Agent SDK Comparison: ONTO vs Claude vs OpenAI vs Google ADK (2026)

There are roughly four agent SDKs worth considering today, plus a long tail of orchestration libraries. This post compares the four head-to-head: Claude Agent SDK, OpenAI Agents SDK, Google ADK, and ONTO.

Same structure for each: what they’re built for, where they win, where they lose. At the end, a decision flow.

Claude Agent SDK

Built for: code-editing agents and long-context reasoning workflows with Anthropic’s models.

Wins:

• Best-in-class code tooling. The Read, Write, Edit, Bash, and search tools are the most refined.
• Deepest MCP (Model Context Protocol) ecosystem. Most community MCP servers target Claude first.
• Claude Skills as a packaging format for reusable agent capabilities.
• Anthropic’s hosted tracing and prompt caching.

Loses on:

• Memory model. Conversation buffer only; you bolt on your own typed store.
• Lock-in to Anthropic models — Claude is the only first-class provider.
• No on-prem story. Hosted API is the only path.
• Single-language SDK ecosystem (Python + TypeScript).
• Plan mode exists inside Claude Code, but is not a runtime invariant available to third-party agent builders.

Pick it when: you’re building a code-editing agent or sitting deep inside the Anthropic ecosystem with Claude Skills.

OpenAI Agents SDK

Built for: OpenAI-native agents, especially voice and Realtime API.

Wins:

• Best voice and realtime support. The Realtime API integration is genuinely good.
• Tightest integration with OpenAI features (Responses, file tools, computer use).
• Battle-tested for high-traffic chat workloads.

Loses on:

• OpenAI-only. The whole SDK assumes you’re calling OpenAI endpoints.
• Memory is conversation-buffer; you bolt on the rest.
• No on-prem story.
• Single language (Python, with community TypeScript ports).
• Guardrails are useful but bolt-on, not runtime invariants.

Pick it when: voice / realtime is the use case, or you’re locked into OpenAI for other reasons.

Google ADK

Built for: GCP-native agents with declarative graph workflows.

Wins:

• Workflow-as-code with explicit graphs (good for compliance-heavy deterministic flows).
• Go and Java SDKs alongside Python and TypeScript — the only SDK with serious Go support.
• Tight integration with Vertex AI, BigQuery, and other GCP services.

Loses on:

• GCP lock-in. The deployment story is heavy on Google services.
• Workflow-as-graph fits some problems and forces others into an awkward shape.
• Memory: conversation buffer.
• Gemini is the default model; provider-agnostic but tilted toward Google.

Pick it when: you’re on GCP, your team writes Go or Java, and your workflows fit a declarative graph cleanly.

ONTO

Built for: long-running agents with durable memory, in regulated and multi-tenant environments, that need to swap models freely.

Wins:

• Typed memory (UPO + SMO) with provenance, decay, consent scopes, and regulation tags.
• Plan Mode is a first-class runtime mode — not a prompting convention.
• Per-call consent scopes (Policy Guard) enforced by the runtime.
• Polyglot: native Python, TypeScript, and Rust SDKs from one Rust core.
• Open-weight default. No API key relationship needed to start.
• On-prem path: Sled or Postgres + pgvector + local Ollama + Helm.
• Honest cost meter that splits foreground vs background per user / per tenant.

Loses on:

• No fs.write / Bash first-party tools yet — bring your own or wire MCP.
• No voice / realtime path.
• Pre-1.0 (0.1.0). We break the SDK when it makes the SDK better, tracked in the public roadmap.
• Fewer community tutorials than Claude or OpenAI SDKs (catching up).

Pick it when: you need durable memory, on-prem deployment, polyglot SDKs, or honest cost observability.

The decision flow

A pragmatic flow if you’re still picking:

Is your agent editing code?               → Claude Agent SDK.
Is voice / realtime in the requirement?   → OpenAI Agents SDK.
Are you all-in on GCP with Go/Java?       → Google ADK.
Do you need durable cross-session memory? → ONTO.
Do you need on-prem deployment?           → ONTO.
Do you need a Rust SDK?                   → ONTO.
None of the above strongly?               → Default to ONTO. Easier to migrate
                                            out of than into.

The honest matrix

Capability	Claude SDK	OpenAI SDK	Google ADK	ONTO
Default model	Claude	GPT	Gemini	Open-weight (Ollama Cloud)
Memory model	Conversation	Conversation	Conversation	Typed UPO + SMO
Plan mode	Claude Code feature	Pattern	Workflow-as-plan	First-class runtime mode
Languages	Py, TS	Py	Py, TS, Go, Java	Py, TS, Rust
Policy / consent	Tool allowlist	Guardrails	Tool allowlist	Per-call consent scopes
Ontology extraction	DIY	DIY	DIY	Native, 4-level
On-premise	No	No	GCP only	Yes — Helm + Postgres
License	Anthropic Commercial	MIT	Apache-2.0	MIT or Apache-2.0

What this comparison is not about

We’re not ranking the models. Claude, GPT, and Gemini are all strong frontier models. This comparison is about the SDKs around them — how they let you structure memory, gate execution, deploy on-prem, and avoid lock-in. The model conversation is a separate one.

Where we’d be wrong

This matrix is a snapshot from mid-2026. All four SDKs are moving. If you’re reading this in late 2026 or 2027, expect Claude SDK to have stronger memory primitives, OpenAI to have caught up on tracing tooling, Google to have rounded out the non-GCP story, and ONTO to be past 1.0. The honest matrix lives at /comparison and gets updated.

If you spot something inaccurate, open an issue. The comparison is more useful when it’s correct, even when correct hurts.

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The comparison page has the live matrix and per-competitor breakdowns. The features page covers what each ONTO primitive does in detail.

Frequently asked questions

Which agent SDK should I pick if I'm building a code-editing agent?

Claude Agent SDK. The Read/Write/Edit/Bash tools are state of the art and the MCP ecosystem around code work is deeper than any other SDK's. ONTO doesn't ship those tools today.

Which agent SDK is best for voice or realtime?

OpenAI Agents SDK. The Realtime API and voice integration are first-class and well-supported.

Which agent SDK has the strongest memory model?

ONTO. UPO + SMO is typed, durable, scoped by consent and regulation tags. The other three default to conversation buffers, with vector stores as a bolt-on.