# Hyperparam Quick Start Guide

Canonical: https://hyperparam.app/docs/quick-start-guide

Get started with Hyperparam in just 3 minutes. This guide walks you through loading agent traces or chat logs and running your first analysis.

## Step 1: Sign In

Navigate to [hyperparam.app](https://hyperparam.app) and sign in with Google. Signing in unlocks AI-powered analysis, workspaces, and export.

![Hyperparam Home](https://hyperparam.app/assets/docs/home-dragdrop.png)

## Step 2: Load Your Data

### Upload a File

Drag a `.parquet`, `.csv`, or `.jsonl` file onto the drop zone (Claude Code transcripts, Codex sessions, ChatGPT exports, Langfuse / LangSmith / Phoenix traces, or any structured log file). Your data uploads to your Hyperparam storage and is ready to explore immediately.

### Connect to Cloud Storage

Paste a URL to data stored in S3, GCS, Azure Blob, or any public endpoint. Hyperparam streams the data directly, no download required. See [CORS Configuration](https://hyperparam.app/docs/cors-configuration) if you're connecting to a private bucket.

> **Don't have logs yet?** See [Exporting Chat Logs](https://hyperparam.app/docs/exporting-chat-logs) for guides on pulling logs from Claude Code, ChatGPT, Langfuse, LangSmith, Phoenix, Datadog, and more.

## Step 3: Explore Your Data

Once loaded, you'll see the interactive table view:

- **Scroll infinitely** through millions of rows
- **Click column headers** to sort
- **Double-click cells** to inspect nested data (conversations, tool calls, JSON)
- **Drag columns** to reorder

## Step 4: Analyze in a Workspace

Click "Edit data in workspace" to open your data in a workspace. From here you can:

- **Ask questions in chat**, e.g. "Which conversations had failed tool calls?"
- **Add AI-generated columns**: failure classification, quality scores, root-cause categories
- **Create SQL views**: filter, join, and project across your data
- **Export results**: save filtered datasets as Parquet, CSV, or JSONL

## Step 5: Try AI-Powered Analysis

Try these prompts in the workspace chat:

```
"Which sessions had failed tool calls? Add a column."
```

```
"Classify each failure as avoidable (prompt or schema error) or necessary (probing the environment)."
```

```
"Find conversations where the agent got stuck in a loop or burned tokens without progressing."
```

```
"Score each chat 0-1 for user frustration based on the user's messages."
```

## Your First Workflow

Here's a workflow to try with your own agent or chat logs:

1. **Load your logs** (or try an example dataset)
2. **Sign in** to unlock AI features
3. **Ask chat** to surface failed tool calls or agent errors
4. **Add a column** classifying each failure as avoidable or necessary
5. **Create a SQL view** filtering to avoidable failures
6. **Export** the filtered dataset, or save the analysis as a skill to re-run next week

## What's Next?

- [How to Debug Wasted Tool Calls](https://hyperparam.app/docs/debugging/how-debug-wasted-tool-calls): step-by-step walkthrough
- [Exporting Chat Logs](https://hyperparam.app/docs/exporting-chat-logs): get logs from your platform into Hyperparam

## Need Help?

- Check our [FAQ](https://hyperparam.app/docs/faq)
- Review [data sources](https://hyperparam.app/docs/data-sources)