helium-agent 0.1.9

A terminal-focused AI agent with RAG and local tool capabilities

Helium Agent

Demo

Watch the demo video →

Helium is a lightweight local AI agent that does everything your everyday agent does but without the massive bill and with your own customization. You can run any LLM be it local or cloud inside Helium. But to have the total experience of freedom I would suggest to integrate a local LLM either using ollama, llama.cpp or any inference technology of your choice.

Helium is still developing and more features are added as you read this. I would really love for you to contribute to this and make a part of Helium your own.

[!TIP] If you directly want to try go to usage section.

What It Can Do

• Answer everyday questions: Just like any other agent it can repsond to any mundane queries you might have. It won't judge you.
• Tool calling: Helium can calls tools it has to perform complex operations in order to respond to your queries.
• Coding: It can perform long coding tasks with the help of agentic loop build inside it.
• Deep Research: For queries that include an in-depth knowledge and information retrieval Helium will take help of its research tool to provide with most accurate repsonse with proper citations.
• Web Search: It can use DuckDuckGoSearch API to get web results and if necessary it will use playwright to dig deeper into complex websites all to make sure you get the best answer.
• RAG: Currently a simple RAG pipeline is integrated where only 1 file at a time can be given to Helium and it will respond accordingly. [Future plans to scale this]
• Bash execution: Helium can perform safe bash operations in its terminal.
• Long-term memory: It uses a in-memory sqlite database which is currently session-scoped to remember important facts.

Prerequisites

Helium is optimized for macOS on Apple Silicon but I have tested it across platforms so you shouldn't face any problems but if you do please raise issue.

You will need:

• Python 3.11+
• A local LLM service, usually llama.cpp
• If you have an API endpoint to any LLM you can use that too.

Default service URLs are configured in config/settings.py and can be overridden in config/settings.toml.

Usage

Helium is packaged to pypi so you can just download it and use it directly.

Just install it using pip:

pip install helium-agent

Now, go to the directory where you want Helium to work and just call it:

helium .

For more commands type /help in the chat.

Docker

[!NOTE] Use this if you just want to chat without worrying the technical complexities but make sure to have you env configured accordingly.

It will take care of RAG pipeline automatically.

You can build and run the entire terminal application using:

docker compose up --build

You might need to wait for a bit. So, go have a coffee while it is building.

This will run the image:

docker compose run --rm --service-ports helium

The API container is configured to reach host services through host.docker.internal. Keep llama.cpp instance running on the host, then update docker-compose.yml if your ports differ.

Dev Installation

[!NOTE] Use this only if you want to run it manually otherwise go to Docker section.

1. Clone the repository:

   git clone <repository-url>
   cd helium-agent
   

2. Create and activate a virtual environment:

   python -m venv .venv
   source .venv/bin/activate
   

3. Install Python dependencies:

   pip install -r requirements.txt
   pip install -r requirements-rag.txt
   

4. Doctor command for RAG check:

   python -m rag_service doctor
   

Local Services

[!NOTE] You can either use llama.cpp or any LLM provider API.

Start llama.cpp

Run a compatible instruction-tuned GGUF model on port 3000:

./llama-server -m /path/to/your/model.gguf -c 4096 --port 3000

Helium expects the default completion endpoint to be OPENAI compatible version:

http://127.0.0.1:3000/v1/chat/completion

Use LLM API

Run helium and follow the setup wizard. Persistent API keys are stored in your operating system credential manager:

• macOS: Keychain
• Windows: Credential Manager
• Linux: Secret Service or KWallet, depending on your desktop environment

Non-secret defaults such as the API URL, model, and Playwright toggle are stored in your user Helium config directory.

You can still use environment variables for CI, Docker, or temporary overrides:

LLM_API_KEY=your-llm-api-key
LLM_API_URL=your-llm-url
LLM_MODEL=your-model

If you previously used ~/.helium.env, Helium can import it into secure storage during setup. The legacy file is left untouched, so remove it yourself after confirming the import worked.

Start Playwright

Helium comes with playwright compatibility. So, if you want to get more in-depth results from web you can turn on this feature by updating use_playwright=true in config/settings.toml

Then install playwright and chromium.

pip install playwright
playright install chromium

These are not added in requirements.txt because Helium aims to be lightweight. But you can do whatever you want!

[!TIP] Playwright is heavy as it downloads chromium so it can take some of your memory. Use with caution.

Start RAG pipeline

Helium comes with its own RAG pipeline. This allows you to add files with @ prefix to the file path to your file. Then you can ask anything about that file.

Currently it is good enough to answer what is inside it, summarize it, and other basic questions. Later I intend to deepen the understanding of the file using local embeddings.

This is an optional feature. Look into rag_service directory for more detail.

Run The Assistant

Only TEXT mode is ready for use.

1. Confirm the LLM service is running.

2. Confirm your web services are running if you want better results.

3. Start Helium:

   python main.py --mode text
   

4. Wait for:

Animation to load and welcome message to be shown.

5. Type your query and enjoy Helium.

Example requests:

What is the latest news on AI?
Remember that I prefer concise responses.
Create a file named hello.txt that says hi.
Open Safari.
Compare India and China GDP in 2025.
Why is the Indian Rupee falling recently?
Give me a report on the latest AI regulation changes in the EU.

RAG request example:

@README.md what does this project do?
@docs/plan.pdf summarize the risks

Testing

Run the test suite from the repository root:

python -m unittest discover -s tests