OpenAI AgentKit vs Google ADK vs Inngest: Which Agent Platform Should You Choose?

Three platforms are competing to become the standard for building AI agents: OpenAI's AgentKit, Google's Agent Development Kit (ADK), and Inngest. Each solves the same problem — building AI systems that execute multi-step tasks — with a different philosophy. The differences come down to architecture, memory, debugging, and integration.

Platform overview

Platform	Primary focus	Best for
OpenAI AgentKit	LLM-powered workflow orchestration	Rapid prototyping, GPT integration
Google ADK	Multi-agent systems, enterprise scale	Data-heavy apps, strict compliance
Inngest	Event-driven durable workflows	Background jobs, reliable automation

How they actually work

AgentKit runs on the Responses API, keeping conversation history and context within OpenAI's infrastructure. Its Agent Builder is a visual canvas for designing workflows with drag-and-drop nodes. Partners like Ramp reportedly went from blank canvas to working agent in hours; Klarna's support agent handles two-thirds of tickets.

Google ADK distributes work across microservices. State lives in Firestore, Spanner, or Bigtable depending on your needs, and multiple specialized agents coordinate hierarchically. It's the same framework Google uses internally for Agentspace, deployable across GCP regions with full enterprise data access.

Inngest checkpoints every step. If a server crashes mid-execution, the workflow resumes exactly where it left off — a model borrowed from Temporal and AWS Step Functions. It doesn't include AI reasoning; you write standard TypeScript, Python, or Go that calls any LLM, and Inngest handles orchestration and state.

Memory, debugging, and security

Memory is the clearest divider. AgentKit handles context within its environment — great for prototyping, limiting for long-term persistence. ADK gives you full control over storage and audit requirements. Inngest's durable state lets workflows pause for hours or days and resume cleanly.

On security, AgentKit processes data in OpenAI's infrastructure (SOC 2 Type II). ADK keeps data in your own cloud project with VPC Service Controls, CMEK, and HIPAA/FedRAMP/GDPR support. Inngest is an orchestration layer recording workflow metadata while your business data flows through your own functions — a middle ground, with a self-hosted option.

Pricing

AgentKit is pay-per-token plus function overhead — simple but scales with usage. ADK charges compute and storage separately, integrating with Vertex credits; flexible but complex. Inngest bills per trigger and execution — predictable for background tasks with a generous free tier.

Choosing your platform

Pick AgentKit when time-to-market beats infrastructure control and the GPT ecosystem fits. Pick Google ADK when data must stay in your infrastructure, you need fine-grained control, or compliance is critical. Pick Inngest when reliability trumps everything and you want freedom in your AI provider.

The hybrid pattern

Many teams combine all three: AgentKit for user-facing reasoning, Google ADK for data-heavy analysis, and Inngest as the durable orchestration backbone — handling triggering, state, and recovery while the AI platform focuses on language and decisions. The best choice isn't which platform is objectively superior; it's which aligns with your technical capabilities, compliance needs, and direction.