Rethinking AI Agents: How OpenAI Agents SDK and Google ADK Outshines LangGraph
- Sameer Kankute
- 2 days ago
- 7 min read
2025 is the year of agents. It seems like everyone is building agentic applications or has an opinion of one. We at Newtuple have developed more than 10 agent based applications in the last 2 years and have worked with nearly every agent framework out there. Here’s our opinion on the different libraries & SDKs available.
Firstly, why do we need agents?
An agent is a system that can perceive its environment, make decisions, and take actions to achieve a goal often using tools or external knowledge. Building agents requires rethinking how your systems make decisions and handle complexity. Unlike conventional automation, agents are uniquely suited to workflows where traditional deterministic and rule based approaches fall short.
To build these kinds of agents, we rely on agentic frameworks specialized tools and architectures designed to support decision making, tool usage, and adaptive behavior in complex environments.
What are the different kinds of agent frameworks available?
LangGraph was among the first ones to build an agentic framework, which contributed to its popularity. However, its graph based architecture, while powerful for defining flows, also introduced limitations particularly around flexibility in communication between agents. Because of its rigid edge based design, developers have less runtime control over how agents interact.
Recently, OpenAI launched its Agents SDK to help developers build task driven AI agents with built in tools and seamless API integration. Around the same time, Google introduced its Agent Development Kit (ADK), an open source framework for building multi agent systems with real time capabilities and Gemini model integration.
It’s clear from these launches that both OpenAI and Google have directly addressed developers’ frustrations with LangGraph’s static edge based design, introducing dynamic task handoffs and modular orchestration to deliver the flexibility and runtime control teams have been asking for.
Let’s explore both development kits in detail and discuss the advantages of using each one.
1. OpenAI Agents SDK
The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy to use package with very few abstractions.
Core concepts:
Agents: LLMs configured with instructions, tools, guardrails, and handoffs
Handoffs: A specialized tool call used by the Agents SDK for transferring control between agents
Guardrails: Configurable safety checks for input and output validation
Tracing: Built in tracking of agent runs, allowing you to view, debug and optimize your workflows
1.1 Why use the OpenAI Agents SDK
The SDK has two driving design principles:
Enough features to be worth using, but few enough primitives to make it quick to learn.
Works great out of the box, but you can customize exactly what happens.
Here are the main features of the SDK:
Agent loop: Built in agent loop that handles calling tools, sending results to the LLM, and looping until the LLM is done.
Python first: Use built in language features to orchestrate and chain agents, rather than needing to learn new abstractions.
Handoffs: A powerful feature to coordinate and delegate between multiple agents.
Guardrails: Run input validations and checks in parallel to your agents, breaking early if the checks fail.
Function tools: Turn any Python function into a tool, with automatic schema generation and Pydantic powered validation.
Tracing: Built in tracing that lets you visualize, debug and monitor your workflows, as well as use the OpenAI suite of evaluation, fine tuning and distillation tools.
1.2 Code Example
2. Google’s ADK
This is an open source framework from Google designed to simplify the full stack end to end development of agents and multi agent systems. ADK empowers developers to build production ready agentic applications with greater flexibility and precise control.
2.1 Core Pillars of ADK: Build, Interact, Evaluate, Deploy
ADK provides capabilities across the entire agent development lifecycle:
Multi Agent by Design: Build modular and scalable applications by composing multiple specialized agents in a hierarchy. Enable complex coordination and delegation.
Rich Model Ecosystem: Choose the model that works best for your needs. ADK works with your model of choice whether it is Gemini or your model accessible via Vertex AI Model Garden. The framework also offers LiteLLM integration letting you choose from a wide selection of models from providers like Anthropic, Meta, Mistral AI, AI21 Labs, and many more!
Rich Tool Ecosystem: Equip agents with diverse capabilities: use pre built tools (Search, Code Exec), Model Context Protocol (MCP) tools, integrate 3rd party libraries (LangChain, LlamaIndex), or even use other agents as tools (LangGraph, CrewAI, etc).
Built in streaming: Interact with your agents in human-like conversations with ADK's unique bidirectional audio and video streaming capabilities. With just a few lines of code, you can create natural interactions that change how you work with agents – moving beyond text into rich, multimodal dialogue.
Flexible Orchestration: Define workflows using workflow agents (Sequential, Parallel, Loop) for predictable pipelines, or leverage LLM driven dynamic routing (LlmAgent transfer) for adaptive behavior.
Integrated Developer Experience: Develop, test, and debug locally with a powerful CLI and a visual Web UI. Inspect events, state, and agent execution step by step.
Built in Evaluation: Systematically assess agent performance by evaluating both the final response quality and the step by step execution trajectory against predefined test cases.
Easy Deployment: Containerize and deploy your agents anywhere.
2.2 Code Example
3. Key Similarities Between OpenAI Agents SDK & Google ADK
LLM Support via LiteLLM: Both SDKs use LiteLLM to easily connect and switch between different language models.
Built in Support for Sub agents: Both define sub agents (called handoffs in OpenAI and subagents in ADK) directly within the agent structure with no need for connecting them with edges like in LangGraph.
Tool and MCP Integration: Both support tool usage and have mechanisms for Model Context Protocol (MCP), enabling agents to perform actions or retrieve information.
Voice Interaction Capabilities: Both SDKs offer support for building voice enabled agents.
Traceability: Both provide ways to trace and monitor what agents are doing, making debugging and analysis easier.
These are some of the common challenges us developers have faced or features we've long wanted when building agentic AI applications.
4. OpenAI Agents SDK or Google ADK? Here’s How to Decide
OpenAI Agents SDK: Minimal and Flexible
OpenAI’s Agents SDK is built for simplicity. It gives developers just a few basic building blocks like agents, task handoffs, and guardrails so they can quickly start building AI agents. It focuses on doing one thing well: helping an agent use tools through an LLM. Instead of offering a full framework, it lets developers use regular Python to add whatever else they need, like memory or complex logic. It’s great for those who want speed, control, and the freedom to shape things their own way while enjoying the OpenAI models integration.
Google Agent Development Kit (ADK): Structured and Complete
Google’s ADK, on the other hand, is a more complete and structured toolkit. It’s designed for teams building larger or enterprise level systems. It comes with many features already built-in like different types of agents, memory handling, tools, and ways to evaluate performance. It also fits well with Google Cloud for deployment. This makes it ideal for those who prefer a more guided and ready to use approach to building powerful agent systems.
5. How OpenAI Agents SDK and Google ADK fix LangGraph’s pain points
Problem in LangGraph | OpenAI Agents SDK | Google ADK |
It takes time to learn. You must draw nodes and edges before you run “hello world.” | Tiny parts. One Agent, optional handoff, and any Python function becomes a tool. You stay in normal Python. | Ready shapes. Drop‑in Sequential / Parallel / Loop agents. |
Lots of plumbing. Tools, safety checks, memory, and logs are all add‑ons you wire up | Batteries included. Web search, code exec, guardrails, tracing, and evals ship in the box. | Full stack built‑in. Search, Code Exec, Model Context tools, eval harness, and Gemini / Model‑Garden hook‑ups come pre‑wired. |
Edges are fixed and Conditional edges are hard to handle | Dynamic hand‑offs. One agent can spin up or pass work to others on the fly. | Dynamic hand‑offs. One agent can spin up or pass work to others on the fly. |
State management is strict and manual. You must define a typed schema for shared state, update keys carefully across nodes, and write extra code to pass or extract values making changes error prone and slow | No state dict needed. All context (tool outputs, sub agent replies) is passed through the conversation history as system messages simple and automatic. | Flexible session state. Each agent writes to a shared session object using output_key, and the next agent reads it by key, no need to predefine all keys, and runtime updates are smooth. |
Conclusion: A New Era for Building AI Agents
The rise of OpenAI's Agents SDK and Google's Agent Development Kit (ADK) marks a turning point in how developers build, orchestrate, and scale intelligent systems. Both frameworks reflect a deeper understanding of the needs of modern AI developers: flexibility, modularity, real time coordination, and powerful built-ins that drastically reduce boilerplate and increase productivity.
While LangGraph paved the way for visualizing and structuring agent flows, it now faces competition from frameworks that prioritize runtime dynamism, ease of use, and richer integrations. OpenAI’s SDK focuses on minimalist power and smooth LLM based handoffs, while Google’s ADK brings a full stack solution with native multi-agent support, multimodal interaction, and deployment readiness.
Ultimately, the choice depends on your development style and project goals:
Choose OpenAI Agents SDK if you want lightweight, Python first development with tight OpenAI integration and just enough structure to get started fast.
Opt for Google ADK if you need a robust, enterprise ready agent framework with deep tooling, evaluation, and deployment pipelines built in.
As agentic computing continues to mature, the ability to rapidly iterate, compose, and manage intelligent agents will become a core skill and with these frameworks, that future is closer than ever.
6. References:
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