agents-crewai

Multi-agent workflows coordinate specialized AI agents by defining role-based goals, chaining task context sequentially or hierarchically, and integrating tools for automated task completion. This framework supports research-to-writing pipelines and delegated analysis flows.

A research-to-writing pipeline uses role-based AI agents where a Researcher fetches findings and a Writer composes a structured summary using chained task outputs. Sequential process execution ensures predictable automation from research to drafting.

CrewAI provides multi-agent orchestration without LangChain dependencies, offering deterministic process execution instead. It uses role-based agent definitions, task context chaining, and built-in tool integrations for reliable workflow automation.

Production agent automation supports short-term and long-term memory, tracing, and token usage reporting. These memory and observability features ensure reliable execution for complex, multi-step workflows in production environments.

Sequential multi-agent workflows execute predictable pipelines by passing context between tasks, while hierarchical workflows use manager-delegated task execution. Both process modes support role-based agent definitions and tool integrations for complex task completion.

AI agents support built-in tool integrations for web search, scraping, document handling, databases, and AI services. These tools extend agent capabilities within sequential and hierarchical multi-agent workflows for production automation.