Work modes

Chat

+ Inline Vision and Image generation

In PyGPT, this mode mirrors ChatGPT, allowing you to chat with models like GPT-5, GPT-4, o1, o3, Claude, Gemini, Grok, Perplexity (Sonar), Deepseek, and more. It works with the OpenAI SDK using the Responses API and ChatCompletions API. You can also use SDKs from Google GenAI, Anthropic, or xAI if the native SDK is enabled. You can set the endpoint for ChatCompletions in Config -> Settings -> API Keys.

Note

This mode uses the provider SDK directly. If there’s no native client built into the app, models like Sonar, or Llama3 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

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 send photos from your disk or images from your camera for analysis, and the selected model does not support Vision, you must enable the Vision (inline) plugin in the Plugins menu. This plugin allows you to send photos or images from your camera for analysis in any Chat mode.

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 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.

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 / 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 context database - you can use data from database (your context history) as additional context in discussion. Options for indexing existing context history or enabling real-time indexing new ones (from database) are available in Settings / LlamaIndex section.

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

• 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 -> 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.

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.

Note

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

Image and video generation

PyGPT enables quick and easy image creation with models like DALL-E 3, gpt-image-1, Imagen 3/4, and Nano Banana, as well as video generation using Veo3 and Sora2. 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 VertexAI 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’s user data folder.

Assistants

This mode uses the OpenAI’s Assistants API.

This mode expands on the basic chat functionality by including additional external tools like a Code Interpreter for executing code, Retrieval Files for accessing files, and custom Functions for enhanced interaction and integration with other APIs or services. In this mode, you can easily upload and download files. PyGPT streamlines file management, enabling you to quickly upload documents and manage files created by the model.

Setting up new assistants is simple - a single click is all it takes, and they instantly sync with the OpenAI API. Importing assistants you’ve previously created with OpenAI into PyGPT is also a seamless process.

In Assistant mode you are allowed to storage your files in remote vector store (per Assistant) and manage them easily from app:

Vector stores (via Assistants API)

Assistant mode supports the use of external vector databases offered by the OpenAI API. This feature allows you to store your files in a database and then search them using the Assistant’s API. Each assistant can be linked to one vector database—if a database is linked, all files uploaded in this mode will be stored in the linked vector database. If an assistant does not have a linked vector database, a temporary database is automatically created during the file upload, which is accessible only in the current thread. Files from temporary databases are automatically deleted after 7 days.

To enable the use of vector stores, enable the Chat with Files checkbox in the Assistant settings. This enables the File search tool in Assistants API.

To manage external vector databases, click the DB icon next to the vector database selection list in the Assistant creation and editing window (screen below). In this management window, you can create a new vector database, edit an existing one, or import a list of all existing databases from the OpenAI server:

You can define, using Expire days, how long files should be automatically kept in the database before deletion (as storing files on OpenAI incurs costs). If the value is set to 0, files will not be automatically deleted.

The vector database in use will be displayed in the list of uploaded files, on the field to the right—if a file is stored in a database, the name of the database will be displayed there; if not, information will be shown indicating that the file is only accessible within the thread.

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

Includes built-in agents (Legacy):

• OpenAI Assistants

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 -> LlamaIndex agents -> Max evaluation steps. 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.

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 / Agent (autonomous) / Sub-mode to use

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 / Agent (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 / Agent (autonomous) / 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.

The Computer use remote tool is used here: https://platform.openai.com/docs/guides/tools-computer-use, combined with the Mouse and Keyboard plugin.

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 browsers) - 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 / 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.