azureaicommunity-agent-language-middleware 0.1.0

Language detection and translation middleware for AI agent frameworks

🌐 AzureAICommunity - Agent - Language Middleware

Automatic language detection, translation, and response back-translation middleware for AI agent applications built on the Agent Framework.

Your agent always reasons in its best language — your users always receive replies in theirs.

Getting Started · Configuration · Usage · Contributing

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Overview

azureaicommunity-agent-language-middleware is a plug-and-play translation layer for AI agent pipelines built on agent-framework. It automatically detects the user's language, translates incoming messages to the agent's target language, and back-translates the response — using Azure Translator with an optional LLM agent as fallback.

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✨ Features

	Feature
🔍	Language detection — automatically detect the user's language with configurable confidence threshold
🔄	Round-trip translation — translate incoming messages and back-translate agent responses
☁️	Azure Translator — production-grade speed and accuracy with 100+ languages
🤖	LLM fallback — silently falls back to LLM translation when Azure is unavailable
🔧	Builder pattern — fluent create() API to compose and customize middleware
🔌	Framework integration — drops directly into agent-framework middleware pipelines

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📦 Installation

pip install azureaicommunity-agent-language-middleware

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🚀 Quick Start

import asyncio
from agent_framework import Agent
from agent_framework.ollama import OllamaChatClient
from language_middleware import LanguageTranslationMiddleware

async def main():
    client = OllamaChatClient(model="gemma3:4b")
    agent = Agent(client)

    middleware = LanguageTranslationMiddleware.create(
        llm_agent=agent,
        target_language="en",
    )

    try:
        result = await agent.run("Wie heißt die Hauptstadt von Frankreich?", middleware=middleware)
        print(result.text)  # "Die Hauptstadt von Frankreich ist Paris."
    finally:
        await middleware.aclose()

asyncio.run(main())

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⚙️ Configuration

Parameter	Type	Default	Description
target_language	str	"en"	ISO 639-1 code of the language the agent should reason in
min_confidence	float	0.8	Minimum detection confidence to skip translation
azure_config	AzureTranslatorConfig	None	Azure Translator credentials (optional)
llm_agent	Agent	None	LLM agent used as translation fallback or primary service

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🧑‍💻 Usage

Option 1 — Azure Translator + LLM Fallback (Recommended)

import asyncio
import os
from agent_framework import Agent
from agent_framework.ollama import OllamaChatClient
from language_middleware import LanguageTranslationMiddleware, AzureTranslatorConfig

azure_config = AzureTranslatorConfig(
    key=os.environ.get("AZURE_TRANSLATOR_KEY", ""),
    region=os.environ.get("AZURE_TRANSLATOR_REGION", ""),
    endpoint=os.environ.get("AZURE_TRANSLATOR_ENDPOINT", ""),
)

async def main():
    client = OllamaChatClient(model="gemma3:4b")
    agent = Agent(client)

    middleware = LanguageTranslationMiddleware.create(
        azure_config=azure_config,
        target_language="en",
        min_confidence=0.8,
        llm_agent=agent,
    )

    try:
        tamil_query = "தஞ்சாவூரின் வரலாற்று சிறப்பு என்ன?"
        tamil_result = await agent.run(tamil_query, middleware=middleware)
        print("Tamil response:", tamil_result.text)

        german_query = "Was ist die historische Bedeutung von Thanjavur?"
        german_result = await agent.run(german_query, middleware=middleware)
        print("German response:", german_result.text)
    finally:
        await middleware.aclose()

asyncio.run(main())

Option 2 — LLM-Only (No Azure Subscription Required)

import asyncio
from agent_framework import Agent
from agent_framework.ollama import OllamaChatClient
from language_middleware import LanguageTranslationMiddleware

async def main():
    client = OllamaChatClient(model="gemma3:4b")
    agent = Agent(client)

    middleware = LanguageTranslationMiddleware.create(
        llm_agent=agent,
        target_language="en",
    )

    try:
        result = await agent.run("Wie heißt die Hauptstadt von Frankreich?", middleware=middleware)
        print(result.text)  # "Die Hauptstadt von Frankreich ist Paris."
    finally:
        await middleware.aclose()

asyncio.run(main())

Option 3 — Azure Only (No LLM Fallback)

middleware = LanguageTranslationMiddleware.create(
    azure_config=azure_config,
    target_language="en",
)

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⚙️ How It Works

1. Intercept      →  middleware captures the last user message
2. Detect         →  language is detected with a confidence score
3. Translate      →  message is translated to the target language (if needed)
4. Agent runs     →  the primary LLM processes the translated message
5. Back-translate →  agent response is translated back to the user's original language
6. Return         →  user receives the response in their native language

Translation providers (with automatic fallback):

Priority	Provider	Capability
1st	Azure Translator	Detect language + translate (fast, accurate, 100+ languages)
2nd	LLM Agent (fallback)	Detect language + translate when Azure is unavailable

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🤝 Contributing

Contributions are welcome! Please open an issue to discuss what you'd like to change before submitting a pull request.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Commit your changes (git commit -m 'Add my feature')
4. Push to the branch (git push origin feature/my-feature)
5. Open a Pull Request

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📄 License

MIT — see LICENSE for details.