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Inngest Middleware

Organization
Asymmetric-al
inngest-middleware

Use when adding cross-cutting concerns to durable functions — structured logging or tracing across all functions, error tracking with Sentry, payload encryption for sensitive data, dependency injection of clients (DB, Stripe, etc.) into function handlers, custom telemetry, or behavior that should apply uniformly across many functions. Covers Inngest middleware lifecycle, creating custom middleware, dependencyInjectionMiddleware, @inngest/middleware-encryption, @inngest/middleware-sentry, and custom middleware patterns.

Overview

PublisherAsymmetric-al
Repositorycore
Skill nameinngest-middleware
Stars
383
Forks
7
Bundled files
2
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Asymmetric-al on GitHub. Read the source before you install it.

Installation

Install the Inngest Middleware AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Asymmetric-al/core.git /tmp/core
mkdir -p .claude/skills
cp -r /tmp/core/docs/ai/skills/inngest-middleware .claude/skills/inngest-middleware
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Inngest Middleware in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Inngest Middleware on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Inngest Middleware is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Inngest Middleware

Master Inngest middleware to handle cross-cutting concerns like logging, error tracking, dependency injection, and data transformation. Middleware runs at key points in the function lifecycle, enabling powerful patterns for observability and shared functionality.

These skills are focused on TypeScript. For Python or Go, refer to the Inngest documentation for language-specific guidance. Core concepts apply across all languages.

Note: The middleware system was significantly rewritten in v4. The lifecycle hooks documented here reflect the v4 API. If migrating from v3, consult the migration guide for details on breaking changes.

⚠ For Realtime use the inngest-realtime skill, NOT this one. Inngest v3 used realtimeMiddleware() from @inngest/realtime to inject a publish arg into function handlers. v4 ships realtime nativelystep.realtime.publish is built-in, no middleware required. Do NOT install @inngest/realtime on a v4 project (it's a v3-era package and produces TypeError: Cls is not a constructor at runtime). See the inngest-realtime skill for the v4 pattern.

What is Middleware?

Middleware allows code to run at various points in an Inngest client's lifecycle - during function execution, event sending, and more. Think of middleware as hooks into the Inngest execution pipeline.

When to use middleware:

  • Observability: Add logging, tracing, or metrics
  • Dependency injection: Share client instances across functions
  • Data transformation: Encrypt/decrypt, validate, or enrich data
  • Error handling: Custom error tracking and alerting
  • Authentication: Validate user context or permissions

Middleware Lifecycle

Middleware can be registered at client-level (affects all functions) or function-level (affects specific functions).

Execution Order

typescript
const inngest = new Inngest({
  id: "my-app",
  middleware: [
    loggingMiddleware, // Runs 1st
    errorMiddleware, // Runs 2nd
  ],
});

inngest.createFunction(
  {
    id: "example",
    middleware: [
      authMiddleware, // Runs 3rd
      metricsMiddleware, // Runs 4th
    ],
    triggers: [{ event: "test" }],
  },
  async () => {
    /* function code */
  },
);

Order matters: Client middleware runs first, then function middleware, in the order specified.

Creating Custom Middleware

Basic Middleware Structure

typescript
import { InngestMiddleware } from "inngest";

const loggingMiddleware = new InngestMiddleware({
  name: "Logging Middleware",
  init() {
    // Setup phase - runs when client initializes
    const logger = setupLogger();

    return {
      // Function execution lifecycle
      // Note: `fn` is loosely typed in middleware generics; fn.id works at runtime
      onFunctionRun({ ctx, fn }) {
        return {
          beforeExecution() {
            logger.info("Function starting", {
              functionId: fn.id,
              eventName: ctx.event.name,
              runId: ctx.runId,
            });
          },

          afterExecution() {
            logger.info("Function completed", {
              functionId: fn.id,
              runId: ctx.runId,
            });
          },

          transformOutput({ result }) {
            // Log function output
            logger.debug("Function output", {
              functionId: fn.id,
              output: result.data,
            });

            // Return unmodified result
            return { result };
          },
        };
      },

      // Event sending lifecycle
      onSendEvent() {
        return {
          transformInput({ payloads }) {
            logger.info("Sending events", {
              count: payloads.length,
              events: payloads.map((p) => p.name),
            });

            // Spread to convert readonly array to mutable array
            return { payloads: [...payloads] };
          },
        };
      },
    };
  },
});

Python Implementation

Python middleware follows a similar pattern. See Dependency Injection Reference for complete Python examples.


## Dependency Injection

Share expensive or stateful clients across all functions. **See [Dependency Injection Reference](./references/dependency-injection.md) for detailed patterns.**

### Quick Example - Built-in DI

```typescript
import { dependencyInjectionMiddleware } from "inngest";

const inngest = new Inngest({
  id: 'my-app',
  middleware: [
    dependencyInjectionMiddleware({
      openai: new OpenAI(),
      db: new PrismaClient(),
    }),
  ],
});

// Functions automatically get injected dependencies
inngest.createFunction(
  { id: "ai-summary", triggers: [{ event: "document/uploaded" }] },
  async ({ event, openai, db }) => {
    // Dependencies available in function context
    const summary = await openai.chat.completions.create({
      messages: [{ role: "user", content: event.data.content }],
      model: "gpt-4",
    });

    await db.document.update({
      where: { id: event.data.documentId },
      data: { summary: summary.choices[0].message.content }
    });
  }
);

Middleware Packages

Beyond dependencyInjectionMiddleware (built-in, shown above), Inngest provides official middleware as separate packages. See Middleware Reference for complete details.

Encryption Middleware

bash
npm install @inngest/middleware-encryption
typescript
import { encryptionMiddleware } from "@inngest/middleware-encryption";

const inngest = new Inngest({
  id: "my-app",
  middleware: [
    encryptionMiddleware({
      key: process.env.ENCRYPTION_KEY,
    }),
  ],
});

Automatically encrypts all step data, function output, and event data.encrypted field. Supports key rotation via fallbackDecryptionKeys.

Sentry Error Tracking

bash
npm install @inngest/middleware-sentry
typescript
import * as Sentry from "@sentry/node";
import { sentryMiddleware } from "@inngest/middleware-sentry";

Sentry.init({
  /* your Sentry config */
});

const inngest = new Inngest({
  id: "my-app",
  middleware: [sentryMiddleware()],
});

Captures exceptions, adds tracing to each function run, and includes function ID and event names as context. Requires @sentry/*@>=8.0.0.

Common Middleware Patterns

Metrics and Performance Tracking

typescript
const metricsMiddleware = new InngestMiddleware({
  name: "Metrics Tracking",
  init() {
    return {
      onFunctionRun({ ctx, fn }) {
        let startTime: number;

        return {
          beforeExecution() {
            startTime = Date.now();
            metrics.increment("inngest.step.started", {
              function: fn.id,
              event: ctx.event.name,
            });
          },

          afterExecution() {
            const duration = Date.now() - startTime;
            metrics.histogram("inngest.step.duration", duration, {
              function: fn.id,
              event: ctx.event.name,
            });
          },

          transformOutput({ result }) {
            const status = result.error ? "error" : "success";
            metrics.increment("inngest.step.completed", {
              function: fn.id,
              status: status,
            });

            return { result };
          },
        };
      },
    };
  },
});

Advanced Patterns

Authentication: Validate tokens and inject user context Conditional logic: Apply middleware based on event type or function Circuit breakers: Prevent cascading failures from external services

Configuration-Based Middleware

Create reusable middleware with configuration options for different environments and use cases. See reference documentation for complete examples.

Best Practices

Design Principles

  1. Keep middleware focused: One concern per middleware
  2. Handle errors gracefully: Don't let middleware crash functions
  3. Consider performance: Middleware runs on every execution
  4. Use proper typing: Let TypeScript infer middleware types
  5. Test thoroughly: Middleware affects all functions that use it

Common Use Cases to Implement

  • Retry logic for transient failures
  • Circuit breakers for external service calls
  • Request/response logging for debugging
  • User context enrichment from external sources
  • Feature flags for gradual rollouts
  • Custom authentication and authorization checks

Error Handling in Middleware

typescript
const robustMiddleware = new InngestMiddleware({
  name: "Robust Middleware",
  init() {
    return {
      onFunctionRun({ ctx, fn }) {
        return {
          transformOutput({ result }) {
            try {
              // Your middleware logic here
              return performTransformation(result);
            } catch (middlewareError) {
              // Log error but don't break the function
              console.error("Middleware error:", middlewareError);

              // Return original result on middleware failure
              return { result };
            }
          },
        };
      },
    };
  },
});

Testing Middleware

Use Inngest's testing utilities (createMockContext, createMockFunction) to unit test middleware behavior.

For complete implementation examples and advanced patterns, see:

This Repository

These upstream Inngest instructions are vendored for agent tooling and integration work in this monorepo.

Repository Triggers

Use this skill when inngest-middleware matches the current Inngest task. If the right skill is unclear, start with docs/ai/skills/inngest/SKILL.md.

Repository Workflow

  1. Confirm whether the request is agent-tooling guidance or product runtime integration.
  2. Use inngest-brownfield-audit before changing existing app workflows or fragile background work.
  3. Follow this upstream guidance under OpenSpec, root AGENTS.md, repo rulebooks, framework docs, and runtime evidence.
  4. Keep runtime packages, app code, migrations, and INNGEST_* env requirements out of agent-tooling-only changes.

Repository Checklist

  • The task has explicit product-runtime scope before adding Inngest app code or dependencies.
  • Existing workflows were audited before introducing or changing durable workflow behavior.
  • Any MCP usage is backed by a running Inngest dev server on the configured port.
  • Upstream source and license attribution remain documented in docs/ai/skills/inngest/references/upstream.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Inngest Middleware AI skill do?

Use when adding cross-cutting concerns to durable functions — structured logging or tracing across all functions, error tracking with Sentry, payload encryption for sensitive data, dependency injection of clients (DB, Stripe, etc.) into function handlers, custom telemetry, or behavior that should apply uniformly across many functions. Covers Inngest middleware lifecycle, creating custom middleware, dependencyInjectionMiddleware, @inngest/middleware-encryption, @inngest/middleware-sentry, and custom middleware patterns.

Why use Inngest Middleware on TypingMind?

Because you install it once and use it with any model. Inngest Middleware is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Inngest Middleware in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Asymmetric-al/core/tree/develop/docs/ai/skills/inngest-middleware. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Inngest Middleware?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Inngest Middleware?

As many as you like. As long as a model supports skills, you can use Inngest Middleware with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Inngest Middleware AI skill free?

Yes. It is published on GitHub by Asymmetric-al under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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