while providing specialized services.

Key Components

1. LangGraph Framework

LangGraph is a framework for building stateful, multi-actor applications with Large Language Models (LLMs). It enables the creation of complex agent workflows and maintains context across interactions.

2. A2A Protocol

The Agent-to-Agent (A2A) protocol is an open standard created by Google that enables different AI agents to communicate and collaborate using a standardized interface. It allows for:

• Discovery of capabilities
• Task submission
• Progress monitoring
• Result retrieval

3. Currency Conversion Functionality

The agent integrates with the Frankfurter API to provide real-time currency exchange information.

How It Works

I'll explain the workflow using a diagram:

  

    mermaid
    
      
    
  
  

    

      

        
        
      
    
    

      

        
        
      
    
  
sequenceDiagram
    participant Client as A2A Client
    participant Server as A2A Server
    participant Agent as LangGraph Agent
    participant API as Frankfurter API

    Client->>Server: Send task with currency query
    Server->>Agent: Forward query to currency agent

    alt Complete Information
        Agent->>API: Call get_exchange_rate tool
        API->>Agent: Return exchange rate data
        Agent->>Server: Process data & return result
        Server->>Client: Respond with currency information
    else Incomplete Information
        Agent->>Server: Request additional input
        Server->>Client: Set state to input-required
        Client->>Server: Send additional information
        Server->>Agent: Forward additional info
        Agent->>API: Call get_exchange_rate tool
        API->>Agent: Return exchange rate data
        Agent->>Server: Process data & return result
        Server->>Client: Respond with currency information
    end

    alt With Streaming
        Note over Client,Server: Real-time status updates
        Server->>Client: Looking up exchange rates...
        Server->>Client: Processing exchange rates...
        Server->>Client: Final result
    end

Key Features

1. Multi-turn Conversations- The agent can request additional information when needed

   • For example, if a user asks for a currency conversion but doesn't specify both currencies, the agent can prompt for the missing information

2. Real-time Streaming

   • Provides status updates during processing
   • Users can see progress messages like "Looking up exchange rates..." and "Processing exchange rates..."

3. Conversational Memory

   • Maintains context across interactions
   • Remembers previous exchanges to provide more coherent and contextually relevant responses

4. ReAct Pattern Implementation

   • Uses the Reasoning and Acting (ReAct) agent pattern
   • Enables the agent to reason about what information it needs and then take appropriate actions

5. Currency Exchange Tool

   • Integrates with Frankfurter API for real-time exchange rates
   • Can handle various currency conversion scenarios

Implementation Details

Technical Requirements

• Python 3.13or higher
• UV (Python package installer)
• Access to an LLM (typically Google Gemini) and API Key

Setup Process

1. Navigate to the samples directory: cd samples/python/agents/langgraph
2. Create an environment file with your API key: echo "GOOGLE_API_KEY=your_api_key_here" > .env
3. Run the agent: uv run . (default port 10000) or with custom settings: uv run . --host 0.0.0.0 --port 8080

Example Usage

Basic Query

  

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User: "How much is 100 USD in EUR?"
Agent: "Based on the current exchange rate, 1 USD is equivalent to 0.92 EUR. Therefore, 100 USD would be approximately 92 EUR."

Query Requiring Additional Information

  

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User: "Convert 50 dollars"
Agent: "To which currency would you like to convert50 USD?"
User: "CAD"
Agent: "The current exchange rate is 1 USD = 1.4328 CAD. Therefore, 50 USD would be approximately 71.64 CAD."

Streaming Example (Real-time Updates)

When streaming is enabled, users receive real-time updates:

1. "Looking up the exchange rates..."
2. "Processing the exchange rates..."
3. "Based on the current exchange rate, 1 USD is equivalent to 0.77252 GBP. Therefore, 100 USD would be approximately 77.252 GBP."

Integration with Other A2A Agents

As part of the broader A2A ecosystem, the LangGraph Currency Agent can be integrated with other A2A-compatible agents:

1. Direct API Communication: Other agents can invoke the currency conversion capabilities directly
2. Orchestrated Workflows: Multiple agents can be chained together for complex tasks
3. Cross-Platform Compatibility: Works with various LLM platforms that support the A2A protocol (Claude, Gemini, etc.)

Conclusion

The LangGraph Currency Agent with A2A Protocol represents an important implementation of standardized agent communication protocols. It demonstrates how specialized AI agents can be built with LangGraph and exposed through a common interface, enabling interoperability and collaboration between different AI systems. This approach is particularly valuable for creating modular, specialized AI agents that can work together seamlessly.

LangGraph 货币代理与 A2A 协议

概述

LangGraph 货币代理是一个展示如何使用 LangGraph 框架构建货币转换 AI 代理的示例，该代理通过 Google 的代理到代理（Agent-to-Agent，A2A）协议进行暴露。这个实现展示了 AI 代理如何在提供专业服务的同时以标准化方式进行通信。

关键组件

1. LangGraph 框架

LangGraph 是一个用于构建有状态、多参与者应用程序的框架，它使用大型语言模型（LLMs）。它能够创建复杂的代理工作流并在交互过程中维护上下文。

2. A2A 协议

代理到代理（A2A）协议是由 Google 创建的开放标准，它使不同的 AI 代理能够使用标准化接口进行通信和协作。它允许：

• 能力发现
• 任务提交
• 进度监控
• 结果检索

3. 货币转换功能

该代理集成了 Frankfurter API 以提供实时货币汇率信息。

工作原理

我将使用图表来解释工作流程：

  

    mermaid
    
      
    
  
  

    

      

        
        
      
    
    

      

        
        
      
    
  
sequenceDiagram
    participant Client as A2A 客户端
    participant Server as A2A 服务器
    participant Agent as LangGraph 代理
    participant API as Frankfurter API

    Client->>Server: 发送带有货币查询的任务
    Server->>Agent: 将查询转发给货币代理

    alt 完整信息
        Agent->>API: 调用 get_exchange_rate 工具
        API->>Agent: 返回汇率数据
        Agent->>Server: 处理数据并返回结果
        Server->>Client: 响应货币信息
    else 不完整信息
        Agent->>Server: 请求额外输入
        Server->>Client: 状态设置为需要输入
        Client->>Server: 发送额外信息
        Server->>Agent: 转发额外信息
        Agent->>API: 调用 get_exchange_rate 工具
        API->>Agent: 返回汇率数据
        Agent->>Server: 处理数据并返回结果
        Server->>Client: 响应货币信息
    end

    alt 流式传输
        Note over Client,Server: 实时状态更新
        Server->>Client: 正在查询汇率...
        Server->>Client: 正在处理汇率...
        Server->>Client: 最终结果
    end

主要特点

1. 多轮对话

   • 代理可以在需要时请求额外信息
   • 例如，如果用户要求进行货币转换但没有指定两种货币，代理可以提示缺失的信息

2. 实时流式传输

   • 在处理过程中提供状态更新
   • 用户可以看到进度消息，如"正在查询汇率..."和"正在处理汇率..."

3. 对话记忆

   • 在交互过程中维护上下文
   • 记住之前的交流，以提供更连贯和上下文相关的响应

4. ReAct 模式实现

   • 使用推理和行动（Reasoning and Acting，ReAct）代理模式
   • 使代理能够推理它需要什么信息，然后采取适当的行动

5. 货币兑换工具

   • 集成 Frankfurter API获取实时汇率
   • 可以处理各种货币转换场景

实现细节

技术要求

• Python 3.11或更高版本
• UV（Python 包安装器）
• 访问 LLM（通常是 Google Gemini）和 API 密钥

设置过程

1. 导航到示例目录：cd samples/python/agents/langgraph
2. 创建带有 API 密钥的环境文件：echo "GOOGLE_API_KEY=your_api_key_here" > .env
3. 运行代理：uv run .（默认端口 10000）或使用自定义设置：uv run . --host 0.0.0.0--port 8080

使用示例

基本查询

  

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用户："100 美元兑换成欧元是多少？"
代理："根据当前汇率，1 美元等于 0.92 欧元。因此，100 美元大约是 92 欧元。"

需要额外信息的查询

  

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用户："兑换 50 美元"
代理："您想将 50 美元兑换成哪种货币？"
用户："加元"
代理："当前汇率为 1 美元 = 1.4328 加元。因此，50 美元大约是 71.64 加元。"

流式传输示例（实时更新）

启用流式传输时，用户会收到实时更新：

1. "正在查询汇率..."
2. "正在处理汇率..."
3. "根据当前汇率，1 美元等于 0.77252 英镑。因此，100 美元大约是 77.252 英镑。"

与其他 A2A 代理的集成

作为更广泛的 A2A 生态系统的一部分，LangGraph 货币代理可以与其他兼容 A2A 的代理集成：

1. 直接 API 通信：其他代理可以直接调用货币转换功能
2. 编排工作流：多个代理可以链接在一起完成复杂任务
3. 跨平台兼容性：适用于支持 A2A 协议的各种 LLM 平台（Claude、Gemini 等）

结论

LangGraph 货币代理与A2A 协议代表了标准化代理通信协议的重要实现。它展示了如何使用 LangGraph 构建专业AI 代理并通过通用接口暴露，实现不同 AI 系统之间的互操作性和协作。这种方法对于创建能够无缝协作的模块化、专业 AI 代理特别有价值。