Ranked by stars gained over the last 7 days, growth rate and recent commit activity.
Marketing skills for Claude Code and AI agents. CRO, copywriting, SEO, analytics, and growth engineering.
AI-written summary
CrewAI is an open-source Python framework for building multi-agent workflows, offering Crews (role-based agents that collaborate autonomously) and Flows (event-driven automations with precise control that can also call Crews or make single LLM calls). The same company sells a commercial AMP Suite adding managed deployment, observability, and governance.
Developers building multi-agent systems who want to choose between autonomous role-based collaboration and explicit event-driven control within one framework should look at it.
AI-written summary
OmO is a single `omo` command-line agent that runs on senpi, the author's fork of pi, with built-in features including a memory system, CodeMode, and multi-model workflows triggered by keywords like "mass ulw". It installs a native binary via a shell script or `bun`/`npm`, and includes migration tooling (`omo setup`, `omo doctor`) for users coming from the OpenCode edition or LazyCodex.
Developers using OpenCode or LazyCodex, or anyone looking for a terminal agent with multi-model orchestration and session resume/thinking-level controls, may want to evaluate it.
AI-written summary
CopilotKit is an SDK for building agent-native applications, providing chat UI, generative UI, shared state, and human-in-the-loop workflows for React, Angular, Vue, React Native, Slack, and Microsoft Teams. Its team also makes the AG-UI Protocol, and an optional CopilotKit Intelligence layer adds persistent threads, user memories, analytics, and automatic learning.
Developers who need to put an existing agent in front of users across web, mobile, or team chat apps, with agent-driven UI and user approval steps, should look at it.
AI-written summary
An open-source semantic context layer for AWS that combines knowledge graphs, formal ontologies, and rule-based systems, organized as a Scan → Model → Serve workflow. It connects data sources, builds a unified semantic graph, and serves context to AI agents via MCP tools, with namespace isolation and role-based access control.
Developers building AI agents that need to retrieve business context and validate it against ontology and rule-based logic on AWS may want to look at how this repo serves that context over MCP.
AI-written summary