Inngest Agents

Inngest Agents

Use this skill when the user wants to build, migrate, or debug an AI agent, multi-step AI workflow, tool-calling loop, support agent, research agent, human-in-the-loop review flow, or realtime agent UI.

Inngest's AgentKit defines agents with createAgent; when an AgentKit run is owned by an Inngest function, model calls use Inngest step.ai so they retry and cache model results durably. Use the lower-level Inngest step primitives around the agent for database reads/writes, tool side effects, waits, approvals, realtime progress, and flow control.

Official references:

• AgentKit agents: https://agentkit.inngest.com/concepts/agents
• createAgent: https://agentkit.inngest.com/reference/create-agent
• AI inference and step.ai: https://www.inngest.com/docs/features/inngest-functions/steps-workflows/step-ai-orchestration
• AgentKit realtime hooks: https://www.inngest.com/changelog/2025-09-24-agentkit-use-agent

Copyable Example

When starting a durable support or tool-calling agent from scratch, inspect the companion example at ../../examples/durable-agent. It shows the expected agent-first shape: quick HTTP trigger, typed events, AgentKit inside an Inngest function, step-scoped context loading, human approval with step.waitForEvent, and durable side effects after approval.

When to Use Inngest for Agents

Good fit:

• Agent can take longer than one HTTP request.
• Agent calls tools, APIs, databases, browsers, sandboxes, or MCP servers.
• Agent needs to survive deploys, crashes, serverless timeouts, or model/API failures.
• Agent may wait for human approval, external callbacks, scheduled follow-up, or user input.
• Agent progress should stream to a UI from the durable workflow.
• Model/provider calls need concurrency or throttle limits.
• Duplicate sends, charges, writes, or model calls would be costly.

Not usually worth it:

• One short, read-only model call with no side effects and no need for durable progress.
• UI-only autocomplete where losing the request is acceptable.

Architecture

Use this shape unless the repo already has a stronger established pattern:

1. The HTTP/server action layer validates auth, stores the user's intent if needed, emits an event with a stable id, and returns quickly.
2. An Inngest function owns the agent run.
3. Load state and external context inside step.run.
4. Create AgentKit agents inside the function or import agent/network factories.
5. Run model inference through AgentKit / step.ai; wrap non-model tool side effects in step.run.
6. Use step.waitForEvent or step.waitForSignal for human approval and external callbacks.
7. Publish durable progress with native realtime.
8. Apply flow control at the function level for provider and tenant limits.

Basic AgentKit Function

Prefer a small, typed function first; add networks and extra tools after the single-agent path is proven.

import { createAgent, openai } from "@inngest/agent-kit";
import { inngest } from "@/inngest/client";

export const summarizeTicket = inngest.createFunction(
  {
    id: "summarize-ticket",
    triggers: [{ event: "support/ticket.created" }],
    concurrency: [{ key: "event.data.accountId", limit: 2 }]
  },
  async ({ event, step }) => {
    const ticket = await step.run("load-ticket", () => {
      return getTicket(event.data.ticketId);
    });

    const writer = createAgent({
      name: "support-summary-writer",
      system: "Write a concise support-ticket summary with next actions.",
      model: openai({ model: "gpt-4o" })
    });

    const { output } = await writer.run(JSON.stringify(ticket));

    await step.run("save-summary", () => {
      return saveTicketSummary(event.data.ticketId, output);
    });

    return { ticketId: event.data.ticketId };
  }
);

Tool Calls

Tools can be defined with AgentKit, but agent-safe tools should still follow durability rules:

• Read-only tool calls can run as part of the agent when replaying is harmless.
• External side effects should be isolated with stable IDs and step.run boundaries, or implemented as tool handlers that use the provided step.
• Tool outputs should be small enough for step state limits.
• Validate tool parameters with schemas; never trust model-provided arguments.
• Use tenant/user IDs from authenticated event data, not only from model text.

Tool side-effect checklist:

- What external state can this tool change?
- What idempotency key prevents duplicate writes?
- What should happen if the model calls the same tool twice?
- Is the output safe to store in function run state?
- Does the tool need provider-specific concurrency or throttle limits?

Human in the Loop

Use a durable wait instead of polling a database or keeping state in memory.

const approval = await step.waitForEvent("wait-for-approval", {
  event: "support/reply.approved",
  timeout: "3d",
  match: "data.ticketId"
});

if (!approval) {
  await step.run("mark-review-timeout", () => {
    return markTicketNeedsManualReview(event.data.ticketId);
  });
  return { status: "timed_out" };
}

await step.run("send-reply", () => {
  return sendSupportReply({
    ticketId: event.data.ticketId,
    approvalId: approval.data.approvalId
  });
});

Realtime Progress

For v4 native realtime:

• Use step.realtime.publish between steps.
• Use inngest.realtime.publish inside an existing step.run.
• Do not install the v3 @inngest/realtime package for v4 projects.
• Do not build a process-local WebSocket as the only source of progress for a durable function.

For AgentKit-specific UI hooks, check the installed @inngest/agent-kit version and current docs before wiring useAgent or useChat.

Flow Control and Cost

Agent workloads often need provider and tenant limits:

• Use account-scoped concurrency or throttle keys for model providers.
• Key per tenant or account where fairness matters.
• Use deterministic event IDs so duplicate user actions do not spawn duplicate expensive runs.
• Keep successful model/tool results in steps so retrying a later failure does not re-charge earlier model calls.

Example:

{
  id: "support-agent-run",
  triggers: [{ event: "support/agent.requested" }],
  throttle: {
    limit: 120,
    period: "1m",
    key: `"openai"`
  },
  concurrency: [
    { key: "event.data.accountId", limit: 3 }
  ]
}

Brownfield Migration

When migrating an existing agent:

1. Search for model calls, tool loops, in-memory state, streaming handlers, approval polling, and external side effects.
2. Keep prompt/tool behavior stable at first.
3. Move the trigger into an event and an Inngest function.
4. Move model calls to AgentKit / step.ai.
5. Move side-effecting tools into step.run or durable tool handlers.
6. Replace process-local waits with step.waitForEvent or step.waitForSignal.
7. Add realtime after the durable run is working.

Use inngest-brownfield-audit first when the repo has multiple possible workflows and the user has not picked one.

Anti-Patterns

• Agent loop state only in memory.
• One giant try/catch around all model and tool calls.
• Retrying the entire agent after one tool failure.
• Charging repeatedly for successful model calls after a later step fails.
• setTimeout, cron polling, or Redis TTL as the human-review mechanism.
• Side-effecting tools with no idempotency key.
• Streaming progress from a server process that can die while the durable work continues elsewhere.
• Adding AgentKit without registering the surrounding Inngest function.

Verification

• Typecheck the agent, tool schemas, and event payloads.
• Unit-test tool handlers separately from model behavior.
• Test that the HTTP entrypoint emits one deterministic event and returns fast.
• Test that duplicate event IDs do not duplicate final side effects.
• If possible, run the Inngest dev server and inspect the agent steps/traces.