Read Inspect AI Evaluation Files

This skill provides instructions for reading and analyzing Inspect AI evaluation log files using the Python API.

When to Use

Use this skill when the user needs to:

• Read evaluation results from .eval log files
• Extract samples, messages, or events from evaluations
• Analyze model performance metrics and scores
• Convert eval data to dataframes for analysis
• Debug evaluation failures or errors
• Access specific samples by task or epoch

Prerequisites

Requires the inspect_ai package:

pip install inspect-ai

Core Functions

The inspect_ai.log module provides these primary functions:

Reading Eval Logs

Basic Usage

from inspect_ai import log

# List available logs
logs = log.list_eval_logs("./logs")

# Read a specific log
eval_log = log.read_eval_log("path/to/file.eval")

# Check status
print(f"Status: {eval_log.status}")  # "success", "error", or "started"
print(f"Results: {eval_log.results}")

Read Options

# Read only header/metadata (fast, no sample data)
eval_log = log.read_eval_log("file.eval", header_only=True)

# Resolve de-duplicated attachments
eval_log = log.read_eval_log("file.eval", resolve_attachments=True)

# Require successful completion
eval_log = log.read_eval_log("file.eval", all_samples_required=True)

Iterating Samples

For large files, iterate samples without loading all into memory:

from inspect_ai import log

# Read samples as generator (memory efficient)
for sample in log.read_eval_log_samples("file.eval"):
    print(f"Sample {sample.id}: {sample.scores}")

# Or get summaries only (even faster)
for summary in log.read_eval_log_sample_summaries("file.eval"):
    print(f"Sample {summary.id}: score={summary.scores}")

EvalLog Structure

The EvalLog object contains:

eval_log.status      # "success", "error", "started"
eval_log.samples     # List[EvalSample] - individual evaluations
eval_log.results     # Aggregate metrics
eval_log.stats       # Token usage data
eval_log.error       # Exception details if failed
eval_log.eval        # Eval configuration (task, model, etc.)

EvalSample Structure

Each sample contains:

sample.id            # Sample identifier
sample.epoch         # Epoch number
sample.input         # Input to the model
sample.target        # Expected target
sample.output        # Model output
sample.scores        # Dict of scorer results
sample.messages      # List of conversation messages
sample.metadata      # Custom metadata
sample.error         # Error if sample failed

Dataframe API

The inspect_ai.analysis module provides functions to extract dataframes:

Evaluation-Level Data

from inspect_ai import analysis

# One row per eval log
df = analysis.evals_df("./logs")

# Custom columns
df = analysis.evals_df("./logs", columns=analysis.EvalInfo + analysis.EvalResults)

Sample-Level Data

# One row per sample (summary info - fast)
df = analysis.samples_df("./logs")

# Full sample data (slower, use parallel)
df = analysis.samples_df("./logs", full=True, parallel=True)

Message-Level Data

# One row per message
df = analysis.messages_df("./logs", parallel=True)

# Filter by role
df = analysis.messages_df("./logs", filter=["assistant"], parallel=True)

Event-Level Data

# One row per event (model calls, tool use, etc.)
df = analysis.events_df(
    "logs",
    columns=analysis.EventTiming + analysis.ModelEventColumns,
    filter=lambda e: e.event == "model",
    parallel=True
)

Column Groups

Pre-defined column groups for evals_df:

• EvalInfo - Metadata (created, tags, git commit)
• EvalTask - Task configuration
• EvalModel - Model details
• EvalResults - Status, errors, headline metrics
• EvalScores - All scores as separate columns

Pre-defined column groups for samples_df:

• SampleSummary - Default lightweight columns
• SampleScores - Score details (answer, metadata, explanation)
• SampleMessages - Message content as strings

Common Workflows

Analyze Evaluation Results

from inspect_ai import log, analysis

# Load eval
eval_log = log.read_eval_log("results.eval")

if eval_log.status == "success":
    print(f"Total samples: {len(eval_log.samples)}")
    print(f"Results: {eval_log.results}")

    # Accuracy by task
    df = analysis.samples_df([eval_log])
    print(df.groupby("task")["score"].mean())

Extract Conversation Messages

from inspect_ai import log

for sample in log.read_eval_log_samples("results.eval"):
    print(f"\n=== Sample {sample.id} ===")
    for msg in sample.messages:
        role = msg.role
        content = msg.content if isinstance(msg.content, str) else str(msg.content)
        print(f"{role}: {content[:200]}...")

Find Failed Samples

from inspect_ai import log

for sample in log.read_eval_log_samples("results.eval"):
    if sample.error:
        print(f"Sample {sample.id} failed: {sample.error}")

Compare Model Performance

from inspect_ai import analysis

df = analysis.evals_df("./logs")
print(df.groupby("model")["accuracy"].mean().sort_values(ascending=False))

Export to CSV

from inspect_ai import analysis

samples = analysis.samples_df("./logs", parallel=True)
samples.to_csv("samples.csv", index=False)

evals = analysis.evals_df("./logs")
evals.to_csv("evals.csv", index=False)

Working with Downloaded .eval Files

If you downloaded an .eval file using the download-inspect-eval skill, use the standard inspect_ai functions:

from inspect_ai import log

# Read the downloaded file directly
eval_log = log.read_eval_log("2025-12-17T05-42-03+00-00_debug_xyz.eval")

# Iterate samples
for sample in log.read_eval_log_samples("file.eval"):
    print(sample.id, sample.scores)

As a last resort, you can manually extract the archive (.eval files are zip archives):

import zipfile
with zipfile.ZipFile("file.eval", "r") as z:
    z.extractall("extracted")

# Read sample JSON directly
import json
with open("extracted/samples/task_epoch_1.json") as f:
    sample_data = json.load(f)

Error Handling

from inspect_ai import log, analysis

# Handle missing fields gracefully
df, errors = analysis.evals_df("./logs", strict=False)
if errors:
    print(f"Errors: {errors}")

# Check log status before processing
eval_log = log.read_eval_log("file.eval")
if eval_log.status == "error":
    print(f"Evaluation failed: {eval_log.error}")
elif eval_log.status == "started":
    print("Evaluation incomplete")

Performance Tips

1. Use header_only=True when you only need metadata
2. Use read_eval_log_samples() generator for large files
3. Use SampleSummary columns (default) for fast samples_df()
4. Use parallel=True for full sample/message/event extraction
5. Filter early in messages_df() and events_df() to reduce data

Reference

• Eval Logs Documentation
• Dataframe API Documentation