Eunomia leverages a configuration-first approach to orchestrate AI agents. Through YAML, users define agent roles, prompt templates, tool integrations, memory stores, and branching logic. The framework supports synchronous/asynchronous tools, retrieval-augmented generation, and chain-of-thought prompting. An extensible plugin system allows custom tools, memory backends, and logging integrations. Eunomia’s CLI scaffolds projects, validates configs, and runs agents locally or in cloud environments. This enables teams to quickly prototype, iterate on conversational workflows, and maintain agent solutions without heavy custom development.

GPTMe provides a robust platform for orchestrating AI agents that retain conversational context, integrate external tools, and expose a consistent API. Developers install a lightweight Python package, define agents with plug-and-play memory backends, register custom tools (e.g., web search, database queries, file operations), and spin up a local or cloud service. GPTMe handles session tracking, multi-step reasoning, prompt templating, and model switching, delivering production-ready assistants for customer service, productivity, data analysis, and more.