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Designing type-safe sync/async mode support in TypeScript

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  • I recently added sync/async mode support to Optique, a type-safe CLI parser
    for TypeScript. It turned out to be one of the trickier features I've
    implemented—the object() combinator alone needed to compute a combined mode
    from all its child parsers, and TypeScript's inference kept hitting edge cases.

    What is Optique?

    Optique is a type-safe, combinatorial CLI parser for TypeScript, inspired by
    Haskell's optparse-applicative. Instead of decorators or builder patterns,
    you compose small parsers into larger ones using combinators, and TypeScript
    infers the result types.

    Here's a quick taste:

    import { object } from "@optique/core/constructs";
    import { argument, option } from "@optique/core/primitives";
    import { string, integer } from "@optique/core/valueparser";
    import { run } from "@optique/run";
    
    const cli = object({
      name: argument(string()),
      count: option("-n", "--count", integer()),
    });
    
    // TypeScript infers: { name: string; count: number | undefined }
    const result = run(cli);  // sync by default
    

    The type inference works through arbitrarily deep compositions—in most cases,
    you don't need explicit type annotations.

    How it started

    Lucas Garron (@lgarron@mastodon.social) opened an issue requesting
    async support for shell completions. He wanted to provide
    <kbd>Tab</kbd>-completion suggestions by running shell commands like
    git for-each-ref to list branches and tags.

    // Lucas's example: fetching Git branches and tags in parallel
    const [branches, tags] = await Promise.all([
      $`git for-each-ref --format='%(refname:short)' refs/heads/`.text(),
      $`git for-each-ref --format='%(refname:short)' refs/tags/`.text(),
    ]);
    

    At first, I didn't like the idea. Optique's entire API was synchronous, which
    made it simpler to reason about and avoided the “async infection” problem where
    one async function forces everything upstream to become async. I argued that
    shell completion should be near-instantaneous, and if you need async data, you
    should cache it at startup.

    But Lucas pushed back. The filesystem is a database, and many useful
    completions inherently require async work—Git refs change constantly, and
    pre-caching everything at startup doesn't scale for large repos. Fair point.

    What I needed to solve

    So, how do you support both sync and async execution modes in a composable
    parser library while maintaining type safety?

    The key requirements were:

    • parse() returns T or Promise<T>
    • complete() returns T or Promise<T>
    • suggest() returns Iterable<T> or AsyncIterable<T>
    • When combining parsers, if any parser is async, the combined result
      must be async
    • Existing sync code should continue to work unchanged

    The fourth requirement is the tricky one. Consider this:

    const syncParser = flag("--verbose");
    const asyncParser = option("--branch", asyncValueParser);
    
    // What's the type of this?
    const combined = object({ verbose: syncParser, branch: asyncParser });
    

    The combined parser should be async because one of its fields is async.
    This means we need type-level logic to compute the combined mode.

    Five design options

    I explored five different approaches, each with its own trade-offs.

    Option A: conditional types with mode parameter

    Add a mode type parameter to Parser and use conditional types:

    type Mode = "sync" | "async";
    
    type ModeValue<M extends Mode, T> = M extends "async" ? Promise<T> : T;
    
    interface Parser<M extends Mode, TValue, TState> {
      parse(context: ParserContext<TState>): ModeValue<M, ParserResult<TState>>;
      // ...
    }
    

    The challenge is computing combined modes:

    type CombineModes<T extends Record<string, Parser<any, any, any>>> =
      T[keyof T] extends Parser<infer M, any, any>
        ? M extends "async" ? "async" : "sync"
        : never;
    

    Option B: mode parameter with default value

    A variant of Option A, but place the mode parameter first with a default
    of "sync":

    interface Parser<M extends Mode = "sync", TValue, TState> {
      readonly $mode: M;
      // ...
    }
    

    The default value maintains backward compatibility—existing user code keeps
    working without changes.

    Option C: separate interfaces

    Define completely separate Parser and AsyncParser interfaces with
    explicit conversion:

    interface Parser<TValue, TState> { /* sync methods */ }
    interface AsyncParser<TValue, TState> { /* async methods */ }
    
    function toAsync<T, S>(parser: Parser<T, S>): AsyncParser<T, S>;
    

    Simpler to understand, but requires code duplication and explicit conversions.

    Option D: union return types for suggest() only

    The minimal approach. Only allow suggest() to be async:

    interface Parser<TValue, TState> {
      parse(context: ParserContext<TState>): ParserResult<TState>;  // always sync
      suggest(context: ParserContext<TState>, prefix: string):
        Iterable<Suggestion> | AsyncIterable<Suggestion>;  // can be either
    }
    

    This addresses the original use case but doesn't help if async parse() is
    ever needed.

    Option E: fp-ts style HKT simulation

    Use the technique from fp-ts to simulate Higher-Kinded Types:

    interface URItoKind<A> {
      Identity: A;
      Promise: Promise<A>;
    }
    
    type Kind<F extends keyof URItoKind<any>, A> = URItoKind<A>[F];
    
    interface Parser<F extends keyof URItoKind<any>, TValue, TState> {
      parse(context: ParserContext<TState>): Kind<F, ParserResult<TState>>;
    }
    

    The most flexible approach, but with a steep learning curve.

    Testing the idea

    Rather than commit to an approach based on theoretical analysis, I created
    a prototype to test how well TypeScript handles the type inference in practice.
    I published my findings in the GitHub issue:

    Both approaches correctly handle the “any async → all async” rule at the
    type level. (…) Complex conditional types like
    ModeValue<CombineParserModes<T>, ParserResult<TState>> sometimes require
    explicit type casting in the implementation. This only affects library
    internals. The user-facing API remains clean.

    The prototype validated that Option B (explicit mode parameter with default)
    would work. I chose it for these reasons:

    • Backward compatible: The default "sync" keeps existing code working
    • Explicit: The mode is visible in both types and runtime (via a $mode
      property)
    • Debuggable: Easy to inspect the current mode at runtime
    • Better IDE support: Type information is more predictable

    How CombineModes works

    The CombineModes type computes whether a combined parser should be sync or
    async:

    type CombineModes<T extends readonly Mode[]> = "async" extends T[number]
      ? "async"
      : "sync";
    

    This type checks if "async" is present anywhere in the tuple of modes.
    If so, the result is "async"; otherwise, it's "sync".

    For combinators like object(), I needed to extract modes from parser
    objects and combine them:

    // Extract the mode from a single parser
    type ParserMode<T> = T extends Parser<infer M, unknown, unknown> ? M : never;
    
    // Combine modes from all values in a record of parsers
    type CombineObjectModes<T extends Record<string, Parser<Mode, unknown, unknown>>> =
      CombineModes<{ [K in keyof T]: ParserMode<T[K]> }[keyof T][]>;
    

    Runtime implementation

    The type system handles compile-time safety, but the implementation also needs
    runtime logic. Each parser has a $mode property that indicates its execution
    mode:

    const syncParser = option("-n", "--name", string());
    console.log(syncParser.$mode);  // "sync"
    
    const asyncParser = option("-b", "--branch", asyncValueParser);
    console.log(asyncParser.$mode);  // "async"
    

    Combinators compute their mode at construction time:

    function object<T extends Record<string, Parser<Mode, unknown, unknown>>>(
      parsers: T
    ): Parser<CombineObjectModes<T>, ObjectValue<T>, ObjectState<T>> {
      const parserKeys = Reflect.ownKeys(parsers);
      const combinedMode: Mode = parserKeys.some(
        (k) => parsers[k as keyof T].$mode === "async"
      ) ? "async" : "sync";
    
      // ... implementation
    }
    

    Refining the API

    Lucas suggested an important refinement during our
    discussion. Instead of having run() automatically choose between sync and
    async based on the parser mode, he proposed separate functions:

    Perhaps run(…) could be automatic, and runSync(…) and runAsync(…) could
    enforce that the inferred type matches what is expected.

    So we ended up with:

    • run(): automatic based on parser mode
    • runSync(): enforces sync mode at compile time
    • runAsync(): enforces async mode at compile time
    // Automatic: returns T for sync parsers, Promise<T> for async
    const result1 = run(syncParser);  // string
    const result2 = run(asyncParser);  // Promise<string>
    
    // Explicit: compile-time enforcement
    const result3 = runSync(syncParser);  // string
    const result4 = runAsync(asyncParser);  // Promise<string>
    
    // Compile error: can't use runSync with async parser
    const result5 = runSync(asyncParser);  // Type error!
    

    I applied the same pattern to parse()/parseSync()/parseAsync() and
    suggest()/suggestSync()/suggestAsync() in the facade functions.

    Creating async value parsers

    With the new API, creating an async value parser for Git branches looks
    like this:

    import type { Suggestion } from "@optique/core/parser";
    import type { ValueParser, ValueParserResult } from "@optique/core/valueparser";
    
    function gitRef(): ValueParser<"async", string> {
      return {
        $mode: "async",
        metavar: "REF",
        parse(input: string): Promise<ValueParserResult<string>> {
          return Promise.resolve({ success: true, value: input });
        },
        format(value: string): string {
          return value;
        },
        async *suggest(prefix: string): AsyncIterable<Suggestion> {
          const { $ } = await import("bun");
          const [branches, tags] = await Promise.all([
            $`git for-each-ref --format='%(refname:short)' refs/heads/`.text(),
            $`git for-each-ref --format='%(refname:short)' refs/tags/`.text(),
          ]);
          for (const ref of [...branches.split("\n"), ...tags.split("\n")]) {
            const trimmed = ref.trim();
            if (trimmed && trimmed.startsWith(prefix)) {
              yield { kind: "literal", text: trimmed };
            }
          }
        },
      };
    }
    

    Notice that parse() returns Promise.resolve() even though it's synchronous.
    This is because the ValueParser<"async", T> type requires all methods to use
    async signatures. Lucas pointed out this is a minor ergonomic issue. If only
    suggest() needs to be async, you still have to wrap parse() in a Promise.

    I considered per-method mode granularity (e.g., ValueParser<ParseMode, SuggestMode, T>), but the implementation complexity would multiply
    substantially. For now, the workaround is simple enough:

    // Option 1: Use Promise.resolve()
    parse(input) {
      return Promise.resolve({ success: true, value: input });
    }
    
    // Option 2: Mark as async and suppress the linter
    // biome-ignore lint/suspicious/useAwait: sync implementation in async ValueParser
    async parse(input) {
      return { success: true, value: input };
    }
    

    What it cost

    Supporting dual modes added significant complexity to Optique's internals.
    Every combinator needed updates:

    • Type signatures grew more complex with mode parameters
    • Mode propagation logic had to be added to every combinator
    • Dual implementations were needed for sync and async code paths
    • Type casts were sometimes necessary in the implementation to satisfy
      TypeScript

    For example, the object() combinator went from around 100 lines to around
    250 lines. The internal implementation uses conditional logic based on the
    combined mode:

    if (combinedMode === "async") {
      return {
        $mode: "async" as M,
        // ... async implementation with Promise chains
        async parse(context) {
          // ... await each field's parse result
        },
      };
    } else {
      return {
        $mode: "sync" as M,
        // ... sync implementation
        parse(context) {
          // ... directly call each field's parse
        },
      };
    }
    

    This duplication is the cost of supporting both modes without runtime overhead
    for sync-only use cases.

    Lessons learned

    Listen to users, but validate with prototypes

    My initial instinct was to resist async support. Lucas's persistence and
    concrete examples changed my mind, but I validated the approach with a
    prototype before committing. The prototype revealed practical issues (like
    TypeScript inference limits) that pure design analysis would have missed.

    Backward compatibility is worth the complexity

    Making "sync" the default mode meant existing code continued to work
    unchanged. This was a deliberate choice. Breaking changes should require
    user action, not break silently.

    Unified mode vs per-method granularity

    I chose unified mode (all methods share the same sync/async mode) over
    per-method granularity. This means users occasionally write
    Promise.resolve() for methods that don't actually need async, but the
    alternative was multiplicative complexity in the type system.

    Designing in public

    The entire design process happened in a public GitHub issue. Lucas, Giuseppe,
    and others contributed ideas that shaped the final API. The
    runSync()/runAsync() distinction came directly from Lucas's feedback.

    Conclusion

    This was one of the more challenging features I've implemented in Optique.
    TypeScript's type system is powerful enough to encode the “any async means all
    async” rule at compile time, but getting there required careful design work and
    prototyping.

    What made it work: conditional types like ModeValue<M, T> can bridge the gap
    between sync and async worlds. You pay for it with implementation complexity,
    but the user-facing API stays clean and type-safe.

    Optique 0.9.0 with async support is currently in pre-release testing. If
    you'd like to try it, check out PR #70 or install the pre-release:

    npm  add       @optique/core@0.9.0-dev.212 @optique/run@0.9.0-dev.212
    deno add --jsr @optique/core@0.9.0-dev.212 @optique/run@0.9.0-dev.212
    

    Feedback is welcome!

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    We've all been there. You start a quick TypeScript CLI with process.argv.slice(2), add a couple of options, and before you know it you're drowning in if/else blocks and parseInt calls. It works, until it doesn't. In this guide, we'll move from manual argument parsing to a fully type-safe CLI with subcommands, mutually exclusive options, and shell completion. The naïve approach: parsing process.argv Let's start with the most basic approach. Say we want a greeting program that takes a name and optionally repeats the greeting: // greet.ts const args = process.argv.slice(2); let name: string | undefined; let count = 1; for (let i = 0; i < args.length; i++) { if (args[i] === "--name" || args[i] === "-n") { name = args[++i]; } else if (args[i] === "--count" || args[i] === "-c") { count = parseInt(args[++i], 10); } } if (!name) { console.error("Error: --name is required"); process.exit(1); } for (let i = 0; i < count; i++) { console.log(`Hello, ${name}!`); } Run node greet.js --name Alice --count 3 and you'll get three greetings. But this approach is fragile. count could be NaN if someone passes --count foo, and we'd silently proceed. There's no help text. If someone passes --name without a value, we'd read the next option as the name. And the boilerplate grows fast with each new option. The traditional libraries You've probably heard of Commander.js and Yargs. They've been around for years and solve the basic problems: // With Commander.js import { program } from "commander"; program .requiredOption("-n, --name <n>", "Name to greet") .option("-c, --count <number>", "Number of times to greet", "1") .parse(); const opts = program.opts(); These libraries handle help text, option parsing, and basic validation. But they were designed before TypeScript became mainstream, and the type safety is bolted on rather than built in. The real problem shows up when you need mutually exclusive options. Say your CLI works either in "server mode" (with --port and --host) or "client mode" (with --url). With these libraries, you end up with a config object where all options are potentially present, and you're left writing runtime checks to ensure the user didn't mix incompatible flags. TypeScript can't help you because the types don't reflect the actual constraints. Enter Optique Optique takes a different approach. Instead of configuring options declaratively, you build parsers by composing smaller parsers together. The types flow naturally from this composition, so TypeScript always knows exactly what shape your parsed result will have. Optique works across JavaScript runtimes: Node.js, Deno, and Bun are all supported. The core parsing logic has no runtime-specific dependencies, so you can even use it in browsers if you need to parse CLI-like arguments in a web context. Let's rebuild our greeting program: import { object } from "@optique/core/constructs"; import { option } from "@optique/core/primitives"; import { integer, string } from "@optique/core/valueparser"; import { withDefault } from "@optique/core/modifiers"; import { run } from "@optique/run"; const parser = object({ name: option("-n", "--name", string()), count: withDefault(option("-c", "--count", integer({ min: 1 })), 1), }); const config = run(parser); // config is typed as { name: string; count: number } for (let i = 0; i < config.count; i++) { console.log(`Hello, ${config.name}!`); } Types are inferred automatically. config.name is string, not string | undefined. config.count is number, guaranteed to be at least 1. Validation is built in: integer({ min: 1 }) rejects non-integers and values below 1 with clear error messages. Help text is generated automatically, and the run() function handles errors and exits with appropriate codes. Install it with your package manager of choice: npm add @optique/core @optique/run # or: pnpm add, yarn add, bun add, deno add jsr:@optique/core jsr:@optique/run Building up: a file converter Let's build something more realistic: a file converter that reads from an input file, converts to a specified format, and writes to an output file. import { object } from "@optique/core/constructs"; import { optional, withDefault } from "@optique/core/modifiers"; import { argument, option } from "@optique/core/primitives"; import { choice, string } from "@optique/core/valueparser"; import { run } from "@optique/run"; const parser = object({ input: argument(string({ metavar: "INPUT" })), output: option("-o", "--output", string({ metavar: "FILE" })), format: withDefault( option("-f", "--format", choice(["json", "yaml", "toml"])), "json" ), pretty: option("-p", "--pretty"), verbose: option("-v", "--verbose"), }); const config = run(parser, { help: "both", version: { mode: "both", value: "1.0.0" }, }); // config.input: string // config.output: string // config.format: "json" | "yaml" | "toml" // config.pretty: boolean // config.verbose: boolean The type of config.format isn't just string. It's the union "json" | "yaml" | "toml". TypeScript will catch typos like config.format === "josn" at compile time. The choice() parser is useful for any option with a fixed set of valid values: log levels, output formats, environment names, and so on. You get both runtime validation (invalid values are rejected with helpful error messages) and compile-time checking (TypeScript knows the exact set of possible values). Mutually exclusive options Now let's tackle the case that trips up most CLI libraries: mutually exclusive options. Say our tool can either run as a server or connect as a client, but not both: import { object, or } from "@optique/core/constructs"; import { withDefault } from "@optique/core/modifiers"; import { argument, constant, option } from "@optique/core/primitives"; import { integer, string, url } from "@optique/core/valueparser"; import { run } from "@optique/run"; const parser = or( // Server mode object({ mode: constant("server"), port: option("-p", "--port", integer({ min: 1, max: 65535 })), host: withDefault(option("-h", "--host", string()), "0.0.0.0"), }), // Client mode object({ mode: constant("client"), url: argument(url()), }), ); const config = run(parser); The or() combinator tries each alternative in order. The first one that successfully parses wins. The constant() parser adds a literal value to the result without consuming any input, which serves as a discriminator. TypeScript infers a discriminated union: type Config = | { mode: "server"; port: number; host: string } | { mode: "client"; url: URL }; Now you can write type-safe code that handles each mode: if (config.mode === "server") { console.log(`Starting server on ${config.host}:${config.port}`); } else { console.log(`Connecting to ${config.url.hostname}`); } Try accessing config.url in the server branch. TypeScript won't let you. The compiler knows that when mode is "server", only port and host exist. This is the key difference from configuration-based libraries. With Commander or Yargs, you'd get a type like { port?: number; host?: string; url?: string } and have to check at runtime which combination of fields is actually present. With Optique, the types match the actual constraints of your CLI. Subcommands For larger tools, you'll want subcommands. Optique handles this with the command() parser: import { object, or } from "@optique/core/constructs"; import { optional } from "@optique/core/modifiers"; import { argument, command, constant, option } from "@optique/core/primitives"; import { string } from "@optique/core/valueparser"; import { run } from "@optique/run"; const parser = or( command("add", object({ action: constant("add"), key: argument(string({ metavar: "KEY" })), value: argument(string({ metavar: "VALUE" })), })), command("remove", object({ action: constant("remove"), key: argument(string({ metavar: "KEY" })), })), command("list", object({ action: constant("list"), pattern: optional(option("-p", "--pattern", string())), })), ); const result = run(parser, { help: "both" }); switch (result.action) { case "add": console.log(`Adding ${result.key}=${result.value}`); break; case "remove": console.log(`Removing ${result.key}`); break; case "list": console.log(`Listing${result.pattern ? ` (filter: ${result.pattern})` : ""}`); break; } Each subcommand gets its own help text. Run myapp add --help and you'll see only the options relevant to add. Run myapp --help and you'll see a summary of all available commands. The pattern here is the same as mutually exclusive options: or() to combine alternatives, constant() to add a discriminator. This consistency is one of Optique's strengths. Once you understand the basic combinators, you can build arbitrarily complex CLI structures by composing them. Shell completion Optique has built-in shell completion for Bash, zsh, fish, PowerShell, and Nushell. Enable it by passing completion: "both" to run(): const config = run(parser, { help: "both", version: { mode: "both", value: "1.0.0" }, completion: "both", }); Users can then generate completion scripts: $ myapp --completion bash >> ~/.bashrc $ myapp --completion zsh >> ~/.zshrc $ myapp --completion fish > ~/.config/fish/completions/myapp.fish The completions are context-aware. They know about your subcommands, option values, and choice() alternatives. Type myapp --format <TAB> and you'll see json, yaml, toml as suggestions. Type myapp a<TAB> and it'll complete to myapp add. Completion support is often an afterthought in CLI tools, but it makes a real difference in user experience. With Optique, you get it essentially for free. Integrating with validation libraries Already using Zod for validation in your project? The @optique/zod package lets you reuse those schemas as CLI value parsers: import { z } from "zod"; import { zod } from "@optique/zod"; import { option } from "@optique/core/primitives"; const email = option("--email", zod(z.string().email())); const port = option("--port", zod(z.coerce.number().int().min(1).max(65535))); Your existing validation logic just works. The Zod error messages are passed through to the user, so you get the same helpful feedback you're used to. Prefer Valibot? The @optique/valibot package works the same way: import * as v from "valibot"; import { valibot } from "@optique/valibot"; import { option } from "@optique/core/primitives"; const email = option("--email", valibot(v.pipe(v.string(), v.email()))); Valibot's bundle size is significantly smaller than Zod's (~10KB vs ~52KB), which can matter for CLI tools where startup time is noticeable. Tips A few things I've learned building CLIs with Optique: Start simple. Begin with object() and basic options. Add or() for mutually exclusive groups only when you need them. It's easy to over-engineer CLI parsers. Use descriptive metavars. Instead of string(), write string({ metavar: "FILE" }) or string({ metavar: "URL" }). The metavar appears in help text and error messages, so it's worth the extra few characters. Leverage withDefault(). It's better than making options optional and checking for undefined everywhere. Your code becomes cleaner when you can assume values are always present. Test your parser. Optique's core parsing functions work without process.argv, so you can unit test your parser logic: import { parse } from "@optique/core/parser"; const result = parse(parser, ["--name", "Alice", "--count", "3"]); if (result.success) { assert.equal(result.value.name, "Alice"); assert.equal(result.value.count, 3); } This is especially valuable for complex parsers with many edge cases. Going further We've covered the fundamentals, but Optique has more to offer: Async value parsers for validating against external sources, like checking if a Git branch exists or if a URL is reachable Path validation with path() for checking file existence, directory structure, and file extensions Custom value parsers for domain-specific types (though Zod/Valibot integration is usually easier) Reusable option groups with merge() for sharing common options across subcommands The @optique/temporal package for parsing dates and times using the Temporal API Check out the documentation for the full picture. The tutorial walks through the concepts in more depth, and the cookbook has patterns for common scenarios. That's it Building CLIs in TypeScript doesn't have to mean fighting with types or writing endless runtime validation. Optique lets you express constraints in a way that TypeScript actually understands, so the compiler catches mistakes before they reach production. The source is on GitHub, and packages are available on both npm and JSR. Questions or feedback? Find me on the fediverse or open an issue on the GitHub repo.
  • #Optique 0.9.0 is here!

    Uncategorized cli async optique typescript
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    #Optique 0.9.0 is here! This release brings #async/await support to #CLI parsers. Now you can validate input against external resources—databases, APIs, Git repositories—directly at parse time, with full #TypeScript type safety. The new @optique/git package showcases this: validate branch names, tags, and commit SHAs against an actual Git repo, complete with shell completion suggestions. Other highlights: Hidden option support for deprecated/internal flags Numeric choices in choice() Security fix for shell completion scripts Fully backward compatible—your existing parsers work unchanged. https://github.com/dahlia/optique/discussions/75
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    When I started building Fedify, an ActivityPub server framework, I ran into a problem that surprised me: I couldn't figure out how to add logging. Not because logging is hard—there are dozens of mature logging libraries for JavaScript. The problem was that they're primarily designed for applications, not for libraries that want to stay unobtrusive. I wrote about this a few months ago, and the response was modest—some interest, some skepticism, and quite a bit of debate about whether the post was AI-generated. I'll be honest: English isn't my first language, so I use LLMs to polish my writing. But the ideas and technical content are mine. Several readers wanted to see a real-world example rather than theory. The problem: existing loggers assume you're building an app Fedify helps developers build federated social applications using the ActivityPub protocol. If you've ever worked with federation, you know debugging can be painful. When an activity fails to deliver, you need to answer questions like: Did the HTTP request actually go out? Was the signature generated correctly? Did the remote server reject it? Why? Was there a problem parsing the response? These questions span multiple subsystems: HTTP handling, cryptographic signatures, JSON-LD processing, queue management, and more. Without good logging, debugging turns into guesswork. But here's the dilemma I faced as a library author: if I add verbose logging to help with debugging, I risk annoying users who don't want their console cluttered with Fedify's internal chatter. If I stay silent, users struggle to diagnose issues. I looked at the existing options. With winston or Pino, I would have to either: Configure a logger inside Fedify (imposing my choices on users), or Ask users to pass a logger instance to Fedify (adding boilerplate) There's also debug, which is designed for this use case. But it doesn't give you structured, level-based logs that ops teams expect—and it relies on environment variables, which some runtimes like Deno restrict by default for security reasons. None of these felt right. So I built LogTape—a logging library designed from the ground up for library authors. And Fedify became its first real user. The solution: hierarchical categories with zero default output The key insight was simple: a library should be able to log without producing any output unless the application developer explicitly enables it. Fedify uses LogTape's hierarchical category system to give users fine-grained control over what they see. Here's how the categories are organized: Category What it logs ["fedify"] Everything from the library ["fedify", "federation", "inbox"] Incoming activities ["fedify", "federation", "outbox"] Outgoing activities ["fedify", "federation", "http"] HTTP requests and responses ["fedify", "sig", "http"] HTTP Signature operations ["fedify", "sig", "ld"] Linked Data Signature operations ["fedify", "sig", "key"] Key generation and retrieval ["fedify", "runtime", "docloader"] JSON-LD document loading ["fedify", "webfinger", "lookup"] WebFinger resource lookups …and about a dozen more. Each category corresponds to a distinct subsystem. This means a user can configure logging like this: await configure({ sinks: { console: getConsoleSink() }, loggers: [ // Show errors from all of Fedify { category: "fedify", sinks: ["console"], lowestLevel: "error" }, // But show debug info for inbox processing specifically { category: ["fedify", "federation", "inbox"], sinks: ["console"], lowestLevel: "debug" }, ], }); When something goes wrong with incoming activities, they get detailed logs for that subsystem while keeping everything else quiet. No code changes required—just configuration. Request tracing with implicit contexts The hierarchical categories solved the filtering problem, but there was another challenge: correlating logs across async boundaries. In a federated system, a single user action might trigger a cascade of operations: fetch a remote actor, verify their signature, process the activity, fan out to followers, and so on. When something fails, you need to correlate all the log entries for that specific request. Fedify uses LogTape's implicit context feature to automatically tag every log entry with a requestId: await configure({ sinks: { file: getFileSink("fedify.jsonl", { formatter: jsonLinesFormatter }) }, loggers: [ { category: "fedify", sinks: ["file"], lowestLevel: "info" }, ], contextLocalStorage: new AsyncLocalStorage(), // Enables implicit contexts }); With this configuration, every log entry automatically includes a requestId property. When you need to debug a specific request, you can filter your logs: jq 'select(.properties.requestId == "abc-123")' fedify.jsonl And you'll see every log entry from that request—across all subsystems, all in order. No manual correlation needed. The requestId is derived from standard headers when available (X-Request-Id, Traceparent, etc.), so it integrates naturally with existing observability infrastructure. What users actually see So what does all this configuration actually mean for someone using Fedify? If a Fedify user doesn't configure LogTape at all, they see nothing. No warnings about missing configuration, no default output, and minimal performance overhead—the logging calls are essentially no-ops. For basic visibility, they can enable error-level logging for all of Fedify with three lines of configuration. When debugging a specific issue, they can enable debug-level logging for just the relevant subsystem. And if they're running in production with serious observability requirements, they can pipe structured JSON logs to their monitoring system with request correlation built in. The same library code supports all these scenarios—whether the user is running on Node.js, Deno, Bun, or edge functions, without extra polyfills or shims. The user decides what they need. Lessons learned Building Fedify with LogTape taught me a few things: Design your categories early. The hierarchical structure should reflect how users will actually want to filter logs. I organized Fedify's categories around subsystems that users might need to debug independently. Use structured logging. Properties like requestId, activityId, and actorId are far more useful than string interpolation when you need to analyze logs programmatically. Implicit contexts turned out to be more useful than I expected. Being able to correlate logs across async boundaries without passing context manually made debugging distributed operations much easier. When a user reports that activity delivery failed, I can give them a single jq command to extract everything relevant. Trust your users. Some library authors worry about exposing too much internal detail through logs. I've found the opposite—users appreciate being able to see what's happening when they need to. The key is making it opt-in. Try it yourself If you're building a library and struggling with the logging question—how much to log, how to give users control, how to avoid being noisy—I'd encourage you to look at how Fedify does it. The Fedify logging documentation explains everything in detail. And if you want to understand the philosophy behind LogTape's design, my earlier post covers that. LogTape isn't trying to replace winston or Pino for application developers who are happy with those tools. It fills a different gap: logging for libraries that want to stay out of the way until users need them. If that's what you're looking for, it might be a better fit than the usual app-centric loggers.