As AI systems move beyond passive prompts toward autonomous decision-making, the concept of agentic AI has emerged as a foundational shift. Agentic AI refers to systems that can reason, act autonomously, interact with tools and APIs, and complete multi-step goals. OpenAI’s new Agentic SDK provides a standardized framework for building such AI agents—bridging large language models with structured tool use, memory, and reasoning loops.
This article offers a practical, human-readable guide on how to get started with the OpenAI Agentic SDK. You’ll learn how to create your first AI agent, define tools, manage context, and handle task planning—all using a clean and production-friendly approach.
The OpenAI Agentic SDK is a Python or Typescript framework that enables developers to create agentic AI systems using OpenAI models (e.g., GPT-4o). It simplifies the orchestration of:
Traditional prompt-based usage of LLMs is stateless and limited to single-turn reasoning. In contrast, agentic AI systems can:
The Agentic SDK offers a flexible yet opinionated scaffold for building these types of systems safely and systematically.
Before you start coding, it helps to understand the core building blocks:
| Component | Description |
|---|---|
| Agent | The main controller that receives tasks and decides how to execute them |
| Tools | Functions or APIs the agent can use (e.g., calculator, web search, database query) |
| Planner | Determines how to break tasks into sub-steps |
| Memory | Stores and retrieves historical context |
| Observer/Reporter | Logs actions or emits real-time feedback to UIs |
Let’s walk through creating a simple AI agent that can perform calculations, look up current weather, and answer user questions based on those actions.
The Agentic SDK is part of OpenAI’s Python client as of mid-2024. Make sure your version is up to date:
1 bash
2 pip install --upgrade openai
Tools are functions the agent can call. You register them using decorators so that GPT knows how to use them.
1 python
2 from openai import tool
@tool
def add_numbers(a: int, b: int) -> int:
"""Adds two numbers and returns the result."""
return a + b
@tool
def get_weather(city: str) -> str:
"""Returns fake weather for demo."""
return f"The weather in {city} is 29°C and sunny."
Each tool should have:
Now instantiate an agent and give it access to the tools.
1 python
2 from openai import AssistantAgent
agent = AssistantAgent(
tools=[add_numbers, get_weather],
model="gpt-4o"
)
The model you choose must support function calling (e.g., GPT-4o or GPT-4-turbo). The agent will automatically invoke tools as needed.
You can now start sending messages to the agent. It will determine whether to respond directly or call a tool.
1 python
2 response = agent.chat("What's the weather in Miami and also add 12 and 15?")
print(response)
Under the hood:
You can extend the agent’s reasoning loop by defining a Planner. This is a strategy engine that breaks high-level tasks into steps.
For example:
1 python
2 from openai import TaskPlanner
class CustomPlanner(TaskPlanner):
def plan(self, task, tools, memory):
# Insert logic to decide task order or retry paths
return super().plan(task, tools, memory)
You can inject this planner into the agent during creation. Planners are useful when your AI agents need to coordinate multi-step workflows—such as reading emails, extracting data, making decisions, and sending replies.
By default, agents can maintain a short-term memory within a single session. You can extend this to longer-term or persistent memory via:
For instance:
1 python
2 agent.memory.save_context("user_name", "Zylker")
agent.memory.retrieve_context("user_name") # returns "Zylker"
This becomes crucial in applications like personal assistants or customer support bots where context continuity improves user experience.
| Use case | How agents help |
|---|---|
| Customer support bots | Can look up ticket history, perform account actions, and suggest solutions |
| Workflow automation | Agents can read emails, fetch data from APIs, and fill out forms |
| Data assistants | Pull structured data from databases and generate insights on demand |
| AI copilots | Help engineers or analysts write queries, generate reports, or test APIs |
Autonomous AI introduces new safety and governance concerns. When using the Agentic SDK, consider:
You can also use human-in-the-loop designs where agents propose actions, and humans approve them.
| Feature | OpenAI Agentic SDK | LangChain | Semantic Kernel |
|---|---|---|---|
| Tight OpenAI integration | ✅ | ❌ | ❌ |
| Clean function interface | ✅ | ✅ | ✅ |
| Native model support | GPT-4o, GPT-4-turbo | Multi-provider | Multi-provider |
| Ideal for | Production OpenAI workloads | Rapid prototyping | Enterprise orchestration |
If you are primarily working with OpenAI models and want high-fidelity tool calling, the Agentic SDK is the most seamless choice.
The Agentic SDK brings structure, safety, and scalability to agent-based AI systems. It abstracts the repetitive tasks of routing, tool invocation, and state management—letting you focus on task design and user experience.
By starting with simple tools and layering on planning and memory, you can gradually evolve your agents from basic assistants to fully autonomous copilots that operate within your domain.