OpenAI Agent SDK
AIOpen SourceLightweight SDK for building provider-agnostic, multi-agent AI applications with minimal abstractions.
-(0 Reviews)
Free Available
Starting from Free
OpenAI Agent SDK
AIOpen Source
Lightweight SDK for building provider-agnostic, multi-agent AI applications with minimal abstractions.
-(0 Reviews)
Free Available
Starting from Free
About OpenAI Agent SDK
The OpenAI Agent SDK is a lightweight framework for building agentic applications and multi-agent workflows. It provides minimal, easy-to-use abstractions to define agents, coordinate roles, attach tools and memory, and orchestrate multi-step reasoning. The SDK is provider-agnostic, able to work with OpenAI Responses as well as self-hosted or compatible LLMs by configuring model details. It emphasizes fast prototyping and production readiness, with integrations and patterns for long-running workflows, observability, and extensibility to adapt agents to custom toolchains and deployments.
Key Features
Lightweight SDK: Minimal abstractions and a compact API surface designed to let developers quickly define agents, agent roles, and control flow without heavy framework overhead.
Multi-Agent Workflows: Built-in primitives to define and coordinate multiple agents in workflows, enabling role-based collaboration, chaining tasks, and orchestrated decision-making.
Provider-Agnostic Model Support: Configurable adapters to use OpenAI Responses as well as self-hosted or OpenAI-compatible LLMs by specifying model details and endpoints.
Tool and Memory Integration: Mechanisms to attach external tools, APIs, and persistent or short-term memory stores to agents for retrieval-augmented workflows and stateful behavior.
Production Integration & Scalability: Patterns and integrations for running production-ready, long-running processes (examples include integrations like Temporal), enabling durable task orchestration and retry semantics.
Extensibility and Observability: Extension points for custom agent logic, hooks for logging/telemetry, and support for debugging and inspecting agent interactions for iterative development and monitoring.
Lightweight Python SDK for building agentic applications with few abstractions
Support for multi-agent workflows and orchestration
Provider-agnostic model integration (OpenAI Responses and other OpenAI-compatible/self-hosted models)
Documentation, tutorials, and dynamic examples for rapid onboarding
Extensible APIs to compose agents and customize behaviors
Compatibility with workflow systems (examples include Temporal integration)
Example projects and GitHub-hosted reference implementation
Use Cases
Customer Support Automation: Compose agents to retrieve user data, summarize account history, and generate context-aware responses while coordinating multiple specialist agents (retriever, summarizer, responder).
Multi-Agent Research Pipelines: Orchestrate agents that perform data collection, analysis, and report generation in sequence—each agent handling a specific step and passing structured outputs to the next.
On-Prem or Hybrid Deployments: Run agent workflows against self-hosted or compatible LLMs by configuring provider adapters, enabling compliance-sensitive or offline use cases.
Production Workflow Automation: Integrate with orchestration platforms (e.g., Temporal) to implement long-running, durable agent processes such as automated claims processing, approvals, or scheduled data workflows.
Rapid Prototyping of Agentic Apps: Quickly prototype complex agent behaviors with minimal boilerplate to validate concepts like multi-step reasoning, tool use, and role-based coordination.
Developer Tooling and CI Automation: Build developer-facing agents that coordinate code generation, test execution, and deployment steps, delegating discrete tasks to specialized agents.
Orchestrating multi-agent pipelines for task automation and coordination
Building production-ready agentic applications and assistants
Integrating LLM-driven agents into backend workflows and orchestrators (e.g., Temporal)
Prototyping and testing agent behaviors with different model providers, including self-hosted models
Combining multiple specialized agents to solve complex, multi-step problems
Screenshots
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