Yes on tool-calling and handoffs (their term for delegating to a sub-agent). Agentmatic's Supervisor pattern covers the same use case with explicit graph control flow.

Can I use OpenAI's newer features (Realtime, Computer Use) with Agentmatic?

Yes via the OpenAI provider in Agentmatic. We track the OpenAI Python SDK; new features land in our provider within days.

OpenAI Agents SDK is simpler for a single-step OpenAI-only agent. Agentmatic is simpler for anything multi-step or multi-provider.

Comparison

Agentmatic vs OpenAI Agents SDK — open framework vs vendor-led toolkit

OpenAI's Agents SDK is a clean toolkit for building agents against OpenAI's API. Agentmatic is an open framework against any LLM, with explicit graphs, multi-language SDKs, and production resilience primitives.

Published May 14, 2026

Two different value propositions

OpenAI Agents SDK is a thin, well-designed wrapper for building agents that use OpenAI models. It optimizes for: minimal API surface, tight integration with the latest OpenAI features (Responses API, Realtime, Computer Use, Codex), excellent docs.

Agentmatic is a vendor-neutral agent orchestration framework. It optimizes for: explicit graph control flow, multi-provider support, checkpointing, distributed execution, multi-language SDKs.

When the OpenAI SDK fits

OpenAI-only stack. You’re committed to OpenAI; new features (Computer Use, Realtime audio) matter to you.
Short-chain agents. One agent, a few tools, maybe a handoff or two.
Minimal abstraction surface. You want code that looks close to raw OpenAI API calls.

When Agentmatic fits

Multi-provider. You use Anthropic for code, OpenAI for math, Gemini for vision, Bedrock for compliance.
Multi-step workflows. Supervisors, sub-agents, branching control flow, HITL.
Production resilience. Circuit breakers, retry, dead-letter queues, distributed clusters.
Polyglot teams. Python on the backend, TypeScript on the edge, Go in the platform layer.
Vendor neutrality. You want to avoid lock-in to any one LLM provider.

Side-by-side: handoff vs supervisor

# OpenAI Agents SDK — handoff
from openai_agents import Agent, handoff

triage = Agent(
    name="triage",
    instructions="Route to billing or docs.",
    handoffs=[billing_agent, docs_agent],
)

# Agentmatic — supervisor
from agentmatic.prebuilt import create_supervisor

supervisor = create_supervisor(
    llm=OpenAI(),
    agents={"billing": billing_agent, "docs": docs_agent},
)

Same idea, different vocabulary. Agentmatic’s supervisor exposes the routing decision as a node you can checkpoint and debug.

Headline

If you’re OpenAI-only and your agents are short, the Agents SDK is a great choice. If you need multi-provider, multi-step, multi-language, or production hardening — that’s our lane.

Pick Agentmatic

You need to use Anthropic, Gemini, Bedrock, Ollama — or any non-OpenAI model.
You need explicit graph orchestration with checkpointing.
You need built-in resilience primitives (CB, retry, DLQ).
You need polyglot SDKs (Python + TS + Go + Java + Rust).
You want a vendor-neutral framework that won't drift toward one model provider.

Pick OpenAI Agents SDK

You're 100% on OpenAI and likely to stay there.
You want the tightest possible integration with OpenAI's latest model features (Realtime, GPT-4o-audio, Computer Use, Codex).
Your agents are single-step or short-chain — the SDK shines for those.

Pricing notes

OpenAI Agents SDK is open source (Apache-2.0); you pay for OpenAI usage. Agentmatic is MIT, supports OpenAI + Anthropic + Gemini + Bedrock + Ollama + any OpenAI-compatible endpoint.