The code for querying an agent is the same regardless of whether it is running
locally or
deployed remotely. Therefore, in this
page, the term agent refers to either local_agent or remote_agent
interchangeably. As the set of supported operations varies across frameworks, we
provide usage instructions for framework-specific templates:
| Framework | Description |
|---|---|
| Agent Development Kit | Designed based on Google's internal best practices for developers building AI applications or teams needing to rapidly prototype and deploy robust agent-based solutions. |
| Agent2Agent (preview) | The Agent2Agent (A2A) protocol is an open standard designed to enable seamless communication and collaboration between AI agents. |
| LangChain | Easier to use for basic use cases because of its predefined configurations and abstractions. |
| LangGraph | Graph-based approach to defining workflows, with advanced human-in-the-loop and rewind/replay capabilities. |
| AG2 (formerly AutoGen) | AG2 provides multi-agent conversation framework as a high-level abstraction for building LLM workflows. |
| LlamaIndex (preview) | LlamaIndex's query pipeline offers a high-level interface for creating Retrieval-Augmented Generation (RAG) workflows. |
| Custom | Agents that were developed and deployed without the use of a framework-specific template. |