CAMEL-AI
The world's first autonomous multi-agent framework for communicative AI societies.
Lightweight, stateless multi-agent orchestration for seamless autonomous handoffs.
OpenAI Swarm is a highly ergonomic, experimental framework designed for orchestrating multi-agent systems. Unlike heavier orchestration frameworks that rely on complex state management or memory abstractions, Swarm focuses on two core primitives: Routines and Handoffs. By 2026, Swarm has established itself as the industry standard for low-latency, stateless agentic workflows, allowing developers to define 'Agents' as simple sets of instructions and tools that can transition control to other agents dynamically. The architecture is built on top of the Chat Completions API, making it natively compatible with function calling and the latest GPT-4o models. Swarm’s technical value lies in its 'hand-off' mechanism, where an agent can return another agent's instance to signify a transfer of responsibility, mimicking a human organizational structure. This makes it ideal for high-volume environments where maintaining long-term state within the orchestration layer would introduce unnecessary overhead and latency. While classified as experimental by OpenAI, it serves as the architectural blueprint for developers building scalable, production-grade agent swarms that require transparency and predictable execution paths without the 'black box' complexity of more automated, recursive agents.
Uses the 'transfer' pattern where an agent returns an instance of another agent in a function call to move the conversation context.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Encapsulates instructions and tools into portable objects that can be modified at runtime based on context variables.
A mechanism to pass global or local state variables across agent boundaries during a handoff.
Supports dynamic string formatting within agent instructions based on real-time inputs.
Every agent action is treated as a function call, making integration with external APIs native and reliable.
The client.run() loop handles all iterations between agent responses and tool calls automatically.
Capable of triggering multiple tool calls simultaneously if the underlying model supports it.
General chatbots fail at technical troubleshooting while technical bots are too expensive for general greeting.
Registry Updated:2/7/2026
Hardware Specialist hands back to Triage for closing survey.
Linear scripts struggle with varying data formats and unexpected web structures.
Users need to switch between browsing, checking order status, and processing returns within one session.