agentlib vs the OpenAI Agents SDK — when to pick which
Side-by-side: agentlib (Dart, native mobile, multi-provider, deep primitives) vs the OpenAI Agents SDK (Python/JS, cloud OpenAI, handoff-first). Different runtimes, different focuses.
OpenAI’s Agents SDK has the cleanest handoff design of any framework. It’s also Python/JS-only and OpenAI-only. If those constraints fit yours, it’s a great pick. If they don’t, agentlib covers a different surface.
Runtime
- OpenAI Agents SDK: Python (
openai-agents) or JavaScript. Cloud OpenAI by default; can point at OpenAI-compatible endpoints. - agentlib: Dart, native mobile. Seven providers — cloud + on-device.
Where each shines
OpenAI Agents SDK strengths:
- The handoff primitive — “this conversation is now this other agent’s problem” — is the cleanest in the ecosystem.
- Tight integration with OpenAI’s hosted Agent Platform features (when/if you use them).
- Apache-2.0 license, well-documented, OpenAI-team-maintained.
agentlib strengths:
- Runs natively in mobile apps.
- On-device inference (Apple Foundation Models, Gemini Nano, llama.cpp).
- Multi-provider cloud (Anthropic, OpenAI, Google) behind one route.
- Snapshots with public fork / revert API.
- 21 hooks vs the OpenAI SDK’s smaller guardrails surface.
- Subagents in parallel and background, not just handoffs.
Where they overlap
- Both support handoffs. agentlib’s is functional but less mature.
- Both support OpenAI as a model.
- Both support MCP (agentlib via HTTP and WebSocket; OpenAI through their tool ecosystem).
- Both have streaming.
The real decision
If your agent is OpenAI-only, server-side, and handoff-driven — the OpenAI Agents SDK is built for that exact shape. Don’t try to do it with agentlib; you’d be reinventing.
If your agent runs on a phone, or you need on-device, or you need multi-provider — agentlib. Use OpenAI as the
fallback provider behind a ModelRoute.preferOnDevice([...], fallback: OpenAIProvider(...)) if you want OpenAI for
the cloud tier.
Combining them
If your product has both a server-side workflow (OpenAI Agents SDK) and a mobile app (agentlib), they interoperate through standard protocols: MCP for shared tools, HTTPS for the app calling the server’s agent. The shapes don’t clash.
Bottom line
The OpenAI Agents SDK is the right framework for OpenAI-first, server-side, handoff-driven workflows. agentlib is the right framework for mobile-native, multi-provider, primitive-rich workflows. Different problems, both well-solved.
- Your agent runs inside a Flutter, iOS, or Android app.
- You need on-device models or multi-provider cloud routing.
- You want subagents in parallel or background, not just handoffs.
- You need snapshots, fork, revert — a full safety net for destructive actions.
- You need lifecycle hooks (suspend / resume / low battery / network change).
- Your agent runs on a Python or JS backend.
- OpenAI is your model and you want first-class GPT-4o / o-series integration.
- Your workflow is handoff-driven — clean transitions between specialised agents.
- You're using OpenAI's hosted Agent Platform features.
- You want the cleanest handoff primitive in the ecosystem.
FAQ
- agentlib has handoffs too — what's different?
- agentlib added handoffs in 1.1.0 inspired by the OpenAI Agents SDK pattern. The OpenAI SDK's handoff design is still cleaner and more mature; agentlib's is functional. If handoffs are the central pattern of your workflow, OpenAI's SDK is the gold standard.
- Does agentlib support OpenAI as a model?
- Yes — OpenAIProvider talks to the Chat Completions API with full streaming + tool calling. You can use GPT-4o, o-series, or any OpenAI-compatible endpoint.