PyGPT - Desktop AI Assistant

Release: 2.8.14 | build: 2026-09-10 | Python: >=3.10, <3.14

Official website: https://pygpt.net | Documentation | Discord

Get it from: PyPi | Snap Store | Microsoft Store | AppImage

Compiled version for Linux and Windows: Download (64bit)

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Overview

PyGPT is an all-in-one desktop AI assistant supporting models from OpenAI (GPT-6 Astra, GPT-5.6, GPT-4, o1, o3), Google Gemini, Anthropic Claude, xAI Grok, Perplexity / Sonar, DeepSeek, and models available through HuggingFace, LlamaIndex, OpenAI-compatible APIs, and local Ollama installations such as Gemma 4, Qwen 3.6, Llama 4, Mistral Small 3.2, DeepSeek, Bielik, Nemotron, and gpt-oss.

It supports chat, Agents v2 (beta) and other agent workflows, completions, Chat with Files (via LlamaIndex), image and video generation, and image analysis. Models can work with files, run Python and system or custom commands, transfer files, call external APIs, and search the web with DuckDuckGo, Google and Microsoft Bing.

PyGPT also provides speech synthesis through OpenAI, Microsoft Azure, Google Cloud / GenAI, Eleven Labs and xAI, plus speech recognition with OpenAI Whisper (API or local), Google / Google Cloud / GenAI, Bing and xAI Grok Voice. It stores conversation history and memory, supports reusable presets, and can be extended with built-in or custom plugins for tools, automation and external integrations.

Showcase (mp4, version 2.8.3, build 2026-08-16):

https://github.com/user-attachments/assets/22972e34-dc9f-451d-ae64-23a91e945e97

Screenshots (version 2.8.4, build 2026-08-16):

Dark theme:

Light theme:

You can download compiled 64-bit versions for Windows and Linux here: https://pygpt.net/#download

Features

• Desktop AI Assistant for Linux, Windows and Mac, written in Python.
• Works similarly to ChatGPT, but locally (on a desktop computer).
• 11 modes of operation: Chat, Chat with Files, Realtime + audio, Research (Perplexity), Completion, Image and Video generation, Experts, Computer use, Agents v2 (beta), Agents and Autonomous Mode.
• Supports multiple models like OpenAI GPT-6 Astra, GPT-5.6, GPT-4, o1, o3, o4, Google Gemini, Anthropic Claude, xAI Grok, DeepSeek V3/R1, Perplexity / Sonar, and any model accessible through LlamaIndex and Ollama such as Gemma 4, Qwen 3.6, Llama 4, Mistral Small 3.2, DeepSeek, Bielik, Nemotron, gpt-oss, etc.
• Chat with your own Files: integrated LlamaIndex support: chat with data such as: txt, pdf, csv, html, md, docx, json, epub, xlsx, xml, webpages, Google, GitHub, video/audio, images and other data types, or use conversation history as additional context provided to the model.
• Built-in vector databases support and automated files and data embedding.
• Image generation via models like gpt-image, Imagen, Gemini, and Nano Banana.
• Video generation via models like Veo3 and Sora2.
• Internet access via DuckDuckGo, Google and Microsoft Bing.
• Speech synthesis via OpenAI, Microsoft Azure, Google Cloud / GenAI, Eleven Labs and xAI Text-To-Speech services.
• Speech recognition via OpenAI Whisper (API or local), Google / Google Cloud / GenAI, Microsoft Bing and xAI Grok Voice.
• Plugins support with built-in plugins like Files I/O, Code Interpreter, Web Search, Google, Facebook, X/Twitter, Slack, Telegram, GitHub, MCP, and many more.
• MCP support.
• Camera capture for real-time image analysis in Chat and other supported modes.
• Image analysis via vision models.
• Included support features for individuals with disabilities: customizable keyboard shortcuts, voice control, and translation of on-screen actions into audio via speech synthesis.
• Handles and stores the full context of conversations (short and long-term memory).
• Integrated calendar, day notes and search in contexts by selected date.
• Tools and commands execution (via plugins: access to the local filesystem, Python/OS, system commands execution, and more).
• Custom commands creation and execution.
• Crontab / Task scheduler included.
• Built-in Python/OS tool with real-time Python / IPython execution.
• Manages files and attachments with options to upload, download, and organize.
• Context history with the capability to revert to previous contexts (long-term memory).
• Allows you to easily manage prompts with handy editable presets.
• Provides an intuitive operation and interface.
• Includes a notepad.
• Includes simple painter / drawing tool.
• Includes an node-based Agents Builder.
• Includes Agents v2 (beta), an advanced orchestrated multi-agent mode with a user-facing Orchestrator and dynamically managed worker agents.
• Supports multiple languages.
• Requires no previous knowledge of using AI models.
• Fully configurable.
• Themes support.
• Real-time code syntax highlighting.
• Built-in token usage calculation.
• Open source; source code is available on GitHub.
• Utilizes the user's own API key.
• and many more.

The application is free, open-source, and runs on PCs with Linux, Windows 10, Windows 11 and Mac. Full Python source code is available on GitHub.

PyGPT uses your own API credentials to connect to supported AI providers such as OpenAI, Google, Anthropic, xAI, Perplexity, Mistral, OpenRouter, and others. Depending on the selected model and provider, you may need an account and a valid API key for that service. Local models do not require external API credentials. You can also use built-it LlamaIndex support to connect to other Large Language Models (LLMs), such as those on HuggingFace. Additional API keys may be required.

Installation

Binaries (Linux, Windows 10 and 11)

You can download compiled binary versions for Linux and Windows (10/11).

PyGPT binaries require a PC with Windows 10, 11, or Linux. Simply download the installer or the archive with the appropriate version from the download page at https://pygpt.net, extract it, or install it, and then run the application. A binary version for Mac is not available, so you must run PyGPT from PyPi or from the source code on Mac. Currently, only 64-bit binaries are available.

Linux version requires GLIBC >= 2.35.

Microsoft Store (Windows)

For Windows 10/11, you can install PyGPT directly from Microsoft Store:

Link to MS Store: https://apps.microsoft.com/detail/XP99R4MX3X65VQ

AppImage (Linux)

You can download the latest PyGPT AppImage for Linux from the release page:

Releases: https://github.com/szczyglis-dev/py-gpt/releases

Tip: Remember to give execution permissions to the downloaded file:

chmod +x ./PyGPT-X.X.X-x86_64.AppImage

To manage future updates you can use AppImageUpdate tool:

You can download it from: https://github.com/AppImage/AppImageUpdate/releases

After downloading, run the following command in terminal:

appimageupdatetool ./PyGPT-X.X.X-x86_64.AppImage

Snap Store (Linux)

You can install PyGPT directly from Snap Store:

sudo snap install pygpt

To manage future updates use:

sudo snap refresh pygpt

Using camera: to use camera in Snap version you must connect the camera with:

sudo snap connect pygpt:camera

Using microphone: to use microphone in Snap version you must connect the microphone with:

sudo snap connect pygpt:audio-record :audio-record
sudo snap connect pygpt:alsa

Using audio output: to use audio output in Snap version you must connect the audio with:

sudo snap connect pygpt:audio-playback
sudo snap connect pygpt:alsa

Connecting IPython in Docker in Snap version:

To use IPython in the Snap version, you must connect PyGPT to the Docker daemon:

sudo snap connect pygpt:docker-executables docker:docker-executables

sudo snap connect pygpt:docker docker:docker-daemon

PyPi (pip)

The application can also be installed from PyPi using pip install:

1. Create virtual environment:

python3 -m venv venv
source venv/bin/activate

2. Install from PyPi:

pip install pygpt-net

3. Once installed run the command to start the application:

pygpt

Running from GitHub source code

An alternative method is to download the source code from GitHub and execute the application using the Python interpreter (>=3.10, <3.14).

Install with pip

1. Clone git repository or download .zip file:

git clone https://github.com/szczyglis-dev/py-gpt.git
cd py-gpt

2. Create a new virtual environment:

python3 -m venv venv
source venv/bin/activate

3. Install requirements:

pip install -r requirements.txt

4. Run the application:

python3 run.py

Install with Poetry

1. Clone git repository or download .zip file:

git clone https://github.com/szczyglis-dev/py-gpt.git
cd py-gpt

2. Install Poetry (if not installed):

pip install poetry

3. Create a new virtual environment that uses Python 3.10:

poetry env use python3.10
poetry shell

or (Poetry >= 2.0):

poetry env use python3.10
poetry env activate

4. Install requirements:

poetry install

5. Run the application:

poetry run python3 run.py

Tip: you can use PyInstaller to create a compiled version of the application for your system (required version 6.4.0).

Troubleshooting

If you have a problems with xcb plugin with newer versions of PySide on Linux, e.g. like this:

qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. 
Reinstalling the application may fix this problem.

...then install libxcb:

sudo apt install libxcb-cursor0

If you have a problems with audio on Linux, then try to install portaudio19-dev and/or libasound2:

sudo apt install portaudio19-dev

sudo apt install libasound2
sudo apt install libasound2-data 
sudo apt install libasound2-plugins

Problems with GLIBC on Linux

If you encounter error:

Error loading Python lib libpython3.10.so.1.0: dlopen: /lib/x86_64-linux-gnu/libm.so.6: version GLIBC_2.35 not found (required by libpython3.10.so.1.0)

when trying to run the compiled version for Linux, try updating GLIBC to version 2.35, or use a newer operating system that has at least version 2.35 of GLIBC.

Access to camera in Snap version:

sudo snap connect pygpt:camera

Access to microphone in Snap version:

To use microphone in Snap version you must connect the microphone with:

sudo snap connect pygpt:audio-record :audio-record

Snap and AppArmor permission denied

Snap installs AppArmor profiles for each application by default. The profile for PyGPT is created at:

/var/lib/snapd/apparmor/profiles/snap.pygpt.pygpt

The application should work with the default profile; however, if you encounter errors like:

PermissionError: [Errno 13] Permission denied: '/etc/httpd/conf/mime.types'

add the appropriate access rules to the profile file, for example:

# /var/lib/snapd/apparmor/profiles/snap.pygpt.pygpt

...

/etc/httpd/conf/mime.types r

and reload the profiles.

Alternatively, you can try removing snap and reinstalling it:

sudo snap remove --purge pygpt

sudo snap install pygpt

Access to a microphone and audio in Windows version:

If you have a problems with audio or a microphone in the non-binary PIP/Python version on Windows, check to see if FFmpeg is installed. If it's not, install it and add it to the PATH. You can find a tutorial on how to do this here: https://phoenixnap.com/kb/ffmpeg-windows. The binary version already includes FFmpeg.

Windows and VC++ Redistributable

On Windows, the proper functioning requires the installation of the VC++ Redistributable, which can be found on the Microsoft website:

https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist

The libraries from this environment are used by PySide6 - one of the base packages used by PyGPT. The absence of the installed libraries may cause display errors or completely prevent the application from running.

It may also be necessary to add the path C:\path\to\venv\Lib\python3.x\site-packages\PySide6 to the PATH variable.

WebEngine/Chromium renderer and OpenGL problems

If you have a problems with WebEngine / Chromium renderer you can force the legacy mode by launching the app with command line arguments:

python3 run.py --legacy=1

and to force disable OpenGL hardware acceleration:

python3 run.py --disable-gpu=1

You can also manualy enable legacy mode by editing config file - open the %WORKDIR%/config.json config file in editor and set the following options:

"render.engine": "legacy",
"render.open_gl": false,

Other requirements

For operation, an internet connection is needed (for API connectivity), a registered OpenAI account, and an active API key that must be input into the program. Local models served through Ollama do not require an OpenAI account or external API keys.

Debugging and logging

Please go to Debugging and Logging section for instructions on how to log and diagnose issues in a more detailed manner.

Quick Start

Setting-up API Key(s)

You can configure API keys for various providers, such as OpenAI, Anthropic, Google, xAI, Perplexity, OpenRouter, and more. This flexibility allows you to use different providers based on your needs.

During the initial setup, configure your API keys within the application.

To do so, navigate to the menu:

Config -> Settings -> API Keys

Here, you can add or manage API keys for any supported provider.

Configuring Provider

1. Select the Provider: Choose a tab with provider.
2. Enter the API Key: Paste the corresponding API key for the selected provider.

Example

• OpenAI: Obtain your API key by registering on the OpenAI website: https://platform.openai.com and navigating to https://platform.openai.com/account/api-keys.
• Anthropic, Google, etc.: Follow similar steps on their respective platforms.

Note: The ability to use models or services depends on your access level with the respective provider. If you wish to use custom API endpoints or local APIs that do not require API keys, simply enter any value into the API key field to bypass prompts about an empty key.

Work modes

Chat

+ Inline vision and image generation

In PyGPT, this mode lets you chat with models such as GPT-6 Astra, GPT-5.6, GPT-4, o1, o3, Claude, Gemini, Grok, Perplexity (Sonar), DeepSeek, and many others. PyGPT can use native SDKs from supported providers, including OpenAI, Google, Anthropic, and xAI, when enabled. It can also connect to providers and local services through OpenAI-compatible APIs, including Responses API and ChatCompletions API compatible endpoints where supported.

Tip: This mode uses the provider SDK directly. If there's no native client built into the app, models like Sonar or local Ollama models such as Qwen 3.6 and Gemma 4 are supported in Chat mode via LlamaIndex or OpenAI-compatible API endpoints. The app automatically switches to these endpoints when using non-OpenAI models. You can enable or disable the use of the native API SDK (per provider) in Settings -> API Keys. If the native SDK is disabled, the OpenAI SDK will be used via the compatible ChatCompletions API endpoint.

Currently built-in native clients:

• Anthropic SDK
• OpenAI SDK
• Google GenAI SDK
• xAI SDK

Local Ollama models are also supported.

The main part of the interface is a chat window where you see your conversations. Below it is a message box for typing. On the right side, you can set up or change the model and system prompt. You can also save these settings as presets to easily switch between models or tasks.

Above where you type your messages, the interface shows you the number of tokens your message will use up as you type it – this helps to keep track of usage. There is also a feature to attach and upload files in this area. Go to the Files and Attachments section for more information on how to use attachments.

Vision: If you want to analyze photos from disk, screenshots, or camera captures and the currently selected model cannot accept image input, enable the Vision (inline) plugin in the Plugins menu. The plugin uses a separately configured image-capable Chat model only for the image-analysis turn. The fallback model can come from any supported provider (for example OpenAI, Google, Anthropic, xAI, OpenRouter, or another OpenAI-compatible provider), as long as the model is configured for Chat and image input.

With this plugin, you can capture an image with your camera or attach an image and send it for analysis to discuss the photograph:

Image generation: If you want to generate images directly in chat you must enable plugin Image generation (inline) in the Plugins menu. Plugin allows you to generate images in Chat mode:

Chat with Files (LlamaIndex)

This mode enables chat interaction with your documents and entire context history through conversation. It seamlessly incorporates LlamaIndex into the chat interface, allowing for immediate querying of your indexed documents.

Tip: If you do not want to call tools/commands, disable the checkbox +Tools. It will speed up the response time when using local models. You can also enable the ReAct agent for tool calls in: Settings -> Indexes / LlamaIndex -> Chat -> Use ReAct agent for Tool calls in Chat with Files mode. Stream mode is disabled if the ReAct agent and +Tools checkbox are active.

Querying single files

You can also query individual files "on the fly" using the query_file command from the Files I/O plugin. This allows you to query any file by simply asking a question about that file. A temporary index will be created in memory for the file being queried, and an answer will be returned from it. From version 2.1.9 similar command is available for querying web and external content: Directly query web content with LlamaIndex.

For example:

If you have a file: data/my_cars.txt with content My car is red.

You can ask for: Query the file my_cars.txt about what color my car is.

And you will receive the response: Red.

Note: this command indexes the file only for the current query and does not persist it in the database. To store queried files also in the standard index you must enable the option Auto-index readed files in plugin settings. Remember to enable + Tools checkbox to allow usage of tools and commands from plugins.

Using Chat with Files mode

In this mode, you are querying the whole index, stored in a vector store database. To start, you need to index (embed) the files you want to use as additional context. Embedding transforms your text data into vectors. If you're unfamiliar with embeddings and how they work, check out this article:

https://stackoverflow.blog/2023/11/09/an-intuitive-introduction-to-text-embeddings/

For a visualization from OpenAI's page, see this picture:

Source: https://cdn.openai.com/new-and-improved-embedding-model/draft-20221214a/vectors-3.svg

To index your files, simply copy or upload them into the data directory and initiate indexing (embedding) by clicking the Index all button, or right-click on a file and select Embed into index. Additionally, you have the option to utilize data from indexed files in any Chat mode by activating the Chat with Files (LlamaIndex, inline) plugin.

After the file(s) are indexed (embedded in vector store), you can use context from them in chat mode:

Built-in file loaders:

Files:

• CSV files (csv)
• Epub files (epub)
• Excel .xlsx spreadsheets (xlsx)
• HTML files (html, htm)
• IPYNB Notebook files (ipynb)
• Image (vision) (jpg, jpeg, png, gif, bmp, tiff, webp)
• JSON files (json)
• Markdown files (md)
• PDF documents (pdf)
• Plain-text files (txt)
• Video/audio (mp4, avi, mov, mkv, webm, mp3, mpeg, mpga, m4a, wav)
• Word .docx documents (docx)
• XML files (xml)

Web/external content:

• Bitbucket
• ChatGPT Retrieval Plugin
• GitHub Issues
• GitHub Repository
• Google Calendar
• Google Docs
• Google Drive
• Google Gmail
• Google Keep
• Google Sheets
• Microsoft OneDrive
• RSS
• SQL Database
• Sitemap (XML)
• Twitter/X posts
• Webpages (crawling any webpage content)
• YouTube (transcriptions)

You can configure data loaders in Settings / Indexes / LlamaIndex / Data Loaders by providing list of keyword arguments for specified loaders. You can also develop and provide your own custom loader and register it within the application.

LlamaIndex is also integrated with the context database, so conversation history can be indexed and used as additional RAG context. Context indexing is configured separately from file indexing in Settings -> Indexes / LlamaIndex -> Context indexing.

File, context, and project indexing

PyGPT separates file indexing from conversation-context indexing:

• File indexing indexes files and directories into a selected persistent vector index. Use Settings -> Indexes / LlamaIndex -> File indexing, the Index all action, or Files -> RMB -> Embed into index.
• Context indexing indexes stored conversation items from the context database. It is configured in Settings -> Indexes / LlamaIndex -> Context indexing.
• Project indexes are isolated runtime indexes associated with projects. They are created and resolved automatically and do not need to be added to the normal configured indexes list.

The Conversation auto-indexing setting has three modes:

• Off - disables automatic conversation-context indexing.
• Auto-index all conversations - enables automatic context indexing for conversations both inside and outside projects.
• Auto-index only in projects - enables automatic context indexing only for conversations assigned to projects.

The Use isolated index per project option controls where conversations inside projects are stored. When enabled, each project uses its own isolated index. PyGPT exposes it in the UI as Current project and internally resolves it to a project-specific ID such as proj_<project_id>. These project indexes are created and updated on demand and are not added to the normal Indexes list. When this option is disabled, project conversations use the global auto-indexing index or indexes selected in Indexes for global auto-indexing.

The Enable auto-indexing in modes setting further limits which work modes may trigger automatic context indexing. Project context indexing is incremental: PyGPT tracks the last indexed conversation item and continues from that point on subsequent updates.

Project-aware indexing is also available outside automatic context indexing:

• In Chat with Files, select Current project to query the active project's isolated index.
• In the Files tab, RMB -> Embed into index -> Current project indexes the selected file or directory into the active project.
• The Chat with Files (LlamaIndex, inline) and Files I/O plugins can automatically use the active project index when their Use project index if in use option is enabled (default: enabled).
• The project context menu provides Update project index and Truncate project index actions. Updating continues incrementally; truncating removes the project's index data and resets its indexing state.
• Deleting a project also removes its project index. Duplicating a project rebuilds a corresponding isolated index only when the source project had one.

Removing an entry from Settings -> Indexes / LlamaIndex -> Indexes removes only the configuration entry; it does not delete data already stored in the vector store. Use the Clear and truncate tab to permanently remove a selected index or all tracked project indexes.

WARNING: remember that when indexing content, API calls to the embedding model are used. Each indexing consumes additional tokens. Always control the number of tokens used on the provider's page.

Tip: Using the Chat with Files mode, you have default access to files manually indexed from the /data directory. However, you can use additional context by attaching a file - such additional context from the attachment does not land in the main index, but only in a temporary one, available only for the given conversation.

Token limit: When you use Chat with Files in non-query mode, LlamaIndex adds extra context to the system prompt. If you use a plugins (which also adds more instructions to system prompt), you might go over the maximum number of tokens allowed. If you get a warning that says you've used too many tokens, turn off plugins you're not using or turn off the "+ Tools" option to reduce the number of tokens used by the system prompt.

Available vector stores (provided by LlamaIndex):

- ChromaVectorStore
- ElasticsearchStore
- PinecodeVectorStore
- QdrantVectorStore
- RedisVectorStore
- SimpleVectorStore

You can configure selected vector store by providing config options like api_key, etc. in Settings -> LlamaIndex window. See the section: Configuration / Vector stores for configuration reference.

Configuring data loaders

In the Settings -> Indexes / LlamaIndex -> Data loaders section you can define the additional keyword arguments to pass into data loader instance. See the section: Configuration / Data Loaders for configuration reference.

Chat with Audio

This mode works like the Chat mode but with native support for audio input and output using a Realtime and Live APIs. In this mode, audio input and output are directed to and from the model directly, without the use of external plugins. This enables faster and better audio communication.

Currently, in beta.

At this moment, only OpenAI real-time models (via the Realtime API) and Google Gemini real-time models (via the Live API) are supported.

Research

This mode (when using Sonar and R1 models) operates using the Perplexity API: https://perplexity.ai.

It allows for deep web searching and utilizes Sonar models, available in Perplexity AI.

It requires a Perplexity API key, which can be generated at: https://perplexity.ai.

From version 2.5.27 also OpenAI deep-research models are available in this mode.

Google Remote MCP: Google Remote MCP can be enabled in Config -> Settings -> Remote Tools -> Google. In the current PyGPT implementation it is available only in Research mode through Google's Interactions API / Deep Research path. Configure MCP servers in Remote MCP configuration as a JSON object or list. Google currently supports Streamable HTTP MCP servers on this path; SSE servers are not supported.

Completion

An older mode of operation that allows working in the standard text completion mode. However, it allows for a bit more flexibility with the text by enabling you to initiate the entire discussion in any way you like.

Similar to chat mode, on the right-hand side of the interface, there are convenient presets. These allow you to fine-tune instructions and swiftly transition between varied configurations and pre-made prompt templates.

Additionally, this mode offers options for labeling the AI and the user, making it possible to simulate dialogues between specific characters - for example, you could create a conversation between Batman and the Joker, as predefined in the prompt. This feature presents a range of creative possibilities for setting up different conversational scenarios in an engaging and exploratory manner.

From version 2.0.107 the davinci models are deprecated and has been replaced with gpt-3.5-turbo-instruct model in Completion mode.

Image and video generation

PyGPT enables quick and easy image creation with image-generation models such as gpt-image, Imagen, Gemini, Nano Banana, and Grok, as well as video generation using models such as Veo and Sora. Generating images and videos is akin to a chat conversation - a user's prompt triggers the generation, followed by downloading, saving to the computer, and displaying the image onscreen. You can send raw prompt to the model in Image generation mode or ask the model for the best prompt.

Image generation using image models is also available in every mode via plugin Image Generation (inline). Just ask any model, in any mode, like e.g. GPT or Gemini to generate an image and it will do it inline, without need to mode change.

If you want to generate images directly in chat you must enable plugin Image generation (inline) in the Plugins menu. Plugin allows you to generate images in Chat mode:

For OpenAI models, you can also enable remote image generation in Config -> Settings -> Remote Tools. If enabled, image generation will be available natively within the conversation, without plugins, in Chat mode.

To use Imagen models you must enable Use Vertex AI in Config -> Settings -> API Keys -> Google -> Advanced options.

Remix, Edit, or Extend

To remix or extend from a previous image or video instead of creating a new one from scratch, enable the Remix/Extend option checkbox in the toolbox. The last generated image or video in the current context will be used as a reference for your prompt, allowing you to request changes to the generated content. If the Remix/Extend option is enabled, uploading an image attachment as a reference will not take effect.

Raw mode

There is an option for switching prompt generation mode.

If Raw Mode is enabled, a model will receive the prompt exactly as you have provided it. If Raw Mode is disabled, a model will generate the best prompt for you based on your instructions.

Image storage

Once you've generated an image, you can easily save it anywhere on your disk by right-clicking on it. You also have the options to delete it or view it in full size in your web browser.

Tip: Use presets to save your prepared prompts. This lets you quickly use them again for generating new images later on.

The app keeps a history of all your prompts, allowing you to revisit any session and reuse previous prompts for creating new images.

Images are stored in img directory in PyGPT user data folder.

Agents v2 (beta)

Beta: Agents v2 is currently an experimental beta feature introduced in version 2.8.10. Its behavior, preset options, workflow rules, and provider compatibility may change in subsequent releases.

Agents v2 is a new orchestrated multi-agent mode designed for complex tasks that benefit from planning, delegation, parallel execution, tool use, and verification. It uses a dedicated orchestration runtime built on LlamaIndex agent workflows and is separate from the older Agent (LlamaIndex), Agent (OpenAI), and Agent (Autonomous) modes.

A single Orchestrator is responsible for the user-facing conversation and owns the task from start to finish. During a task it can dynamically create specialist worker agents, assign or update their roles, start or reuse them for follow-up work, inspect their state, wait for their results, stop them, or remove them. Workers keep their in-memory history while the current Agents v2 runtime is active, allowing the Orchestrator to continue or refine their work without recreating them. Up to 16 workers can exist in one runtime, and independent workers can execute concurrently.

The Orchestrator can answer very small tasks directly, but for multi-step or action-oriented work it delegates execution to workers and integrates their results into the final response. Worker output is private to the orchestration runtime; the user sees the Orchestrator's streamed response plus a transient progress/status line.

Tools and provider capabilities

Agents v2 can use both local and provider-side capabilities:

• Local tools from enabled PyGPT plugins can be made available to the Orchestrator and workers.
• Remote tools exposed by the selected provider can be made available when supported by the provider/model and enabled in PyGPT.
• Local and remote tools can be enabled or disabled independently in the Agents v2 preset with Allow local tools and Allow remote tools.
• Models with native function calling use it when available; the runtime can fall back to a ReAct agent for compatible models without native function calling.

Settings

Agent-related application settings are organized under Settings -> Agents and experts. The General / v2 tab contains settings intended for the current Agents v2 workflow. Show full tool-chain in Agents v2 is disabled by default; when enabled, the final response stores and displays the complete chain of normal tool calls executed during the workflow, with a separate expandable Request/Response pair for each call. Internal orchestration and worker-management calls are not included.

Settings kept only for older agent implementations are separated into the Legacy tab. Display full agent output in chat view controls rendering of full output from legacy agent modes, while Display a tray notification when the goal is achieved controls legacy agent completion notifications. These Legacy options do not control the Agents v2 tool-chain display.

RAG, attachments and artifacts

If a valid index is selected in the preset, Agents v2 exposes it as a RAG query tool. User attachments and extracted attachment context are shared with the workflow, and image attachments are also passed as native image input when the selected model supports images. Files, images, URLs and attachments produced by workers or provider-side tools are collected by the runtime and propagated to the main response.

Memory and workflow lifecycle

The Orchestrator keeps its own hidden conversation memory across turns in the current conversation/preset, subject to the normal PyGPT token-window limits. Worker memory is runtime-local and is retained when the same worker is reused during that workflow. The Orchestrator cannot finalize a workflow while required workers are still running or when a newly created worker has never been started; it must first wait for, stop, run or remove those workers and then produce the final answer.

Recommended use cases include coding and file operations, research with independent verification, RAG-assisted tasks, multi-stage analysis, workflows that combine multiple tools, and tasks that can be split into independent parallel subtasks.

Note: Because Agents v2 is currently beta, use it with appropriate supervision when tools can modify files, execute code or system commands, or perform external actions.

Agent (LlamaIndex)

Mode that allows the use of agents offered by LlamaIndex.

Includes built-in agents (Workflow):

• FunctionAgent
• ReAct
• Structured Planner (sub-tasks)
• CodeAct (connected to Code Interpreter plugin)
• Supervisor + worker

In the future, the list of built-in agents will be expanded.

You can create your own types (workflows/patterns) using the built-in visual node-based editor found in the Tools -> Agents Builder.

You can also create your own agent by creating a new provider that inherits from pygpt_net.provider.agents.base.

Tools and Plugins

In this mode, all commands from active plugins are available (commands from plugins are automatically converted into tools for the agent on-the-fly).

RAG - using indexes

If an index is selected in the agent preset, a tool for reading data from the index is automatically added to the agent, creating a RAG automatically.

Multimodality is currently unavailable, only text is supported. Vision support will be added in the future.

Loop / Evaluate Mode

You can run the agent in autonomous mode, in a loop, and with evaluation of the current output. When you enable the Loop / Evaluate checkbox, after the final response is given, the quality of the answer will be rated on a percentage scale of 0% to 100% by another agent. If the response receives a score lower than the one expected (set using a slider at the bottom right corner of the screen, with a default value 75%), a prompt will be sent to the agent requesting improvements and enhancements to the response.

Setting the expected (required) score to 0% means that the response will be evaluated every time the agent produces a result, and it will always be prompted to self-improve its answer. This way, you can put the agent in an autonomous loop, where it will continue to operate until it succeeds.

You can choose between two methods of evaluation:

• By the percentage of tasks completed
• By the accuracy (score) of the final response

You can set the limit of steps in such a loop by going to Settings -> Agents and experts -> Agents -> Max evaluation steps in loop. The default value is 3, meaning the agent will only make three attempts to improve or correct its answer. If you set the limit to zero, there will be no limit, and the agent can operate in this mode indefinitely (watch out for tokens!).

You can change the prompts used for evaluating the response in Settings -> Prompts -> Agent: evaluation prompt in loop. Here, you can adjust it to suit your needs, for example, by defining more or less critical feedback for the responses received.

Agent (OpenAI)

The mode operates on the openai-agents library integrated into the application:

https://github.com/openai/openai-agents-python

It allows running agents for OpenAI models and models compatible with the OpenAI API.

In this mode, you can use pre-configured Experts in Expert mode presets - they will be launched as agents (in the openai_agents_experts type, which allows launching one main agent and subordinate agents to which queries will be appropriately directed).

Agent types (workflows/patterns):

• Agent with experts - uses attached experts as sub-agents
• Agent with experts + feedback - uses attached experts as sub-agents + feedback agent in a loop
• Agent with feedback - single agent + feedback agent in a loop
• Planner - planner agent, 3 sub-agents inside: planner, base agent + feedback
• Research bot - researcher, 3 sub-agents inside: planner, searcher and writer as base agent
• Simple agent - a single agent.
• Evolve - in each generation (cycle), the best response from a given parent agent is selected; in the next generation, the cycle repeats.
• B2B - bot-to-bot communication, involving two bots interacting with each other while keeping a human in the loop.
• Supervisor + Worker - one agent (supervisor) acts as a bridge between the user and the second agent (worker). The user provides a query to the supervisor, who then sends instructions to the worker until the task is completed by the worker.

You can create your own types (workflows/patterns) using the built-in visual node-based editor found in the Tools -> Agents Builder.

There are also predefined presets added as examples:

• Coder
• Experts agent
• Planner
• Researcher
• Simple agent
• Writer with Feedback
• 2 bots
• Supervisor + worker

In the Agents (OpenAI) mode, all remote tools are available for the base agent according to the configuration in the Config -> Settings -> Remote tools menu.

Remote tools for experts can be selected separately for each expert in the preset configuration.

Local tools (from plugins) are available for agents and experts according to the enabled plugins, as in other modes.

In agents with feedback and plans, tools can be allowed in a preset configuration for each agent. They also have separate prompts that can be configured in presets.

Description of how different types of agents work:

Below is a pattern for how different types of agents work. You can use these patterns to create agents for different tasks by modifying the appropriate prompts in the preset for the specific task.

Simple Agent

• The agent completes its task and then stops working.

Agent with Feedback

• The first agent answers a question.
• The second agent (feedback) evaluates the answer and, if necessary, goes back to the first agent to enforce corrections.
• The cycle repeats until the feedback agent is satisfied with the evaluation.

Agent with Experts

• The agent completes the assigned task on its own or delegates it to the most suitable expert (another agent).

Agent with Experts + Feedback

• The first agent answers a question or delegates it to the most suitable expert.
• The second agent (feedback) evaluates and, if necessary, goes back to the first agent to enforce corrections.
• The cycle repeats until the feedback agent is satisfied with the evaluation.

Research Bot

• The first agent (planner) prepares a list of phrases to search.
• The second agent (search) finds information based on the phrases and creates a summary.
• The third agent (writer) prepares a report based on the summary.

Planner

• The first agent (planner) breaks down a task into sub-tasks and sends the list to the second agent.
• The second agent performs the task based on the prepared task list.
• The third agent, responsible for feedback, evaluates, requests corrections if needed, and sends the request back to the first agent. The cycle repeats.

Evolve

• You select the number of agents (parents) to operate in each generation (iteration).
• Each agent prepares a separate answer to a question.
• The best agent (producing the best answer) in a generation is selected by the next agent (chooser).
• Another agent (feedback) verifies the best answer and suggests improvements.
• A request for improving the best answer is sent to a new pair of agents (new parents).
• From this new pair, the best answer is selected again in the next generation, and the cycle repeats.

B2B

• A human provides a topic for discussion.
• Bot 1 generates a response and sends it to Bot 2.
• Bot 2 receives the response from Bot 1 as input, provides an answer, and sends the response back to Bot 1 as its input. This cycle repeats.
• The human can interrupt the loop at any time and update the entire discussion.

Supervisor + Worker

• A human provides a query to the Supervisor.
• The Supervisor prepares instructions for the Worker and sends them to the Worker.
• The Worker completes the task and returns the result to the Supervisor.
• If the task is completed, the Supervisor returns the result to the user. If not, the Supervisor sends another instruction to the Worker to complete the task or asks the user if there are any questions.
• The cycle repeats until the task is completed.

Tip: Starting from version 2.5.97, you can assign and use Experts in all of the agent types.

Limitations:

• When the Computer use tool is selected for an expert or when the computer-use model is chosen, all other tools will not be available for that model.

Agent (Autonomous)

This is an older version of the Agent mode, still available as legacy. However, it is recommended to use the newer mode: Agent (LlamaIndex).

WARNING: Please use this mode with caution - autonomous mode, when connected with other plugins, may produce unexpected results!

The mode activates autonomous mode, where AI begins a conversation with itself. You can set this loop to run for any number of iterations. Throughout this sequence, the model will engage in self-dialogue, answering his own questions and comments, in order to find the best possible solution, subjecting previously generated steps to criticism.

WARNING: Setting the number of run steps (iterations) to 0 activates an infinite loop which can generate a large number of requests and cause very high token consumption, so use this option with caution! Confirmation will be displayed every time you run the infinite loop.

This mode is similar to Auto-GPT - it can be used to create more advanced inferences and to solve problems by breaking them down into subtasks that the model will autonomously perform one after another until the goal is achieved.

You can create presets with custom instructions for multiple agents, incorporating various workflows, instructions, and goals to achieve.

All plugins are available for agents, so you can enable features such as file access, command execution, web searching, image generation, vision analysis, etc., for your agents. Connecting agents with plugins can create a fully autonomous, self-sufficient system. All currently enabled plugins are automatically available to the Agent.

When the Auto-stop option is enabled, the agent will attempt to stop once the goal has been reached.

In opposition to Auto-stop, when the Always continue... option is enabled, the agent will use the "always continue" prompt to generate additional reasoning and automatically proceed to the next step, even if it appears that the task has been completed.

Options

The agent is essentially a virtual mode that internally sequences the execution of a selected underlying mode. You can choose which internal mode the agent should use in the settings:

Settings -> Agents and experts -> Autonomous -> Sub-mode for agents

Default mode is: Chat.

If you want to use the LlamaIndex mode when running the agent, you can also specify which index LlamaIndex should use with the option:

Settings -> Agents and experts -> Autonomous -> Index to use

Experts (co-op, co-operation mode)

Expert mode allows for the creation of experts (using presets) and then consulting them during a conversation. In this mode, a primary base context is created for conducting the conversation. From within this context, the model can make requests to an expert to perform a task and return the results to the main thread. When an expert is called in the background, a separate context is created for them with their own memory. This means that each expert, during the life of one main context, also has access to their own memory via their separate, isolated context.

In simple terms - you can imagine an expert as a separate, additional instance of the model running in the background, which can be called at any moment for assistance, with its own context and memory, as well as its own specialized instructions in a given subject.

Experts do not share contexts with one another, and the only point of contact between them is the main conversation thread. In this main thread, the model acts as a manager of experts, who can exchange data between them as needed.

An expert is selected based on the name in the presets; for example, naming your expert as: ID = python_expert, name = "Python programmer" will create an expert whom the model will attempt to invoke for matters related to Python programming. You can also manually request to refer to a given expert:

Call the Python expert to generate some code.

Experts can be activated or deactivated - to enable or disable use RMB context menu to select the Enable/Disable options from the presets list. Only enabled experts are available to use in the thread.

Experts can also be used in Agent (autonomous) mode - by creating a new agent using a preset. Simply move the appropriate experts to the active list to automatically make them available for use by the agent.

You can also use experts in "inline" mode - by activating the Experts (inline) plugin. This allows for the use of experts in any mode, such as normal chat.

Expert mode, like agent mode, is a "virtual" mode - you need to select a target mode of operation for it, which can be done in the settings at Settings -> Agents and experts -> Experts -> Sub-mode for experts.

You can also ask for a list of active experts at any time:

Give me a list of active experts.

Computer use

This mode allows for autonomous computer control.

In this mode, the model takes control of the mouse and keyboard and can navigate within the user's environment. PyGPT uses the selected provider's native Computer use capability when supported by the current model (OpenAI, Google, or Anthropic), combined with the built-in Mouse and Keyboard integration.

Example of use:

Click on the Start Menu to open it, search for the Notepad in the list, and run it.

You can change the environment in which the navigation mode operates by using the list at the bottom of the toolbox.

Available Environments:

• Browser
• Linux
• Windows
• Mac

You can run this mode in Sandbox (using Playwright - https://playwright.dev/) - to do it, just enable the Sandbox switch in the toolbox. Playwright browsers must be installed on your system. To do so, run:

pip install playwright
playwright install <chromium|firefox|webkit>

After that, set the path to directory with installed browsers in Mouse and Keyborad plugin settings option: Sandbox (Playwright) / Browsers directory.

Compiled binary and Snap versions have chromium preinstalled in the package.

Tip: DO NOT enable the Mouse and Keyboard plugin in Computer use mode—it is already connected to Computer use mode "in the background."

Context and memory

Short and long-term memory

PyGPT features a continuous chat mode that maintains a long context of the ongoing dialogue. It preserves the entire conversation history and automatically appends it to each new message (prompt) you send to the AI. Additionally, you have the flexibility to revisit past conversations whenever you choose. The application keeps a record of your chat history, allowing you to resume discussions from the exact point you stopped.

Handling multiple contexts

On the left side of the application interface, there is a panel that displays a list of saved conversations. You can save numerous contexts and switch between them with ease. This feature allows you to revisit and continue from any point in a previous conversation. PyGPT automatically generates a summary for each context, akin to the way ChatGPT operates and gives you the option to modify these titles itself.

You can disable context support in the settings by using the following option:

Config -> Settings -> Use context 

Clearing history

You can clear the entire memory (all contexts) by selecting the menu option:

File -> Clear history...

Context storage

On the application side, the context is stored in the SQLite database located in the working directory (db.sqlite). In addition, all history is also saved to .txt files for easy reading.

Once a conversation begins, a title for the chat is generated and displayed on the list to the left. This process is similar to ChatGPT, where the subject of the conversation is summarized, and a title for the thread is created based on that summary. You can change the name of the thread at any time.

Files And Attachments

Uploading attachments

Using Your Own Files as Additional Context in Conversations

You can use your own files (for example, to analyze them) during any conversation. You can do this in two ways: by indexing (embedding) your files in a vector database, which makes them available all the time during a "Chat with Files" session, or by adding a file attachment (the attachment file will only be available during the conversation in which it was uploaded).

Attachments

PyGPT makes it simple for users to upload files and send them to the model for tasks like analysis, similar to attaching files in ChatGPT. There's a separate Attachments tab next to the text input area specifically for managing file uploads.

Tip: Project-wide attachment sharing is optional. Enable Settings -> Files and attachments -> Make attachments available in the whole project to make attachments added in one chat available to all chats in the same project. The option is disabled by default; when disabled, attachments remain available only in the chat where they were added.

You can use attachments to provide additional context to the conversation. By default, uploaded files are processed locally using loaders from LlamaIndex and can be converted into text and/or indexed in the vector store. You can upload any file format supported by the application through LlamaIndex. Supported formats include:

Text-based types:

• CSV files (csv)
• Epub files (epub)
• Excel .xlsx spreadsheets (xlsx)
• HTML files (html, htm)
• IPYNB Notebook files (ipynb)
• JSON files (json)
• Markdown files (md)
• PDF documents (pdf)
• Plain-text files (txt and etc.)
• Word .docx documents (docx)
• XML files (xml)

Media-types:

• Image (using vision) (jpg, jpeg, png, gif, bmp, tiff, webp)
• Video/audio (mp4, avi, mov, mkv, webm, mp3, mpeg, mpga, m4a, wav)

Archives:

• zip
• tar, tar.gz, tar.bz2

Native file upload

In the Attachments tab you can enable Prefer native file upload when supported. This option is disabled by default.

When enabled, PyGPT will try to upload each supported attachment directly through the selected provider's native file API instead of reading the file locally and appending its extracted content to the prompt. Native upload is used only when it is supported by the current provider, model, file type, and file size. The native upload path is available for supported OpenAI, Google Gemini, Anthropic, and xAI configurations.

For a file that is successfully sent natively, native upload overrides the selected attachment context mode (Full context, RAG, or Summary) for that file. If native upload is unavailable or fails, PyGPT automatically falls back to the standard local attachment processing, so existing attachment behavior is preserved.

Archive files such as ZIP and TAR are unpacked locally first. Their contents are then handled individually: supported files can be uploaded natively, while unsupported files use the normal local-processing fallback. Attachments sent through the native path are marked with the (Native) suffix in the uploaded attachments list.

Note: Native upload sends the original file content to the selected API provider. Provider-specific file type, size, model, retention, and availability limits may apply.

Tip: To see native-upload activity in the console, enable Settings -> Debug -> Log attachments usage to console. Native upload messages are printed only when attachment logging is enabled.

The content from the uploaded attachments will be used in the current conversation and will be available throughout (per context). There are 3 modes available for working with additional context from attachments:

• Full context: Provides best results. This mode attaches the entire content of the read file to the user's prompt. This process happens in the background and may require a large number of tokens if you uploaded extensive content.

• RAG: The indexed attachment will only be queried in real-time using LlamaIndex. This operation does not require any additional tokens, but it may not provide access to the full content of the file 1:1.

• Summary: When queried, an additional query will be generated in the background and executed by a separate model to summarize the content of the attachment and return the required information to the main model. You can change the model used for summarization in the settings under the Files and attachments section.

In the RAG and Summary mode, you can enable an additional setting by going to Settings -> Files and attachments -> Use history in RAG query. This allows for better preparation of queries for RAG. When this option is turned on, the entire conversation context is considered, rather than just the user's last query. This allows for better searching of the index for additional context. In the RAG limit option, you can set a limit on how many recent entries in a discussion should be considered (0 = no limit, default: 3).

Important: When using Full context mode, the entire content of the file is included in the prompt, which can result in high token usage each time. If you want to reduce the number of tokens used, instead use the RAG option, which will only query the indexed attachment in the vector database to provide additional context.

Images as Additional Context

Files such as jpg, png, and similar images are a special case. By default, images are not used as additional context; they are analyzed in real-time using a vision model. If you want to use them as additional context instead, you must enable the "Allow images as additional context" option in the settings: Files and attachments -> Allow images as additional context.

Uploading larger files and auto-index

To use the RAG mode, the file must be indexed in the vector database. This occurs automatically at the time of upload if the Auto-index on upload option in the Attachments tab is enabled. When uploading large files, such indexing might take a while - therefore, if you are using the Full context option, which does not use the index, you can disable the Auto-index option to speed up the upload of the attachment. In this case, it will only be indexed when the RAG option is called for the first time, and until then, attachment will be available in the form of Full context and Summary.

Embeddings

When using RAG to query attachments, the documents are indexed into a temporary vector store. With multiple providers and models available, you can select the model used for querying attachments in: Config -> Settings -> Files and Attachments. You can also choose the embedding models for specified providers in Config -> Settings -> Indexes / LlamaIndex -> Embeddings -> Default embedding providers for attachments list. By default, when querying an attachment using RAG, the default embedding model and provider corresponding to the RAG query model will be used. If no default configuration is provided for a specific provider, the global embedding configuration will be used.

For example, if the RAG query model is gpt-4o-mini, then the default model for the provider OpenAI will be used. If the default model for OpenAI is not specified on the list, the global provider and model will be used.

Downloading files

PyGPT enables the automatic download and saving of files created by the model. This is carried out in the background, with the files being saved to an data folder located within the user's working directory. To view or manage these files, users can navigate to the Files tab which features a file browser for this specific directory. Here, users have the interface to handle all files sent by the AI.

This data directory is also where the application stores files that are generated locally by the AI, such as code files or any other data requested from the model. Users have the option to execute code directly from the stored files and read their contents, with the results fed back to the AI. This hands-off process is managed by the built-in plugin system and model-triggered commands. You can also indexing files from this directory (using integrated LlamaIndex) and use it's contents as additional context provided to discussion.

The Files I/O plugin takes care of file operations in the data directory, while the Code Interpreter plugin allows for the execution of code from these files.

To allow the model to manage files or python code execution, the + Tools option must be active, along with the above-mentioned plugins:

Presets

What is preset?

Presets in PyGPT are essentially templates used to store and quickly apply different configurations. Each preset includes settings for the mode you want to use (such as chat, completion, or image generation), an initial system prompt, an assigned name for the AI, a username for the session, and the desired "temperature" for the conversation. A warmer "temperature" setting allows the AI to provide more creative responses, while a cooler setting encourages more predictable replies. These presets can be used across various modes and with models accessed via the OpenAI API or LlamaIndex.

The application lets you create as many presets as needed and easily switch among them. Additionally, you can clone an existing preset, which is useful for creating variations based on previously set configurations and experimentation.

Example usage

The application includes several sample presets that help you become acquainted with the mechanism of their use.

Profiles

You can create multiple profiles for an app and switch between them. Each profile uses its own configuration, settings, context history, and a separate folder for user files. This allows you to set up different environments and quickly switch between them, changing the entire setup with just one click.

The app lets you create new profiles, edit existing ones, and duplicate current ones.

To create a new profile, select the option from the menu: Config -> Profile -> New Profile...

To edit saved profiles, choose the option from the menu: Config -> Profile -> Edit Profiles...

To switch to a created profile, pick the profile from the menu: Config -> Profile -> [Profile Name]

Each profile uses its own user directory (workdir). You can link a newly created or edited profile to an existing workdir with its configuration.

The name of the currently active profile is shown as (Profile Name) in the window title.

Models

Built-in models

PyGPT has a preconfigured list of models (as of 2026-09-09):

- `claude-fable-5` (Anthropic)
- `claude-fable-5-1` (Anthropic)
- `claude-haiku-4-5` (Anthropic)
- `claude-opus-4-5` (Anthropic)
- `claude-opus-5` (Anthropic)
- `claude-sonnet-4-5` (Anthropic)
- `claude-sonnet-5` (Anthropic)
- `deepseek-v4-flash` (DeepSeek)
- `deepseek-v4-pro` (DeepSeek)
- `deep-research-max-preview-04-2026` (Google)
- `deep-research-preview-04-2026` (Google)
- `deep-research-pro-preview-12-2025` (Google)
- `gemini-2.5-computer-use-preview-10-2025` (Google)
- `gemini-2.5-flash` (Google)
- `gemini-2.5-flash-image` (Google)
- `gemini-2.5-flash-native-audio-latest` (Google, real-time)
- `gemini-2.5-pro` (Google)
- `gemini-3-flash-preview` (Google)
- `gemini-3-pro-image` (Google)
- `gemini-3.1-flash-image` (Google)
- `gemini-3.1-flash-lite-image` (Google)
- `gemini-3.1-flash-live-preview` (Google, real-time)
- `gemini-3.1-pro-preview` (Google)
- `gemini-3.5-flash` (Google)
- `gemini-3.5-flash-lite` (Google)
- `gemini-3.6-flash` (Google)
- `gemini-3.7-flash` (Google)
- `gemini-flash-latest` (Google)
- `gemini-pro-latest` (Google)
- `imagen-4.0-generate-001` (Google)
- `nano-banana-pro-preview` (Google)
- `veo-3.1-fast-generate-preview` (Google)
- `veo-3.1-generate-preview` (Google)
- `veo-3.1-lite-generate-preview` (Google)
- `openai/gpt-oss-120b:novita` (HuggingFace Router)
- `openai/gpt-oss-20b:novita` (HuggingFace Router)
- `deepseek-r1:8b` (Ollama)
- `gemma4:e4b` (Ollama)
- `gpt-oss:120b` (Ollama)
- `gpt-oss:20b` (Ollama)
- `llama2-uncensored` (Ollama)
- `llama3.1` (Ollama)
- `llama4:scout` (Ollama)
- `mistral-small3.2:latest` (Ollama)
- `nemotron-3.5-lightning:30b` (Ollama)
- `qwen3.5:9b` (Ollama)
- `qwen3.6:27b` (Ollama)
- `SpeakLeash/bielik-11b-v3.0-instruct:Q4_K_M` (Ollama)
- `gpt-3.5-turbo` (OpenAI)
- `gpt-3.5-turbo-instruct` (OpenAI)
- `gpt-4` (OpenAI)
- `gpt-4-turbo` (OpenAI)
- `gpt-4o` (OpenAI)
- `gpt-4o-mini` (OpenAI)
- `gpt-5.3-codex (high)` (OpenAI)
- `gpt-5.3-codex (low)` (OpenAI)
- `gpt-5.3-codex (medium)` (OpenAI)
- `gpt-5.3-codex (xhigh)` (OpenAI)
- `gpt-5.6-luna (high)` (OpenAI)
- `gpt-5.6-luna (low)` (OpenAI)
- `gpt-5.6-luna (medium)` (OpenAI)
- `gpt-5.6-sol (high)` (OpenAI)
- `gpt-5.6-sol (low)` (OpenAI)
- `gpt-5.6-sol (medium)` (OpenAI)
- `gpt-5.6-terra (high)` (OpenAI)
- `gpt-5.6-terra (low)` (OpenAI)
- `gpt-5.6-terra (medium)` (OpenAI)
- `gpt-6-astra (high)` (OpenAI)
- `gpt-6-astra (low)` (OpenAI)
- `gpt-6-astra (medium)` (OpenAI)
- `gpt-image-1` (OpenAI)
- `gpt-image-1.5` (OpenAI)
- `gpt-image-2` (OpenAI)
- `gpt-realtime` (OpenAI, real-time)
- `gpt-realtime-2.1` (OpenAI, real-time)
- `gpt-realtime-2.1-mini` (OpenAI, real-time)
- `o1` (OpenAI)
- `o1-pro` (OpenAI)
- `o3` (OpenAI)
- `o3-mini (high)` (OpenAI)
- `o3-mini (low)` (OpenAI)
- `o3-mini (medium)` (OpenAI)
- `o3-pro` (OpenAI)
- `o4-mini` (OpenAI)
- `sora-2` (OpenAI)
- `sora-2-pro` (OpenAI)
- `sonar` (Perplexity)
- `sonar-deep-research` (Perplexity)
- `sonar-pro` (Perplexity)
- `sonar-reasoning-pro` (Perplexity)
- `grok-2-vision` (xAI)
- `grok-3-mini` (xAI)
- `grok-3-mini-fast` (xAI)
- `grok-4.3` (xAI)
- `grok-4.5` (xAI)
- `grok-4.6` (xAI)
- `grok-imagine-image` (xAI)
- `grok-imagine-image-quality-latest` (xAI)
- `grok-imagine-video` (xAI)
- `grok-imagine-video-1.5` (xAI)

All models are specified in the configuration file models.json, which you can customize. This file is located in your working directory. You can add new models provided directly by OpenAI API (or compatible), Google Gen AI API, Anthropic API, xAI API, and those supported by LlamaIndex or Ollama to this file. Configuration for LlamaIndex in placed in llama_index key.

You can import new models by manually editing models.json or by using the model importer in the Config -> Models -> Import menu.

Tip: The models on the list are sorted by provider, not by manufacturer. A model from a particular manufacturer may be available through different providers (e.g., OpenAI models can be provided by the OpenAI API or by OpenRouter). If you want to use a specific model through a particular provider, you need to configure the provider in Config -> Models -> Edit, or import it directly via Config -> Models -> Import.

Tip: Anthropic and Deepseek API providers use VoyageAI for embeddings (Chat with Files and attachments RAG), so you must also configure the Voyage API key if you want to use embeddings from these providers.

Adding a custom model

You can add your own models. See the section Extending PyGPT / Adding a new model for more info.

There is built-in support for those LLM providers:

• Anthropic
• Azure OpenAI (native SDK)
• Deepseek API
• Eden AI
• Forge
• Google (native SDK)
• HuggingFace API
• HuggingFace Router (wrapper for OpenAI compatible ChatCompletions)
• LiteLLM
• Local models (OpenAI API compatible)
• Mistral AI
• Ollama
• OpenAI (native SDK)
• OpenRouter
• Perplexity
• xAI (native SDK)

Custom providers (OpenAI-compatible)

You can add OpenAI Chat Completions-compatible providers at runtime without editing PyGPT source code or creating a custom launcher. Open:

Config -> Settings -> Custom providers

and add a row with:

• Provider name - the name shown in provider selectors.
• API base URL - the OpenAI-compatible API base, for example https://example.com/v1.
• API key - the provider API key. It may be left empty when the endpoint does not require authentication.

Custom providers are stored in config.json under the api_custom_providers key. After saving Settings, they are registered immediately and become available anywhere PyGPT uses the LLM provider registry, including the Models Editor and Config -> Models -> Import. The importer obtains the model list from the provider's OpenAI-compatible /models endpoint.

Models assigned to a custom provider use the native OpenAI Python SDK with the Chat Completions API in normal Chat mode. In Chat with Files (LlamaIndex) and LlamaIndex-based flows, PyGPT uses the LlamaIndex OpenAILike wrapper with the same API base URL and API key. Custom runtime providers intentionally use Chat Completions compatibility; they do not enable the OpenAI Responses API.

Once the provider is saved, import its models from Config -> Models -> Import, or create/edit a model manually and select the custom provider from the provider list. Model-specific API base / API key values in the Models Editor, when provided, override the custom provider values for that model.

How to use local or non-GPT models

Gemma 4, Qwen 3.6, Llama 4, Mistral, DeepSeek, Bielik, gpt-oss, and other local models

How to use locally installed Gemma 4, Qwen 3.6, Llama 4, DeepSeek, Mistral, Bielik, and other models:

1. Choose a working mode: Chat or Chat with Files.

2. On the models list - select, edit, or add a new model (with ollama provider). You can edit the model settings through the menu Config -> Models -> Edit, then configure the model parameters in the advanced section.

3. Download and install Ollama from here: https://github.com/ollama/ollama

For example, on Linux:

curl -fsSL https://ollama.com/install.sh | sh

4. Run the model locally on your machine. For example, on Linux:

ollama run gemma4:e4b

5. Return to PyGPT and select the correct model from models list to chat with selected model using Ollama running locally.

Example available models

• gemma4:e4b
• qwen3.5:9b
• qwen3.6:27b
• llama4:scout
• mistral-small3.2
• deepseek-r1:8b
• SpeakLeash/bielik-11b-v3.0-instruct:Q4_K_M

etc.

You can add more models by editing the models list.

Real-time importer

You can also import models in real-time from a running Ollama instance using the Config -> Models -> Import... tool.

Custom Ollama endpoint

The default endpoint for Ollama is: http://localhost:11434

You can change it globally by setting the environment variable OLLAMA_API_BASE in Settings -> General -> Advanced -> Application environment.

You can also change the "base_url" for a specific model in its configuration:

Config -> Models -> Edit, then in the Advanced -> [LlamaIndex] ENV Vars section add the variable:

NAME: OLLAMA_API_BASE VALUE: http://my_endpoint.com:11434

List of all models supported by Ollama

https://ollama.com/library

https://github.com/ollama/ollama

IMPORTANT: Remember to define the correct model name in the **kwargs list in the model settings.

Using local embeddings

Refer to: https://docs.llamaindex.ai/en/stable/examples/embeddings/ollama_embedding/

You can use an Ollama instance for embeddings. Simply select the ollama provider in:

Config -> Settings -> Indexes / LlamaIndex -> Embeddings -> Embeddings provider

Define parameters like model name and Ollama base URL in the Embeddings provider **kwargs list, e.g.:

• name: model_name, value: gemma4:e4b, type: str

• name: base_url, value: http://localhost:11434, type: str

Google Gemini, Anthropic Claude, xAI Grok, etc.

If you want to use non-OpenAI models in Chat with Files and Agents (LlamaIndex) modes, then remember to configure the required parameters like API keys in the model config fields. Chat mode works via OpenAI SDK (compatible API), Chat with Files and Agents (LlamaIndex) modes works via LlamaIndex.

Google Gemini

Required ENV:

• GOOGLE_API_KEY = {api_key_google}

Required **kwargs:

• model

Anthropic Claude

Required ENV:

• ANTHROPIC_API_KEY = {api_key_anthropic}

Required **kwargs:

• model

xAI Grok (Chat mode only)

Required ENV:

• OPENAI_API_KEY = {api_key_xai}
• OPENAI_API_BASE = {api_endpoint_xai}

Required **kwargs:

• model

Mistral AI

Required ENV:

• MISTRAL_API_KEY = {api_key_mistral}

Required **kwargs:

• model

Perplexity

Required ENV:

• PPLX_API_KEY = {api_key_perplexity}

Required **kwargs:

• model

HuggingFace API

Required ENV:

• HUGGING_FACE_TOKEN = {api_key_hugging_face}

Required **kwargs:

• model_name | model
• token
• provider = auto

Plugins

Overview

PyGPT can be enhanced with plugins that add tools, integrations, automation, multimodal features, and additional context directly to conversations.

The following plugins are currently available:

• API calls - Connects models to external services through user-defined API endpoints, request methods, parameters, and payloads.

• Audio Input - Adds speech recognition and microphone input using providers such as OpenAI Whisper, local Whisper, Google, Bing, and xAI Grok Voice.

• Audio Output - Adds text-to-speech output using providers such as OpenAI, Microsoft Azure, Google, Eleven Labs, and xAI.

• Autonomous Agent (inline) - Runs an autonomous multi-step conversation loop inside standard chat modes and can cooperate with other enabled plugins to complete tasks.

• Bitbucket - Connects to Bitbucket Cloud for repository, file, issue, pull request, workspace, and account operations.

• Chat with Files (LlamaIndex, inline) - Adds RAG and LlamaIndex retrieval to standard conversations, allowing models to use indexed files, project indexes, and stored context as additional knowledge.

• Code Interpreter - Lets models execute Python code locally or in a Docker sandbox, maintain IPython state, and work with files created during the conversation.

• Context history (calendar, inline) - Gives models access to saved conversation history and calendar day notes, including reading, searching, creating, and updating stored entries.

• Crontab / Task scheduler - Lets models create and manage scheduled prompts and tasks using cron-based schedules.

• Custom Commands - Exposes user-defined system commands and scripts as callable tools with configurable arguments and execution rules.

• Experts (inline) - Makes enabled expert presets available from standard chat modes so the current model can delegate specialized tasks to them.

• Facebook - Connects to the Facebook Graph API for working with pages, posts, photos, and related account information.

• Files I/O - Gives models controlled access to local files and directories for reading, writing, copying, moving, downloading, searching, and indexing data.

• GitHub - Connects to GitHub for repository, file, issue, pull request, code search, and account operations.

• Google - Integrates Gmail, Drive, Calendar, Contacts, Keep, Docs, Maps, Colab, and YouTube so models can work with Google services from conversations.

• Image Generation (inline) - Adds image generation and editing directly to conversations using a separately configured image model without requiring a mode change.

• Mailer - Provides email access through configured mail services, including sending and reading messages where supported.

• Memory (inline) - Maintains a compact database-backed long-term memory, using a global scope outside projects and an isolated memory scope for each project.

• MCP - Connects models to external Model Context Protocol servers and exposes discovered remote tools through stdio, SSE, or Streamable HTTP transports.

• Mouse and Keyboard - Lets models control the mouse and keyboard, capture screenshots, and interact with the desktop or supported sandbox environment.

• OpenStreetMap - Adds geocoding, place search, routing, and map utilities based on OpenStreetMap services.

• Real Time - Appends the current date and/or time to system prompts so models can receive up-to-date local time context.

• Serial port / USB - Gives models access to configured serial and USB devices for reading data and sending commands.

• Server (SSH/FTP) - Connects to remote servers through SSH, SFTP, or FTP for command execution, file transfers, and filesystem operations.

• Slack - Connects to Slack workspaces for reading conversations, managing messages, working with users, and transferring files.

• System Prompt Extra (append) - Automatically appends reusable custom instructions or additional context to the active system prompt.

• System (OS) - Provides access to the operating system and executes system commands through PyGPT's host or sandbox execution mechanisms.

• Telegram - Connects to Telegram bots or user accounts for messaging, chat access, contacts, media, and file transfers.

• Tuya (IoT) - Connects to Tuya Cloud so models can inspect, search, and control supported smart-home and IoT devices.

• TwelveLabs - Adds video understanding and multimodal embeddings using TwelveLabs Pegasus and Marengo models.

• Vision (inline) - Adds image analysis to supported chat modes by routing image input through a separately configured vision-capable model.

• Voice Control (inline) - Lets spoken commands trigger configured PyGPT actions directly while a conversation is active.

• Web Search - Adds real-time web search, webpage retrieval, crawling, and external-content indexing using supported search providers and LlamaIndex loaders.

• Wikipedia - Provides Wikipedia search, article lookup, summaries, geographic discovery, and random-page access.

• Wolfram Alpha - Adds computational knowledge, symbolic and numeric mathematics, unit conversions, matrix operations, and generated plots through Wolfram Alpha.

• X/Twitter - Connects to X for searching and reading posts, publishing content, managing interactions, bookmarks, and media.

Tip: Inline plugins do not require the + Tools option in the toolbox. Once enabled, they remain active throughout the conversation and can provide their functionality automatically when applicable.

API calls

PyGPT lets you connect the model to the external services using custom defined API calls.

To activate this feature, turn on the API calls plugin found in the Plugins menu.

In this plugin you can provide list of allowed API calls, their parameters and request types. The model will replace provided placeholders with required params and make API call to external service.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#api-calls

Audio Input

The plugin facilitates speech recognition. OpenAI Whisper is the default provider; local Whisper, Google, Google Cloud, Google GenAI, Bing, and xAI Grok Voice providers are also available. It allows for voice commands to be relayed to the AI using your own voice. Whisper doesn't require any extra API keys or additional configurations; it uses the main OpenAI key. In the plugin's configuration options, you should adjust the volume level (min energy) at which the plugin will respond to your microphone. Once the plugin is activated, a new Speak option will appear at the bottom near the Send button - when this is enabled, the application will respond to the voice received from the microphone.

The plugin can be extended with other speech recognition providers.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#audio-input

Audio Output

The plugin lets you turn text into speech using OpenAI TTS or providers such as Microsoft Azure, Google Cloud TTS, Google GenAI TTS, Eleven Labs, and xAI TTS. You can add more text-to-speech providers to it too. OpenAI TTS does not require any additional API keys or extra configuration; it utilizes the main OpenAI key. Provider-specific credentials are required where applicable: Azure and Eleven Labs use plugin credentials, Google GenAI uses the Google API key from Settings, and xAI TTS uses the xAI API key from Settings. Configure voices, regions, and provider-specific options in the plugin settings.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#audio-output

Autonomous Agent (inline)

WARNING: Please use autonomous mode with caution! - this mode, when connected with other plugins, may produce unexpected results!

The plugin activates autonomous mode in standard chat modes, where AI begins a conversation with itself. You can set this loop to run for any number of iterations. The plugin also has an Always continue option (default: disabled) that forces another iteration even when the goal has already been reached. Throughout this sequence, the model will engage in self-dialogue, answering his own questions and comments, in order to find the best possible solution, subjecting previously generated steps to criticism.

This mode is similar to Auto-GPT - it can be used to create more advanced inferences and to solve problems by breaking them down into subtasks that the model will autonomously perform one after another until the goal is achieved. The plugin is capable of working in cooperation with other plugins, thus it can utilize tools such as web search, access to the file system, or image generation.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#autonomous-agent-inline

Bitbucket

The Bitbucket plugin allows for seamless integration with the Bitbucket Cloud API, offering functionalities to manage repositories, issues, and pull requests. This plugin provides highly configurable options for authentication, cached convenience, and manages HTTP requests efficiently.

• Retrieve details about the authenticated user.
• Get information about a specific user.
• List available workspaces.
• List repositories in a workspace.
• Get details about a specific repository.
• Create a new repository.
• Delete an existing repository.
• Retrieve contents of a file in a repository.
• Upload a file to a repository.
• Delete a file from a repository.
• List issues in a repository.
• Create a new issue.
• Comment on an existing issue.
• Update details of an issue.
• List pull requests in a repository.
• Create a new pull request.
• Merge an existing pull request.
• Search for repositories.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#bitbucket

Chat with Files (LlamaIndex, inline)

Plugin integrates LlamaIndex storage in any chat and provides additional knowledge into context. The plugin also provides the Image model setting used by the Image (vision) data loader when API mode is active (default: gpt-4o). Audio/video transcription is not configured here; it uses the provider selected in the Audio Input plugin.

When Use project index if in use is enabled (default), the plugin automatically queries the isolated Current project index whenever the active conversation belongs to a project. Outside a project it uses the configured regular index or indexes.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#chat-with-files-llamaindex-inline

Code Interpreter

Executing Code

From version 2.4.13 with built-in IPython.

The plugin operates similarly to the Code Interpreter in ChatGPT, with the key difference that it works locally on the user's system. It allows for the execution of any Python code on the computer that the model may generate. When combined with the Files I/O plugin, it facilitates running code from files saved in the data directory. You can also prepare your own code files and enable the model to use them or add your own plugin for this purpose. You can execute commands and code on the host machine or in Docker container.

IPython: Starting from version 2.4.13, it is highly recommended to adopt the new option: IPython, which offers significant improvements over previous workflows. IPython provides a robust environment for executing code within a kernel, allowing you to maintain the state of your session by preserving the results of previous commands. This feature is particularly useful for iterative development and data analysis, as it enables you to build upon prior computations without starting from scratch. Moreover, IPython supports the use of magic commands, such as !pip install <package_name>, which facilitate the installation of new packages directly within the session. This capability streamlines the process of managing dependencies and enhances the flexibility of your development environment. Overall, IPython offers a more efficient and user-friendly experience for executing and managing code.

To use IPython in sandbox mode, Docker must be installed on your system.

You can find the installation instructions here: https://docs.docker.com/engine/install/

Tip: connecting IPython in Docker in Snap version:

To use IPython in the Snap version, you must connect PyGPT to the Docker daemon:

sudo snap connect pygpt:docker-executables docker:docker-executables

sudo snap connect pygpt:docker docker:docker-daemon

Code interpreter: PyGPT includes the Python/OS tool for real-time Python and IPython execution. Click the <> icon to open the Python/OS window. Plugin code input/output is mirrored there only when Connect to the Python/OS window is enabled (default: enabled). The window keeps 30 input/output blocks by default (0 = unlimited), and Always run code in a fresh kernel is disabled by default.

INFO: Executing Python code using IPython in compiled versions requires an enabled sandbox (Docker container). You can connect the Docker container via Plugins -> Settings.

Tip: always remember to enable the + Tools option to allow execute commands from the plugins.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#code-interpreter

Context history (calendar, inline)

Provides access to context history database. Plugin also provides access to reading and creating day notes.

Examples of use, you can ask e.g. for the following:

Give me today day note

Save a new note for today

Update my today note with...

Get the list of yesterday conversations

Get contents of conversation ID 123

etc.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#context-history-calendar-inline

Crontab / Task scheduler

Plugin provides cron-based job scheduling - you can schedule tasks/prompts to be sent at any time using cron-based syntax for task setup.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#crontab-task-scheduler

Custom Commands

With the Custom Commands plugin, you can integrate PyGPT with your operating system and scripts or applications. You can define an unlimited number of custom commands and instruct model on when and how to execute them. Configuration is straightforward, and PyGPT includes a simple tutorial command for testing and learning how it works:

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#custom-commands

Experts (inline)

The plugin allows calling experts in any chat mode. This is the inline Experts (co-op) mode.

See the Work modes -> Experts section for more details.

Facebook

The plugin integrates with Facebook's Graph API to enable various actions such as managing pages, posts, and media uploads. It uses OAuth2 for authentication and supports automatic token exchange processes.

• Retrieving basic information about the authenticated user.
• Listing all Facebook pages the user has access to.
• Setting a specified Facebook page as the default.
• Retrieving a list of posts from a Facebook page.
• Creating a new post on a Facebook page.
• Deleting a post from a Facebook page.
• Uploading a photo to a Facebook page.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#facebook

Files I/O

The plugin allows for file management within the local filesystem. It enables the model to create, read, write and query files located in the data directory, which can be found in the user's work directory. With this plugin, the AI can also generate Python code files and thereafter execute that code within the user's system.

Plugin capabilities include:

• Sending files as attachments
• Reading files
• Appending to files
• Writing files
• Deleting files and directories
• Listing files and directories
• Creating directories
• Downloading files
• Copying files and directories
• Moving (renaming) files and directories
• Reading file info
• Indexing files and directories using LlamaIndex
• Querying files using LlamaIndex
• Searching for files and directories

If a file being created (with the same name) already exists, a prefix including the date and time is added to the file name.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#files-i-o

GitHub

The plugin provides seamless integration with GitHub, allowing various operations such as repository management, issue tracking, pull requests, and more through GitHub's API. This plugin requires authentication, which can be configured using a Personal Access Token (PAT) or OAuth Device Flow.

• Retrieve details about your GitHub profile.
• Get information about a specific GitHub user.
• List repositories for a user or organization.
• Retrieve details about a specific repository.
• Create a new repository.
• Delete an existing repository.
• Retrieve the contents of a file in a repository.
• Upload or update a file in a repository.
• Delete a file from a repository.
• List issues in a repository.
• Create a new issue in a repository.
• Add a comment to an existing issue.
• Close an existing issue.
• List pull requests in a repository.
• Create a new pull request.
• Merge an existing pull request.
• Search for repositories based on a query.
• Search for issues based on a query.
• Search for code based on a query.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#github

Google (Gmail, Drive, Calendar, Contacts, YT, Keep, Docs, Maps, Colab)

The plugin integrates with various Google services, enabling features such as email management, calendar events, contact handling, and document manipulation through Google APIs.

• Gmail

  • Listing recent emails from Gmail.
  • Listing all emails from Gmail.
  • Searching emails in Gmail.
  • Retrieving email details by ID in Gmail.
  • Sending an email via Gmail.

• Google Calendar

  • Listing recent calendar events.
  • Listing today's calendar events.
  • Listing tomorrow's calendar events.
  • Listing all calendar events.
  • Retrieving calendar events by a specific date.
  • Adding a new event to the calendar.
  • Deleting an event from the calendar.

• Google Keep

  • Listing notes from Google Keep.
  • Adding a new note to Google Keep.

• Google Drive

  • Listing files from Google Drive.
  • Finding a file in Google Drive by its path.
  • Downloading a file from Google Drive.
  • Uploading a file to Google Drive.

• YouTube

  • Retrieving information about a YouTube video.
  • Retrieving the transcript of a YouTube video.

• Google Contacts

  • Listing contacts from Google Contacts.
  • Adding a new contact to Google Contacts.

• Google Docs

  • Creating a new document.
  • Retrieving a document.
  • Listing documents.
  • Appending text to a document.
  • Replacing text in a document.
  • Inserting a heading in a document.
  • Exporting a document.
  • Copying from a template.

• Google Maps

  • Geocoding an address.
  • Reverse geocoding coordinates.
  • Getting directions between locations.
  • Using the distance matrix.
  • Text search for places.
  • Finding nearby places.
  • Generating static map images.

• Google Colab

  • Listing notebooks.
  • Creating a new notebook.
  • Adding a code cell.
  • Adding a markdown cell.
  • Getting a link to a notebook.
  • Renaming a notebook.
  • Duplicating a notebook.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#google-gmail-drive-calendar-contacts-yt-keep-docs-maps-colab

Image Generation (inline)

The plugin integrates image generation with any chat mode. Select the image-generation model in the plugin settings, enable the plugin, and ask the current model to create an image. The model can then call the plugin's image tool with a dedicated image prompt. The plugin does not require the + Tools option to be enabled.

By default, the plugin appends a short image-generation instruction to the system prompt so the current model knows when and how to use the image tool. You can disable this behavior with Append image prompt to system prompt while keeping the image tool available.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#image-generation-inline

Mailer

Enables the sending, receiving, and reading of emails from the inbox. Currently, only SMTP is supported. More options coming soon.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#mailer

Memory (inline)

The Memory (inline) plugin provides a compact long-term memory cache stored in the local database. It uses one global memory outside projects and a separate memory for each project; when a conversation belongs to a project, the project-specific memory is used instead of the global one. Because it is an inline plugin, it does not require the + Tools option to be enabled.

After a completed conversation turn, Memory can update the active memory asynchronously using the configured model. The updater rewrites the memory as a compact canonical state: it keeps important durable information, merges related facts instead of accumulating duplicates, reconciles newer information with older entries, and drops routine or transient details. Global memory focuses on durable information about the user, while project memory keeps information relevant to that project.

Options:

• Memory update model - model used for automatic memory updates and, when enabled, for refining manual memory_add calls.
• Maximum memory characters - target memory size. Default: 15000 characters. A 300-character safety margin is allowed before hard truncation.
• Refine memory before adding - applies only to manual memory_add calls. When enabled (default), the configured model merges the new information into the existing memory instead of appending raw text. Automatic end-of-context updates are always refined regardless of this option.
• Auto attach memory to every conversation - appends the active memory to the system prompt in <context_memory>...</context_memory>. Default: False.
• Auto attach memory only in projects - automatically attaches memory when the current conversation belongs to a project. Default: True.

Tools:

• memory_get - reads the complete memory for the current scope. Enabled by default.
• memory_add - selectively adds an important, durable fact and can merge it with existing memory. Disabled by default.
• memory_update - replaces the complete memory content for the current scope. Disabled by default.
• memory_clear - clears the current memory only after explicit user confirmation. Enabled by default.

MCP (Model Context Protocol)

With the MCP plugin, you can connect PyGPT to remote tools exposed by Model Context Protocol servers (stdio, Streamable HTTP, or SSE). The plugin discovers available tools on your configured servers and publishes them to the model as callable commands with proper parameter schemas. You can whitelist/blacklist tools per server. Tool discovery caching is enabled by default with a 300-second TTL.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#mcp

Mouse And Keyboard

Introduced in version: 2.4.4 (2024-11-09)

WARNING: Use this plugin with caution - allowing all options gives the model full control over the mouse and keyboard

The plugin allows for controlling the mouse and keyboard by the model. With this plugin, you can send a task to the model, e.g., "open notepad, type something in it" or "open web browser, do search, find something."

Plugin capabilities include:

• Get mouse cursor position
• Control mouse cursor position
• Control mouse clicks
• Control mouse scroll
• Control the keyboard (pressing keys, typing text)
• Making screenshots

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#mouse-and-keyboard

OpenStreetMap

Provides everyday mapping utilities using OpenStreetMap services:

• Forward and reverse geocoding via Nominatim
• Search with optional near/bbox filters
• Routing via OSRM (driving, walking, cycling)
• Generate openstreetmap.org URL (center/zoom or bbox; optional marker)
• Utility helpers: open an OSM website URL centered on a point; download a single XYZ tile

Images are saved under data/openstreetmap/ in the user data directory.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#openstreetmap

Real Time

This plugin automatically adds the current date and time to each system prompt you send. You have the option to include just the date, just the time, or both.

When enabled, it quietly enhances each system prompt with current time information before sending it to model.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#real-time

Serial port / USB

Provides commands for reading and sending data to USB ports.

Tip: in Snap version you must connect the interface first: https://snapcraft.io/docs/serial-port-interface

You can send commands to, for example, an Arduino or any other controllers using the serial port for communication.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#serial-port-usb

Server (SSH/FTP)

The Server plugin provides integration for remote server management via SSH, SFTP, and FTP protocols. This plugin allows executing commands, transferring files, and managing directories on remote servers.

For security reasons, the model will not see any credentials, only the server name and port fields (see the docs)

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#server-ssh-ftp

Slack

The Slack plugin integrates with the Slack Web API, enabling interaction with Slack workspaces through the application. This plugin supports OAuth2 for authentication, which allows for seamless integration with Slack services, enabling actions such as posting messages, retrieving users, and managing conversations.

• Retrieving a list of users.
• Listing all conversations.
• Accessing conversation history.
• Retrieving conversation replies.
• Opening a conversation.
• Posting a message in a chat.
• Deleting a chat message.
• Uploading files to Slack.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#slack

System (OS)

The plugin provides access to the operating system and executes system commands. sys_exec input/output is mirrored to the Python/OS window when Connect to the Python/OS window is enabled (default: enabled). Docker sandbox execution is disabled by default; the stock sandbox runs as the unprivileged pygpt user by default, with passwordless sudo available when elevated commands are needed.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#system-os

System Prompt Extra (append)

The plugin appends additional system prompts (extra data) from a list to every current system prompt. You can enhance every system prompt with extra instructions that will be automatically appended to the system prompt.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#system-prompt-extra-append

Telegram

The plugin enables integration with Telegram for both bots and user accounts through the Bot API and the Telethon library respectively. It allows sending and receiving messages, managing chats, and handling updates.

• Sending text messages to a chat or channel.
• Sending photos with an optional caption to a chat or channel.
• Sending documents or files to a chat or channel.
• Retrieving information about a specific chat or channel.
• Polling for updates in bot mode.
• Downloading files using a file identifier.
• Listing contacts in user mode.
• Listing recent dialogs or chats in user mode.
• Retrieving recent messages from a specific chat or channel in user mode.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#telegram

Tuya (IoT)

The Tuya plugin integrates with Tuya's Smart Home platform, enabling seamless interactions with your smart devices via the Tuya Cloud API. This plugin provides a user-friendly interface to manage and control devices directly from your assistant.

• Provide your Tuya Cloud Client ID, Client Secret, and linked App Account UID to enable communication; access/refresh tokens and the device cache are managed automatically.
• Access and list all smart devices connected to your Tuya app account.
• Retrieve detailed information about each device, including its status and supported functions.
• Effortlessly search for devices by their names using cached data for quick access.
• Control devices by turning them on or off, toggle states, and set specific device parameters.
• Send custom commands to devices for more advanced control.
• Read sensor values and normalize them for easy interpretation.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#tuya-iot

TwelveLabs

The TwelveLabs plugin brings native video understanding to PyGPT through the TwelveLabs API. The model can analyze videos with the Pegasus model and create multimodal embeddings with the Marengo model.

• Analyze a video and answer a prompt about it (summary, description, Q&A) from a public video URL or an already-indexed video_id (tl_analyze_video, Pegasus).
• Create a multimodal text embedding that lives in the same vector space as Marengo video embeddings, useful for text-to-video search (tl_embed_text, Marengo).
• Configure the Pegasus and Marengo model names, default max tokens, temperature, and request timeout.

Provide your API key in the plugin settings, or set the TWELVELABS_API_KEY environment variable. Defaults are pegasus1.5 for analysis, marengo3.0 for embeddings, 2048 max tokens, temperature 0.2, and a 300-second request timeout. You can grab a free API key at https://twelvelabs.io — there is a generous free tier.

Vision (inline)

The plugin adds image analysis to supported chat modes without relying on the deprecated standalone Vision mode. When an image attachment, screenshot, or camera capture is detected, the request is handled through Chat with the image-capable model configured in the plugin. This preserves the plugin's dedicated-model behavior while removing the dependency on the legacy Vision mode.

The plugin model list is filtered by capabilities (Chat + image input), not by provider, so supported models from OpenAI, Google, Anthropic, xAI, OpenRouter, local/OpenAI-compatible endpoints, and other configured providers can be selected. Native Google, Anthropic, and xAI SDK routing is respected when enabled; otherwise the configured OpenAI-compatible Chat endpoint is used where applicable.

Tip: The + Vision label at the bottom of the Chat window is an availability indicator for inline image analysis. Image handling is automatic when compatible image content is supplied; there is no separate legacy Vision-mode switch to enable.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#vision-inline

Voice Control (inline)

The plugin provides voice control command execution within a conversation. The optional Magic prefix for voice commands defaults to Execute voice command.

See the Accessibility section for more details.

Web Search

PyGPT lets you connect model to the internet and carry out web searches in real time as you make queries.

To activate this feature, turn on the Web Search plugin found in the Plugins menu.

Web searches can use DuckDuckGo, Google Custom Search Engine, or Microsoft Bing and can be extended with other search engine providers. DuckDuckGo does not require an API key. The default provider is Google Custom Search; the plugin opens at most 3 URLs at once by default, fetches thumbnail images, and currently has SSL verification disabled for crawling by default.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#web-search

Wikipedia

The Wikipedia plugin allows for comprehensive interactions with Wikipedia, including language settings, article searching, summaries, and random article discovery. This plugin offers a variety of options to optimize your search experience.

• Set your preferred language for Wikipedia queries.
• Retrieve and check the current language setting.
• Explore a list of supported languages.
• Search for articles using keywords or get suggestions for queries.
• Obtain summaries and detailed page content.
• Discover articles by geographic location or randomly.
• Open articles directly in your web browser.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#wikipedia

Wolfram Alpha

Provides computational knowledge via Wolfram Alpha: short answers, full JSON pods, numeric and symbolic math (solve, derivatives, integrals), unit conversions, matrix operations, and plots rendered as images. Images are saved under data/wolframalpha/ in the user data directory.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#wolfram-alpha

X/Twitter

The X/Twitter plugin integrates with the X platform, allowing for comprehensive interactions such as tweeting, retweeting, liking, media uploads, and more. This plugin requires OAuth2 authentication and offers various configuration options to manage API interactions effectively.

• Retrieve user details by providing their username.
• Fetch user information using their unique ID.
• Access recent tweets from a specific user.
• Search for recent tweets using specific keywords or hashtags.
• Create a new tweet and post it on the platform.
• Remove an existing tweet from your profile.
• Reply to a specific tweet with a new comment.
• Quote a tweet while adding your own comments or thoughts.
• Like a tweet to show appreciation or support.
• Remove a like from a previously liked tweet.
• Retweet a tweet to share it with your followers.
• Undo a retweet to remove it from your profile.
• Hide a specific reply to a tweet.
• List all bookmarked tweets for easy access.
• Add a tweet to your bookmarks for later reference.
• Remove a tweet from your bookmarks.
• Upload media files such as images or videos for tweeting.
• Set alternative text for uploaded media for accessibility.

Documentation: https://pygpt.readthedocs.io/en/latest/plugins.html#x-twitter

Creating Your Own Plugins

PyGPT can be extended with custom plugins, models, LLM wrappers, vector stores, data loaders, audio providers, web providers, and agents.

For implementation guides, launcher examples, plugin APIs, and complete code samples, see the full documentation:

https://pygpt.readthedocs.io/en/latest/extending.html

Functions, commands and tools

PyGPT supports native API function calls as well as its internal command/tool system. Commands exposed by enabled plugins can be called by compatible models when the + Tools option is active. Native function calls can be configured in Config -> Settings -> Prompts, and model-level support is controlled by the Tool calls option in the Models Editor.

Custom commands and API function schemas can be used together and are translated by PyGPT when required.

For command syntax, JSON schemas, custom command examples, and complete tool-call integration examples, see:

https://pygpt.readthedocs.io/en/latest/functions.html

Tools

PyGPT features several useful tools, including:

• Notepad
• Painter
• Calendar
• Indexer
• Media Player
• Image viewer
• Text editor
• Transcribe audio/video files
• OpenAI Vector Stores
• Google Vector Stores
• Python/OS
• HTML/JS Canvas (built-in HTML renderer)
• Translator
• Web Browser (Chromium)
• Agents Builder (beta)

Notepad

The application has a built-in notepad, divided into several tabs. This can be useful for storing information in a convenient way, without the need to open an external text editor. The content of the notepad is automatically saved whenever the content changes.

Painter

Using the Painter tool, you can create quick sketches and submit them to the model for analysis. You can also edit opened from disk or captured from camera images, for example, by adding elements like arrows or outlines to objects. Additionally, you can capture screenshots from the system - the captured image is placed in the drawing tool and attached to the query being sent.

To capture the screenshot just click on the Ask with screenshot option in a tray-icon dropdown:

Calendar

Using the calendar, you can go back to selected conversations from a specific day and add daily notes. After adding a note, it will be marked on the list, and you can change the color of its label by right-clicking and selecting Set label color. By clicking on a particular day of the week, conversations from that day will be displayed.

Indexer

This tool allows indexing of local files or directories and external web content to a vector database, which can then be used with the Chat with Files mode. Using this tool, you can manage local indexes and add new data with built-in LlamaIndex integration.

Media Player

A simple video/audio player that allows you to play video files directly from within the app.

Image Viewer

A simple image browser that lets you preview images directly within the app.

Text Editor

A simple text editor that enables you to edit text files directly within the app.

Transcribe Audio/Video Files

An audio transcription tool with which you can prepare a transcript from a video or audio file. It will use a speech recognition plugin to generate the text from the file.

OpenAI / Google Vector Stores

Remote vector stores management.

Python/OS

This tool allows you to run Python code directly from within the app. It is integrated with the Code Interpreter plugin, ensuring that code generated by the model is automatically available from the interpreter. In the plugin settings, you can enable the execution of code in a Docker environment.

INFO: Executing Python code using IPython in compiled versions requires an enabled sandbox (Docker container). You can connect the Docker container via Plugins -> Settings.

HTML/JS Canvas

Allows to render HTML/JS code in HTML Canvas (built-in renderer based on Chromium). To use it, just ask the model to render the HTML/JS code in built-in browser (HTML Canvas). Tool is integrated with the Code Interpreter plugin.

Translator

Enables translation between multiple languages using an AI model.

Web Browser

A built-in web browser based on Chromium, allowing you to open webpages directly within the app. SECURITY NOTICE: For your protection, avoid using the built-in browser for sensitive or critical tasks. It is intended for basic use only.

Agents Builder (beta)

To launch the Agent Editor, navigate to:

Tools -> Agents Builder

This tool allows you to create workflows for agents using a node editor, without writing any code. You can add a new agent type, and it will appear in the list of presets.

To add a new element, right-click on the editor grid and select Add to insert a new node.

Types of Nodes:

• Start: The starting point for agents (user input).
• Agent: A single agent with customizable default parameters, such as system instructions and tool usage. These settings can be overridden in the preset.
• Memory: Shared memory between agents (shared Context).
• End: The endpoint, returning control to the user.

Agents with connected shared memory share it among themselves. Agents without shared memory only receive the latest output from the previous agent.

The first agent in the sequence always receives the full context passed by the user.

Connecting agents and memory is done using node connections via slots. To connect slots, simply drag from the input port to the output port (Ctrl + mouse button removes a connection).

Node Editor Navigation:

• Right-click: Add node, undo, redo, clear
• Middle-click + drag: Pan view
• Ctrl + Mouse wheel: Zoom
• Left-click a port: Create connection
• Ctrl + Left-click a port: Rewire or detach connection
• Right-click or DELETE a node/connection: Remove node/connection

Tip: Enable agent debugging in Settings -> Debug -> Log Agents usage to console to log the full workflow to the console.

Agents built using this tool are compatible with both OpenAI Agents and LlamaIndex.

Notes: Multi-branch agent flows automatically receive an internal routing instruction that tells the current agent which downstream route can be selected.

For the complete routing schema, injected system-instruction example, and Agents Builder details, see:

https://pygpt.readthedocs.io/en/latest/tools.html#agents-builder-beta

INFO: Agents Builder is in beta.

Token usage calculation

Input tokens

The application features a token calculator. It attempts to forecast the number of tokens that a particular query will consume and displays this estimate in real time. This gives you improved control over your token usage. The app provides detailed information about the tokens used for the user's prompt, the system prompt, any additional data, and those used within the context (the memory of previous entries).

Remember that these are only approximate calculations and do not include, for example, the number of tokens consumed by some plugins. You can find the exact number of tokens used on provider's website.

Total tokens

After receiving a response from the model, the application displays the actual total number of tokens used for the query (received from the API).

Accessibility

Since version 2.2.8, PyGPT has added beta support for disabled people and voice control. This may be very useful for blind people.

In the Config / Accessibility menu, you can turn on accessibility features such as:

• activating voice control

• translating actions and events on the screen with audio speech

• setting up keyboard shortcuts for actions.

Using voice control

Voice control can be turned on in two ways: globally, through settings in Config -> Accessibility, and by using the Voice control (inline) plugin. Both options let you use the same voice commands, but they work a bit differently - the global option allows you to run commands outside of a conversation, anywhere, while the plugin option lets you execute commands directly during a conversation – allowing you to interact with the model and execute commands at the same time, within the conversation.

In the plugin (inline) option, you can also turn on a special trigger word that will be needed for content to be recognized as a voice command. You can set this up by going to Plugins -> Settings -> Voice Control (inline):

Magic prefix for voice commands

Tip: When the voice control is enabled via a plugin, simply provide commands while providing the content of the conversation by using the standard Microphone button.

Enabling voice control globally

Turn on the voice control option in Config / Accessibility:

Enable voice control (using the microphone)

Once you enable this option, an Voice Control button will appear at the bottom right corner of the window. When you click on this button, the microphone will start listening; clicking it again stops listening and starts recognizing the voice command you said. You can cancel voice recording at any time with the ESC key. You can also set a keyboard shortcut to turn voice recording on/off.

Voice command recognition works based on a model, so you don't have to worry about saying things perfectly.

Here's a list of commands you can ask for by voice:

• Get the current application status
• Exit the application
• Enable audio output
• Disable audio output
• Enable audio input
• Disable audio input
• Add a memo to the calendar
• Clear memos from calendar
• Read the calendar memos
• Enable the camera
• Disable the camera
• Capture image from camera
• Create a new context
• Go to the previous context
• Go to the next context
• Go to the latest context
• Focus on the input
• Send the input
• Clear the input
• Get current conversation info
• Get available commands list
• Stop executing current action
• Clear the attachments
• Read the last conversation entry
• Read the whole conversation
• Rename current context
• Search for a conversation
• Clear the search results
• Send the message to input
• Append message to current input without sending it
• Switch to chat mode
• Switch to chat with files (llama-index) mode
• Switch to the next mode
• Switch to the previous mode
• Switch to the next model
• Switch to the previous model
• Add note to notepad
• Clear notepad contents
• Read current notepad contents
• Switch to the next preset
• Switch to the previous preset
• Switch to the chat tab
• Switch to the calendar tab
• Switch to the draw (painter) tab
• Switch to the files tab
• Switch to the notepad tab
• Switch to the next tab
• Switch to the previous tab
• Start listening for voice input
• Stop listening for voice input
• Toggle listening for voice input

More commands coming soon.

Just ask for an action that matches one of the descriptions above. These descriptions are also known to the model, and relevant commands are assigned to them. When you voice a command that fits one of those patterns, the model will trigger the appropriate action.

For convenience, you can enable a short sound to play when voice recording starts and stops. To do this, turn on the option:

Audio notification for microphone listening start/stop

To enable a sound notification when a voice command is recognized and command execution begins, turn on the option:

Audio notification for voice command execution

For voice translation of on-screen events and information about completed commands via speech synthesis, you can turn on the option:

Use voice synthesis to describe events on the screen.

Configuration

Settings

Application settings are available from:

Config -> Settings...

The Settings window contains configuration for API providers, layout, files and attachments, context, remote tools, models, prompts, media, LlamaIndex, agents, security, accessibility, updates, debugging, and other application features.

For the complete configuration options reference, including descriptions and default values for all settings, see:

https://pygpt.readthedocs.io/en/latest/configuration.html#settings

JSON files

The configuration is stored in JSON files for easy manual modification outside of the application. These configuration files are located in the user's work directory within the following subdirectory:

{HOME_DIR}/.config/pygpt-net/

Manual configuration

PyGPT stores its configuration and user data in the working directory, which by default is:

{HOME_DIR}/.config/pygpt-net/

Configuration files such as config.json and models.json can also be edited manually.

For the complete manual configuration reference, including configuration files and workdir contents, see:

https://pygpt.readthedocs.io/en/latest/configuration.html#manual-configuration

Setting the Working Directory Using Command Line Arguments

To set the current working directory using a command-line argument, use:

python3 ./run.py --workdir="/path/to/workdir"

or, for the binary version:

pygpt.exe --workdir="/path/to/workdir"

Translations / Locale

PyGPT supports custom translations and user overrides for locale and CSS files. Locale files use the .ini format and are loaded automatically by the application.

Custom locale, CSS, and font files can also be placed in the PyGPT working directory to override the bundled resources.

For the complete translation, locale, CSS override, and custom font reference, see:

https://pygpt.readthedocs.io/en/latest/configuration.html#translations-locale

Data Loaders

Configuring data loaders

In the Settings -> Indexes / LlamaIndex -> Data loaders section you can define additional keyword arguments passed to data loader instances.

PyGPT includes built-in loaders for common file types and external/web content. In most cases, LlamaIndex loaders are used internally. You can also develop and register your own custom loader.

Tip: To index external data or web content, you can use the Web Search plugin and ask the model to index a supported resource, such as a webpage or YouTube video. The appropriate data loader is selected automatically when possible.

For the complete list of built-in data loaders, supported parameters, defaults, and configuration details, see:

https://pygpt.readthedocs.io/en/latest/configuration.html#configuration-data-loaders

Vector stores

Available vector stores (provided by LlamaIndex):

- ChromaVectorStore
- ElasticsearchStore
- PinecodeVectorStore
- QdrantVectorStore
- RedisVectorStore
- SimpleVectorStore

You can configure selected vector store by providing config options like api_key, etc. in Settings -> LlamaIndex window.

Arguments provided here (on list: Vector Store (**kwargs) in Advanced settings will be passed to selected vector store provider. You can check keyword arguments needed by selected provider on LlamaIndex API reference page:

https://docs.llamaindex.ai/en/stable/api_reference/storage/vector_store.html

Which keyword arguments are passed to providers?

For ChromaVectorStore and SimpleVectorStore all arguments are set by PyGPT and passed internally (you do not need to configure anything).

For other providers you can provide these arguments:

ElasticsearchStore

Keyword arguments for ElasticsearchStore(**kwargs):

• index_name (default: current index ID, already set, not required)
• any other keyword arguments provided on list

PinecodeVectorStore

Keyword arguments for Pinecone(**kwargs):

• api_key
• index_name (default: current index ID, already set, not required)

QdrantVectorStore

Keyword arguments for QdrantVectorStore(**kwargs):

• url - str, default: http://localhost:6333
• api_key - str, default: None (for Qdrant Cloud)
• collection_name (default: current index ID, already set, not required)
• any other keyword arguments provided on list

RedisVectorStore

Keyword arguments for RedisVectorStore(**kwargs):

• index_name (default: current index ID, already set, not required)
• any other keyword arguments provided on list

You can extend list of available providers by creating custom provider and registering it on app launch.

By default, you are using chat-based mode when using Chat with Files. If you want to only query index (without chat) you can enable Query index only (without chat) option.

Adding custom vector stores and data loaders

You can create a custom vector store provider or data loader for your data and develop a custom launcher for the application.

See the section Extending PyGPT / Adding a custom Vector Store provider for more details.

Updates

Updating PyGPT

PyGPT comes with an integrated update notification system. When a new version with additional features is released, you'll receive an alert within the app.

To get the new version, simply download it and start using it in place of the old one. All your custom settings like configuration, presets, indexes, and past conversations will be kept and ready to use right away in the new version.

Debugging and Logging

In Settings -> Developer dialog, you can enable the Show debug menu option to turn on the debugging menu. The menu allows you to inspect the status of application elements. In the debugging menu, there is a Logger option that opens a log window. In the window, the program's operation is displayed in real-time.

Logging levels:

By default, all errors and exceptions are logged to the file:

{HOME_DIR}/.config/pygpt-net/app.log

To increase the logging level (ERROR level is default), run the application with --debug argument:

python3 run.py --debug=1

or

python3 run.py --debug=2

The value 1 enables the INFOlogging level.

The value 2 enables the DEBUG logging level (most information).

Compatibility (legacy) mode

If you have a problems with WebEngine / Chromium renderer you can force the legacy mode by launching the app with command line arguments:

python3 run.py --legacy=1

and to force disable OpenGL hardware acceleration:

python3 run.py --disable-gpu=1

You can also manualy enable legacy mode by editing config file - open the %WORKDIR%/config.json config file in editor and set the following options:

"render.engine": "legacy",
"render.open_gl": false,

Extending PyGPT

PyGPT can be extended with custom models, plugins, LLM wrappers, vector stores, data loaders, audio input/output providers, web providers, and custom agents. Extension components can be registered through a custom launcher.

The repository also contains ready-to-use examples in the examples directory.

For complete Python examples, event handling, custom model configuration, provider interfaces, launcher code, and extension API reference, see:

https://pygpt.readthedocs.io/en/latest/extending.html

DISCLAIMER

This application is not officially associated with OpenAI. The author shall not be held liable for any damages resulting from the use of this application. It is provided "as is," without any form of warranty. Users are reminded to be mindful of token usage - always verify the number of tokens utilized by the model on the API website and engage with the application responsibly. Activating plugins, such as Web Search, may consume additional tokens that are not displayed in the main window.

Always monitor your actual token usage on the OpenAI, Google, Anthropic, xAI, etc. websites.

---

CHANGELOG

Recent changes:

2.8.14 (2026-09-10)

• Security, stability, and provider fixes - PR #208 by @atharvaHJoshi.
• Fixed list selectors for vision models by removing checks for the deprecated vision mode.
• Fixed the halt and acknowledgement flow in Computer Use.
• Moved the native Perplexity LlamaIndex provider to the shared OpenAI-compatible wrapper.
• Fixed legacy beta headers for Anthropic in Chat with Files mode.
• Fixed support for remote tools and Computer Use in Chat with Files mode.
• Fixed race conditions and restart loops in the IPython plugin.
• Added splitter anchors to the CSS in the light theme.
• Added a new model: gpt-image-2.5.
• Added an Insert date/time option to the Notepad right-click menu.
• Added support for Completion-only mode with models and providers other than OpenAI gpt-3.5-instruct.
• Added support for local Jupyter/IPython in compiled builds.
• Set IPython’s default stdin to DEVNULL when running code with interactive input to prevent freezes.
• Added support for Computer Use in Realtime + Audio, Legacy Agents, and Autonomous modes.
• Various UI and CSS fixes.

2.8.13 (2026-09-09)

• Fixed issue with empty parameters in the Anthropic API remote tool for computer use.
• Added support for the use of computer use remote tool in Agents v2 for Anthropic and Google.
• Added OSINT v2 preset to Agents v2.
• Added a new plugin: Memory (inline).
• Updated IPython Dockerfile: included default installation of pandas, matplotlib, scikit-learn, and other useful libraries.
• Integrated Google remote tool - MCP.
• Added validation for pasting large directory attachments.
• Enhanced CSS in chat view for better aesthetics.
• Improved handling of multiple tabs.
• Fixed issue with hiding date separators in context list view.
• Corrected profile switch components restoration.
• Other fixes.

2.8.12 (2026-09-08)

• Improved, extended, and fixed several bugs in the following modes: Chat, Realtime + Audio, Computer Use, Autonomous Agent, and Agents v2.
• Added new models: GPT-6 Astra and Claude Fable 5.1.
• Added a new remote tool in Settings: Computer Use, which allows you to control the computer in standard Chat mode.
• Added item limits, a Show more option, and collapsible items to the project list.
• Model responses are now split into partials and stored in the database.
• Fixed multi-column context handling.
• Fixed sandbox image URLs.
• Removed redundant tool input/output from message footers.
• Removed old and deprecated models.

Credits and links

Official website: https://pygpt.net

Documentation: https://pygpt.readthedocs.io

Support and donate: https://pygpt.net/#donate

GitHub: https://github.com/szczyglis-dev/py-gpt

Discord: https://pygpt.net/discord

Snap Store: https://snapcraft.io/pygpt

Microsoft Store: https://apps.microsoft.com/detail/XP99R4MX3X65VQ

PyPI: https://pypi.org/project/pygpt-net

Author: Marcin Szczygliński (Poland, EU)

Contact: info@pygpt.net

License: MIT License

Special thanks

GitHub's community:

• @atharvaHJoshi

• @ba2512005

• @BillionShields

• @gfsysa

• @glinkot

• @kaneda2004

• @KingOfTheCastle

• @linnflux

• @LittleBallOfPurr

• @lukasz-pekala

• @mohit-twelvelabs

• @moritz-t-w

• @MVS-source

• @oleksii-honchar

• @RheagalFire

• @robertnama

• @WildGreenRose

• @yf007

• @Yiiii0

Third-party libraries

Full list of external libraries used in this project is located in the requirements.txt file in the main folder of the repository.

All used SVG icons are from Material Design Icons provided by Google:

https://github.com/google/material-design-icons

https://fonts.google.com/icons

Monaspace fonts provided by GitHub: https://github.com/githubnext/monaspace

Code of the LlamaIndex offline loaders integrated into app is taken from LlamaHub: https://llamahub.ai

Awesome ChatGPT Prompts (used in templates): https://github.com/f/awesome-chatgpt-prompts/

Code syntax highlight powered by: https://highlightjs.org

Markdown parsing powered by: https://github.com/markdown-it/markdown-it

LaTeX support by: https://katex.org

Playwright: https://playwright.dev/