AgenticX

MetaGPT organizes product managers, architects, project managers, and engineers into a coordinated multi-agent workflow. From a one-line requirement, it can produce user stories, requirements, data structures, APIs, documentation, and a code repository. It runs from the command line or as a Python library, and requires Python 3.9 through 3.11 plus Node.js and pnpm for actual use.

Microsoft Agent Framework targets Python and .NET applications that need more than a single prompt-and-response loop. It supports sequential, concurrent, handoff, and group-collaboration graphs, along with checkpointing, streaming, human-in-the-loop control, OpenTelemetry tracing, and Foundry hosting samples. Its scope suits teams building restartable, observable agent workflows rather than a small one-off script.

RuoYi AI is a full-stack platform for enterprise agent applications. It connects multiple model providers, builds local RAG knowledge bases, and lets teams compose model calls, email, human review, and other steps in a visual workflow. Its agent layer supports multiple decision modes, multi-agent coordination, MCP, and Skills extensions. A Docker Compose setup can start the backend, administration UI, user UI, and supporting services together.

agent-runbook lets skill authors describe multi-agent workflows in YAML and compile them into SKILL.md files with checkpoint support. Steps declare typed inputs and JSON Schema outputs, while the compiler checks references, dependency closure, and cycles before producing the artifact. Runbooks can dispatch agents, run scripts, branch, loop, execute parallel work, and resume from a checkpoint; the project notes that its API is still changing.