superagentx

Agent Squad selects the most suitable specialized agent from each request and preserves conversation context across agents and sessions. It provides Python, TypeScript, and Swift runtimes with replaceable classifiers, storage, retrievers, and tools. The Swift implementation can keep routing, MCP tools, voice, and chat history on Apple devices, while GroundedAgent separates data gathering from response writing so answers stay limited to actual tool results.

BeeAI Framework supplies Python and TypeScript components for agents, multi-agent handoffs, model backends, tools, and RAG, helping developers assemble applications that reason, act, and collaborate. Requirements can constrain agent behavior consistently across different models, while MCP, ACP, and custom tools extend the system. Examples work with hosted providers and can also run locally through Ollama.

LangChain provides common interfaces and modular components for building agents and other LLM-powered applications. Developers can connect models, embeddings, vector stores, retrievers, and third-party tools while swapping providers without rebuilding the whole application. The core framework works on its own; LangGraph handles lower-level orchestration, while LangSmith products support debugging, evaluation, and deployment workflows.

MLflow records and manages the development and operation of LLM applications, AI agents, and machine-learning models. Teams can start a server, enable automatic tracing, inspect calls and metrics in the UI, and use evaluation, prompt optimization, experiment tracking, model registry, and deployment workflows to improve their systems. It can run locally, on private infrastructure, or in cloud environments, making it useful when generative AI and traditional model lifecycles need a shared operational layer.