Domo is your personal AI agent on your computer

This AI agent is designed to serve as a fully private, locally running personal assistant that helps users manage and understand their digital workspace without relying on external services. Its core aim is to provide a free, secure alternative to cloud-based tools by enabling intelligent operations on local documents (such as searching for information, summarizing files or entire folders, classifying and organizing content, and ranking documents by relevance) while also augmenting its capabilities with internet-based research. By combining local data access with AI-driven reasoning, it allows users to efficiently navigate and act on their information while keeping full control over their data.

Domo is your personal assistant on your computer

Run

Start the app with:

./run_app.sh

Run the job workflow directly with:

.venv/bin/python -m tools.job.main

Process a single local job folder with:

.venv/bin/python -m tools.job.main data/jobs/my-job

Notes:

• Open the local URL shown by Streamlit, typically http://localhost:8051.
• The Streamlit assistant now uses a session-scoped chat UI with three panes:
  • chat for clarification and confirmation
  • a context panel showing retained parameter values, their source, and their status
  • an activity panel showing agent decisions and workflow progress, with raw workflow output available per run
• Domo always asks for confirmation in chat before executing a workflow.
• This repo must be run with the local .venv, not a global Streamlit install.
• ./run_app.sh uses .venv/bin/streamlit explicitly, which avoids ModuleNotFoundError from Anaconda/global Python.
• If you prefer the raw command, use .venv/bin/streamlit run app/streamlit_app.py --server.port 8051.
• Use the streamlit CLI to run the app, not python app/streamlit_app.py.
• Start Ollama on http://localhost:11434 before using the assistant or the job workflow.
• python -m tools.job.main with no argument runs the default ATS search using config.yaml.
• python -m tools.job.main <folder> accepts a folder containing job_description_raw.txt, cleaned_job_description.txt, job_description.txt, job description.txt, job_description.pdf, or job description.pdf.
• You can also pass a company-name-style hint like checkr; the workflow will search data/jobs/ and pick the closest dated matching folder.
• Generated artifacts are written under the configured outputs folder, which defaults to data/outputs/.
• Main application settings now live in config.yaml, including paths, debug mode, Ollama settings, and job-search parameters.
• In the assistant, run_job_agent is the online search workflow and create_job_files is the local-folder workflow for generating output files from an existing job folder or company-name hint.
• The assistant can also override job_search.role, job_search.location, job_search.ignore_location, and job_search.remote_only for a single run based on your prompt.
• The allowed per-prompt overrides are listed explicitly in config.yaml under prompt_overrides; currently only run_job_agent has overrideable fields, while create_job_files and match_cv are empty.

Structure

domo/
├── README.md
├── config.yaml
├── pyproject.toml
├── requirements.txt
├── run_app.sh
├── app/
│   └── streamlit_app.py
├── assistant/
│   ├── audit.py
│   ├── domo_agent.py
│   ├── policy.py
│   ├── registry.py
│   ├── router.py
│   ├── runtime.py
│   └── schemas.py
├── integrations/
│   └── ollama_client.py
├── img/
│   ├── domo_blue.webp
│   └── domo_yellow.webp
├── tools/
│   ├── job/
│   │   ├── __init__.py
│   │   ├── clean_job_description.py
│   │   ├── create_job_files.py
│   │   ├── discover_jobs.py
│   │   ├── export_job_pdf.py
│   │   ├── filesystem.py
│   │   ├── generate_application_materials.py
│   │   ├── main.py
│   │   ├── models.py
│   │   ├── prompts.py
│   │   └── run_job_agent.py
└── data/
    ├── jobs/
    ├── logs/
    └── outputs/

Current job workflow

• assistant/runtime.py now manages a conversational session, retains structured context values, prepares confirmations, and records structured activity events before dispatching tools.
• assistant/policy.py validates tool arguments and restricts job folder paths to the project data roots.
• tools/job/run_job_agent.py launches the online job-search workflow through the project .venv.
• tools/job/create_job_files.py launches the local job-folder workflow through the same processor.
• tools/job/main.py supports three modes:
  • no argument: search ATS sources using the parameters in config.yaml
  • folder containing job_description_raw.txt: clean the raw job ad, generate a PDF, and generate application materials
  • folder containing cleaned_job_description.txt: skip cleaning and generate the remaining outputs from the cleaned text
  • folder containing job_description.txt or job description.txt: normalize the text into job_description_raw.txt, create job_metadata.json if needed, then continue through the normal flow
  • folder containing job_description.pdf or job description.pdf: extract the PDF text into job_description_raw.txt, create job_metadata.json if needed, then continue through the normal flow
• Main configuration lives in config.yaml, including:
  • debug.enabled
  • paths
  • ollama
  • job_workflow
  • prompt_overrides
  • job_search
• Job search parameters in config.yaml include:
  • role
  • location
  • ignore_location
  • remote_only
  • sources
  • companies
  • max_jobs
  • max_results_per_source
  • max_company_attempts_per_source
• Batch search writes discovered job inputs under the configured jobs folder, which defaults to data/jobs/.
• Local folder mode can resolve a company-name hint such as checkr to the closest dated matching folder under the jobs directory.
• The assistant workflow create_job_files uses that local-folder mode directly and writes the six generated job files under the outputs directory.
• Generated artifacts are written under timestamped folders in the configured outputs folder, which defaults to data/outputs/.
• Current generated files are:
  • cleaned_job_description.txt
  • job_description.pdf
  • application_notes.txt
  • summary.txt
  • skills.txt
  • sample_cv.txt
• The workflow can search and prepare documents, but it does not currently submit applications through portals or monitor application status.

License

This project is licensed under the MIT License.