For developers building GTM agents and backend automations, Inngest is a durable execution engine that runs event-driven functions as retryable, queued steps — handling retries, concurrency, and scheduling without separate queue infrastructure.
Presence & Market Position
Tracked · below panel floor — earned mentions are accruing toward the next scored window.
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Inngest occupies the execution layer under GTM agents and backend automation — a durable, event-driven runtime that runs functions as retryable, queued steps. The build-out points at getting agents into production rather than just prototyped, evaluating full agent runs against business outcomes and pairing with voice-agent platforms to take over once a call needs retries and durable orchestration. Conversation around it is still early and tracks close to the company's own release cadence, amplifying new eval tooling and production-agent workflows more than independent comparison.
Vellum
Relevance AI
Lindy
CrewAI
DustSoundCloud, Tripadvisor, Replit, Cohere, ElevenLabs, Resend, GitBook, Windmill, Day AI, Aomni
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Business Profile
Inngest launched in 2021 building for a developer audience that needed durability and retries in event-driven backend code, well before AI agents became the default use case on its site today. The team has stayed small — 27 people — while raising roughly $30 million from investors including Andreessen Horowitz and Altimeter, funding a shift toward framing the product around AI agents specifically as agentic workloads grew inside its own customer base, which includes AI-native names like Cohere and ElevenLabs. The company remains independent and continues to describe itself around a single core primitive, durable execution, rather than a broader platform pitch.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Inngest ships API, SDK, CLI, and MCP surfaces, plus tooling built for agents: skills and plugins that teach coding agents like Claude Code and Cursor to write durable Inngest functions, an MCP server that lets those agents inspect and test running functions directly, and an AgentKit-based skill for building durable AI agents that handle model calls, tool calls, and human-approval steps with retries built in. It has also shipped Agent Evals, scoring full runs against outcomes rather than single responses.
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