Analysing Eval Log Files

This skill covers how to view and analyse Inspect evaluation log files using the Python API and CLI commands.

Quick Reference

CLI Commands

# List all logs in the default log directory (./logs or INSPECT_LOG_DIR)
uv run inspect log list --json

# List logs with specific status
uv run inspect log list --json --status success
uv run inspect log list --json --status error

# List retryable logs (error/cancelled without subsequent success)
uv run inspect log list --json --retryable

# Dump a log file as JSON (works with any format: .eval or .json)
uv run inspect log dump <log_file_path>

# Convert between log formats
uv run inspect log convert source.json --to eval --output-dir log-output
uv run inspect log convert logs/ --to eval --output-dir logs-eval

# Get JSON schema for log files
uv run inspect log schema

Interactive Log Viewer

# Start the interactive log viewer (updates automatically)
uv run inspect view

Python API

Key Imports

from inspect_ai.log import (
    # Listing and reading
    list_eval_logs,
    read_eval_log,
    read_eval_log_sample,
    read_eval_log_samples,
    read_eval_log_sample_summaries,
    
    # Writing
    write_eval_log,
    
    # Utilities
    retryable_eval_logs,
    recompute_metrics,
    resolve_sample_attachments,
    
    # Types
    EvalLog,
    EvalLogInfo,
    EvalSample,
    EvalSampleSummary,
)

Listing Logs

# List all logs in default directory
logs = list_eval_logs()

# List logs in a specific directory
logs = list_eval_logs(log_dir="./experiment-logs")

# Filter by format
logs = list_eval_logs(formats=["eval"])  # Only .eval files

# Filter with a custom function (receives header-only EvalLog)
logs = list_eval_logs(filter=lambda log: log.status == "success")

# Non-recursive listing
logs = list_eval_logs(recursive=False)

Reading Logs

# Read full log
log = read_eval_log("path/to/logfile.eval")

# Read header only (fast, excludes samples)
log = read_eval_log("path/to/logfile.eval", header_only=True)

# Read with attachments resolved
log = read_eval_log("path/to/logfile.eval", resolve_attachments=True)

Reading Samples

# Read a single sample by ID and epoch
sample = read_eval_log_sample("path/to/logfile.eval", id=42, epoch=1)

# Read a single sample by UUID
sample = read_eval_log_sample("path/to/logfile.eval", uuid="sample-uuid")

# Stream all samples (memory efficient - one at a time)
for sample in read_eval_log_samples("path/to/logfile.eval"):
    process(sample)

# Stream samples from incomplete logs
for sample in read_eval_log_samples("path/to/logfile.eval", all_samples_required=False):
    process(sample)

# Read sample summaries (fast, includes scoring info)
summaries = read_eval_log_sample_summaries("path/to/logfile.eval")

Filtering Samples

# Read only samples with errors using summaries for filtering
errors = []
for summary in read_eval_log_sample_summaries(log_file):
    if summary.error is not None:
        errors.append(
            read_eval_log_sample(log_file, summary.id, summary.epoch)
        )

EvalLog Structure

The EvalLog object contains:

Always Check Status

log = read_eval_log("path/to/logfile.eval")
if log.status == "success":
    # Safe to analyse results
    for score in log.results.scores:
        print(f"{score.name}: {score.metrics}")

EvalSample Structure

Each sample contains:

Common Analysis Patterns

Get Aggregate Metrics

log = read_eval_log(log_file, header_only=True)
if log.results:
    for score in log.results.scores:
        print(f"Scorer: {score.name}")
        for metric_name, metric in score.metrics.items():
            print(f"  {metric_name}: {metric.value}")

Analyse Failed Samples

log = read_eval_log(log_file)
if log.samples:
    failed = [s for s in log.samples if s.error is not None]
    for sample in failed:
        print(f"Sample {sample.id}: {sample.error.message}")

Extract Model Usage

log = read_eval_log(log_file, header_only=True)
for model, usage in log.stats.model_usage.items():
    print(f"{model}: {usage.input_tokens} in, {usage.output_tokens} out")

Compare Multiple Runs

logs = list_eval_logs(filter=lambda l: l.eval.task == "my_task")
for log_info in logs:
    log = read_eval_log(log_info, header_only=True)
    if log.results and log.results.scores:
        accuracy = log.results.scores[0].metrics.get("accuracy")
        print(f"{log.eval.model}: {accuracy.value if accuracy else 'N/A'}")

Find Retryable Logs

all_logs = list_eval_logs()
retryable = retryable_eval_logs(all_logs)
for log_info in retryable:
    print(f"Can retry: {log_info.name}")

Log File Formats

Both formats are fully supported by the API and can be intermixed.

Working with Large Logs

For logs too large to fit in memory:

1. Use .eval format - supports compression and incremental access
2. Read header only - read_eval_log(log_file, header_only=True)
3. Stream samples - read_eval_log_samples() yields one at a time
4. Use summaries - read_eval_log_sample_summaries() for quick overview

Modifying Logs

# Read, modify, and write back
log = read_eval_log(log_file)
# ... modify log ...
write_eval_log(log)  # Uses log.location automatically

# Write to a new location
write_eval_log(log, location="new_path.eval")

# Recompute metrics after score edits
recompute_metrics(log)
write_eval_log(log)

Environment Variables

Related Tools

• Inspect Scout - Transcript analysis
• Inspect Viz - Data visualization
• Log Dataframes - Extract pandas DataFrames from logs (see inspect_ai.analysis)