Seamless Collaboration Solution for Realizing Multilingual Intelligent Agents
While traditional AI agent development is often limited to a single language ecosystem, Motia solves this problem perfectly with the following design:
- Mixed Language Container Architecture: The bottom layer adopts the common JSON data format for transmission, and each language module only needs to realize the standard input and output interfaces.
- automatic type conversion mechanism: Python's dict is automatically converted to TypeScript's Record, and Ruby hash tables are bi-directionally compatible with JavaScript objects.
- unified runtime environment (URE): Build lightweight sandboxes based on Node.js, support for calling other language interpreters through child processes
Specific implementation steps:
- Create different language files in the steps directory (e.g. py/js/rb suffixes)
- Ensure that all handler functions receive and return the standard dictionary structure
- On-demand referencing of multi-language steps in flows configuration files
Debugging Tips:Use Workbench's cross-language tracing capabilities to observe the state of data flow between steps, with a particular focus on automated processing of timestamps and character encoding.
This answer comes from the articleMotia: a development framework for rapidly building intelligences in codeThe
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