CrewAI is a framework for designing and orchestrating multi-agent AI systems, providing built-in guardrails, memory, and observability. It supports structured agent outputs, long-running workflow persistence, and various process types including human-in-the-loop. Enterprise features facilitate deployment, integration with external services, and team management.

The OpenAI Agents SDK provides a lightweight Python package for building agentic AI applications. It offers primitives like agents, handoffs, and guardrails with a built-in loop, tracing, and state management. It is designed for multi-step, complex agent workflows and offers customization over default behaviors.

• +Built-in guardrails, memory, knowledge, and observability for multi-agent systems.
• +Supports structured agent outputs using Pydantic.
• +Enables orchestration of long-running, stateful workflows with persistence and resumption.
• +Allows defining diverse process types: sequential, hierarchical, hybrid, with human-in-the-loop triggers.
• +Provides enterprise features for deployment, environment management, and live run monitoring.
• +Offers extensive integrations with external services like Gmail, Slack, Salesforce, and Bedrock Agents.
• +Includes team management capabilities with Role-Based Access Control (RBAC) for production automations.

• +Lightweight, easy-to-use package with few abstractions.
• +Production-ready, upgrading previous experimentation for agents.
• +Python-first design, leveraging built-in language features for orchestration.
• +Built-in agent loop handles tool invocation and task completion automatically.
• +Supports agent delegation and coordination via 'Agents as tools' (Handoffs).
• +Provides isolated workspaces for specialist agents using Sandbox agents.
• +Includes guardrails for parallel input/output validation and safety checks.
• +Automatic schema generation and Pydantic validation for Python function tools.
• +Persistent memory layer for maintaining working context across agent turns via Sessions.
• +Built-in tracing for visualizing, debugging, evaluating, and fine-tuning agent workflows.
• +Supports building low-latency voice agents with `gpt-realtime-2.1`.

• −Adds a runtime layer that may be unnecessary overhead for short-lived workflows or simple model responses.
• −Limits direct developer control over the agent loop, tool dispatch, and state handling for those who prefer full manual management.

The framework manages state within flows, allowing for persistence and resuming long-running workflows.

The framework manages state through persistent memory sessions that maintain working context across agent turns.