Openai Agents Python Examples. effort="none" with gpt-5. Contribute to langchain-ai/lan
effort="none" with gpt-5. Contribute to langchain-ai/langchain development by creating an account on GitHub. video. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Feb 9, 2024 路 This is the first part in a multi-part series on building Agents with OpenAI's Assistant API using the Python SDK. 馃馃敆 The platform for reliable agents. docs: add ExecPlan guidance and template by @seratch in #2298 Update examples by @seratch in #2300 feat: add examples auto-run skill and refresh example scripts by @seratch in #2303 Add two more agent skills by @seratch in #2304 misc: add docs-sync skill by @seratch in #2306 chore: add release workflows and review prompt by @seratch in #2309 4 days ago 路 Bring enterprise-grade durability to your AI agents with Restate and OpenAI Agents SDK. start() and trace. Learn how to use Azure OpenAI's new stateful Responses API. It equips developers with tools to create task-specific agents, integrate external functionalities, manage inter-agent task delegation, enforce input/output validation, and monitor execution flows. Built-in tools The Responses API has built-in tools that you can use instead of building your own: Web search: allow models to search the web for the latest information before generating a response. The agent can determine which tools to use based on the user's input and execute them as needed. Nov 18, 2025 路 This page documents the model abstraction layer that enables the SDK to integrate with multiple LLM providers through a unified interface. For example, an MCP server connects Claude to your database, while a Skill teaches Claude your data model and query patterns. OpenAI Agents SDK OpenAI Agents SDK The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. py file and create a simple function to generate text using the OpenAI API. With Codex, developers can simultaneously deploy multiple agents to independently handle coding tasks such as writing features, answering questions about your codebase, fixing bugs, and proposing pull requests for review. Mar 11, 2025 路 The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. py. See the example code and #2320 for more details. Browse a collection of snippets, advanced techniques and walkthroughs. Explore the capabilities of OpenAI's o1 series for complex reasoning and problem-solving. finish(). Core concepts: Agents: LLMs configured with instructions, tools May 28, 2025 路 Why this matters The "agents as a tool" pattern is a powerful way to build transparent, auditable, and scalable multi-agent collaboration . str) outputs. Here’s a simple example to get started: 1. openai. If you want the agent to produce a particular type of output, you can use the output_type parameter. e. Tools & Dependency Injection Example Here is a concise example using Pydantic AI to build a support agent for a bank: Learn how to understand or generate images with the OpenAI API. from langchain_openai import ChatOpenAI from langchain. The tool simply runs the Codex CLI as a subprocess, so all existing Codex configuration, skills, and capabilities are available without any additional setup. Non-strict output types Previous response ID usage customer_service: Example customer service system for an airline. g. Non-GPT-5 models If you pass a non–GPT-5 model name without custom model_settings, the SDK reverts to generic ModelSettings compatible with any model. In this notebook, we use a deliberately simple example (an agent that summarizes sections of a document) to illustrate the iterative improvement loop. Function calling: wrap any Python function as a tool. May 16, 2025 路 Introducing Codex: a cloud-based software engineering agent that can work on many tasks in parallel, powered by codex-1. Agents Reference docs This page contains reference documentation for Agents. Nov 4, 2025 路 The process consists of the following steps: Baseline Agent The process begins with a baseline agent. This example demonstrates how to combine deep specialization, parallel execution, and robust orchestration using the OpenAI Agents SDK. Extend the model's capabilities with built-in tools for file search, web search, computer use, and more. Learn about their features and how they compare to GPT-4o models. updateSession({ instructions: `You // After connecting the OpenAI agent as shown above const realtimeClient = await streamClient. middleware import wrap_model_call, ModelRequest, ModelResponse basic_model = ChatOpenAI(model="gpt-4o-mini") advanced_model = ChatOpenAI(model="gpt-4o") @wrap_model_call def dynamic_model_selection(request: ModelRequest, handler Jul 14, 2025 路 The openai Python package The openai-agents package An OpenAI API key set as OPENAI_API_KEY in your environment variables 1. This function will power our AI agent: Here is the code: A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Skills vs. Local runtime tools: run in your environment (computer use, shell, apply patch). OpenAI provides models with agentic strengths, a toolkit for agent creation and deploys, and dashboard features for monitoring and optimizing agents. Contribute to openai/openai-python development by creating an account on GitHub. Code interpreter: allow models to write In DSPy, you can use any of the dozens of LLM providers supported by LiteLLM. The user sets their desired structured output schema, and when the model generates the structured data, it’s captured, validated, and returned in the 'structured_response' key of the agent’s state. It covers the minimal setup required to mock OpenAI API calls in a pytest test function using the `@openairesp The official prompt engineering guide by OpenAI is usually the best place to start for prompting tips. financial_research_agent: A financial research agent that demonstrates structured research workflows with agents and tools for financial data analysis. Each agent specializes in either detecting or rewriting a specific type of issue: To use the OpenAI API in Python, you can use the official OpenAI SDK for Python. Output types By default, agents produce plain text (i. This repository provides examples of many popular Python AI agent frameworks using LLMs from GitHub Models. Supports text and image inputs, and text outputs. You can also manually call trace. You have two options to do so: Recommended: use the trace as a context manager, i. This allows you to push progress updates at the level of "message generated", "tool ran", etc, instead of each token. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs. updateSession({ instructions: `You Using the OpenAI API in Python is straightforward, especially with the official OpenAI Python library and available quickstart examples. Traces need to be started and finished. Agents are systems that intelligently accomplish tasks—from simple goals to complex, open-ended workflows. Aug 29, 2025 路 This guide shows how to add persistent memory to your OpenAI Agents using Memori, an open-source memory engine that makes AI agents remember conversations and learn from past interactions. System Overview The prompt optimization system uses a collaborative multi-agent approach to analyze and improve prompts. This example demonstrates how to use Agno's built-in OpenAI moderation guardrail to detect and block content that violates OpenAI's content policy. Those models are free to use for anyone with a GitHub account, up to a daily rate limit. For lower latency, using reasoning. Creating traces You can use the trace() function to create a trace. PythonCodeExecAgent: This agent will generate the Python code to answer the user's question and execute the code in the Docker container. **Install the OpenAI Pytho OpenAI Computer Use Use OpenAI models with computer use capabilities for browser automation OpenAI’s Computer Use Agent (openai-cua) leverages GPT models with vision and reasoning capabilities for screenshot-based browser automation. In real-world or enterprise settings, the baseline agent could be much more complex. updateSession({ voice: "alloy" }); // Set detailed instructions for the AI agent realtimeClient. agents import create_agent from langchain. 20 hours ago 路 This blog demonstrated how the OCI OpenAI package makes it easy to build agentic applications on OCI Generative AI using multiple frameworks. with trace() as my_trace. By implementing a simple weather agent across the OpenAI SDK, OpenAI Agents SDK, LangChain, LangGraph, and the Microsoft Agent Framework. Below we present a number of prompt formats we find work well, but feel free to explore different formats, which may fit your task better. This guide walks you through creating a Python application that combines OpenAI's GPT models with CData Connect AI to enable conversational access to your data. Below, you can see how to extract information from unstructured text that conforms to a schema defined in code. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Nov 18, 2025 路 Model Implementations OpenAIChatCompletionsModel The OpenAIChatCompletionsModel implementation uses OpenAI's Chat Completions API (/v1/chat/completions), which is the standard API for most OpenAI models and compatible providers. Learn how to integrate Accelo with open-ai-agents-sdk using the Model Context Protocol (MCP). A lightweight, powerful framework for multi-agent workflows - Renol1/openai-agents-python-Nordrelos Responses OpenAI's most advanced interface for generating model responses. Learn how function calling enables large language models to connect to external data and systems. Deep Agents are equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - making them well-equipped Build resilient language agents as graphs. The gpt-4. Create stateful interactions with the model, using the output of previous responses as input. Feb 3, 2025 路 We will define two agents: FileAccessAgent: This agent will read the file and provide the context to the PythonCodeExecAgent. Aug 27, 2025 路 When using the OpenAI Agents SDK in Python with Azure OpenAI as the default model (via the async Azure client), the SDK ignores the Azure configuration and still requires OPENAI_API_KEY. connectOpenAi({ call, openAiApiKey, agentUserId: "support-agent", }); // Change the voice to 'alloy' realtimeClient. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python LangChain’s create_agent handles structured output automatically. as the result of a handoff). Deep Agents is an agent harness built on langchain and langgraph. Explain your reasoning at each step and include examples", ) Apr 30, 2024 路 Generate Text with OpenAI API Open the main. Some examples: If your provider offers an OpenAI-compatible endpoint, just add an openai/ prefix to your full model name. handoffs: See practical examples of agent handoffs with message filtering. Anaconda is a library that includes Python and many useful packages for Python, as well as an environment manager called conda that makes package management simple. In collaboration with Microsoft, OpenAI provides an officially supported API client for C#. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Oct 2, 2025 路 In this example, we show you how to build a Durable Agent using the OpenAI Agents SDK Integration for Temporal. This article provides an overview of the Microsoft Foundry SDKs and endpoints and how to get started using them. You have a few options for getting started with this repository. The model layer handles API calls to language models, request. Agents as tools: expose an agent as a callable tool without a full handoff. Installing Python ¶ We recommend installing Python through Anaconda. It serves as an efficient middleware that enables rapid delivery of enterprise-grade solutions. Share your own examples and guides. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform. Oct 21, 2025 路 The OpenAI Agents SDK is a Python library engineered to simplify the development of AI agents powered by OpenAI’s language models. For a deep dive into the architecture and real-world applications of Agent Skills, read Equipping agents for the real world with Agent Skills. 2 days ago 路 This page demonstrates how to write your first test using the `openai-responses` library. com is returning 400 Bad Request on some INVITEs? // After connecting the OpenAI agent as shown above const realtimeClient = await streamClient. Step-by-step guide with Python and TypeScript code examples. from agents import Agent agent = Agent( name="Math Tutor", instructions="You provide help with math problems. Learn how to integrate Replicate with open-ai-agents-sdk using the Model Context Protocol (MCP). 1 family (including mini and nano variants) also remains a solid choice for building interactive agent apps. The quickest way to get started is Mar 11, 2025 路 A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Mar 16, 2025 路 spent the weekend exploring the new agents sdk — built a simple multi-llm call workflow and shared my first impressions the code example put together guardrails & streaming too! the code example puts together guardrails & streaming too! thoughts: “agents as tools” vs “handoffs” the built-in traces ui is outstanding guardrails and streaming outputs: room for improvement? sometimes GenAI Agent Framework, the Pydantic way You can learn more about the differences between the Responses API and Chat Completions API in the OpenAI API docs. OpenTelemetry instrumentation for Python modules. updateSession({ instructions: `You Using the OpenAI API in Python is straightforward with the official OpenAI Python library. Non-OpenAI models You can use most other non Explore resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's developer platform. Learn how to deploy and manage containerized AI agents with zero infrastructure setup by using the feature of hosted agents in Microsoft Foundry. By the end, you will be ready to create and interact with your own simple OpenAI agent. You will learn how to define an agent with specific instructions, run it synchronously using the Runner class, and extract the agent’s final output. [!NOTE] Looking for the JavaScript/TypeScript version? Check out Agents SDK JS/TS. Here’s a concise guide to help you get started: // After connecting the OpenAI agent as shown above const realtimeClient = await streamClient. A common choice is to use Pydantic objects, but we support any type that can be wrapped in a Pydantic TypeAdapter - dataclasses, lists, TypedDict, etc. Design real-world AI agents visually using OpenAI’s AgentBuilder—no heavy coding required. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Jan 18, 2023 路 Open-source examples and guides for building with the OpenAI API. Use the OpenAI Agent Builder to start from templates, compose nodes, preview runs, and export workflows to code. Contribute to open-telemetry/opentelemetry-python-contrib development by creating an account on GitHub. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Realtime framework for voice, video, and physical AI agents. Jan 7, 2024 路 Build you own AI agent from scratch in 30 mins using simple Python Introduction OpenAI released ChatGPT on November 30, 2022, and due to its success, 2023 became the year of AI chat apps. MCP: Skills tell Claude how to use tools; MCP provides the tools. Not very interesting yet, but we can easily add tools, dynamic instructions, and structured outputs to build more powerful agents. 2 is recommended. agents. Learn how to integrate Airtable with open-ai-agents-sdk using the Model Context Protocol (MCP). Get started by installing the SDK using pip: Execute the code with python example. This guide delivers a detailed, technical walkthrough of LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves Dec 29, 2025 路 A curated list of skills, tools, tutorials, and capabilities for AI coding agents (Claude, Codex, Copilot, VS Code) - heilcheng/awesome-agent-skills In addition to supporting JSON Schema in the REST API, the OpenAI SDKs for Python and JavaScript also make it easy to define object schemas using Pydantic and Zod respectively. The AI agent we build will have access to tools (Temporal Activities) to answer user questions. Simply follow their instructions for which {PROVIDER}_API_KEY to set and how to write pass the {provider_name}/ {model_name} to the constructor. This lesson introduces you to the OpenAI Agents SDK in Python, explaining what agents are, how they differ from basic chat LLMs, and how the agent loop works. Learn how to integrate Pdf4me with open-ai-agents-sdk using the Model Context Protocol (MCP). memory: Examples of different memory implementations for agents, including: SQLite session storage Advanced SQLite session storage Redis session storage SQLAlchemy session storage Encrypted session storage OpenAI session storage model_providers: Explore how to use non-OpenAI models with the SDK, including custom providers and LiteLLM integration. Similarly, AgentUpdatedStreamEvent gives you updates when the current agent changes (e. In a few moments, you should see the output of your API request. As a result, real-time mode fails because the SDK always attempts to authenticate against OpenAI’s public API instead of using the Azure client credentials. If you run an agent with codex_tool() on a host where Codex is installed, the agent will use Codex as a tool to answer the question. Step 3: Set up Agentic Orchestration to run the application Learn how to build agents with the OpenAI Agents SDK. 1 day ago 路 Building Autonomous AI Agents: A Practical Guide with LlamaIndex, OpenAI, and Self-Evaluation Can autonomous AI agents truly revolutionize how we interact with technology? Jun 24, 2024 路 Semantic Kernel is a lightweight, open-source development kit that lets you easily build AI agents and integrate the latest AI models into your C#, Python, or Java codebase. The starter projects also include an AGENTS. For example, this will ignore raw events and stream updates to the user. See the docs for conceptual guides, tutorials, and examples on using Agents. Consider the example of payment fraud analysis. api. md file with instructions for using the MCP server and other important information about building agents with LiveKit. This will automatically start and end the trace at the right time. In contrast, an LLM agent functions more like a seasoned investigator, evaluating context, considering subtle patterns, and identifying suspicious activity even when clear-cut rules aren't violated. The current trace is tracked via a Python Oct 25, 2020 路 The official Python library for the OpenAI API. This nuanced reasoning capability is exactly what Developer Guide - Build AI-Powered Data Assistants with OpenAI Python SDK Build intelligent data assistants that can query your live data sources using natural language. Overview OpenAI Swarm OpenAI Swarm is an open鈥憇ource, lightweight multi鈥慳gent orchestration framework designed to coordinate many LLM鈥慸riven agents via simple Python and function鈥慶alling, emphasizing simplicity, retrieval鈥慼eavy workflows, and scalable multi鈥慳gent setups. Contribute to langchain-ai/langgraph development by creating an account on GitHub. 6 days ago 路 Check the SIP side for project proj_6ThBeJvaJTKbbGyke5l6Dzah and the example Twilio Call SID (s) above, to see why sip. Jan 10, 2026 路 To prepare AI agents for office work, the company is asking contractors to upload projects from past jobs, leaving it to them to strip out confidential and personally identifiable information. Jul 28, 2025 路 OpenAI Codex: What is it, how to access it, steps to login, examples on how to use OpenAI Codex for software development tasks. A traditional rules engine works like a checklist, flagging transactions based on preset criteria. LiveKit CLI Use the LiveKit CLI to manage LiveKit API keys and deploy your agent to LiveKit Cloud. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python Hosted OpenAI tools: run alongside the model on OpenAI servers. The agent factory - Design, customize, manage, and support AI applications and agents at scale. A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python A lightweight, powerful framework for multi-agent workflows - openai/openai-agents-python LangChain’s create_agent handles structured output automatically. Follow the installation instructions for Anaconda here. This ensures your agent has access to the latest documentation and examples.