Crewai

CrewAI is one of the most widely adopted multi-agent frameworks — its role-based crew model (define agents by role, goal, backstory; assign tasks; let the crew execute collaboratively) aligns well with how teams think about dividing complex AI work. Strong for pipelines where specialization helps: a researcher agent, an analyst agent, and a writer agent working together. Python-first, LLM-agnostic, active community. CrewAI+ adds hosting and observability. Confidence is docs-derived.

Python SDK: pip install crewai. Define Agent(role, goal, backstory, tools, llm). Define Task(description, expected_output, agent). Create Crew(agents, tasks, process=Process.sequential|hierarchical). crew.kickoff() executes the pipeline. Tools: any LangChain tool or custom @tool function. CrewAI+ REST API for hosted crew management and execution monitoring.

No CrewAI API key for open-source library usage. LLM provider keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.) required. CrewAI+ hosted platform: API key for crew management API. Open-source access model with no unusual auth complexity.

Agent execution failures surface as Python exceptions per task. Hierarchical process: manager agent handles delegation failures. Task retry configurable. LLM provider errors propagate as crew execution failures. Open-source with active GitHub maintenance. CrewAI+ platform reliability tracked separately.

docs.crewai.com covers quickstart, agent/task/crew definitions, tool integration, process types, and CrewAI+ platform. Getting started: pip install crewai, configure LLM key, first crew in under 10 minutes. Large community via CrewAI Discord and GitHub. Extensive cookbook examples. Among the most accessible multi-agent frameworks.