Diagrid Catalyst vs. Inngest: Comparing Durable Workflows for Production AI Agents
Inngest and Diagrid Catalyst both support durable AI workloads. Inngest provides event-driven durable functions; Catalyst provides Dapr-based durable workflows plus a shared identity, policy, and governance layer across agent frameworks, MCP servers, and applications.
Diagrid
Diagrid Team
Inngest and Diagrid Catalyst both support durable AI workloads. Inngest provides event-driven durable functions with persisted steps, flow control, and developer-oriented infrastructure. Catalyst provides Dapr-based durable workflows and a shared identity, policy, and governance layer across Agent frameworks, MCP servers, and applications.
Comparison summary
| Criterion | Diagrid Catalyst | Inngest |
|---|---|---|
| Authoring | Dapr Workflows and supported Agent-framework runners | TypeScript, Python, and Go functions using step primitives |
| Recovery | Event-sourced workflow replay | Step-level memoization and deterministic replay |
| Agent composition | Existing frameworks, workflows, pub/sub | Durable steps, events, sessions, and sub-agent invocation |
| Platform scope | Execution, identity, policy, MCP, app communication | Durable functions, events, queues, flow control, observability |
| Deployment question | Managed and enterprise customer-infrastructure options | Hosted and self-hosted options; confirm edition-specific capabilities |
Choose Inngest when
The team wants an approachable developer-first model for background jobs, event-driven functions, long-running tasks, and AI workflows. Inngest's durable-agent documentation explains how LLM and tool results are memoized so completed steps are not re-executed after failure.
Evaluate Catalyst when
Multiple teams use different Agent frameworks or languages and need a common production standard. Catalyst's value expands beyond durable steps to workload identity, policy-controlled Agent and MCP access, Dapr application APIs, and governance across deployment environments.
Proof-of-concept checklist
Compare recovery after an LLM call, a state-changing tool call, a deploy, and a long human wait. Then compare language and framework fit, security boundaries, self-hosting responsibilities, operator controls, and evidence available after the run.
Inngest may be the better fit for application teams prioritizing durable-function developer experience. Catalyst may be the better fit when the requirement is an enterprise execution and governance layer for heterogeneous Agent and application workloads.
