Frequently asked questions

SIMI is a multi-AI workspace designed to bring multiple artificial intelligence models and AI agents into one organized environment. Instead of having to open separate applications or browser tabs for different AI providers, SIMI allows users to connect different models, create agents around those models, organize conversations, and use multiple agents as part of a single workflow.

The purpose of SIMI is not simply to provide another chatbot. It is designed to help users manage and coordinate AI more effectively. A user can create different agents for different purposes, assign them different AI models, communicate with them independently, or bring several agents together for a group discussion.

SIMI also provides tools for organizing conversations, combining related chats, exporting conversations, managing memory, monitoring AI activity, and organizing agents into containers. This makes it useful for people who use AI extensively for research, analysis, content creation, business work, development, planning, and other knowledge-intensive tasks.

In simple terms, SIMI acts as a centralized workspace where users can organize and work with multiple AI capabilities instead of treating every AI provider as a completely separate environment.

SIMI provides a centralized environment for connecting, organizing, and working with multiple AI agents and models.

A user can connect AI providers through API keys, create individual agents, assign models to those agents, and then communicate with them through the Chats section. Instead of treating every AI model as an isolated tool, SIMI allows users to build a structured AI workspace around the models they already want to use.

One of SIMI’s important capabilities is multi-agent interaction. Users can create group chats containing several agents and send a question or task to the group. Each agent can provide its own response, allowing the user to compare different perspectives. The agents can also be instructed to discuss a subject with one another, which can be useful when a task benefits from multiple independent analyses.

SIMI also includes features for sharing and combining conversations, exporting chats, managing memory, monitoring usage, and organizing agents into containers. This means SIMI addresses not only the generation of AI responses but also the organization and management of AI workflows around those responses.

SIMI and ChatGPT serve different purposes.

ChatGPT is an AI service and conversational assistant that provides users with access to OpenAI’s models and features. SIMI, on the other hand, is designed as a multi-AI workspace where users can connect and organize AI models and agents from different providers.

The distinction becomes especially important when someone wants to compare AI models or use several providers during the same project. Instead of moving between separate AI applications, SIMI can provide a centralized environment where different agents can be accessed and organized together.

For example, a user researching a complex topic could create several agents using different models and ask each of them to analyze the subject. The user can then compare the responses or bring those agents into a group discussion.

Therefore, SIMI should not simply be viewed as "another ChatGPT." Its value is in the layer of organization, multi-provider access, agent management, comparison, and collaboration that it provides around multiple AI models.

Yes. Multi-model and multi-agent usage is one of the central concepts behind SIMI.

Users can create multiple agents and connect those agents to different AI models. This makes it possible to work with several AI systems within the same SIMI environment.

For example, one agent could be configured around one provider while another agent uses a completely different provider. The user can ask the agents the same question independently and compare their answers, or place several agents into a group chat and have them participate in the same task.

This is particularly useful because different AI models can produce different interpretations, reasoning approaches, writing styles, or recommendations for the same prompt. Instead of relying entirely on one model’s output, users can deliberately introduce multiple AI perspectives into their workflow.

SIMI therefore makes multi-model experimentation and comparison much easier to manage than manually switching between unrelated AI applications.

SIMI is designed for people who want to use AI as a serious productivity and workflow tool rather than simply having an occasional conversation with a chatbot.

Potential users include researchers who need multiple perspectives on complex subjects, content creators who compare AI-generated ideas, developers working with different AI models, marketers researching and producing content, business users managing AI-assisted workflows, students and educators working on research and planning, and AI enthusiasts who want to compare different models.

It can also be useful for users who already subscribe to or have access to multiple AI providers and want a more organized way to work with them.

The common factor is not a specific profession. The common factor is the need to work with AI efficiently, especially when more than one model or agent is involved.

SIMI can be downloaded through the official SIMI website.

Users should visit the SIMI website, review the available information and terms, and then select the appropriate download option for their computer. During installation, the application may present terms and confirmation options that need to be accepted before continuing.

Because software versions and supported platforms can change over time, users should always obtain SIMI from the official SIMI distribution channel rather than relying on unofficial download websites.

The official website is: simimulti.com

SIMI is intended as a desktop application, but the exact operating systems and versions supported can change as the software develops.

Users should therefore check the current download section of the official SIMI website for the latest platform-specific versions rather than assuming that every operating system is supported by every release.

This is especially important because desktop applications can have different installation requirements depending on the operating system.

For the most accurate and current information, users should refer to the official SIMI download section before installing the application.

In normal use, an internet connection is required for AI features that communicate with external AI providers.

SIMI connects agents to AI models through provider services and API connections. Since those AI models generally operate through online infrastructure, the request needs to reach the relevant provider and the provider needs to return the response.

The exact behavior of offline functionality can depend on the version of SIMI and the particular feature being used. However, users should generally expect that interacting with cloud-based AI models requires an active internet connection.

This is also why the reliability of the connection and the availability of the connected AI provider can affect the response experience.

Creating an agent generally begins with connecting an AI provider through the API Keys section.

The user first adds the appropriate API key, selects the model they want to use, and saves the configuration. Once the provider and model have been configured, the user can create an agent based on that model.

The resulting agent can then appear independently within the SIMI workspace and be used in chats. Multiple agents can be created, allowing users to assign different models or purposes to different agents.

For example, a user could create one agent dedicated to research, another dedicated to writing, and another dedicated to critical analysis. The important concept is that an agent becomes a manageable unit within SIMI rather than simply being a one-time conversation.

For connecting external AI providers through SIMI, API keys are an important part of the setup.

An API key allows SIMI to communicate with the corresponding AI provider on the user’s behalf. The user selects the provider and model, enters the required credentials, and then creates an agent around that configuration.

This approach also means that users should understand the billing and usage policies associated with their AI providers. API usage may be subject to the provider’s own pricing, limits, quotas, and terms.

SIMI provides the workspace and agent-management layer, while the connected AI provider supplies the underlying model. Users should therefore keep their API credentials secure and follow the security requirements of each provider.

SIMI is designed around connecting multiple AI providers and models rather than limiting users to a single AI ecosystem.

The specific providers and models available can change as SIMI develops and as providers introduce, retire, or modify their APIs. SIMI has been designed to work with multiple major AI ecosystems, including models associated with providers such as OpenAI, Google, Anthropic, Meta, Kimi, and GLM, among others where supported.

Because model availability can change, the most reliable way to determine whether a particular provider or model is currently supported is to check SIMI’s current API-key and model-selection interface.

This multi-provider approach is important because it allows users to build their AI workspace around more than one model instead of being locked into a single AI provider.

SIMI is designed specifically to make working with multiple AI ecosystems possible from a centralized workspace, subject to the models and APIs currently supported by SIMI.

The practical advantage is that users do not necessarily need to treat each AI provider as a separate workspace. They can create different agents and associate those agents with different models.

For example, a user might create Agent A using one provider, Agent B using another, and Agent C using another. These agents can then be used separately or brought together in a group chat.

This becomes particularly useful when the user wants to compare answers rather than simply asking one AI system and accepting its first response.

The exact model list can change, so users should check SIMI’s current model-selection options for the latest supported combinations.

Yes. This is one of the important reasons to create multiple agents within SIMI.

Each agent can be configured around a particular AI model, allowing users to build an environment where different agents perform different roles.

For example, one agent could be configured with a model the user prefers for writing, another with a model preferred for research, and another with a model the user wants to use for alternative analysis.

This makes the agent system more flexible than simply having multiple copies of the same chatbot. The user can deliberately construct a team of AI agents with different underlying models and then decide whether they should work independently or together.

It also makes experimentation easier because the user can compare how different models approach the same problem.

If a connected AI provider experiences an outage, API problem, quota limitation, authentication issue, or another service-related problem, the affected agent may not be able to generate a response.

The important advantage of a multi-provider workspace is that other independently connected agents may still be available.

For example, if Agent A depends on one provider that is temporarily unavailable, an agent connected to another provider can potentially continue working. This does not mean SIMI can guarantee uninterrupted service from every provider, because the underlying AI services remain outside SIMI’s direct control.

Users should also check whether the problem is caused by the provider itself, an expired or invalid API key, an account limit, or a network connection.

SIMI is designed to organize AI conversations and agents separately from the underlying model selection, but the exact behavior can depend on how the agent and conversation are configured.

The important advantage is that conversations are managed inside a structured workspace rather than being scattered across unrelated applications.

When changing models or agent configurations, users should understand whether they are continuing an existing conversation, creating a new agent, or changing the underlying configuration of an existing agent.

This distinction matters because different models may interpret previous context differently. For important projects, users should also preserve valuable conversations through SIMI’s export and sharing capabilities.

A normal SIMI chat is primarily a conversation between the user and an individual AI agent.

A group chat is designed for multiple agents to participate in the same conversation. Instead of asking one model for an answer, the user can select several agents and give them a common topic or task.

Each agent can produce its own perspective. The user can then compare the responses, identify areas of agreement or disagreement, and use the discussion to obtain a broader analysis.

This can be particularly useful for research, brainstorming, planning, content development, decision support, and situations where seeing multiple AI perspectives is valuable.

The group-chat concept is therefore one of the features that most clearly demonstrates SIMI’s multi-agent approach.

To start a multi-agent discussion, the user can create a new group chat from the Group Chat section and give the group an appropriate name.

The user then selects the agents that should participate and sends them a question, task, or subject for analysis.

Each selected agent can respond independently. The user can also use SIMI’s discussion functionality to allow the agents to continue interacting rather than ending after their first responses.

This can create a workflow where different agents challenge, expand upon, or respond to the ideas produced by the other agents.

The user remains in control of the process and can stop the discussion when the desired result has been reached. This makes the feature useful when the user wants AI agents to explore a topic collaboratively rather than simply returning several unrelated answers.

Yes. SIMI’s discussion functionality allows the user to stop an ongoing agent discussion when they decide that sufficient information has been produced.

This is important because a multi-agent conversation can continue generating additional responses even after the user already has enough information.

For example, if three agents have reached a useful conclusion and begin repeating similar points, the user can stop the discussion instead of allowing unnecessary additional exchanges.

The user can then review the resulting output and determine whether it satisfies the original objective.

This gives the user control over the balance between deeper AI interaction and unnecessary additional model usage.

Shared chats are designed to help users bring related conversations together so they can be reviewed and analyzed as a larger body of information.

For example, a user might have separate conversations with different AI agents about the same research topic. Instead of reviewing every conversation independently, related conversations can be combined through the shared-chat workflow.

This can make it easier to compare different AI responses, identify common conclusions, spot disagreements, and develop a more complete understanding of the subject.

Shared chats are therefore particularly useful when a project involves multiple conversations rather than a single linear chat.

They can also help users maintain better organization when AI is being used repeatedly for the same project.

Yes. SIMI provides a conversation-locking capability that can be used to protect important conversations from accidental changes.

This is useful when a conversation contains research, completed work, important instructions, business information, or another result that the user wants to preserve.

Locking a conversation adds another organizational layer to the workspace because not every conversation needs to remain equally editable.

For users who generate a large number of AI conversations, this type of organization can become increasingly valuable. Instead of treating every chat as temporary, important conversations can be preserved as part of an organized AI workflow.

Yes. SIMI provides conversation export capabilities so users can preserve their AI-generated work outside the application.

Exporting conversations can be useful for documentation, research, reporting, archiving, collaboration, publishing workflows, or simply keeping an independent copy of important work.

This is particularly valuable for users who rely heavily on AI because an important conversation may contain research, decisions, prompts, analysis, or generated material that needs to be retained.

Rather than keeping everything exclusively inside the chat interface, exporting allows users to move useful information into other workflows and applications.

SIMI provides export options including PDF, CSV, and HTML.

Each format can be useful for a different purpose.

PDF is useful when a user wants a readable document that can be saved, printed, or shared.

CSV can be useful when structured conversation information needs to be processed or analyzed using spreadsheet or data tools.

HTML is useful when the user wants a web-compatible representation of the conversation that can be opened in a browser.

Providing several export formats gives users more flexibility than keeping conversations exclusively inside the SIMI interface.

Yes. SIMI includes sharing functionality that allows users to share conversations when collaboration or review is required.

This can be useful when someone wants another person to examine an AI conversation without manually copying and pasting every response.

For example, a researcher could share an AI analysis with a colleague, a content creator could share a conversation related to a project, or a team could review AI-generated research together.

Users should still be careful when sharing conversations containing confidential, personal, proprietary, or otherwise sensitive information. Sharing functionality makes distribution easier, but users remain responsible for deciding what information should be shared.

Containers are an organizational feature that allows users to group agents into logical categories.

Instead of having a long, unstructured collection of agents, users can organize them around projects, departments, clients, topics, or specific workflows.

For example, a user could create containers for "Research," "Marketing," "Development," or a particular project and place the relevant agents into the appropriate container.

This becomes increasingly useful as the number of agents grows. A user with only two agents may not need much organization, but someone managing many agents can quickly benefit from a structured system.

SIMI’s container approach helps turn a collection of AI agents into a more manageable workspace.

SIMI’s current container structure is designed around one-container-per-agent organization.

This means an individual agent is assigned to a single container rather than simultaneously belonging to multiple containers.

The advantage of this approach is consistency. Each agent has a clear organizational location, making it easier to understand where it belongs and which project or workflow it is associated with.

If the same underlying AI model is needed for multiple purposes, users can structure their agents and workflows accordingly rather than relying on one agent being duplicated across many containers.

As SIMI evolves, organizational capabilities may change, so users should check the current application behavior for the latest implementation.

SIMI includes a Memory section designed to help manage information associated with AI conversations and agent workflows.

Memory is important because AI interactions can become much more useful when relevant context can be retained rather than recreated manually every time.

SIMI provides memory allocation and storage management, allowing users to monitor and manage the amount of memory available within their workspace.

The purpose is to make longer-term AI workflows more organized. Instead of treating every conversation as completely isolated, memory can help support continuity where the relevant configuration and feature allow it.

Users should nevertheless distinguish between SIMI’s own memory functionality and the context or memory capabilities provided independently by the underlying AI model or provider.

SIMI provides memory management capabilities that allow users to monitor and control available memory allocation.

This is useful because different users have different requirements. Someone using AI occasionally may need relatively little stored information, while a user running many agents and maintaining extensive conversations may require more storage.

Managing memory also gives users greater visibility into how their workspace resources are being used.

Rather than treating storage as an invisible background process, SIMI exposes memory-related information so users can make more informed decisions about their AI workspace.

The exact available allocation and limits depend on the user’s SIMI plan and current application configuration.

The Monitoring section provides visibility into AI activity and usage across the SIMI workspace.

Depending on the current SIMI configuration, monitoring can include information such as total requests, average latency, total agents, conversations, memory entries, LLM calls, and tokens used.

These metrics can be valuable because they provide a broader picture of how the AI workspace is being used.

For example, latency information can help users understand response performance, while token and request information can help users understand the scale of their AI activity. Agent and conversation statistics can also provide an overview of how extensively the workspace is being used.

Monitoring is therefore particularly useful for users who want more visibility and control rather than simply sending prompts without knowing what is happening across their AI environment.

SIMI provides settings that allow users to control whether chat history is maintained between sessions.

When chat history is enabled, users can continue working with previous conversations rather than starting from scratch each time they open the application.

The Settings section also provides data-management controls, including the ability to clear data where supported.

Users should understand that there can be multiple layers of data involved: information stored by SIMI itself, information transmitted to connected AI providers, and information retained according to the policies of those providers.

For that reason, users working with sensitive information should review SIMI’s current privacy documentation as well as the policies of each connected AI provider.

SIMI provides privacy and data-management settings intended to give users greater control over their workspace and stored information. However, privacy in a multi-AI environment involves more than SIMI alone.

When a user connects an external AI provider through an API, information sent to that provider may be subject to that provider’s own security, privacy, retention, and data-processing policies.

Therefore, users should consider the entire AI workflow when evaluating privacy: what information is entered into SIMI, where that information is sent, which AI provider processes it, how API credentials are managed, and what retention policies apply.

Users should avoid placing highly sensitive or confidential information into AI systems unless they have verified that the relevant services and configurations meet their security requirements.

SIMI’s role is to provide the workspace, agent, conversation, organization, and management layer, while the underlying AI providers remain responsible for the processing performed by their respective services.

For the most accurate security and privacy information, users should review SIMI’s current terms and privacy documentation together with the policies of every AI provider they connect.