Building Context-Aware Multilingual AI Agents with Cognee and Crowdin

In the era of autonomous AI applications, LLMs are increasingly deployed to interact with global audiences across dozens of languages. From multilingual customer support agents to automated documentation generators, modern systems must communicate fluently and accurately in the target dialect.

However, LLMs possess a well-known vulnerability: terminology drift. When asked to translate domain-specific software concepts, models frequently invent literal, inconsistent, or prohibited translations rather than adhering to officially approved glossaries.

Enter Crowdin: the enterprise localization platform trusted by GitHub, GitLab, Discord, and Microsoft open source to orchestrate translations, brand glossaries, and string catalogs.

With the release of the Crowdin Connector for Cognee, engineering teams can now stream localization repositories, approved terminology glossaries, and translation memory directly into Cognee's AI-native knowledge graph.

Integrating Crowdin into Cognee's memory engine empowers autonomous agents with:

The Crowdin connector is designed for production reliability:

Install Cognee and the community Crowdin connector:

pip install cognee cognee-community-connector-crowdin Enter fullscreen mode Exit fullscreen mode Or using uv:

uv add cognee cognee-community-connector-crowdin Enter fullscreen mode Exit fullscreen mode 2. Ingesting Localization Knowledge import asyncio import os import cognee from cognee_community_connector_crowdin import ( CrowdinConfig, crowdin_connector, ) async def main (): # Configure the connector config = CrowdinConfig ( api_token = os. getenv ( " CROWDIN_PERSONAL_TOKEN ", " your-crowdin-token " ), include_glossaries = True, include_strings = True, ) print ( " Fetching Crowdin localization knowledge... " ) documents = await crowdin_connector ( config ) print ( f " Retrieved { len ( documents ) } localization documents.

" ) # Ingest into Cognee Memory print ( " Ingesting into Cognee... " ) await cognee. add ( documents, dataset_name = " localization_knowledge " ) await cognee. cognify ( dataset_name = " localization_knowledge " ) # Natural Language Terminology Reasoning query = " What is the approved German translation for Passkey? " print ( f " \n Querying Cognee: ' { query } '" ) results = await cognee. search ( query, dataset_name = " localization_knowledge " ) for r in results: print ( f " \n Memory Result: { r } " ) if __name__ == " __main__ ": asyncio. run ( main ()) Enter fullscreen mode Exit fullscreen mode What Does Ingested Memory Look Like?

The connector parses Crowdin REST API v2 JSON into structured Markdown reports optimized for graph extraction:

# Terminology Glossary: Acme Technical Lexicon (ID: 501) **Total Terms Recorded**: 2 ## Approved Terms & Conceptual Definitions ### Term: `Passkey` (EN) - **Term ID**: 9001 - **Part of Speech**: noun - **Status**: approved - **Definition / Context**: FIDO2 WebAuthn cryptographic credential. ### Term: `Passkey` (DE) - **Term ID**: 9002 - **Part of Speech**: noun - **Status**: approved - **Definition / Context**: Do not translate as Passwort. Keep as Passkey. Enter fullscreen mode Exit fullscreen mode When Cognee processes this markdown through cognify(), it constructs knowledge graph nodes representing:

The Crowdin connector is an open-source contribution to the Cognee Community ecosystem.

Empower your AI agents with reliable, multilingual memory today!