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+---
+title: 'One Flag, Every Chunk: Debug Logging Lands in TanStack AI'
+published: 2026-04-22
+excerpt: "Your AI pipeline is a black box: a missing chunk, a middleware that doesn't fire, a tool call with mystery args. TanStack AI now ships pluggable, category-toggleable debug logging across every activity and adapter. Flip one flag and the pipeline prints itself."
+authors:
+  - Alem Tuzlak
+---
+
+![Debug Logging for TanStack AI](/blog-assets/debug-logging-for-tanstack-ai/header.png)
+
+You kick off a `chat()` call. A chunk goes missing. A middleware you wrote last week doesn't seem to fire. A tool gets called with arguments you can't explain. Your stream finishes, the UI looks wrong, and you have no idea which layer lied to you.
+
+Up until now your options were limited. You could wrap the SDK in a tracing platform, spend a day wiring OpenTelemetry, or sprinkle `console.log` into your own code and hope the problem lives where you can see it. Neither helps when the bug is **inside** the pipeline: a raw provider chunk that got dropped, a middleware that mutated config, a tool call the agent loop reissued.
+
+TanStack AI now has a built-in answer. **Flip one flag and the entire pipeline prints itself.**
+
+## Turn it on
+
+Add `debug: true` to any activity call:
+
+```typescript
+import { chat } from '@tanstack/ai'
+import { openaiText } from '@tanstack/ai-openai/adapters'
+
+const stream = chat({
+  adapter: openaiText(),
+  model: 'gpt-4o',
+  messages: [{ role: 'user', content: 'Hello' }],
+  debug: true,
+})
+```
+
+Every internal event now prints to the console, prefixed with an emoji-tagged category so you can scan dense streaming logs without squinting:
+
+```
+📤 [tanstack-ai:request] 📤 activity=chat provider=openai model=gpt-4o messages=1 tools=0 stream=true
+🔁 [tanstack-ai:agentLoop] 🔁 run started
+📥 [tanstack-ai:provider] 📥 provider=openai type=response.output_text.delta
+📨 [tanstack-ai:output] 📨 type=TEXT_MESSAGE_CONTENT
+🧩 [tanstack-ai:middleware] 🧩 hook=onOutput
+🔧 [tanstack-ai:tools] 🔧 tool=getTodos phase=before
+```
+
+That is the whole setup. No exporters, no sidecar, no dashboard. Just what your pipeline is actually doing, right now, in the terminal you already have open.
+
+## Eight categories, not a log level
+
+Most logging libraries give you `debug`, `info`, `warn`, `error` and ask you to pick one. That mapping is wrong for an AI pipeline. **The noise isn't a severity, it's a source.** When you're chasing a tool bug you don't want provider chunks. When you're chasing a provider bug you don't want middleware chatter.
+
+So `debug` accepts a config object where every category toggles independently:
+
+```typescript
+chat({
+  adapter: openaiText(),
+  model: 'gpt-4o',
+  messages,
+  debug: { middleware: false }, // everything except middleware
+})
+```
+
+Omitted categories default to `true`, so the common case is "turn off the one thing that's drowning you." Every category maps to a real pipeline surface:
+
+| Category     | What it logs                                                   |
+| ------------ | -------------------------------------------------------------- |
+| `request`    | Outgoing call to a provider (model, message count, tool count) |
+| `provider`   | Every raw chunk or frame from the provider SDK                 |
+| `output`     | Every chunk or result yielded to the caller                    |
+| `middleware` | Inputs and outputs around every middleware hook                |
+| `tools`      | Before and after tool call execution                           |
+| `agentLoop`  | Agent-loop iterations and phase transitions                    |
+| `config`     | Config transforms returned by middleware `onConfig` hooks      |
+| `errors`     | Every caught error anywhere in the pipeline                    |
+
+Chat-only categories like `tools` and `agentLoop` just don't fire for `summarize()` or `generateImage()`, because they don't exist in those pipelines. You don't have to think about it.
+
+## Pipe it anywhere
+
+`console.log` is the right default for local work. It is the wrong default for production, where you want structured JSON going to a log shipper, not ANSI colors going to stdout.
+
+Pass your own `Logger` and the entire category system routes through it:
+
+```typescript
+import type { Logger } from '@tanstack/ai'
+import pino from 'pino'
+
+const pinoLogger = pino()
+const logger: Logger = {
+  debug: (msg, meta) => pinoLogger.debug(meta, msg),
+  info: (msg, meta) => pinoLogger.info(meta, msg),
+  warn: (msg, meta) => pinoLogger.warn(meta, msg),
+  error: (msg, meta) => pinoLogger.error(meta, msg),
+}
+
+chat({
+  adapter: openaiText(),
+  model: 'gpt-4o',
+  messages,
+  debug: { logger },
+})
+```
+
+The `Logger` interface is four methods. Anything that writes a line of text fits. Pino, winston, bunyan, a `fetch` to a logging service, a no-op that forwards to your existing observability layer. All valid.
+
+### Your logger can't break the pipeline
+
+This is the detail we lost sleep over. If your custom logger throws (a cyclic-meta `JSON.stringify`, a transport that rejects synchronously, a typo in a bound `this`), the exception should **not** bubble up and mask the real error that triggered the log call in the first place.
+
+Internally, every user-logger invocation is wrapped in a try/catch. A broken logger silently drops the log line. Your actual pipeline error still reaches you through thrown exceptions and `RUN_ERROR` chunks, exactly where you were looking for it.
+
+If you need to know when your own logger is failing, guard inside your implementation. The SDK will not guess how loud you want logger failures to be.
+
+## Every activity, every provider
+
+Debug logging isn't a chat-only feature. Every activity in TanStack AI accepts the same option:
+
+```typescript
+summarize({ adapter, text, debug: true })
+generateImage({ adapter, prompt: 'a cat', debug: { logger } })
+generateSpeech({ adapter, text, debug: { request: true } })
+generateTranscription({ adapter, audio, debug: true })
+generateVideo({ adapter, prompt, debug: true })
+```
+
+Realtime session adapters (`openaiRealtime`, `elevenlabsRealtime`) take it too.
+
+On the provider side, **every adapter in every provider package is wired through the structured logger**: OpenAI, Anthropic, Gemini, Grok, Groq, Ollama, OpenRouter, fal, and ElevenLabs. 25 adapters total. Zero ad-hoc `console.*` calls remain in adapter source code. Whether you're debugging an Anthropic text stream or an ElevenLabs realtime session, the output shape is the same.
+
+## The small decisions that add up
+
+A few calls that look cosmetic but matter once you're staring at a thousand-line log.
+
+**Emoji prefixes on both sides of the tag.** `📨 [tanstack-ai:output] 📨 ...` reads faster than raw brackets in a dense stream, and your eye can hop categories without parsing text.
+
+**`console.dir` with `depth: null`.** Node's default console formatting stops at depth 2, so nested provider chunks render as `[Object]` and you lose the thing you were trying to see. Debug logs surface the entire structure. In browsers, the raw object still lands in DevTools for interactive inspection.
+
+**Errors log unconditionally.** You don't have to remember to turn them on. If you really want total silence, `debug: false` or `debug: { errors: false }` does it. Otherwise errors flow through even when you haven't asked for any other category.
+
+**Internal devtools middleware is muted.** If you have the TanStack AI devtools middleware installed, its own hooks don't flood the `middleware` category. You see the middleware **you** wrote, not the plumbing.
+
+Each of these is a small call on its own. Together they're the difference between "debug output I actually read" and "debug output I pipe to `/dev/null` within ten seconds."
+
+## Getting it
+
+Debug logging ships in the latest `@tanstack/ai`. It's additive, backward-compatible, and available on every activity today. No config file, no exporter, no platform.
+
+Upgrade, add `debug: true` to the call you can't explain, and read the output.
+
+For the full reference, see the [Debug Logging guide](https://tanstack.com/ai/latest/docs/advanced/debug-logging). And if you want an even faster turnaround: TanStack AI ships an agent skill under `packages/typescript/ai/skills/` so your LLM-powered dev tools can discover the flag on their own.
+
+One flag. Every chunk. Your streams are no longer a black box.