Upsonic: An AI agent framework with client-server architecture
Three months ago, we started developing an open source agent framework. We previously tried existing frameworks in our enterprise product but faced challenges in certain areas. Problems we experienced: * We risked our stateless architecture when we wanted to add an agented feature to our existing system. Current frameworks lack server-client architecture, requiring significant effort to maintain statelessness when adding an agent framework to your application. * Scaling problem - needed to write Docker configurations as existing frameworks lack official Docker support. Each agent in my…
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Three months ago, we started developing an open source agent framework. We previously tried existing frameworks in our enterprise product but faced challenges in certain areas. Problems we experienced: * We risked our stateless architecture when we wanted to add an agented feature to our existing system. Current frameworks lack server-client architecture, requiring significant effort to maintain statelessness when adding an agent framework to your application. * Scaling problem - needed to write Docker configurations as existing frameworks lack official Docker support. Each agent in my application required a separate container (e.g., Twitter page analysis, website scraping, automatic documentation writing, etc.), necessitating individual agent deployment and health checks monitoring. * Needed LLM calls for simple tasks - both fast and cost-effective solutions. With increased model capabilities, the framework should offer this option. I could handle LLM calls myself, but structured outputs required extra work within task structure. Due to these problems, we decided to build a dockerized agent framework with server-client architecture. Though server-client architecture slowed development, we observe many benefits for users. We're developing a task-centric approach as we expect agents to complete simple tasks and assist with work. As tool support is crucial for completing tasks, we built a structure officially supporting MCP servers. Client-server architecture proved beneficial for MCP server stability. Finally, we prioritized simplicity in the framework, developing a structure where outputs are more easily coded with object responses. We'd be very happy if you could check our repo. I'd love to hear any questions you may have.,
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