single-file-agents

What if we could pack single purpose, powerful AI Agents into a single python file?

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Single File Agents (SFA) is a collection of powerful single-file agents built on top of uv, a modern Python package installer and resolver. These agents aim to perform specific tasks efficiently, demonstrating precise prompt engineering and GenAI patterns. The repository contains agents built across major GenAI providers like Gemini, OpenAI, and Anthropic. Each agent is self-contained, minimal, and built on modern Python for fast and reliable dependency management. Users can run these scripts from their server or directly from a gist. The agents are patternful, emphasizing the importance of setting up effective prompts, tools, and processes for reusability.

README:

Single File Agents (SFA)

Premise: #1: What if we could pack single purpose, powerful AI Agents into a single python file?

Premise: #2: What's the best structural pattern for building Agents that can improve in capability as compute and intelligence increases?

What is this?

A collection of powerful single-file agents built on top of uv - the modern Python package installer and resolver.

These agents aim to do one thing and one thing only. They demonstrate precise prompt engineering and GenAI patterns for practical tasks many of which I share on the IndyDevDan YouTube channel. Watch us walk through the Single File Agent in this video.

You can also check out this video where we use Devin, Cursor, Aider, and PAIC-Patterns to build three new agents with powerful spec (plan) prompts.

This repo contains a few agents built across the big 3 GenAI providers (Gemini, OpenAI, Anthropic).

Quick Start

Export your API keys:

export GEMINI_API_KEY='your-api-key-here'

export OPENAI_API_KEY='your-api-key-here'

export ANTHROPIC_API_KEY='your-api-key-here'

export FIRECRAWL_API_KEY='your-api-key-here' # Get your API key from https://www.firecrawl.dev/

JQ Agent:

uv run sfa_jq_gemini_v1.py --exe "Filter scores above 80 from data/analytics.json and save to high_scores.json"

DuckDB Agent (OpenAI):

# Tip tier
uv run sfa_duckdb_openai_v2.py -d ./data/analytics.db -p "Show me all users with score above 80"

DuckDB Agent (Anthropic):

# Tip tier
uv run sfa_duckdb_anthropic_v2.py -d ./data/analytics.db -p "Show me all users with score above 80"

DuckDB Agent (Gemini):

# Buggy but usually works
uv run sfa_duckdb_gemini_v2.py -d ./data/analytics.db -p "Show me all users with score above 80"

SQLite Agent (OpenAI):

uv run sfa_sqlite_openai_v2.py -d ./data/analytics.sqlite -p "Show me all users with score above 80"

Meta Prompt Generator:

uv run sfa_meta_prompt_openai_v1.py \
    --purpose "generate mermaid diagrams" \
    --instructions "generate a mermaid valid chart, use diagram type specified or default flow, use examples to understand the structure of the output" \
    --sections "user-prompt" \
    --variables "user-prompt"

Bash Editor Agent (Anthropic)

(sfa_bash_editor_agent_anthropic_v2.py)

An AI-powered assistant that can both edit files and execute bash commands using Claude's tool use capabilities.

Example usage:

# View a file
uv run sfa_bash_editor_agent_anthropic_v2.py --prompt "Show me the first 10 lines of README.md"

# Create a new file
uv run sfa_bash_editor_agent_anthropic_v2.py --prompt "Create a new file called hello.txt with 'Hello World!' in it"

# Replace text in a file
uv run sfa_bash_editor_agent_anthropic_v2.py --prompt "Create a new file called hello.txt with 'Hello World!' in it. Then update hello.txt to say 'Hello AI Coding World'"

# Execute a bash command
uv run sfa_bash_editor_agent_anthropic_v2.py --prompt "List all Python files in the current directory sorted by size"

Polars CSV Agent (OpenAI)

(sfa_polars_csv_agent_openai_v2.py)

An AI-powered assistant that generates and executes Polars data transformations for CSV files using OpenAI's function calling capabilities.

Example usage:

# Run Polars CSV agent with default compute loops (10)
uv run sfa_polars_csv_agent_openai_v2.py -i "data/analytics.csv" -p "What is the average age of the users?"

# Run with custom compute loops
uv run sfa_polars_csv_agent_openai_v2.py -i "data/analytics.csv" -p "What is the average age of the users?" -c 5

Web Scraper Agent (OpenAI)

(sfa_scrapper_agent_openai_v2.py)

An AI-powered web scraping and content filtering assistant that uses OpenAI's function calling capabilities and the Firecrawl API for efficient web scraping.

Example usage:

# Basic scraping with markdown list output
uv run sfa_scrapper_agent_openai_v2.py -u "https://example.com" -p "Scrap and format each sentence as a separate line in a markdown list" -o "example.md"

# Advanced scraping with specific content extraction
uv run sfa_scrapper_agent_openai_v2.py \
    --url https://agenticengineer.com/principled-ai-coding \
    --prompt "What are the names and descriptions of each lesson?" \
    --output-file-path paic-lessons.md \
    -c 10

Features

Self-contained: Each agent is a single file with embedded dependencies
Minimal, Precise Agents: Carefully crafted prompts for small agents that can do one thing really well
Modern Python: Built on uv for fast, reliable dependency management
Run From The Cloud: With uv, you can run these scripts from your server or right from a gist (see my gists commands)
Patternful: Building effective agents is about setting up the right prompts, tools, and process for your use case. Once you setup a great pattern, you can re-use it over and over. That's part of the magic of these SFA's.

Test Data

The project includes a test duckdb database (data/analytics.db), a sqlite database (data/analytics.sqlite), and a JSON file (data/analytics.json) for testing purposes. The database contains sample user data with the following characteristics:

User Table

30 sample users with varied attributes
Fields: id (UUID), name, age, city, score, is_active, status, created_at
Test data includes:
- Names: Alice, Bob, Charlie, Diana, Eric, Fiona, Jane, John
- Cities: Berlin, London, New York, Paris, Singapore, Sydney, Tokyo, Toronto
- Status values: active, inactive, pending, archived
- Age range: 20-65
- Score range: 3.1-96.18
- Date range: 2023-2025

Perfect for testing filtering, sorting, and aggregation operations with realistic data variations.

Agents

Note: We're using the term 'agent' loosely for some of these SFA's. We have prompts, prompt chains, and a couple are official Agents.

JQ Command Agent

(sfa_jq_gemini_v1.py)

An AI-powered assistant that generates precise jq commands for JSON processing

Example usage:

# Generate and execute a jq command
uv run sfa_jq_gemini_v1.py --exe "Filter scores above 80 from data/analytics.json and save to high_scores.json"

# Generate command only
uv run sfa_jq_gemini_v1.py "Filter scores above 80 from data/analytics.json and save to high_scores.json"

DuckDB Agents

(sfa_duckdb_openai_v2.py, sfa_duckdb_anthropic_v2.py, sfa_duckdb_gemini_v2.py, sfa_duckdb_gemini_v1.py)

We have three DuckDB agents that demonstrate different approaches and capabilities across major AI providers:

DuckDB OpenAI Agent (sfa_duckdb_openai_v2.py, sfa_duckdb_openai_v1.py)

An AI-powered assistant that generates and executes DuckDB SQL queries using OpenAI's function calling capabilities.

Example usage:

# Run DuckDB agent with default compute loops (10)
uv run sfa_duckdb_openai_v2.py -d ./data/analytics.db -p "Show me all users with score above 80"

# Run with custom compute loops 
uv run sfa_duckdb_openai_v2.py -d ./data/analytics.db -p "Show me all users with score above 80" -c 5

DuckDB Anthropic Agent (sfa_duckdb_anthropic_v2.py)

An AI-powered assistant that generates and executes DuckDB SQL queries using Claude's tool use capabilities.

Example usage:

# Run DuckDB agent with default compute loops (10)
uv run sfa_duckdb_anthropic_v2.py -d ./data/analytics.db -p "Show me all users with score above 80"

# Run with custom compute loops
uv run sfa_duckdb_anthropic_v2.py -d ./data/analytics.db -p "Show me all users with score above 80" -c 5

DuckDB Gemini Agent (sfa_duckdb_gemini_v2.py)

An AI-powered assistant that generates and executes DuckDB SQL queries using Gemini's function calling capabilities.

Example usage:

# Run DuckDB agent with default compute loops (10)
uv run sfa_duckdb_gemini_v2.py -d ./data/analytics.db -p "Show me all users with score above 80"

# Run with custom compute loops
uv run sfa_duckdb_gemini_v2.py -d ./data/analytics.db -p "Show me all users with score above 80" -c 5

Meta Prompt Generator (sfa_meta_prompt_openai_v1.py)

An AI-powered assistant that generates comprehensive, structured prompts for language models.

Example usage:

# Generate a meta prompt using command-line arguments.
# Optional arguments are marked with a ?.
uv run sfa_meta_prompt_openai_v1.py \
    --purpose "generate mermaid diagrams" \
    --instructions "generate a mermaid valid chart, use diagram type specified or default flow, use examples to understand the structure of the output" \
    --sections "examples, user-prompt" \
    --examples "create examples of 3 basic mermaid charts with <user-chart-request> and <chart-response> blocks" \
    --variables "user-prompt"

# Without optional arguments, the script will enter interactive mode.
uv run sfa_meta_prompt_openai_v1.py \
    --purpose "generate mermaid diagrams" \
    --instructions "generate a mermaid valid chart, use diagram type specified or default flow, use examples to understand the structure of the output"

# Interactive Mode
# Just run the script without any flags to enter interactive mode.
# You'll be prompted step by step for:
# - Purpose (required): The main goal of your prompt
# - Instructions (required): Detailed instructions for the model
# - Sections (optional): Additional sections to include
# - Examples (optional): Example inputs and outputs
# - Variables (optional): Placeholders for dynamic content
uv run sfa_meta_prompt_openai_v1.py

Git Agent

Up for a challenge?

Requirements

Python 3.8+
uv package manager
GEMINI_API_KEY (for Gemini-based agents)
OPENAI_API_KEY (for OpenAI-based agents)
ANTHROPIC_API_KEY (for Anthropic-based agents)
jq command-line JSON processor (for JQ agent)
DuckDB CLI (for DuckDB agents)

Installing Required Tools

jq Installation

macOS:

brew install jq

Windows:

Download from stedolan.github.io/jq/download
Or install with Chocolatey: choco install jq

DuckDB Installation

macOS:

brew install duckdb

Windows:

Download the CLI executable from duckdb.org/docs/installation
Add the executable location to your system PATH

Installation

Install uv:

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

Clone this repository:

git clone <repository-url>

Set your Gemini API key (for JQ generator):

export GEMINI_API_KEY='your-api-key-here'

# Set your OpenAI API key (for DuckDB agents):
export OPENAI_API_KEY='your-api-key-here'

# Set your Anthropic API key (for DuckDB agents):
export ANTHROPIC_API_KEY='your-api-key-here'

Shout Outs + Resources for you

uv - The engineers creating uv are built different. Thank you for fixing the python ecosystem.
Simon Willison - Simon introduced me to the fact that you can use uv to run single file python scripts with dependencies. Massive thanks for all your work. He runs one of the most valuable blogs for engineers in the world.
Building Effective Agents - A proper breakdown of how to build useful units of value built on top of GenAI.
Part Time Larry - Larry has a great breakdown on the new Python GenAI library and delivers great hands on, actionable GenAI x Finance information.
Aider - AI Coding done right. Maximum control over your AI Coding Experience. Enough said.

AI Coding

Context Priming

Read README.md, CLAUDE.md, ai_docs/*, and run git ls-files to understand this codebase.

License

MIT License - feel free to use this code in your own projects.

If you find value from my work: give a shout out and tag my YT channel IndyDevDan.

For Tasks:

Click tags to check more tools for each tasks

filter data generate prompts execute sql queries web scraping generate meta prompts

For Jobs:

data scientist ai engineer machine learning engineer python developer ai researcher

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