⚡ Kiro Users Report

A Streamlit dashboard for visualizing Kiro usage metrics. Connects to your Kiro user report data in S3 via AWS Glue and Athena, and presents interactive charts covering messages, conversations, credits, client types, user engagement, and more.

Prerequisites

• Kiro user report data export enabled in the AWS console (see below)
• Terraform >= 1.0
• AWS CLI configured with appropriate credentials
• Python 3.8+

Enable Kiro User Reports

Before using this dashboard, you need to enable user report data export in the AWS console. Navigate to the Kiro settings page and configure the S3 bucket where user reports will be delivered.

Once enabled, Kiro will deliver daily user report CSV files to your specified S3 bucket.

For detailed instructions, see the Kiro User Activity documentation.

Quick Start

git clone https://github.com/aws-samples/sample-kiro-user-analytics-dashboard.git
cd sample-kiro-user-analytics-dashboard

# 1. Configure your variables
cp terraform/terraform.tfvars.example terraform/terraform.tfvars
# Edit terraform/terraform.tfvars with your values (see Configuration below)

# 2. Deploy everything (infra + crawler + app config)
./deploy.sh

# 3. Start the dashboard
cd app
pip install -r requirements.txt
streamlit run app.py

The dashboard will be available at http://localhost:8501.

Configuration

Edit terraform/terraform.tfvars with your values:

Variable	Required	Description
aws_region	No (default: us-east-1)	AWS region for all resources
aws_account_id	Yes	Your AWS account ID
s3_bucket_name	Yes	S3 bucket name including prefix (e.g. kiro-dev/user)
glue_database_name	No	Glue catalog database name
glue_crawler_schedule	No	Cron schedule for the Glue crawler
identity_store_id	Yes	IAM Identity Center Identity Store ID (e.g. d-1234567890) for resolving user IDs to usernames
project_name	No	Prefix for resource naming
tags	No	Tags applied to all resources

The S3 data path is constructed automatically as:

s3://{s3_bucket_name}/AWSLogs/{aws_account_id}/KiroLogs/user_report/{aws_region}/

The Glue table name is auto-discovered from the database at runtime — no need to configure it manually.

What deploy.sh Does

1. Runs terraform init and terraform apply to provision:
   • Glue database, crawler, and IAM role
   • Athena workgroup and results S3 bucket (bucket name includes account ID suffix to ensure global uniqueness)
   • IAM policies for app access
2. Generates app/.env from Terraform outputs
3. Starts the Glue crawler and waits for it to finish

After that, you just start the Streamlit app.

Data Schema

The dashboard expects Kiro user report CSV data with these columns:

Column	Type	Description
date	string	Date of the report activity (YYYY-MM-DD)
userid	string	ID of the user for whom the activity is reported
client_type	string	KIRO_IDE, KIRO_CLI, or PLUGIN
chat_conversations	integer	Number of conversations by the user during the day
credits_used	double	Credits consumed from the user subscription plan during the day
overage_cap	double	Overage limit set by admin (or max credits for plan if overage not enabled)
overage_credits_used	double	Total overage credits used, if overage is enabled
overage_enabled	string	Whether overage is enabled for this user
profileid	string	Profile associated with the user activity
subscription_tier	string	Kiro subscription plan (Pro, ProPlus, Power)
total_messages	integer	Messages sent to and from Kiro (prompts, tool calls, responses)

Dashboard Sections

• Overall Metrics — users, messages, conversations, credits, overage
• Usage by Client Type — KIRO_CLI vs KIRO_IDE breakdown
• Top 10 Users — leaderboard by messages
• Daily Activity Trends — messages, conversations, credits, active users over time
• Daily Trends by Client Type — per-client daily line charts
• Credits Analysis — top users by credits, base vs overage split
• Subscription Tier Breakdown — users and credits by tier
• User Engagement — segmentation (Power / Active / Light / Idle)
• User Activity Timeline — recency, active days, filterable detail table
• Engagement Funnel — conversion rates across engagement stages
• User Filtering — filter dashboard data by specific users or user groups
• Data Export — export filtered data and charts to CSV for offline analysis

Project Structure

.
├── deploy.sh                        # One-click deploy script
├── terraform/
│   ├── main.tf                      # Glue, Athena, S3, IAM resources
│   ├── variables.tf                 # Input variables
│   ├── outputs.tf                   # Outputs (fed into app/.env)
│   └── terraform.tfvars.example     # Example configuration
└── app/
    ├── app.py                       # Streamlit dashboard
    ├── config.py                    # Environment variable loader
    ├── requirements.txt             # Python dependencies
    └── .env.example                 # Example environment file

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.