ai-core/debug-logging

Debug Logging

Dependency note: This skill builds on ai-core. Read it first for critical rules.

Use this skill when you need to turn debug logging on or off, narrow what's printed, or pipe logs into a custom logger (pino, winston, etc.). The same debug option works on every activity — chat(), summarize(), generateImage(), generateSpeech(), generateTranscription(), generateVideo().

Turn it on

import { chat } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'

const stream = chat({
  adapter: openaiText('gpt-5.2'),
  messages,
  debug: true, // all categories on, prints to console
})

Each log line is prefixed with an emoji and [tanstack-ai:<category>]:

📤 [tanstack-ai:request] 📤 activity=chat provider=openai model=gpt-5.2 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

Turn it off

chat({
  adapter: openaiText('gpt-5.2'),
  messages,
  debug: false, // silence everything, including errors
})

Omitting debug is not the same as debug: false. When omitted, the errors category is still on (errors are cheap and important). Use debug: false or debug: { errors: false } for true silence.

DebugOption — the accepted shapes

type DebugOption = boolean | DebugConfig

interface DebugConfig {
  // Per-category flags. Any flag omitted from a DebugConfig defaults to true.
  request?: boolean
  provider?: boolean
  output?: boolean
  middleware?: boolean
  tools?: boolean
  agentLoop?: boolean
  config?: boolean
  errors?: boolean
  // Optional custom logger. Defaults to ConsoleLogger.
  logger?: Logger
}

Resolution rules for the debug?: DebugOption field on every activity:

Narrow what's printed

Pass a DebugConfig object. Unspecified categories default to true, so it's easiest to toggle by setting specific flags to false:

chat({
  adapter: openaiText('gpt-5.2'),
  messages,
  debug: { middleware: false }, // everything except middleware
})

To print only a specific set, set the rest to false explicitly:

chat({
  adapter: openaiText('gpt-5.2'),
  messages,
  debug: {
    provider: true,
    output: true,
    middleware: false,
    tools: false,
    agentLoop: false,
    config: false,
    errors: true, // keep errors on — they're cheap and important
    request: false,
  },
})

Pipe into your own logger

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('gpt-5.2'),
  messages,
  debug: { logger }, // all categories on, piped to pino
})

The default console logger is exported as ConsoleLogger if you want to wrap it:

import { ConsoleLogger } from '@tanstack/ai'

Categories

Chat-only categories simply never fire for non-chat activities — those concepts don't exist in their pipelines.

Non-chat activities

Same debug option everywhere:

summarize({ adapter, text, debug: true })
generateImage({ adapter, prompt: 'a cat', debug: { logger } })
generateSpeech({ adapter, text, debug: { request: true } })
generateTranscription({ adapter, audio, debug: false })
generateVideo({ adapter, prompt: 'a wave', debug: { output: true } })

Realtime session adapters in provider packages (e.g. openaiRealtime, elevenlabsRealtime) accept the same debug?: DebugOption on their session options. They emit request, provider, and errors lines; the chat-only categories don't apply.

Common Mistakes

a. HIGH: Treating omitted debug as silent

// WRONG — expecting this to be completely silent
chat({ adapter, messages })
// Errors still print via [tanstack-ai:errors] ... on failure.

// CORRECT — explicit silence
chat({ adapter, messages, debug: false })
chat({ adapter, messages, debug: { errors: false } })

debug undefined means "only errors"; debug: false means "nothing at all".

Source: docs/advanced/debug-logging.md

b. MEDIUM: Reaching for middleware when debug would do

// WRONG — writing logging middleware to see chunks flow
const chunkLogger: ChatMiddleware = {
  name: 'chunk-logger',
  onChunk: (ctx, chunk) => {
    console.log(chunk.type, chunk)
  },
}
chat({ adapter, messages, middleware: [chunkLogger] })

// CORRECT — just turn on the relevant categories
chat({
  adapter,
  messages,
  debug: { provider: true, output: true },
})

For observing the built-in pipeline, the debug option is strictly faster than writing logging middleware. Reach for middleware when you need to transform chunks, not just see them.

Source: docs/advanced/debug-logging.md

c. LOW: Logger implementation that can throw

A user-supplied Logger that throws will have its exception swallowed by the SDK so it never masks the real error that triggered the log call. Still, prefer implementations that don't throw — silenced exceptions are harder to debug than loud ones.

// WRONG — a logger that can throw on serialization
const fragile: Logger = {
  debug: (msg, meta) => console.debug(msg, JSON.stringify(meta)), // cyclic meta → throws
  /* ... */
}

// CORRECT — guard serialization in the logger itself
const safe: Logger = {
  debug: (msg, meta) => {
    try {
      console.debug(msg, meta)
    } catch {
      console.debug(msg)
    }
  },
  /* ... */
}

Source: packages/ai/src/logger/internal-logger.ts

Cross-References

• See also: ai-core/middleware/SKILL.md — if you need to transform chunks/config, not just observe them.
• See also: Observability (docs/advanced/observability.md) — the programmatic event client for a richer, structured feed beyond log lines.