CrewAI 的核心概念就三个Agent角色、Crew团队、Task任务——定义几个有专长的 AI 角色组成团队分配任务像同事一样协作。技术上独立于 LangChain 从零构建QA 任务实测比 LangGraph 快 5.76 倍。双模式架构Crews 模式 Agent 自治决策适合探索性任务Flows 模式事件驱动精确控制适合生产环境。团队背景创始人 João Moura 曾在 Clearbit 负责 AI 工程2024 年获 1,800 万美元融资Insight Partners 等GitHub 38,100 颗星10 万认证开发者。低代码CLI这是两个模式。CrewAI simplifies the agent production process without sacrificing the control enterprises demand.Know what to automate before you buildCrewAI Discovery matches patterns observed over billions of agent runs against your tickets, chats, apps, and workflows. You get a list of automation opportunities ranked by effort, value, and readiness.CrewAI Discovery 通过分析数十亿次智能体运行记录识别出与您的工单、聊天、应用和工作流相匹配的模式。您将获得一份自动化机会清单该清单根据实施难度、业务价值和就绪度进行了排序。Agentic use case generator powered by billions of agent runsInteractive suggestions refine and improve recommended automationsAgent automations in shareable presentation format accelerates team alignmentAccelerated path to build automations with one-click context基于数十亿次智能体运行驱动的智能体用例生成器交互式建议助力优化与完善自动化推荐方案以可分享演示形式呈现的智能体自动化方案加速团队达成共识依托“一键获取上下文”功能加速自动化构建流程Easy to build multi-agent workflowsAnyone who needs to build an agent can work with CrewAI. Use simple visual build tools to sophisticated CLI or APIs for complex multi-agent orchestrations, CrewAI flexes to meet you where you are.任何需要构建智能体Agent的人都可以使用 CrewAI。无论你是使用简单的可视化构建工具还是利用复杂的 CLI 或 API 来编排多智能体系统CrewAI 都能灵活适应你的需求。No-code visual editor, exportable to PythonCode-first API built for total controlRole-based agents separate and simplify agent orchestrationCreate deterministic agent workflows支持导出为 Python 代码的无代码可视化编辑器专为实现全面掌控而构建的“代码优先”型 API基于角色的智能体机制实现智能体编排的解耦与简化构建确定性的智能体工作流Manage production agents with control and confidenceCrewAIs Control Plane sits in the execution path of every workflow, ensuring every agent interaction is observable, compliant, and reversibleCrewAI 的控制平面Control Plane位于每个工作流的执行路径上确保每一次智能体交互都可观测、合规且可回溯。Real-time tracing of every LLM call, tool call, and memory read is observable with full cost accountingRBAC and audit provide granular control, immutable audit trails, and Enterprise IAMHuman-in-the-loop approval gates and intervention during executionRuntime hooks inject PII redaction and policy checks at every LLM and tool call可实时追踪每一次 LLM 调用、工具调用及内存读取并进行完整的成本核算RBAC基于角色的访问控制与审计功能提供细粒度控制、不可篡改的审计追踪及企业级 IAM 支持支持执行过程中的人工审批与人工干预利用运行时钩子Runtime hooks在每次 LLM 和工具调用时自动执行 PII个人身份信息脱敏与策略检查Build agents that get better with every runCrewAI turns every production run into training data to sharpen accuracy, save money, and surface the next workflow to buildCrewAI 将每一次生产运行转化为训练数据旨在提升准确性、降低成本并挖掘出下一个值得构建的工作流。Automated and human-guided training for continuous improvementMulti-LLM testing for model swapping at runtime, find the right model for every workflowEvaluation for native tracking with expanded sophistication powered by Arize, Galileo, DataDog, and PatronusReal-time tracing for every LLM call, tool call, and memory read is observable with full cost accounting支持持续改进的自动化与人工引导式训练支持运行时模型切换的多 LLM 测试为各类工作流程匹配最合适的模型基于 Arize、Galileo、DataDog 和 Patronus 赋能的评估功能实现更精细的原生追踪针对每次 LLM 调用、工具调用及内存读取进行实时追踪与可观测性监控并提供完整的成本核算--CrewAICrewAI Documentation - CrewAI
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