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title Logging
id logging
slug /logging
description Logging is crucial for monitoring and debugging LLM applications during development as well as in production. Haystack provides different logging solutions out of the box to get you started quickly, depending on your use case.

import ClickableImage from "@site/src/components/ClickableImage";

Logging

Logging is crucial for monitoring and debugging LLM applications during development as well as in production. Haystack provides different logging solutions out of the box to get you started quickly, depending on your use case.

Standard Library Logging (default)

Haystack logs through Python’s standard library. This gives you full flexibility and customizability to adjust the log format according to your needs.

Changing the Log Level

By default, Haystack's logging level is set to WARNING. To display more information, you can change it to INFO. This way, not only warnings but also information messages are displayed in the console output.

To change the logging level to INFO, run:

import logging

logging.basicConfig(
    format="%(levelname)s - %(name)s -  %(message)s",
    level=logging.WARNING,
)
logging.getLogger("haystack").setLevel(logging.INFO)

Further Configuration

See Python’s documentation on logging for more advanced configuration.

Real-Time Pipeline Logging

Use Haystack's LoggingTracer logs to inspect the data that's flowing through your pipeline in real-time.

This feature is particularly helpful during experimentation and prototyping, as you don’t need to set up any tracing backend beforehand.

Here’s how you can enable this tracer. In this example, we are adding color tags (this is optional) to highlight the components' names and inputs:

import logging
from haystack import tracing
from haystack.tracing.logging_tracer import LoggingTracer

logging.basicConfig(
    format="%(levelname)s - %(name)s -  %(message)s",
    level=logging.WARNING,
)
logging.getLogger("haystack").setLevel(logging.DEBUG)

tracing.tracer.is_content_tracing_enabled = (
    True  # to enable tracing/logging content (inputs/outputs)
)
tracing.enable_tracing(
    LoggingTracer(
        tags_color_strings={
            "haystack.component.input": "\x1b[1;31m",
            "haystack.component.name": "\x1b[1;34m",
        },
    ),
)

Here’s what the resulting log would look like when a pipeline is run:

Structured Logging

Haystack leverages the structlog library to provide structured key-value logs. This provides additional metadata with each log message and is especially useful if you archive your logs with tools like ELK, Grafana, or Datadog.

If Haystack detects a structlog installation on your system, it installs a structlog-based formatting handler on import - but only for Haystack's own logger namespaces (haystack, haystack_integrations, and haystack_experimental). The root logger and the process-global structlog configuration are left untouched, so importing Haystack does not change how your application or other libraries log.

Scoping and Duplicate Log Lines

You can adjust this behavior with an explicit configure_logging call:

  • configure_logging(logger_name="") attaches the formatting handler to the root logger instead, restoring the legacy behavior of formatting every log record in the process.
  • configure_logging(propagate=False) stops Haystack's log records from propagating to ancestor loggers. Use this to avoid duplicate log lines when your application also configures a handler on the root logger.
from haystack.logging import configure_logging

# Format all log records in the process (legacy behavior)
configure_logging(logger_name="")

# Avoid duplicate log lines when the host app configures the root logger
configure_logging(propagate=False)

Console Rendering

To make development a more pleasurable experience, Haystack uses structlog’s ConsoleRender by default to render structured logs as a nicely aligned and colorful output:

:::tip[Rich Formatting]

Install rich to beautify your logs even more! :::

JSON Rendering

We recommend JSON logging when deploying Haystack to production. Haystack will automatically switch to JSON format if it detects no interactive terminal session. If you want to enforce JSON logging:

  • Run Haystack with the environment variable HAYSTACK_LOGGING_USE_JSON set to true.

  • Or, use Python to tell Haystack to log as JSON:

    import haystack.logging

haystack.logging.configure_logging(use_json=True)

Disabling Structured Logging

To disable structured logging despite an existing installation of structlog, set the environment variable HAYSTACK_LOGGING_IGNORE_STRUCTLOG to true when running Haystack.