Claude Agents SDK vs. OpenAI Agents SDK vs. Google ADK: The better framework for building AI agents in 2026

AI agents are no longer experimental. They are being shipped in production systems right now, and the SDKs powering them have matured dramatically.

If you're building with AI in 2026, understanding the tools available to you is no longer optional.

OpenAI, Claude, and Google are providing their SDKs to build these agents in minimal code for production. With their own quirks, they can make it harder for businesses and developers alike.

I just did a comparison between the top-most used frameworks:

• Claude Agent SDK

• OpenAI Agents SDK

• Google ADK
  All source code at the end!

Let’s begin.

Why This Comparison?

First things first, why this comparison, while many others already existed? Here is the deal:

I’ve been building agents across these SDKs, and the reality is they all promise “minimal code” but behave very differently once you go beyond demos.

So I compared them the only way that matters: by actually building with them.

I looked at:

• how quickly I could get something working,

• how much control I had when things got complex, and

• how they handled real-world workflows like multi-agent coordination, tool usage, and state management.
  Here are all my findings.

OpenAI Agents SDK

The OpenAI Agents SDK is an open-source framework and a significant upgrade over Swarm. It is designed to simplify the orchestration of multi-agent workflows.

It is Python & TS-first. Developers use built-in language features to orchestrate and chain agents rather than learning new abstractions.

Core Primitives

• Agents: LLMs with instructions, tools, guardrails, and handoffs.

• Handoffs: Delegating tasks to other agents.

• Tools: Functions, MCP, and hosted tools.

• Guardrails: Safety checks for input/output validation.

• Sessions: Automatic conversation history management.

• Tracing: Built-in visualisation for debugging.

Why Use It

• Multi-Language Support: Provider-agnostic. Supports OpenAI APIs and 100+ other LLMs.

• Realtime & TTS Voice: Build voice agents with interruption detection and context management.

• Observability: Robust tracing exports to Logfire, AgentOps, or OpenTelemetry.

• Advanced Connectivity: Supports WebSocket transport for Responses API and SIP protocol connections.

• GPT-5.x Ready: Updated reasoning effort and cleaner handoff history for downstream context.

Ease of Getting Started

• Minimal setup: Requires just a few lines of code.

• Quick install: pip install openai-agents. Runs in under 10 lines.

• Suited for: Teams wanting rapid prototyping and simple agent coordination.

Developer Experience

• Python-first: Express complex relationships with a small set of primitives.

• Type Safety: Zod-powered validation for TypeScript/JavaScript.

• Visual Tooling: Agent Builder provides a drag-and-drop canvas for composing logic.

Example

I used the OpenAI Agents SDK to build a job search agent that fetches jobs based on the user persona it created by asking me relevant questions.

By default, Openai agents are unable to use Exa search and Google Sheets. This is where composio handle’s that, not only that, but you can also connect it to over 850+ tools and integrations.

# imports 
from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session (connection tools + wait so OAuth can finish before continuing)
session = composio.create(
    user_id=user_id,
    toolkits=["GITHUB", "exa", "googlesheets", "browser_tool" ],
    manage_connections={"enable": True, "wait_for_connections": True},
)
mcp_url = session.mcp.url

# add tool_configs
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=("..."),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],

One thing that stood out was that not only did the agent create a strong persona based on screening questions, but also fetched me a list of relevant job descriptions along with their relevance to the skills, without being explicitly told to do so.

Claude Agent SDK

The Claude Agent SDK is Anthropic's open-source framework for building agents that interact with a real computer.

It evolved from Claude Code. The core principle is simple: give your agent a computer. It uses a shell, a file system, and the web just like a human.

Core Primitives

• Bash tool: Executes shell commands directly.

• Read / Write / Edit: Native file system access.

• Glob & Search: File discovery across project directories.

• Subagents: Spawn parallel or nested agents for subtasks.

• MCP servers: Standardised integrations (Slack, GitHub, Google Drive).

• Permission modes: Fine-grained control via allowed_tools permission_mode.

Why Use It

• Native OS Access: The only SDK where agents directly control a computer.

• In-Process Tools: Custom tools run inside your Python app. No separate process needed.

• Multi-Cloud: Supports AWS Bedrock, Google Vertex AI, and Microsoft Azure AI Foundry.

• Xcode 26 Integration: Full Claude Code power inside the IDE, including capturing Xcode Previews.

Ease of Getting Started

• Minimal entry point: Built-in tools mean no manual plumbing.

• Quick install: pip install claude-agent-sdk.

• Best for: Developers who need deep OS access or agile coding workflows.

Production Readiness

• Battle-proven: Powers Anthropic’s internal research and video creation.

• Context Compaction: Automatic management for long-running tasks.

• Cost Controls: max_budget_usd parameter caps spend per session.

Example

I used Claude Agent’s SDK to build an open-source contributor that takes a repo name, fetches a good issue, reads the contribution file, forks the repo, creates a code fix that matches the original repo style, pushes the code to the forked repo, and raises a PR.

Out of the box, the Claude Agents SDK can’t interact with GitHub. Composio fills that gap, and also lets you connect to 850+ tools and integrations.

# imports
from composio import Composio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions

# fetch all composio toolkits name
def _toolkits() -> list[str]:
    raw = os.getenv("OSS_COMPOSIO_TOOLKITS")
    if raw:
        return [t.strip() for t in raw.split(",") if t.strip()]
    return list(DEFAULT_TOOLKITS)

# create a async chat, connect to composio, fetch user id, define toolkits and mcp server url + configs.
async def chat_with_oss_stack() -> None:
    api_key = os.getenv("COMPOSIO_API_KEY")
    if not api_key:
        raise RuntimeError("COMPOSIO_API_KEY is not set")

    composio = Composio(api_key=api_key)

    user_id = os.getenv("COMPOSIO_USER_ID") or os.getenv("USER_ID")
    if not user_id:
        raise RuntimeError(
            "Set COMPOSIO_USER_ID (or USER_ID) in the environment or .env — Composio needs a stable user id string."
        )

    kits = _toolkits()
    mcp_server = composio.create(
        user_id=user_id,
        toolkits=kits,
    )

    url = mcp_server.mcp.url
    if not url:
        raise ValueError("Session URL not found")

    options = ClaudeAgentOptions(
        permission_mode="bypassPermissions",
        mcp_servers={
            "composio": {
                "type": "http",
                "url": url,
                "headers": {
                    "x-api-key": os.getenv("COMPOSIO_API_KEY", ""),
                },
            }
        },
        system_prompt=OSS_SYSTEM_PROMPT,
        max_turns=int(os.getenv("OSS_MAX_TURNS", "40")),
    )

I asked it to operate on the “gemini-cli” repository, and it created a pr for me.

Alternative

• Ask for a repo name (open source)

• Fetches the repo issues (type - can be defined while giving name

• Reads the CONTRIBUTION.md file

• Fork the repo using composio Github Tools

• Creates a fix

• Push the code to the forked repo using composio Github Tool

• Raise a pr to the main branch using the GitHub tool.

Google Agent Development Kit (ADK)

Google's ADK is a code-first framework. It applies engineering principles like versioning, testing, and modularity to AI.

While optimised for Gemini, it is model-agnostic and built for compatibility with third-party tools.

Core Primitives

• LLM Agents: Use LLMs as the core of their reasoning.

• Sequential/Parallel/Loop Agents: Predictable pipeline execution.

• Graph-based workflows: (ADK 2.0 Alpha) Conditional, branching pipelines.

• Agent2Agent (A2A): Secure protocol for agent-to-agent delegation.

• ADK Web UI: Browser-based interface for inspecting traces and artefacts.

Why Use It

• Widest Language Support: Python, TypeScript, Java, and Go.

• Complex Orchestration: Graph-based logic for branching and retry paths.

• Secure Interoperability: A2A allows delegation without exposing internal memory.

• Vertex AI Integration: Deploy directly to Google Cloud’s managed enterprise runtime.

Ease of Getting Started

• Under 100 lines: Build production agents with bidirectional audio/video.

• Agent Starter Pack: Accelerated deployment path for Google Cloud services.

• Best for: Enterprise-grade systems requiring tight Google ecosystem integration.

Developer Experience

• Advanced State Management: Restores state from failure and allows context "rewinding."

• Enterprise Governance: Integration with Cloud API Registry to curate approved tools.

• Pre-built Connectors: 100+ connectors via Composio.

Example

For demos, these are cool, heard people say, can be used in production, but they failed most of the tool calls, racked me up a bill of 5$ due to repeated tool calls (was stuck in a thinking loop).

Yes, its developer friendly, but it really lacks a lot in terms of performance, and newbies can easily get stuck with adk web or adk cli as it requires a specific folder structure.

However, for simpler tasks, it did quite well. Built an email agent that maps promotional education mails (like Coursera, Deeplearning) to well optimised developer roadmap, which beginner devs can use to learn in a structured manner.

# imports
from composio import Composio
from composio_google import GoogleProvider

# load envs
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

# create composio client
composio_client = Composio(
    api_key=COMPOSIO_API_KEY,
    provider=GoogleProvider(),
    timeout=120,
    max_retries=5,
)

# create a client session with tools
composio_session = composio_client.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["gmail"],
)

# store sessiom url
COMPOSIO_MCP_URL = composio_session.mcp.url

# add composio mcp server connection
composio_toolset = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url=COMPOSIO_MCP_URL,
        headers={"x-api-key": COMPOSIO_API_KEY},
        timeout=30.0,
        sse_read_timeout=600.0,
    )
)

# include it in the agents
root_agent = Agent(
    model="gemini-2.5-flash",
    name="composio_agent",
    description="An agent that uses Composio tools to perform actions.",
    instruction=(
        "You are a helpful assistant connected to Composio. "
        "You have the following tools available: "
        "COMPOSIO_SEARCH_TOOLS, COMPOSIO_MULTI_EXECUTE_TOOL, "
        "COMPOSIO_MANAGE_CONNECTIONS, COMPOSIO_REMOTE_BASH_TOOL, COMPOSIO_REMOTE_WORKBENCH. "
        "Use these tools to help users with GMAIL operations."
    ),  
    tools=[composio_toolset],
)

Comparison Table

Which One to Choose?

• OpenAI Agents SDK: Choose if you want a lightweight framework with strong voice support and the ability to swap LLMs freely.

• Claude Agent SDK: Choose if your agents need deep OS access (developer assistants) or follow a "give the agent a computer" paradigm.

• Google ADK: Choose if you are building enterprise-grade systems on Google Cloud or need multi-language support (Python/Java/Go). Requires a lot of manual plumbing and security.

• For better

Final Thoughts

OpenAI, Claude, and Gemini are all key players. However, the real competitive edge isn't knowing that these SDKs exist. It's the hands-on mastery of architectural decisions:

• When to use a handoff versus a subagent.

• How to design tools that don't bloat the context window.

• When to insert a human checkpoint.
  Frameworks evolve quickly. The deeper intuition for architecting reliable systems comes only from repeated experimentation.

All agents' source code can be seen at https://github.com/DevloperHS/agents-sdk-tests. Feel free to fork and raise pr’s :)

Happy Building.