Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/jg8481/robot-framework-ai-agent-datadriver

This is an evolving toolkit focused on AI agent experiments that combine the capabilities of Codename Goose, MCP tools, the Robot Framework ecosystem, decentralized LLMs on Ollama, and Docker.
https://github.com/jg8481/robot-framework-ai-agent-datadriver

ai-agents codename-goose ollama robotframework rpa

Last synced: 6 months ago
JSON representation

This is an evolving toolkit focused on AI agent experiments that combine the capabilities of Codename Goose, MCP tools, the Robot Framework ecosystem, decentralized LLMs on Ollama, and Docker.

Awesome Lists containing this project

README 

          
# Robot-Framework-AI-Agent-Datadriver



![Robot](./notes-images-and-demonstrations/robotframework-ai-agent.png)



## LLMs, AI agents, MCP tools, combined with RPA



This experimental toolkit is intended to help explore new technologies related to LLMs, AI agents, and [MCP tools](https://www.pulsemcp.com/) combined with battle-tested Robot Framework RPA strategies. My intention is to demonstrate the usefulness of AI agents and LLMs [beyond the "vibecode" trend](https://x.com/karpathy/status/1886192184808149383). To get the gist of what I mean, scroll down to the `Toolkit Roadmap` section.



I've taken some inspiration from the Codename Goose approach to using [instruction files](https://block.github.io/goose/docs/guides/running-tasks#using-an-instruction-file) to automate AI agents. Instead of Markdown files, I am using [robotframework-datadriver](https://github.com/Snooz82/robotframework-datadriver) and Robot Framework tags to control different categories of AI agent prompts. [Pabot](https://github.com/mkorpela/pabot) is also being used to control parallel running Codename Goose Docker containers.



![Agent](./notes-images-and-demonstrations/demonstration1.png)



There are two primary options for using this project. [Both have their strengths and technical challenges.](https://block.github.io/goose/blog/2025/03/31/goose-benchmark/#technical-challenges-with-open-models) I will be writing up more documentation to explain that soon.



- The first option is to use a fully decentralized LLM provider that runs privately and locally on your hardware. The screenshot above shows an example of an automation run using the qwen2.5:14b model on Ollama.

    - In my case, I only use macOS machines for this work, but it is definitely possible to run all of this on Windows or Linux (scroll down to the `Technical Requirements` section for more information).

    - This project was designed, created, and tested on an M3 Max MacBook Pro with the following specifications. **You may need to consider your hardware needs before attempting to run local LLMs.**

        - https://support.apple.com/en-us/117736

- The second option is available to anyone who does not have access to reliable hardware. This option requires the Codename Goose Docker container and an AI cloud provider's API key.

    - *Examples:* Google Gemini, OpenAI, Anthropic, etc.



### Toolkit Roadmap



Years ago, I created another AI project called [robotframework-test-assistant](https://github.com/jg8481/leon/tree/develop/packages/robotframework-test-assistant) that focused mostly on software test activities. I consider this to be its successor and plan to expand this toolkit into the following areas that can assist in other types of activities (some may be useful to testers):



- (April 29, 2024 - **Ready To Use**) An AI agent assistant that can research any topic.

- (April 29, 2024 - **Ready To Use**) An AI agent assistant that can analyze a given text-based dataset using Retrieval Augmented Generation (RAG).

    - *Examples:* Source code, logs, reports, e-books, PDFs, etc.

    - For now, only the [Docker AI agent examples](https://github.com/jg8481/Robot-Framework-AI-Agent-Datadriver/blob/main/agent-instructions/docker-agent1-distributed-instructions.csv) in this toolkit can do this.

- An AI agent assistant that can audit any given source code repo for vulnerabilities.

    - For now, this partially works only in the [Docker AI agent examples.](https://github.com/jg8481/Robot-Framework-AI-Agent-Datadriver/blob/main/agent-instructions/docker-agent1-distributed-instructions.csv) I am working on a version that will use Ollama.

- An AI agent assistant that will utilize relevant and secure MCP Servers from the [4,000+ projects in the Model Context Protocol (MCP) ecosystem](https://www.pulsemcp.com/servers).

- Parallel running Docker AI agents running MCP tools using Anthropic, OpenAI, Groq, etc. at the same time.

    - Basically multiple different cloud AI APIs running different MCP workflows simultaneously on the same hardware.

- I have more ideas that I will be working on in the future.



### Technical Requirements



The following are the basic technical requirements needed to run the AI agent RPA automation. Please note that **this entire toolkit was developed on a macOS machine**, but this should also work for Windows and Linux users.



- **Python 3** -> https://www.python.org/downloads/

- **Robot Framework** -> https://robotframework.org

- **Docker** -> https://docs.docker.com/

- **Homebrew** -> https://brew.sh/

- **Codename Goose**, a framework for creating AI agents.

    - macOS, Windows, and Linux users can learn how to install it properly here -> https://block.github.io/goose/docs/getting-started/installation

- **Ollama**, a framework for building and running language models on local machines.

    - macOS, Windows, and Linux users can learn how to install it properly here -> https://ollama.com/download

    - macOS users can follow this guide to install it using Homebrew -> https://formulae.brew.sh/formula/ollama

    - After finishing the installation, run this command from your terminal -> `ollama run qwen2.5`



## Quick Start Guide



This is a fully functional RPA toolkit you can start using and customizing for your needs today. I recommend looking at all of the example report Markdown files stored in the `automation-resources/ai-agent-output-collector/ai-agent-output` [folder](https://github.com/jg8481/Robot-Framework-AI-Agent-Datadriver/tree/main/automation-resources/ai-agent-output-collector/ai-agent-output) to get an idea of what is currently possible with the AI agent RPA automation being maintained in this project. More documentation will be coming soon, along with a series of related Robot Framework [Medium.com](https://medium.com/@joshua.gorospe) articles I plan to write.



**Before running any of the automation, first make sure that all of the basic technical requirements are installed and working**, then you can run `bash ./start-ai-agent-workflow-experiments.sh Run-Datadriven-Sequential-Local-AI-Tasks ResearchORQuestions` to experiment with a basic workflow. To see more workflow options run `bash ./start-ai-agent-workflow-experiments.sh -h` in your terminal.



***



## Thank You!



Big thank you to the following people and groups. Without their open source contributions, this RPA project would not exist.



- Pekka Klarck

- Mikko Korpela

- René Rohner

- Docker developers

- Ollama developers

- Codename Goose developers