Python vs TypeScript: Choosing Your Agent SDK

Two SDKs, One API Surface

Agent Builder Lib maintains feature parity across its Python and TypeScript SDKs. Both support the same agent lifecycle, tool system, plugin architecture, and A2A protocol compliance. The choice comes down to your team's expertise, your deployment target, and the ecosystem you want to plug into.

Python SDK Strengths

ML and Data Science Ecosystem

If your agent needs to work with NumPy, Pandas, scikit-learn, or any of the hundreds of ML libraries available in Python, the Python SDK is the natural choice. You can import models directly and use them as tools:

from agent_builder import Agent, tool
import torch
from transformers import pipeline

classifier = pipeline("sentiment-analysis")

@tool(description="Classify the sentiment of text")
async def analyze_sentiment(text: str) -> dict:
    result = classifier(text)[0]
    return {"label": result["label"], "score": result["score"]}

Async-First Design

The Python SDK is built on asyncio with full async/await support throughout. Every handler, tool, and middleware hook is asynchronous by default, ensuring your agent can handle concurrent requests without blocking.

Type Hints

While Python's type system is optional, the SDK uses Pydantic models internally. You get runtime validation, automatic schema generation, and IDE autocompletion when you annotate your handlers.

TypeScript SDK Strengths

Compile-Time Type Safety

TypeScript's type system catches errors before your code runs. The SDK leverages advanced generics so that tool inputs, outputs, and agent configurations are fully typed:

import { Agent, defineTool } from '@agent-builder/sdk';

const calculator = defineTool({
  name: 'calculate',
  input: { expression: 'string' },
  output: 'number',
  handler: async ({ expression }) => {
    // 'expression' is inferred as string
    return evaluate(expression);
  },
});

If you pass the wrong type to a tool or misconfigure an agent, the compiler tells you immediately.

Edge and Serverless Deployment

The TypeScript SDK runs on Node.js, Deno, Bun, and edge runtimes like Cloudflare Workers. If you need agents that start in milliseconds and run at the edge, TypeScript is the better fit.

Full-Stack Integration

If your application already uses React, Next.js, or another JavaScript framework, the TypeScript SDK integrates seamlessly. Share types between your frontend and your agent backend with zero translation layer.

Performance Comparison

For most agent workloads, the bottleneck is LLM latency — not SDK overhead. That said, there are differences worth noting:

• Cold start: TypeScript agents start in ~50ms on edge runtimes; Python agents take 200-500ms depending on imports
• Throughput: Both handle hundreds of concurrent requests; Python uses asyncio, TypeScript uses the event loop
• Memory: TypeScript typically uses less memory for I/O-heavy workloads; Python wins when doing heavy numerical computation with native extensions

When to Use Each

• Choose Python when your agent relies on ML models, data processing pipelines, or scientific computing libraries
• Choose TypeScript when you need edge deployment, full-stack type sharing, or serverless functions with fast cold starts
• Use both when you have specialized agents — the A2A protocol lets Python and TypeScript agents communicate seamlessly

Getting Started

Whichever SDK you choose, the getting-started experience is identical. Visit agentbuilderlib.com for installation guides, or jump directly into the Oya.ai platform to deploy your first agent.