CrewAI Enterprise

A code-first API built for total control and sophistication in multi-agent orchestrations, enabling developers to define deterministic agent workflows programmatically.

A Python-based API for building complex multi-agent orchestrations with full control, role-based agent separation, and deterministic workflow creation for developers requiring granular control.

A governance and observability layer for production agent workflows that sits in the execution path, providing real-time tracing of LLM and tool calls, RBAC, audit trails, human-in-the-loop approval gates, and runtime policy injection (PII redaction, compliance checks).

An agentic use case generator that analyzes billions of agent runs to identify automation opportunities in your tickets, chats, apps, and workflows. Ranks recommendations by effort, value, and readiness with interactive refinement and one-click context for agent building.

Native evaluation framework with integrations to Arize, Galileo, DataDog, and Patronus for tracking and improving agent performance.

Approval gates and intervention capabilities that allow human oversight during agent execution for critical workflows.

Runtime model swapping capability that enables testing and selection of optimal LLM models for each workflow.

A continuous learning system that uses production runs as training data to improve agent accuracy, reduce costs, and identify next workflows to automate. Includes multi-LLM testing, human-guided training, and integration with evaluation partners like Arize, Galileo, DataDog, and Patronus.

Continuous improvement engine that turns production runs into training data. Includes automated/human-guided training, multi-LLM testing for runtime model swapping, and integrated evaluation with Arize, Galileo, DataDog, and Patronus.

Role-based access control and immutable audit trail system with Enterprise IAM integration for granular control and compliance.

Comprehensive observability system that tracks every LLM call, tool call, and memory read with full cost accounting and transparency.

Extensible injection points for PII redaction, policy checks, and custom logic at every LLM and tool call during execution.

Visual builder for multi-agent workflows with no-code canvas and Python export capabilities. Enables role-based agent orchestration and deterministic workflow creation.

Automated and human-guided training system that turns production runs into training data to improve agent accuracy and reduce costs.

A no-code visual builder for creating multi-agent workflows that can be exported to Python code, enabling both non-technical and technical users to design agent orchestrations.