iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
An agent handoff transfers control from one AI agent to a specialist that takes responsibility for the next response. In the OpenAI Agents SDK, the model invokes a handoff as a tool; the receiving agent can get the conversation history and, optionally, a small structured payload with routing details. This is an in-run SDK pattern—not, by itself, a protocol for agents built on different systems.
What an agent handoff means
OpenAI’s Agents SDK documentation puts it simply: “Handoffs allow an agent to delegate tasks to another agent.” The key is that delegation also transfers control. When a specialist is the better agent to continue the conversation, the original agent hands off and the specialist owns the next response.
The handoff is a callable boundary represented to the model as a tool. A destination might appear under a generated name such as transfer_to_refund_agent. The model can use that tool when the specialist’s role fits the request. This is not just one agent asking another for advice and then continuing as before.
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Handoff or agents as tools?
Choose between these patterns by deciding who should own the user-facing response after the specialist has done its work.
#1 Best Overall
- AI-Powered Raspberry Pi Robot Dog — PiDog: Powered by Raspberry Pi (5/4B/3B+/3B/Zero 2W), OpenClaw, and multi-LLMs like ChatGPT, Gemini, Grok, DeepSeek, Qwen & Ollama. With 12 servos, camera, gyroscope, hearing & touch sensors, PiDog can see, listen, talk, move, and interact intelligently. Supports OpenCV, MediaPipe, TTS & STT, app control, FPV & Python. A great STEM robotics gift for students, makers & tech enthusiasts—perfect for birthdays and holidays. (Raspberry Pi not included)
- Realistic Dog-like Movements: PiDog's 12 powerful servos enable 32 dog-like actions, including walking, sitting, standing, shaking its head, wagging its tail, and performing playful tricks, closely mimicking a real dog and providing an engaging experience. This is an AI development robot product designed for engineers, suitable for ages 15 and above
- Rich Sensor Suite for Interactive Experiences: PiDog features ultrasonic, touch, gyroscope, sound, camera, speaker and microphone. These provide it with advanced hearing, vision, and touch, enabling it to see, detect obstacles, respond to touch, and recognize sounds, making interactions highly engaging
- AI-Powered Interactions with OpenClaw & Multi-LLMs. PiDog combines voice, vision, and gesture recognition for immersive AI experiences. Powered by OpenClaw and multi-LLMs like ChatGPT, Gemini, Grok, DeepSeek, Qwen, Doubao, and Ollama (local LLMs), it can understand questions, respond naturally through TTS & STT, recognize math problems, interpret hand gestures, and hold smart conversations. OpenClaw also enables customizable AI behaviors and personalized robotics development, helping users create their own intelligent robotic companion
- Comprehensive Learning Resources and Support: PiDog offers detailed online documentation, video tutorials, prompt technical support, and an active forum community, ensuring beginners can easily complete all projects and enjoy a great experience
| Pattern | Control after specialist work | Best fit |
|---|---|---|
| Handoff | Control transfers to the specialist, which owns the next response. | The specialist should take over the conversation branch. |
| Agent as a tool | The specialist provides bounded help; control returns to the manager agent. | A manager should call one or more specialists, synthesize their work, and deliver the final answer. |
OpenAI’s orchestration guidance recommends splitting work among agents when differences in instructions, tools, or policies materially justify the split. Keep each specialist’s role and handoff description concrete so the model has a useful basis for choosing it.
What passes to the receiving agent
Conversation history
The SDK forwards conversation history by default, but input filters or history mapping can change what the next agent receives. History may contain prior tool calls and tool outputs, not just user and assistant messages. Decide deliberately whether the specialist needs the full transcript; if it does not, explicitly select and sanitize the content you pass along.
Rank #2
- Optimized AI Arm Kit for LeRobot & Hugging Face Projects – The SO-ARM101 is an upgraded low-cost robotic arm servo motor kit designed for AI robotics enthusiasts and developers. Fully compatible with LeRobot and Hugging Face frameworks, it supports imitation learning and reinforcement learning, making it ideal for real-world robotics applications. (3D-printed parts not included.)
- Enhanced Wiring & Performance – Compared to the SO-ARM100, the SO-ARM101 features improved wiring to prevent disconnection at joint 3 and eliminates range-of-motion limitations. The leader arm uses optimized gear ratio motors for smoother performance—no external gearboxes required.
- Real-Time Leader-Follower Functionality – New real-time tracking allows the leader arm to follow the follower arm, enabling human intervention and correction during reinforcement learning (RL) training. Perfect for hands-on AI robotics development and research.
- Open-Source, DIY-Friendly & Nvidia-Compatible – Developed by TheRobotStudio, this open-source AI Arm kit integrates seamlessly with the LeRobot platform, offering PyTorch-based datasets, simulation, training, and deployment tools. Fully compatible with Nvidia Jetson edge devices, including reComputer Mini J4012 Orin NX 16 GB.
- Comprehensive Learning Resources – Includes detailed open-source assembly and calibration guides, testing tutorials, and deployment instructions. From wiring to AI training, get everything you need to start building, teaching, and optimizing your robotic arm for grasping and placing tasks.
Nested history changes how history is represented; it does not, by itself, redact sensitive information. Treat filtering and sanitization—not representation—as the privacy boundary.
Optional structured payload
A handoff can include a small structured payload with model-generated details such as a reason, language, priority, or summary. This metadata supplements the receiving agent’s main input. It does not replace that input or choose a different destination: the handoff itself targets the agent it wraps.
Rank #3
- Raspberry Pi AI Robot: powered by Raspberry Pi (5/4B/3B+/3B/Zero 2W), features 12 servos and sensors for vision, hearing, and touch. Integrated with ChatGPT-4o, it responds to complex queries. With app control and FPV, users can manage and see its view in real-time. It supports Python programming
- Realistic Movements: 12 powerful servos enable 32 actions, including walking, sitting, standing, shaking its head, wagging its tail, and performing playful tricks, closely mimicking a real and providing an engaging experience
- Rich Sensor Suite for Interactive Experiences: features ultrasonic, touch, gyroscope, sound, camera, speaker and microphone. These provide it with advanced hearing, vision, and touch, enabling it to see, detect obstacles, respond to touch, and recognize sounds, making interactions highly engaging
- Engaging Interactions with ChatGPT-4o: with ChatGPT-4o enables voice interactions and visual recognition, making it smarter and more responsive. Users can have natural conversations, solve math problems via the camera, and interpret gestures, creating diverse and fun interactions
- Comprehensive Learning Resources and Support: offers detailed online documentation, video tutorials, prompt technical support, and an active forum community, ensuring beginners can easily complete all projects and enjoy a great experience
Keep application state, such as authenticated identity or trusted account data, in application context rather than treating model-generated fields as authoritative. If a callback uses parsed fields to authorize an action, validate them at the start of the callback before any side effects. The SDK documentation says function-tool input guardrails do not apply to handoffs.
How to design a safe handoff
- Decide who owns the next response. Use a handoff if the specialist should continue the conversation. Use an agent as a tool if a manager must retain control and synthesize the final answer.
- Define a narrow destination. Register a distinct handoff for each known destination the model may choose. Give each specialist a clear job and a concrete handoff description; structured input adds metadata, not a new destination.
- Choose the information to forward. Review the history and tool outputs the specialist could receive. Use filters or history mapping to provide only the context it needs, and sanitize sensitive material explicitly.
- Separate metadata from trusted state. Put application-owned state in application context. Use the structured payload for small values the model determines at handoff time.
- Validate before consequential actions. At the start of a callback, check any parsed fields that affect authorization before performing side effects.
Where an SDK handoff ends—and A2A begins
OpenAI’s SDK documentation describes handoffs as transfers within a single run. That is narrower than interoperability between agents built with different frameworks or vendors. A2A describes an open standard for agents to communicate across those systems. An SDK handoff should not be described as an A2A exchange merely because both involve agents passing work along; consult the versioned A2A v1.0.0 specification for its implementation details.
Rank #4
- 【End-to-End Imitation Learning】Hiwonder SO-ARM101 robot arm is an embodied intelligent hardware platform compatible with the Lerobot open-source framework. It provides developers with streamlined access to shared code, templates, and pre-trained models to explore the latest advancements in AI research.
- 【Dual-Camera Vision System】Equipped with both a gripper-mounted camera and an external camera, the system supports both precise manipulation and environmental awareness for accurate imitation learning.
- 【Hiwonder High-Performance Bus Servos】Featuring 12 high-torque bus servo motors with magnetic feedback, the Hiwonder SO-Arm101 robotic arm delivers smooth, stable motion, eliminating issues like power deficiency and jitter.
- 【Professional Control & Debugging】Integrated with the Hiwonder BusLinker V3.0 debugging board, the system supports servo scanning, real-time status monitoring, and trajectory control. The professional PC software simplifies device calibration and debugging, making it accessible for both researchers and hobbyists.
- 【Open-Source Compatibility】The SO-ARM101 robotic arm is designed to be fully compatible with the LeRobot open-source project. We acknowledge the contributions of the open-source community; all trademarks and copyrights belong to their respective owners.
What the evidence does—and does not—establish
The official OpenAI documentation and orchestration guidance support the control-transfer, context, and design distinctions described here. They do not establish comparative performance across agent frameworks or a vendor-neutral security model. No quantitative claim about handoff latency, accuracy, cost, frequency, or outcomes is established by those sources.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
