agent-framework

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.

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.

AgenticX combines a Python SDK, the agx CLI, a Studio server, and the Machi desktop app into one platform for building and operating multi-agent systems. It supports focused automation assistants, graph-based workflows, and collaborating agent teams, with multiple model providers, MCP tools, hierarchical memory, managed skill lifecycles, sandboxed execution, and session management. Developers can embed the core ReAct agent primitive or manage runs through the studio and server interfaces.

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.