Saiki is an open-source agent orchestration framework that empowers developers to build complex AI-driven workflows by writing declarative YAML definitions. Each agent can perform tasks, call external services, or invoke other agents in a chained sequence. Saiki provides a built-in REST API server, execution tracing, detailed log output, and a web-based dashboard for real-time monitoring. It supports retries, fallbacks, and custom extensions, making it easy to iterate, debug, and scale robust automation pipelines.

mini-agi is designed to simplify the creation of autonomous AI agents by providing a minimal, modular framework. Built in Python, it leverages OpenAI’s language models to interpret high-level goals, decompose them into sub-tasks, and orchestrate tool calls such as HTTP requests, file operations, or custom actions. The framework includes memory storage to track agent state and results, a planner module for task decomposition with cost-based heuristics, and an executor module that sequentially invokes tools. With configuration files, users can inject custom tools, define prompt templates, and adjust planning depth. mini-agi’s lightweight architecture makes it ideal for prototyping AI agents that perform research queries, automate workflows, or generate code autonomously.