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Multi-provider LLM CLI agent for editing Power BI reports

Project description

PBI Agent logo

PBI AGENT

Transform data into decisions.

Tests Publish Release Python License

A local agent that creates, edits, and audits Power BI reports through natural language.

pbi-agent turns plain English into production-ready Power BI reports. Instead of clicking through dozens of menus, you describe what you need and the agent handles the rest: scaffolding projects, building visuals, writing DAX measures, and running best-practice audits.

One command to get started:

pbi-agent

That's it. Running pbi-agent with no arguments launches the browser interface on http://localhost:8000 where you can start building reports immediately.

Why pbi-agent?

Power BI development involves a large amount of repetitive, manual work: creating report structures, configuring visuals, writing measures, and enforcing best practices. pbi-agent eliminates that friction by letting you express intent in natural language and delegating the implementation to an LLM-powered agent that understands the Power BI project format (PBIP) natively.

What this means for developer productivity:

  • Minutes instead of hours -- Scaffold a complete report project, add pages, and wire up visuals in a single conversation rather than navigating menus and property panels.
  • Consistent quality -- The built-in audit engine checks 90+ rules across modeling, performance, security, and DAX quality, catching issues that manual reviews miss.
  • Lower barrier to entry -- Junior developers and analysts can produce well-structured reports without deep Power BI expertise; the agent encodes best practices into every action it takes.
  • Repeatable workflows -- Single-turn prompts (pbi-agent run) integrate into scripts and CI pipelines, making report generation and auditing automatable.
  • Bring your own model -- Works with OpenAI, xAI, Google Gemini, Anthropic or any OpenAI-compatible model like OpenRouter.

Use Cases

1. Create a full dashboard from a data file

Drop a CSV (or any flat file) into your workspace and let the agent do the rest. It analyzes the data, imports it into the semantic model, creates measures, and builds a complete dashboard -- no manual configuration required.

Start the agent and describe what you need:

pbi-agent

"Here is sales_data.csv. Analyze the file, import it into the model, and build a dashboard with a revenue trend line chart, a top-10 products bar chart, and KPI cards for total revenue, order count, and average order value."

The agent will:

  1. Inspect the CSV to understand columns, data types, and cardinality.
  2. Import the file into the semantic model as a new table.
  3. Create DAX measures (total revenue, order count, average order value, etc.).
  4. Build report pages with the requested visuals, properly bound to the model.

From a single file and one prompt, you get a working Power BI report ready to open in Power BI Desktop.

Demo: See this workflow in action -- watch the video.

2. Edit an existing report

Point the agent at an existing PBIP directory and describe the changes you need:

pbi-agent

"On the Sales page, replace the table visual with a clustered bar chart grouped by product category. Add a slicer for fiscal year."

The agent reads the report definition files, applies the edits, and preserves existing configuration.

3. Audit a report for issues

Run a comprehensive best-practice audit that checks 90+ rules across seven domains:

pbi-agent audit --report-dir ./my-report

The audit covers:

Domain What it checks
Structure & Star Schema Table relationships, fact/dimension separation
Modeling & Naming Conventions, data types, calculated columns
Performance Query folding, cardinality, aggregation patterns
Security RLS configuration, data exposure risks
DAX Quality Measure patterns, CALCULATE usage, time intelligence
Metadata & Documentation Descriptions, display folders, formatting
Anti-Patterns Hidden fields, unused objects, dead code

Output is written to AUDIT-REPORT.md (detailed findings with severity scores and a letter grade) and AUDIT-TODO.md (a progress checklist you can track).

4. Single-turn scripting

Run one-off prompts for automation or CI integration:

pbi-agent run --prompt "List all measures in the semantic model that lack descriptions."

Prerequisites

  • Python 3.12+
  • uv (recommended) or pip
  • An API key for one of the supported LLM providers:
    • Set PBI_AGENT_API_KEY
    • Use --provider xai for xAI
    • Use --provider google for Google Gemini
    • Use --provider anthropic for Anthropic
    • Use --provider generic for OpenAI-compatible gateways such as OpenRouter

Installing uv

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Installation

From PyPI (recommended)

Install the CLI globally so it can be called from any directory:

uv tool install pbi-agent

Verify the installation:

pbi-agent --help

From source

git clone https://github.com/<your-org>/pbi-agent.git
cd pbi-agent
uv tool install --reinstall .

This installs pbi-agent globally from the local checkout. Use --reinstall to overwrite a previous installation.

Quick Start

Warning: pbi-agent works directly on Power BI projects saved in PBIP format (Power BI Project). You must run the CLI from the directory that contains your .pbip project files, or scaffold a new one with pbi-agent init. Reports saved as .pbix are not supported -- save your report as a PBIP project from Power BI Desktop first (File > Save as > Power BI Project).

  1. Set your API key (or place it in a .env file in your project directory):

macOS / Linux:

export PBI_AGENT_API_KEY="sk-..."

Windows (PowerShell):

$env:PBI_AGENT_API_KEY = "sk-..."
  1. Navigate to your PBIP project directory (or scaffold a new one):
# Option A: start from an existing PBIP project
cd /path/to/my-report

# Option B: scaffold a new project in the current directory
pbi-agent init --dest .
  1. Launch the agent:
pbi-agent

A browser interface opens at http://localhost:8000. Start describing what you need.

Prefer a terminal? Use pbi-agent console for an interactive terminal session, or pbi-agent run --prompt "..." for single-turn execution.

Commands

Command Description
(none) or web Default. Serve the browser interface (http://localhost:8000)
console Interactive terminal interface
run --prompt "..." Execute a single prompt turn and exit
audit Run a best-practice audit, writes AUDIT-REPORT.md
init Scaffold a new PBIP report project from the bundled template

Configuration

Precedence: CLI flags > environment variables > defaults.

Environment variables

Variable Description Default
PBI_AGENT_API_KEY API key for the selected provider --
PBI_AGENT_PROVIDER LLM provider (openai, xai, google, anthropic, or generic) openai
PBI_AGENT_MODEL Model override gpt-5.4-2026-03-05 for OpenAI, grok-4-1-fast-reasoning for xAI, gemini-3-flash-preview for Google, claude-opus-4-6 for Anthropic, provider default for generic
PBI_AGENT_MAX_TOKENS Max output tokens 16384
PBI_AGENT_REASONING_EFFORT Reasoning effort (low, medium, high, xhigh) xhigh
PBI_AGENT_MAX_TOOL_WORKERS Parallel tool execution threads 4
PBI_AGENT_MAX_RETRIES Retry count for transient failures 3
PBI_AGENT_COMPACT_THRESHOLD Context compaction token threshold 150000
PBI_AGENT_RESPONSES_URL Custom provider HTTP endpoint https://api.openai.com/v1/responses for OpenAI, https://api.x.ai/v1/responses for xAI, https://generativelanguage.googleapis.com/v1beta/interactions for Google
PBI_AGENT_GENERIC_API_URL Generic OpenAI-compatible Chat Completions endpoint https://openrouter.ai/api/v1/chat/completions

You can also place these in a .env file in your project root.

If you already have a provider-specific API key in your environment, pbi-agent picks it up automatically -- no need to set PBI_AGENT_API_KEY:

Provider Environment variable
OpenAI OPENAI_API_KEY
xAI XAI_API_KEY
Google GEMINI_API_KEY
Anthropic ANTHROPIC_API_KEY
Generic GENERIC_API_KEY

PBI_AGENT_API_KEY takes precedence when set. Otherwise the provider-specific variable for the active --provider is used.

CLI flags

All environment variables have corresponding CLI flags. Run pbi-agent --help for the full list.

By default, pbi-agent uses the OpenAI provider with GPT-5.4 -- no flags needed:

pbi-agent

To use Anthropic:

pbi-agent --provider anthropic --model claude-opus-4-6

To use xAI:

pbi-agent --provider xai --model grok-4-1-fast-reasoning

To use Google Gemini through the Interactions API:

pbi-agent --provider google --model gemini-3-flash-preview

To use OpenRouter (or any OpenAI-compatible gateway) with a specific model:

pbi-agent --provider generic --model z-ai/glm-5

How It Works

pbi-agent connects to the OpenAI Responses HTTP API, xAI Responses HTTP API, Google Gemini Interactions API, Anthropic Messages API, or a generic OpenAI-compatible Chat Completions API and runs an agentic loop:

  1. Your prompt is sent alongside the agent's system instructions and tool definitions.
  2. The model responds with text, reasoning, or tool calls.
  3. Tool calls are executed locally in parallel (shell commands, file patches, skill lookups, template scaffolding).
  4. Tool results are fed back to the model for the next turn.
  5. The loop continues until the model produces a final text response.

Built-in tools

Tool Description
shell Execute shell commands (workspace-confined, blocks path traversal)
apply_patch Create, update, or delete files via V4A diffs
skill_knowledge Retrieve Power BI knowledge from the bundled skill library (14 topics)
init_report Scaffold the PBIP template into a target directory

Knowledge base

The agent ships with 14 Power BI skill documents covering visual types, TMDL modeling, theme branding, filter propagation, and more. The agent consults these automatically before creating or editing visuals, ensuring correct JSON schemas and best practices.

Security Notes

  • The shell tool is confined to the workspace directory and rejects path traversal attempts.
  • Even with confinement, treat shell execution as powerful. Only run the agent in trusted workspaces.
  • Never commit .env files containing API keys to version control.

Development

# Lint and format
uvx ruff check . --fix && uvx ruff format .

# Run the CLI from source
uv run pbi-agent --help

License

See LICENSE for details.

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