# PAR LLAMA

## Table of Contents

* [About](#about)
* [Features](#features)
   * [Core Capabilities](#core-capabilities)
   * [Advanced Features](#advanced-features)
   * [Technical Excellence](#technical-excellence)
* [Screenshots](#screenshots)
* [Prerequisites for running](#prerequisites-for-running)
* [Prerequisites for dev](#prerequisites-for-dev)
* [Prerequisites for huggingface model quantization](#prerequisites-for-huggingface-model-quantization)
* [Installing using pipx](#pipx)
* [Installing using uv](#using-uv)
* [Installing for dev mode](#installing-for-dev-mode)
* [Command line arguments](#command-line-arguments)
* [Environment Variables](#environment-variables)
* [Running PAR_LLAMA](#running-par_llama)
    * [with pipx installation](#with-pipx-installation)
    * [with pip installation](#with-pip-installation)
* [Running against a remote instance](#running-against-a-remote-instance)
* [Running under Windows WSL](#running-under-windows-wsl)
    * [Dev mode](#dev-mode)
* [Quick start Ollama chat workflow](#Quick-start-Ollama-chat-workflow)
* [Quick start image chat workflow](#Quick-start-image-chat-workflow)
* [Quick start OpenAI provider chat workflow](#Quick-start-OpenAI-provider-chat-workflow)
* [Custom Prompts](#custom-prompts)
* [Memory System](#memory-system)
* [Template Execution](#template-execution)
* [Themes](#themes)
* [Screen Help](https://github.com/paulrobello/parllama/blob/main/src/parllama/help.md)
* [Contributing](#contributing)
* [FAQ](#faq)
* [Roadmap](#roadmap)
    * [Where we are](#where-we-are)Ï
    * [Where we're going](#where-were-going)
* [What's new](#whats-new)
  * [v0.8.2](#v082)
  * [v0.8.1](#v081)
  * [v0.8.0](#v080)
  * [v0.7.0](#v070)
  * [v0.6.1](#v061)
  * [v0.6.0](#v060)
  * [older...](#v050)

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## About
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](https://textual.textualize.io/) and [Rich](https://github.com/Textualize/rich?tab=readme-ov-file) and my [PAR AI Core](https://github.com/paulrobello/par_ai_core).
It runs on all major OS's including but not limited to Windows, Windows WSL, Mac, and Linux.

[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://buymeacoffee.com/probello3)

## 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.

![Chat Dark](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/chat_dark_1.png)

![Chat Thinking Dark](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/chat_thinking_dark_1.png)

![Chat Image Dark](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/chat_image_dark_1.png)

![Local Models Dark](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/local_models_dark_1.png)

![Model View Dark](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/models_view_dark_1.png)

![Site Models Dark](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/site_models_dark_1.png)

![Custom Prompt Dark](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/custom_prompt_dark_1.png)

![Options Dark](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/options_dark_1.png)

![Local Models Light](https://raw.githubusercontent.com/paulrobello/parllama/main/docs/local_models_light_1.png)

## Videos
[V0.3.5 demo](https://www.youtube.com/watch?v=Genv46SKA5o)

## Prerequisites for running
* Install and run [Ollama](https://ollama.com/download)
* Install Python 3.11 or newer
  * [https://www.python.org/downloads/](https://www.python.org/downloads/) has installers for all versions of Python for all os's
  * On Windows the [Scoop](https://scoop.sh/) tool makes it easy to install and manage things like python
    * Install Scoop then do `scoop install python`

## Prerequisites for dev
* Install uv
  * See the [Using uv](#Using-uv) section
* Install GNU Compatible Make command
  * 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:

1. Download [HuggingFaceModelDownloader](https://github.com/bodaay/HuggingFaceModelDownloader) from the releases area
2. Install [Docker Desktop](https://www.docker.com/products/docker-desktop/)
3. Pull the docker image:
```bash
docker pull ollama/quantize
```

## Using uv

### Installing uv
If you don't have uv installed you can run the following:  
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

### PyPi install
```shell
uv tool install parllama
```

To upgrade an existing uv installation use the -U --force flags:
```bash
uv tool install parllama -U --force
```

### Installing / running using uvx
```shell
uvx parllama
```

### Source install from GitHub
```bash
uv tool install git+https://github.com/paulrobello/parllama
```
To upgrade an existing installation use the --force flag:
```bash
uv tool install git+https://github.com/paulrobello/parllama -U --force
```

## pipx
### Installing
If you don't have pipx installed you can run the following:  
```bash
pip install pipx
pipx ensurepath
```

### PyPi install
```shell
pipx install parllama
```

To upgrade an existing pipx installation use the --force flag:
```bash
pipx install parllama --force
```

### Source install from GitHub
```bash
pipx install git+https://github.com/paulrobello/parllama
```

To upgrade an existing installation use the --force flag:
```bash
pipx install git+https://github.com/paulrobello/parllama --force
```


## Installing for dev mode
Clone the repo and run the setup make target. Note `uv` is required for this.
```bash
git clone https://github.com/paulrobello/parllama
cd parllama
make setup
```


## Command line arguments
```
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 to 1
  --load-local-models-on-startup {0,1}
                        Load local models on startup. Defaults to 1
  -p PS_POLL, --ps-poll PS_POLL
                        Interval in seconds to poll ollama ps command. 0 = disable. Defaults to 3
  -a {0,1}, --auto-name-session {0,1}
                        Auto name session using LLM. Defaults to 0
  --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:
```bash
parllama
```

### with pip installation
From parent folder of venv
```bash
source venv/Scripts/activate
parllama
```
## Running against a remote Ollama instance
```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:  
```bash
parllama -u "http://$(hostname).local:11434"
```
Depending on your DNS setup if the above does not work, try this:  
```bash
parllama -u "http://$(grep -m 1 nameserver /etc/resolv.conf | awk '{print $2}'):11434"
```

PAR_LLAMA will remember the -u flag so subsequent runs will not require that you specify it.

### Dev mode
From repo root:
```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:  
```bash
llama-server -m PATH_TO_MODEL
```
or
```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
```
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
1. **Storage**: Memory content is securely stored in your local settings file
2. **Injection**: When starting a new conversation, memory is automatically prepended as a system message
3. **AI Updates**: Slash commands use your current LLM to intelligently modify memory content
4. **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
  - Default includes: `uv`, `python3`, `python`, `node`, `tsc`, `bash`, `sh`, `zsh`, `fish`
  - Add your own trusted commands as needed
  - Changes take effect immediately without restart

### Usage
1. **Enable Execution**: Ensure "Execution enabled" is checked in Options
2. **Create Executable Content**: Paste or type code/commands in a chat message
3. **Execute**: Press **Ctrl+R** on any chat message to execute its content
4. **Review Results**: Execution results appear as a new chat response with:
   - Command that was executed
   - Standard output and error streams
   - Exit code and execution time
   - Any temporary files created

### Execution Templates
The system supports flexible execution templates that can:
- Execute inline code directly from message content
- Create temporary files for multi-line scripts
- Set custom working directories
- Configure environment variables
- Run commands in foreground or background
- Handle different programming languages and tools

### Best Practices
- Start with a restrictive allowed commands list and expand as needed
- Test execution templates with non-destructive commands first
- Be cautious with commands that modify files or system state
- Use the timeout settings to prevent long-running processes
- Review execution results before relying on outputs

This feature transforms PAR LLAMA into a powerful development companion, enabling seamless transitions between conversation and code execution.

## Themes
Themes are json files stored in the themes folder in the data directory which defaults to **~/.parllama/themes**  

The default theme is "par" so can be located in **~/.parllama/themes/par.json**  

Themes have a dark and light mode are in the following format:  
```json
{
  "dark": {
    "primary": "#e49500",
    "secondary": "#6e4800",
    "warning": "#ffa62b",
    "error": "#ba3c5b",
    "success": "#4EBF71",
    "accent": "#6e4800",
    "panel": "#111",
    "surface":"#1e1e1e",
    "background":"#121212",
    "dark": true
  },
  "light": {
    "primary": "#004578",
    "secondary": "#ffa62b",
    "warning": "#ffa62b",
    "error": "#ba3c5b",
    "success": "#4EBF71",
    "accent": "#0178D4",
    "background":"#efefef",
    "surface":"#f5f5f5",
    "dark": false
  }
}
```

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
Start by following the instructions in the section **Installing for dev mode**.  

Please ensure that all pull requests are formatted with ruff, pass ruff lint and pyright.  
You can run the make target **pre-commit** to ensure the pipeline will pass with your changes.  
There is also a pre-commit config to that will assist with formatting and checks.  
The easiest way to setup your environment to ensure smooth pull requests is:  

With uv installed:
```bash
uv tool install pre-commit
```

With pipx installed:
```bash
pipx install pre-commit
```

From repo root run the following:
```bash
pre-commit install
pre-commit run --all-files
```
After running the above all future commits will auto run pre-commit. pre-commit will fix what it can and show what
if anything remains to be fixed before the commit is allowed.

## 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

### v0.8.2

* **Updated Dependencies**: Upgraded core dependencies to latest versions
  - langchain 1.0.7 (from 0.3.27)
  - textual 6.6.0 (from 6.1.0)
  - par-ai-core 0.5.3 (from 0.3.2)
  - ollama 0.6.1 (from 0.5.1)
  - Multiple security and infrastructure library updates
* **Dependency Cleanup**: Optimized uv.lock file for better performance

### v0.8.1

* **Python 3.14 Support**: Added full compatibility for Python 3.14 while maintaining backward compatibility with Python 3.11+
  - Updated project configuration to support Python 3.11, 3.12, 3.13, and 3.14
  - CI/CD pipelines default to Python 3.14 for builds and testing
  - Code quality tools (ruff, pyright) configured to target Python 3.14
  - Modernized codebase to use Python 3.14 features (type statement, generic type parameters)
* **Updated Dependencies**: Refreshed dependency lock file with latest compatible versions

### v0.8.0

* **Python 3.13 Support**: Added compatibility for Python 3.13 while maintaining support for Python 3.11 and 3.12
  - Updated project configuration to support Python 3.11, 3.12, and 3.13
  - CI/CD pipelines default to Python 3.13 for builds and testing
  - Code quality tools (ruff, pyright) configured for Python 3.11 minimum compatibility
  - Dependency updates for multi-version Python support
* **Updated Dependencies**: Refreshed dependency lock file with latest compatible versions

### v0.7.0

* **Configurable Security Patterns**: Enhanced execution security with user-customizable safety controls
  - Security patterns now configurable through Options page instead of hardcoded
  - Default patterns focused only on filesystem safety (rm, del, mkfs, dd, /dev/)
  - User can add/remove security patterns via comma-separated input field
  - Changes take effect immediately without requiring application restart
  - Critical security patterns (sudo, exec, eval) always remain protected
  - Fixes false positives that were blocking legitimate Python f-string usage

* **Improved Execution Result Formatting**: Cleaner display of code execution results
  - CLI parameter scripts now show as `python -c <script>` instead of messy escaped code
  - Executed code displayed in clean syntax-highlighted blocks for short scripts (≤10 lines)
  - Smart language detection for proper syntax highlighting (Python, JavaScript, Bash)
  - Intelligent command truncation for long commands with logical break points
  - File-based executions remain unchanged for optimal readability

### v0.6.1

* **gRPC Warning Suppression**: Fixed annoying gRPC ALTS warnings on startup
  - Configured Google Generative AI to use REST transport instead of gRPC
  - Eliminates "All log messages before absl::InitializeLog() is called" warnings
  - Resolves "ALTS creds ignored" and "Not running on GCP" messages
  - Provides clean startup experience without affecting functionality
* **Memory View Fix**: Fixed crash in Memory tab due to missing TextArea widget ID
  - Added missing `id="memory_textarea"` attribute to prevent AttributeError
  - Ensures proper widget identification for memory management functionality

### v0.6.0

* **Memory System**: Comprehensive persistent user context across all conversations
  - Dedicated Memory tab for managing personal information and preferences
  - Automatic memory injection as first message in new conversations
  - AI-powered memory updates via slash commands (`/remember`, `/forget`, `/memory.status`, `/memory.clear`)
  - Real-time synchronization between slash commands and Memory tab interface
  - Secure local storage with comprehensive file validation

### v0.5.0

* **Template Execution System**: Added secure code execution feature with configurable command allowlists
  - Execute code snippets and commands directly from chat messages using Ctrl+R
  - Comprehensive security controls including command validation and content filtering
  - Configurable execution settings in Options tab (execution enabled toggle, allowed commands list)
  - Support for multiple programming languages and command-line tools
  - Settings now properly persist between application sessions

### v0.4.0

* Fixed type checking issue with ClickableLabel widget accessing incorrect property
* Changed from accessing `renderable` to `content` property to align with Textual API

### v0.3.28

* Fix some outdated dependencies
* Fixed delete chat tab on macOS sometimes not working
* Streamlined markdown fences

### v0.3.27

* Fixed Fabric import due to upstream library changes (PR https://github.com/paulrobello/parllama/pull/61)
* Added better Markdown streaming support thank to upstream library changes
* Fixed chat tab send bar layout issues
* Fixed thinking fence not showing correctly sometimes

### v0.3.26

* **Enhanced Fabric Import with Progress Tracking**
* **Increased File Size Limits for Better Usability**
* Enhanced URL validation to preserve case sensitivity for case-sensitive endpoints
* Improved security logging and error handling throughout secrets management
* Implemented comprehensive file validation and security system
* Added FileValidator class with security checks for:
  - File size limits (configurable per file type: images, JSON, ZIP)
  - Extension validation against allowed lists
  - Path security to prevent directory traversal attacks
  - Content validation for JSON, images, and ZIP files
  - ZIP bomb protection with compression ratio checks
* Created SecureFileOperations utility
* Added new file validation configuration tools
* Enhanced security for all JSON operations
* All file operations now include comprehensive error handling and fallback mechanisms
* Improved error handling for model creation with better error messages
* Added validation for quantization levels with list of valid options
* Added specific error messages for quantization requirements (F16/F32 base models)
* Updated UI to clarify quantization requirements in model creation
* Fixed memory leaks using WeakSet for subscriptions and bounded job queue
* Updated quantization documentation to clarify native Ollama support vs Docker requirements
* Fixed critical threading race conditions in job processing system
* Enhanced exception handling across all job methods (pull, push, copy, create)
* Improved error logging and user notifications for all job operations
* Added state transition logging for debugging threading issues
* Fixed delete key on mac sometimes not working
* Implemented comprehensive configuration management system
  - Added 24 configurable settings for timers, timeouts, and UI behavior
* Major code quality and type safety improvements
* Implemented centralized state management system
* Enhanced provider cache system with configurable per-provider durations
* Added comprehensive provider disable functionality with checkboxes for all AI providers
  - Disabled providers are excluded from model refresh operations and UI dropdowns to prevent timeouts
* Added automatic local model list refresh after successful model downloads

### v0.3.25

* Fixed tab in input history causing a crash.
* Fixed create new model causing crash.
* Fix hard coded config loading.


### v0.3.24

* Sort local models by size then name
* Add support for http basic auth when setting base url for providers

### v0.3.23

* Fixed possible crash on local model details dialog

### v0.3.22

* Add option to import markdown files into session via slash command /session.import and hot key ctrl+i
* Added slash command /session.summarize to summarize and replace entire conversation into a single 1 paragraph message
* Tree-sitter package no longer installed by default

### v0.3.21

* Fix error caused by LLM response containing certain markup
* Added llm config options for OpenAI Reasoning Effort, and Anthropic's Reasoning Token Budget
* Better display in chat area for "thinking" portions of a LLM response
* Fixed issues caused by deleting a message from chat while its still being generated by the LLM
* data and cache locations now use proper XDG locations

### v0.3.20

* Fix unsupported format string error caused by missing temperature setting

### v0.3.19

* Fix missing package error caused by previous update

### v0.3.18

* Updated dependencies for some major performance improvements

### v0.3.17

* Fixed crash on startup if Ollama is not available
* Fixed markdown display issues around fences
* Added "thinking" fence for deepseek thought output
* Much better support for displaying max input context size

### v0.3.16

* Added providers xAI, OpenRouter, Deepseek and LiteLLM

### v0.3.15

* Added copy button to the fence blocks in chat markdown for easy code copy

### v0.3.14

* Fix crash caused some models having some missing fields in model file

### v0.3.13

* Handle clipboard errors

### v0.3.12

* Fixed bug where changing providers that have custom urls would break other providers
* Fixed bug where changing Ollama base url would cause connection timed out

### v0.3.11

* Added ability to set max context size for Ollama and other providers that support it
* Limited support for LLamaCpp running in OpenAI Mode.
* Added ability to cycle through fences in selected chat message and copy to clipboard with `ctrl+shift+c`
* Added theme selector
* Various bug fixes and performance improvements
* Updated core AI library and dependencies
* Fixed crash due to upstream library update

### v0.3.10
* Fixed crash issues on fresh installs
* Images are now stored in chat session json files
* Added API key checks for online providers

### v0.3.9
* Image support for models that support them using /add.image slash command. See the [Quick start image chat workflow](#quick-start-image-chat-workflow)
* Add history support for both single and multi line input modes
* Fixed crash on models that dont have a license
* Fixed last model used not get used with new sessions

### v0.3.8
* Major rework of core to support providers other than Ollama
* Added support for the following online providers: OpenAI, Anthropic, Groq, Google
* New session config panel docked to right side of chat tab (more settings coming soon)
* Better counting of tokens (still not always 100% accurate)

### v0.3.7
* Fix for possible crash when there is more than one model loaded into ollama

### v0.3.6
* Added option to save chat input history and set its length
* Fixed tab switch issue on startup
* Added cache for Fabric import to speed up subsequent imports

### v0.3.5
* Added first time launch welcome
* Added Options tab which exposes more options than are available via command line switches
* Added option to auto check for new versions
* Added ability to import custom prompts from [fabric](https://github.com/danielmiessler/fabric)
* Added toggle between single and multi line input (Note auto complete and command history features not available in multi line edit mode)

### v0.3.4
* Added custom prompt library support  (Work in progress)
* Added cli option and environment var to enable auto naming of sessions using LLM (Work in progress)
* Added tokens per second stats to session info line on chat tab
* Fixed app crash when it can't contact ollama server for PS info
* Fixed slow startup when you have a lot of models available locally
* Fixed slow startup and reduced memory utilization when you have many / large chats
* Fixed session unique naming bug where it would always add a "1" to the session name
* Fixed app sometimes slowing down during LLM generation
* Major rework of internal message handling
* Issue where some footer items are not clickable has been resolved by a library PARLLAMA depends on

### v0.3.3
* Added ability to edit existing messages. select message in chat list and press "e" to edit, then "escape" to exit edit mode
* Add chat input history access via up / down arrow while chat message input has focus
* Added /session.system_prompt command to set system prompt in current chat tab

### v0.3.2
* Ollama ps stats bar now works with remote connections except for CPU / GPU %'s which ollama's api does not provide
* Chat tabs now have a session info bar with info like current / max context length
* Added conversation stop button to abort llm response
* Added ability to delete messages from session
* More model details displayed on model detail screen
* Better performance when changing session params on chat tab

### v0.3.1
* Add chat tabs to support multiple sessions
* Added cli option to prevent saving chat history to disk
* Renamed / namespaced chat slash commands for better consistency and grouping
* Fixed application crash when ollama binary not found

### v0.3.0
* Added chat history panel and management to chat page

### v0.2.51
* Fix missing dependency in package

### v0.2.5
* Added slash commands to chat input
* Added ability to export chat to markdown file
* ctrl+c on local model list will jump to chat tab and select currently selected local model
* ctrl+c on chat tab will copy selected chat message
