PAR LLAMA is a TUI (Text UI) application designed for easy management and use of Ollama based LLMs. (It also works with most major cloud provided LLMs)
The application was built with Textual and Rich and my PAR AI Core.
It runs on all major OS's including but not limited to Windows, Windows WSL, Mac, and Linux.
Features
Core Capabilities
Multi-Provider Support: Seamlessly work with Ollama, OpenAI, Anthropic, Groq, XAI, OpenRouter, Deepseek, and LiteLLM
Vision Model Support: Chat with images using vision-capable models like LLaVA and GPT-4 Vision
Session Management: Save, load, organize, and export chat sessions with full conversation history
Custom Prompts: Create, import, and manage a library of custom system prompts and templates
Fabric Integration: Import and use Fabric patterns as reusable prompts
Model Management: Pull, delete, copy, create, and quantize models with native Ollama support
Advanced Features
Memory System: Persistent user context that remembers information across all conversations with AI-powered updates
Template Execution: Secure code execution system with configurable command allowlists and customizable security patterns
Smart Model Caching: Intelligent per-provider model caching with configurable durations
Provider Management: Enable/disable providers, manage API keys, and configure endpoints
Theme System: Dark/light modes with custom theme support via JSON configuration
Auto-naming: Automatically name chat sessions using LLM-generated titles
Slash Commands: Extensive command system for quick actions and navigation
Export Options: Export conversations as Markdown files for documentation
Security: Comprehensive file validation and secure operations for all data handling
Technical Excellence
Async Architecture: Non-blocking operations for smooth, responsive UI performance
Type Safety: Fully typed Python codebase with comprehensive type checking
Extensible Design: Easy to add new providers, features, and customizations
Cross-Platform: Native support for Windows, macOS, Linux, and WSL
Hot Reload: Development mode with automatic UI updates on code changes
Screenshots
Supports Dark and Light mode as well as custom themes.
On windows if you have scoop installed you can install make with scoop install make
Model Quantization
Native Ollama Quantization
Ollama now supports native model quantization through the create model interface. When creating a new model, you can specify a quantization level (e.g., q4_K_M, q5_K_M) to reduce model size and memory requirements.
Important: Native quantization only works with F16 or F32 base models. If you try to quantize an already-quantized model (like llama3.2:1b which is already Q4_0), you'll receive an error.
Prerequisites for HuggingFace model quantization
For quantizing custom models from HuggingFace that aren't available through Ollama:
Clone the repo and run the setup make target. Note uv is required for this.
hljs language-bash
git clone https://github.com/paulrobello/parllama
cd parllama
make setup
Command line arguments
hljs language-css
usage: parllama [-h] [-v] [-d DATA_DIR] [-u OLLAMA_URL] [-t THEME_NAME] [-m {dark,light}]
[-s {local,site,chat,prompts,tools,create,options,logs}][--use-last-tab-on-startup {0,1}][--load-local-models-on-startup {0,1}][-p PS_POLL][-a {0,1}][--restore-defaults][--purge-cache][--purge-chats][--purge-prompts][--no-save][--no-chat-save]
PAR LLAMA -- Ollama TUI.
options:
-h, --help show this help message and exit
-v, --version Show version information.
-d DATA_DIR, --data-dir DATA_DIR
Data Directory. Defaults to ~/.local/share/parllama
-u OLLAMA_URL, --ollama-url OLLAMA_URL
URL of your Ollama instance. Defaults to http://localhost:11434
-t THEME_NAME, --theme-name THEME_NAME
Theme name. Defaults to par
-m {dark,light}, --theme-mode {dark,light}
Dark / Light mode. Defaults to dark
-s {local,site,chat,prompts,tools,create,options,logs}, --starting-tab {local,site,chat,prompts,tools,create,options,logs}
Starting tab. Defaults to local
--use-last-tab-on-startup {0,1}
Use last tab on startup. Defaults to1--load-local-models-on-startup {0,1}
Load local models on startup. Defaults to1
-p PS_POLL, --ps-poll PS_POLL
Interval in seconds to poll ollama ps command. 0 = disable. Defaults to3
-a {0,1}, --auto-name-session {0,1}
Auto name session using LLM. Defaults to0--restore-defaults Restore default settings and theme
--purge-cache Purge cached data
--purge-chats Purge all chat history
--purge-prompts Purge all custom prompts
--no-save Prevent saving settings for this session
--no-chat-save Prevent saving chats for this session
Unless you specify "--no-save" most flags such as -u, -t, -m, -s are sticky and will be used next time you start PAR_LLAMA.
Environment Variables
Variables are loaded in the following order, last one to set a var wins
HOST Environment
PARLLAMA_DATA_DIR/.env
ParLlama Options Screen
Environment Variables for PAR LLAMA configuration
PARLLAMA_DATA_DIR - Used to set --data-dir
PARLLAMA_THEME_NAME - Used to set --theme-name
PARLLAMA_THEME_MODE - Used to set --theme-mode
OLLAMA_URL - Used to set --ollama-url
PARLLAMA_AUTO_NAME_SESSION - Set to 0 or 1 to disable / enable session auto naming using LLM
Running PAR_LLAMA
with pipx or uv tool installation
From anywhere:
hljs language-bash
parllama
with pip installation
From parent folder of venv
hljs language-bash
source venv/Scripts/activate
parllama
Running against a remote Ollama instance
hljs language-bash
parllama -u "http://REMOTE_HOST:11434"
Running under Windows WSL
Ollama by default only listens to localhost for connections, so you must set the environment variable OLLAMA_HOST=0.0.0.0:11434
to make it listen on all interfaces. Note: this will allow connections to your Ollama server from other devices on any network you are connected to.
If you have Ollama installed via the native Windows installer you must set OLLAMA_HOST=0.0.0.0:11434 in the "System Variable" section
of the "Environment Variables" control panel.
If you installed Ollama under WSL, setting the var with export OLLAMA_HOST=0.0.0.0:11434 before starting the Ollama server will have it listen on all interfaces.
If your Ollama server is already running, stop and start it to ensure it picks up the new environment variable.
You can validate what interfaces the Ollama server is listening on by looking at the server.log file in the Ollama config folder.
You should see as one of the first few lines "OLLAMA_HOST:http://0.0.0.0:11434"
Now that the server is listening on all interfaces you must instruct PAR_LLAMA to use a custom Ollama connection url with the "-u" flag.
The command will look something like this:
hljs language-bash
parllama -u "http://$(hostname).local:11434"
Depending on your DNS setup if the above does not work, try this:
PAR_LLAMA will remember the -u flag so subsequent runs will not require that you specify it.
Dev mode
From repo root:
hljs language-bash
make dev
Quick start Ollama chat workflow
Start parllama.
Click the "Site" tab.
Use ^R to fetch the latest models from Ollama.com.
Use the "Filter Site models" text box and type "llama3".
Find the entry with title of "llama3".
Click the blue tag "8B" to update the search box to read "llama3:8b".
Press ^P to pull the model from Ollama to your local machine. Depending on the size of the model and your internet connection this can take a few min.
Click the "Local" tab to see models that have been locally downloaded.
Select the "llama3:8b" entry and press ^C to jump to the "Chat" tab and auto select the model.
Type a message to the model such as "Why is the sky blue?". It will take a few seconds for Ollama to load the model. After which the LLMs answer will stream in.
Towards the very top of the app you will see what model is loaded and what percent of it is loaded into the GPU / CPU. If a model can't be loaded 100% on the GPU it will run slower.
To export your conversation as a Markdown file type "/session.export" in the message input box. This will open a export dialog.
Press ^N to add a new chat tab.
Select a different model or change the temperature and ask the same questions.
Jump between the tabs to compare responses by click the tabs or using slash commands /tab.1 and /tab.2
Press ^S to see all your past and current sessions. You can recall any past session by selecting it and pressing Enter or ^N if you want to load it into a new tab.
Press ^P to see / change your sessions config options such as provider, model, temperature, etc.
Type "/help" or "/?" to see what other slash commands are available.
Quick start image chat workflow
Start parllama.
Click the "Site" tab.
Use ^R to fetch the latest models from Ollama.com.
Use the "Filter Site models" text box and type "llava-llama3".
Find the entry with title of "llava-llama3".
Click the blue tag "8B" to update the search box to read "llava-llama3:8b".
Press ^P to pull the model from Ollama to your local machine. Depending on the size of the model and your internet connection this can take a few min.
Click the "Local" tab to see models that have been locally downloaded. If the download is complete and it isn't showing up here you may need to refresh the list with ^R.
Select the "llava-llama3" entry and press ^C to jump to the "Chat" tab and auto select the model.
Use a slash command to add an image and a prompt "/add.image PATH_TO_IMAGE describe what's happening in this image". It will take a few seconds for Ollama to load the model. After which the LLMs answer will stream in.
Towards the very top of the app you will see what model is loaded and what percent of it is loaded into the GPU / CPU. If a model can't be loaded 100% on the GPU it will run slower.
Type "/help" or "/?" to see what other slash commands are available.
Quick start OpenAI provider chat workflow
Start parllama.
Select the "Options" tab.
Locate the AI provider you want to use the "Providers" section and enter your API key and base url if needed.
You may need to restart parllama for some providers to fully take effect.
Select the "Chat" tab
If the "Session Config" panel on the right is not visible press ^p
Any providers that have don't need an API key or that do have an API key set should be selectable.
Once a provider is selected available models should be loaded and selectable.
Adjust any other session settings like Temperature.
Click the message entry text box and converse with the LLM.
Type "/help" or "/?" to see what slash commands are available.
LlamaCPP support
Parllama supports LlamaCPP running OpenAI server mode. Parllama will use the default base_url of http://127.0.0.1:8080. This can be configured on the Options tab.
To start a LlamaCPP server run the following command in separate terminal:
hljs language-bash
llama-server -m PATH_TO_MODEL
or
hljs language-bash
llama-server -mu URL_TO_MODEL
Custom Prompts
You can create a library of custom prompts for easy starting of new chats.
You can set up system prompts and user messages to prime conversations with the option of sending immediately to the LLM upon loading of the prompt.
Currently, importing prompts from the popular Fabric project is supported with more on the way.
Memory System
PAR LLAMA features a comprehensive memory system that allows you to maintain persistent context across all your conversations. This addresses the common issue of LLMs forgetting important information about you between chat sessions.
Core Features
Memory Tab: Dedicated interface for managing your personal information and preferences
Automatic Injection: Memory content is automatically injected as the first message in every new conversation
AI-Powered Updates: Use slash commands to intelligently update your memory with AI assistance
Real-time Synchronization: Changes made via commands instantly update the Memory tab interface
Managing Your Memory
Memory Tab Interface
Navigate to the Memory tab to:
Edit Memory Content: Use the large text area to add information about yourself, your preferences, work context, or any details you want the AI to remember
Enable/Disable Memory: Use the checkbox to control whether memory is injected into new conversations
Save and Clear: Use the Save button to persist changes or Clear to remove all memory content
Slash Commands
Memory can be dynamically updated from any chat using these commands:
/remember [information]: Add new information to your memory using AI assistance
Example: /remember I prefer concise technical explanations
Example: /remember I work in Python and React development
/forget [information]: Remove specific information from your memory using AI assistance
Example: /forget my old job title
Example: /forget I mentioned liking verbose responses
/memory.status: View your current memory content and status
/memory.clear: Clear all memory content (with confirmation)
Example Memory Content
hljs language-diff
My name is Alex and I'm a senior software engineer working primarily with:
- Python (FastAPI, Django)- React and TypeScript- AWS cloud infrastructure
I prefer:
- Concise, technical explanations- Code examples with comments- Best practices and security considerations
Current projects:
- Building a microservices API- Learning Rust programming language
How Memory Works
Storage: Memory content is securely stored in your local settings file
Injection: When starting a new conversation, memory is automatically prepended as a system message
AI Updates: Slash commands use your current LLM to intelligently modify memory content
Persistence: All memory changes are immediately saved and synchronized across the interface
The memory system transforms PAR LLAMA into a truly personalized AI assistant that remembers who you are and adapts to your preferences across all conversations.
Template Execution
PAR LLAMA includes a powerful yet secure template execution system that allows you to run code snippets and commands directly from chat messages. This feature enables interactive development workflows, data analysis, and quick testing without leaving the chat interface.
Security & Safety
The execution system is designed with security as a top priority:
Command Allowlists: Only pre-approved commands can be executed
Content Validation: Dangerous patterns are automatically detected and blocked
Sandboxing: Commands run in a controlled environment with restricted permissions
Timeout Protection: All executions have configurable time limits
Output Limiting: Command output is truncated to prevent resource exhaustion
Configuration
Template execution settings are configurable in the Options tab under "Template Execution":
Execution Enabled: Toggle to enable/disable the execution feature (Ctrl+R on chat messages)
Allowed Commands: Comma-separated list of base commands permitted for execution
You must specify at least one of light or dark for the theme to be usable.
Theme can be changed via command line with the --theme-name option.
Contributing
See CONTRIBUTING.md for development setup, code style, commit conventions, and the pull request process.
FAQ
Q: Do I need Docker?
A: Docker is only required if you want to Quantize models downloaded from Huggingface or similar llm repositories.
Q: Does ParLlama require internet access?
A: ParLlama by default does not require any network / internet access unless you enable checking for updates or want to import / use data from an online source.
Q: Does ParLlama run on ARM?
A: Short answer is yes. ParLlama should run any place python does. It has been tested on Windows 11 x64, Windows WSL x64, Mac OSX intel and silicon
Q: Does ParLlama require Ollama be installed locally?
A: No. ParLlama has options to connect to remote Ollama instances
Q: Does ParLlama require Ollama?
A: No. ParLlama can be used with most online AI providers
Q: Does ParLlama support vision LLMs?
A: Yes. If the selected provider / model supports vision you can add images to the chat via /slash commands
Roadmap
Where we are
Core Model Management - Find, maintain, pull, delete, copy, and create new models with full Ollama integration
Multi-Provider Support - Seamless integration with Ollama, OpenAI, Anthropic, Groq, Google, xAI, OpenRouter, Deepseek, and LiteLLM
Advanced Chat System - Multi-tab conversations with full history management and session persistence
Vision Model Support - Chat with images using vision-capable LLMs like LLaVA and GPT-4 Vision
Memory System - Persistent user context across all conversations with AI-powered memory management
Template Execution - Secure code execution system with configurable command allowlists and safety controls
Custom Prompt Library - Create, manage, and import prompts from Fabric and other sources
Theme System - Full dark/light mode support with custom JSON-based theme configuration
Smart Caching - Intelligent per-provider model caching with configurable durations
Comprehensive Slash Commands - Extensive command system for navigation, session management, and memory operations
Export Capabilities - Export conversations as Markdown files for documentation
Security & Validation - Comprehensive file validation and secure operations for all data handling
Cross-Platform Support - Native support for Windows, macOS, Linux, and WSL environments
Where we're going
Better image support via file pickers
RAG for local documents and web pages
Expand ability to import custom prompts of other tools
LLM tool use
What's new
v0.8.7 (Latest)
Chat Cost Tracking: Session status bar now shows cumulative cost for paid providers. Token usage and cost are accumulated across all messages and persisted with the session file
Markdown Export Frontmatter: Exported markdown files now include YAML frontmatter with provider, model, date, token usage, and cost
v0.8.6
Edit & Continue: Press c on any completed assistant message to continue generation from that point. Works with all providers. After stopping generation, edit the partial response with e, then press c to continue (#45)
v0.8.4
First Run Fix: Fixed error message appearing on first run when settings.json doesn't exist yet (#69)
Python 3.11 Compatibility Fix: Fixed syntax errors when running on Python 3.11
