Local AI Chat
Run supported open-weight language models locally while conversation history stays in the local SQLite workspace.
Local-workspace awareLoading OpenMindAI
Loading OpenMindAI
OpenMindAI is designed as a desktop product, not a thin browser wrapper around a remote API.
OpenMindAI combines local inference, project work, model lifecycle controls and system tools in one native desktop application.
Run supported open-weight language models locally while conversation history stays in the local SQLite workspace.
Local-workspace awareDownload, verify, recommend, select and remove local models with explicit control over large model files.
Local-workspace awareRoute requests internally to appropriate local capabilities for chat, code, files and specialized workloads.
Local-workspace awareOpen local folders as projects, inspect and edit scoped files, validate changes and inspect Git state within permission boundaries.
Local-workspace awareKeep models, files, diagnostics, backup, repair and local-library workflows inside one desktop workstation.
Local-workspace awareLocal image, video and voice entry points report missing dependencies instead of silently falling back to a paid cloud API.
Local-workspace awareUse connected services naturally from Chat or Project Work while connection setup stays in Settings → Apps.
Local-workspace awarePlace models, runtimes, databases, cache, logs, generated files and backups on storage you control.
Local-workspace awareNormal local inference, history, settings, installed model/runtime management, projects, diagnostics and local backup workflows are designed to continue offline.
Initial bootstrap, runtime/model downloads and release checks require a network. OpenMindAI does not treat temporary GitHub unavailability as a reason to disable an already-installed local workspace.
Connections live in Settings → Apps. Chat and Project Work remain the interaction surfaces, while credentials stay out of chat history.
OpenMindAI is designed as a complete local-first AI environment rather than a single-purpose chatbot. Its features are built around a simple idea: users should be able to use powerful AI on their own devices, keep more of their work local, choose how models are used, and connect to the internet only when a task genuinely requires live information or external services.
The platform combines local AI chat, reasoning, web-assisted research, document understanding, multimodal input, voice, model management, project workspaces, hardware-aware execution, connected applications, local history, and controlled automation across Windows, macOS, Linux, Android, and iOS.
OpenMindAI is intended to be useful for students, researchers, developers, engineers, professionals, creators, and privacy-conscious users who want a practical AI workspace that can grow from simple conversations into deeper project-based work.
At the center of OpenMindAI is local AI chat. Instead of requiring every prompt to be sent to a commercial cloud model, OpenMindAI can use compatible models installed directly on the user's device. Once the required model and runtime are available, users can ask questions, generate ideas, write content, explain technical concepts, summarize information, plan tasks, debug problems, and continue ongoing conversations without depending on a remote AI provider for every interaction.
The chat experience is designed to feel familiar while still taking advantage of the local-first architecture. Users can maintain conversation history, continue earlier chats, edit or regenerate responses, stop generation, attach supporting files, and switch between available models depending on the task. This makes OpenMindAI useful both as a lightweight everyday assistant and as the entry point into more advanced workflows.
Local chat is especially valuable in situations where internet access is unreliable, unavailable, expensive, or simply unnecessary. A user can continue working with an installed model even when a cloud AI service is down or unreachable.
Some questions need more than a quick response. OpenMindAI includes reasoning-oriented workflows for tasks that require deeper analysis, multiple steps, comparison, planning, debugging, or technical decision-making.
Thinking mode is intended for questions such as system architecture, difficult programming problems, mathematical reasoning, research analysis, engineering decisions, structured planning, or situations where the user wants the AI to examine multiple possibilities before answering.
When a suitable reasoning-capable model is installed, OpenMindAI can route the task toward that model rather than treating every request as ordinary chat. The purpose is not simply to generate longer responses. It is to provide a workflow better suited to complex tasks where careful analysis matters more than speed.
Different AI models are good at different things, and different devices have very different hardware limits. OpenMindAI is designed around the idea that one model should not be forced to handle every task.
The model-routing system can consider the type of task, available models, required capabilities, available RAM, device architecture, model size, runtime compatibility, vision support, previous model outcomes, and user preferences. This makes it possible to use a lightweight model for fast conversation, a reasoning model for deeper work, a vision-capable model for image understanding, or a larger model on a capable desktop system.
On mobile devices, routing becomes even more important because memory, battery, storage, and thermal limits are much tighter. OpenMindAI can use those constraints when deciding which local model is practical for the current task.
This approach helps make the same OpenMindAI ecosystem usable across high-end desktop systems, normal laptops, Android phones, and iPhones without pretending that all hardware behaves the same way.
OpenMindAI includes a local model management system so users can control which AI models are installed on their devices. Models are not treated as invisible cloud services. They are actual local resources that users can install, verify, activate, manage, and remove.
The model manager can support discovery, download progress, pause and resume where available, cancellation, file verification, model installation, compatibility checks, storage management, activation, and deletion. This is especially important because local model files can range from hundreds of megabytes to many gigabytes.
Users can choose smaller models for speed and lower memory use, or install larger models when they want stronger performance and their hardware can support them. Vision or reasoning models can also be installed separately instead of forcing every user to download capabilities they may never use.
The purpose of the model manager is to make local AI ownership practical rather than forcing users to manually manage model folders, download files, and guess which model is suitable.
Local AI performance depends heavily on hardware. A model that works well on a workstation may be unusable on a low-memory phone. OpenMindAI is designed to recognize this reality before starting inference.
The system can consider available RAM, CPU capability, GPU support, GPU memory, operating system, model size, context requirements, runtime compatibility, architecture, vision dependencies, and mobile resource limits.
This hardware-aware approach helps OpenMindAI make safer and more realistic model choices. It can avoid attempting to launch models that clearly exceed available resources and can choose a more appropriate execution path where possible.
On desktop systems, this allows OpenMindAI to take advantage of stronger processors and supported GPU acceleration. On mobile, the focus is on balancing quality, speed, memory pressure, battery life, and thermal behavior.
Local models are useful for many tasks, but they do not automatically know what changed today. OpenMindAI Search is designed to solve this problem by combining current public web information with local AI reasoning.
When Search is used, OpenMindAI can retrieve public web evidence related to the user's question and provide that information to the selected AI model. The model can then use the retrieved material as context instead of relying only on its internal training knowledge.
This is useful for current software documentation, recent news, technology changes, public research, product information, updated policies, current releases, and other topics where fresh information matters.
Search intentionally requires internet access because live information must be retrieved from the web. The rest of the OpenMindAI experience does not need to become cloud-dependent simply because Search exists. Users can choose Search only when current information is actually needed.
Deep Research expands on ordinary Search by supporting more detailed investigation across multiple public sources.
Instead of retrieving a small amount of current information, Research is designed for tasks where the user wants a more complete understanding of a subject. It can gather evidence, compare sources, organize findings, identify important differences, and provide richer context to the local AI model.
This can help with academic exploration, technology research, market analysis, product comparisons, literature discovery, technical documentation review, competitive research, and complex public-information questions.
The purpose of Research is to combine the strengths of current online evidence with the flexibility of local AI reasoning. OpenMindAI remains local-first, but it can intelligently expand beyond local knowledge when the task requires it.
OpenMindAI is designed to work with real documents rather than forcing users to manually copy everything into a chat window.
Depending on the platform and supported format, users can attach documents such as PDF, DOCX, Markdown, plain text, source code, JSON, YAML, CSV, and other text-based files. OpenMindAI can then use the file content as part of the conversation context.
This makes it possible to ask the AI to summarize a report, explain a research paper, compare two sections, identify important findings, review technical documentation, inspect structured data, analyze source files, create study notes, or rewrite content.
For students and researchers, this can turn long documents into more manageable learning material. For developers, it can provide code and configuration context. For professionals, it can help with reports, proposals, internal documents, and technical material.
The goal is to make AI useful around existing work instead of only generating new text from scratch.
OpenMindAI is not limited to answering inside the chat interface. Desktop workflows can support the generation of reusable documents such as PDF and DOCX files.
This makes the platform useful for creating reports, structured notes, research drafts, business documents, technical documentation, proposals, formatted content, and other materials that need to exist outside the chat window.
Instead of copying AI output manually into another application, users can move from conversation and reasoning into a more complete artifact workflow.
This capability is particularly useful for professionals, researchers, students, and developers who regularly convert ideas and analysis into formal documents.
OpenMindAI can work with images when a compatible multimodal model is installed.
A user can provide a screenshot, photograph, diagram, document image, interface, chart, or other visual content and ask the AI to explain or analyze what is visible. This gives the model additional context beyond text.
Vision can be useful for understanding software errors shown in screenshots, explaining diagrams, analyzing visual layouts, reviewing document images, identifying interface elements, understanding photographs, or asking questions about something that is difficult to describe in words.
On mobile, visual input becomes especially useful because the camera can capture real-world context directly. Instead of typing a long description, users can show OpenMindAI what they are looking at and ask for help.
OpenMindAI Lens extends image understanding into a mobile-first experience.
Using the phone camera or an existing image, users can bring visual information directly into an AI conversation. A user might photograph a document, take a picture of a device, capture a whiteboard, analyze a screenshot, or ask questions about an object or diagram.
The purpose of Lens is not just image recognition. It is to make the camera another way to provide context to the AI.
This can be useful for learning, technical troubleshooting, document analysis, accessibility, visual explanation, travel, field work, and everyday questions where showing the AI something is easier than describing it.
OpenMindAI Canvas is designed for local visual creation and lightweight graphical workflows.
On supported platforms, Canvas can generate or assist with visual content such as SVG-based graphics, diagrams, layout concepts, simple design assets, and other safe structured visual outputs.
Users can preview the result, save it, and share it where supported.
Canvas expands OpenMindAI beyond text and makes it possible to use the application for more creative and visual tasks while still keeping the workflow local whenever possible.
OpenMindAI Hear allows users to speak instead of typing.
Speech input can be useful for dictation, quick notes, hands-free prompting, mobile use, brainstorming, accessibility, or situations where typing is inconvenient.
Supported speech recognition can process voice and convert it into text that becomes part of the OpenMindAI conversation.
The goal is to make interaction more natural and flexible, particularly on mobile devices where voice may be faster than typing.
OpenMindAI Speak allows generated responses to be read aloud using supported text-to-speech technology.
This can improve accessibility and make the assistant more practical for hands-free use, long responses, language practice, listening while working, or mobile scenarios where reading a screen is inconvenient.
Voice output helps OpenMindAI act more like an interactive assistant instead of requiring every interaction to remain text-only.
OpenMindAI stores conversation history locally so users can return to previous work.
Users can continue earlier discussions, rename chats, search conversation history, remove old conversations, regenerate responses, edit prompts, and export conversations where supported.
Local history is important because useful AI work often continues across days or weeks. A research discussion, development problem, study topic, or writing project should not have to restart every time the application opens.
Keeping history locally also supports the broader OpenMindAI philosophy of giving users more control over their data.
On desktop, OpenMindAI Projects provide a more structured environment for long-running work.
A project can bring together instructions, conversations, reference files, attached folders, source code, documents, and local context. This allows the AI to work within a defined project instead of treating every chat as unrelated.
For example, a developer may create a project for a software repository, a researcher may create one for a paper or experiment, and a professional may create one for a business or documentation workflow.
Project workspaces are intended to make OpenMindAI useful for deeper work where context must remain available over time.
OpenMindAI includes local agent-oriented capabilities for project work.
A local project agent can operate within a controlled workspace and help inspect files, understand project structure, read source material, create files, modify files, rename or delete scoped files, review Git status, inspect diffs, validate changes, and help repair problems.
The agent is designed to work with the local OpenMindAI model rather than requiring a paid cloud coding agent.
A major design principle is permission control. The agent should work inside the approved project scope and should not automatically receive unrestricted access to the entire computer.
This creates a foundation for more capable automation while keeping the user in control of what the AI can access or change.
OpenMindAI can already assist with coding-related tasks through local chat, project context, file understanding, and agent workflows.
Developers can ask the AI to explain code, identify bugs, compare implementation approaches, generate functions, review architecture, create documentation, inspect configuration, understand error messages, improve existing code, help with tests, or reason about project structure.
Because OpenMindAI can work with local files and project context, coding assistance can go beyond generic examples and become more relevant to the actual project.
This capability is especially useful for developers who want private code assistance without sending every source file to an external cloud AI provider.
OpenMindAI is expanding its development capabilities with a dedicated OpenMindAI Code Agent, designed specifically for software engineering workflows.
The Code Agent will build on the existing local-first project and agent architecture but focus more deeply on real coding tasks. Instead of behaving like a normal chatbot that only suggests code in a message, the goal is for the Code Agent to understand a project, inspect relevant files, reason about the existing architecture, make controlled code changes, review its own work, and validate results inside the user's approved workspace.
The upcoming Code Agent is intended to make OpenMindAI more useful for developers who want a practical coding assistant running around their own project context.
The OpenMindAI Code Agent is planned to work with the real structure of a project rather than treating every question as an isolated prompt.
It should be able to inspect folders, understand source organization, read related files, identify frameworks and dependencies, follow existing coding patterns, and use project instructions before making changes.
This is important because good software engineering depends on context. A code change that is technically correct in isolation can still be wrong for the project if it ignores existing architecture, conventions, interfaces, tests, or dependencies.
The Code Agent is being designed around project awareness so its work can become more consistent with the actual codebase.
The upcoming Code Agent is intended to support controlled file changes inside an approved workspace.
Depending on user permissions, this can include creating files, editing existing files, refactoring code, updating configuration, modifying documentation, repairing bugs, adding tests, and making related changes across multiple parts of a project.
The key idea is that the user remains in control.
The Code Agent should not silently edit unrelated locations or receive unrestricted access to the whole computer. Its actions are intended to remain scoped to the project or explicit permissions provided by the user.
This keeps automation useful while reducing unnecessary risk.
A major focus of the Code Agent will be debugging.
Instead of only reading an error and suggesting a possible solution, the agent can be designed to inspect the surrounding implementation, identify likely causes, review related files, apply a fix, and then validate whether the change actually addresses the problem.
For example, a future workflow may involve reading a failing test, tracing the relevant code, applying a targeted modification, running a validation step, reviewing the output, and correcting the implementation again if necessary.
This type of iterative repair is closer to how developers actually work and is one of the main reasons a dedicated Code Agent is more useful than ordinary coding chat.
The OpenMindAI Code Agent is planned around the idea that editing code is only part of the task.
A reliable coding assistant should also validate its changes.
Where tools and permissions allow, the Code Agent can be designed to run tests, inspect build results, review linting or type-checking errors, examine Git changes, and confirm that its modifications do not introduce obvious new problems.
The goal is to move from "AI generated some code" toward "AI made a controlled change and checked whether the result works."
Validation will remain dependent on the project, available tools, environment, and permissions granted by the user.
The Code Agent is also intended to work naturally with Git-based development.
It can use Git status and diff information to understand what has changed, distinguish existing user work from agent changes, review modifications, and help prepare clean updates.
Future workflows may also support structured assistance around commits, branches, pull requests, or repository review depending on connected capabilities and user authorization.
The purpose is not to replace Git. It is to help the AI understand the state of a real development project before acting.
One of the most important goals of the OpenMindAI Code Agent is local-first development assistance.
Source code can be sensitive. Private repositories, internal business logic, experimental research code, credentials, architecture decisions, and unreleased products may not be appropriate for automatic cloud processing.
By using compatible local models, OpenMindAI aims to give developers another option: coding assistance that can operate on their own hardware and project files without requiring every task to be sent to a paid cloud coding model.
The final capability will always depend on the quality of the installed local model and the available hardware, but the architecture is being designed to keep local control at the center.
A capable coding agent may sometimes need more than file access. It may need to run a test command, inspect build output, execute a formatter, check a package script, or use other development tools.
OpenMindAI is designed to treat this as a permission boundary.
The future Code Agent should not automatically gain unlimited terminal access. Broader execution capabilities should remain explicit, scoped, and controlled, with safeguards such as limits, timeouts, validation, and failure handling where applicable.
This approach allows the Code Agent to become more powerful without treating the AI model as fully trusted software.
The Code Agent is intended to be useful across different programming languages and project types rather than being tied to a single framework.
Its effectiveness will depend on the installed model, project context, and available tools, but the broader goal is to support common development workflows across web applications, backend systems, desktop applications, mobile development, scripts, research code, and technical projects.
Examples may include JavaScript, TypeScript, React, Next.js, Node.js, Rust, Python, Flutter, Dart, PHP, Laravel, C/C++, and other languages that a compatible local model can understand.
This makes the Code Agent a natural extension of OpenMindAI's broader goal of becoming a personal local AI workstation.
OpenMindAI can optionally connect with external services for users who want AI assistance to extend beyond local files.
Supported or planned workflows can include services such as Google Workspace, GitHub, Microsoft 365, Slack, Notion, Dropbox, and MCP-compatible tools.
These integrations are optional and require network access and appropriate authorization.
The purpose of connected apps is to let the user decide when OpenMindAI should interact with services they already use, while keeping normal local workflows independent from those services.
For developers, GitHub integration can connect OpenMindAI with real repository workflows.
Depending on the permissions available, OpenMindAI can work with repository information, files, issues, pull requests, Actions, and releases.
This is especially useful alongside project and coding features because the assistant can connect local development context with remote repository information when the user explicitly wants that connection.
As the Code Agent evolves, GitHub integration can become an important part of a broader developer workflow while still remaining permission-based.
OpenMindAI is designed to adapt to the way each user works.
Personalization can include local profile settings, preferred model behavior, project instructions, interface options, task preferences, conversation behavior, themes, and other user-controlled settings.
The purpose is not to create hidden profiling. It is to make the assistant more useful by allowing the user to define how they want it to behave.
In project workflows, instructions can also help the AI follow specific coding standards, writing styles, research goals, or project requirements.
OpenMindAI includes a modern interface designed for regular daily use across desktop and mobile.
Users can work with system, light, or dark themes where supported. Mobile experiences can include compact layouts, haptic feedback, notifications, streaming controls, and touch-friendly interactions.
The interface is intended to hide unnecessary technical complexity while still making important model, storage, permission, and execution information available when the user needs it.
This balance is important because local AI can be technically complex, but the user should not need to become an AI infrastructure expert just to have a conversation.
OpenMindAI can use notifications on supported platforms to let users know when a long response, task, or download has completed.
This is particularly useful on mobile, where the user may leave the application while a model is generating a response or downloading a large file.
Notifications remain controlled by the operating system and require the relevant permission from the user.
Local AI models, history, documents, and project data can require significant storage, so OpenMindAI is designed to give users visibility and control over where their resources live.
Desktop users can use configured storage locations for models and application data, while mobile models remain inside app-managed storage.
Users can remove models they no longer need and reinstall them later.
This gives users more control over disk usage and makes it possible to maintain different model collections depending on available storage and hardware.
OpenMindAI's local-first design makes it useful in environments where cloud AI is inconvenient or unavailable.
A developer can work on a laptop without relying on a constant network connection. A researcher can keep documents and model inference local. A traveler can continue using a mobile model while offline. A technical user can maintain large models on controlled storage.
The broader idea of portability is not only about carrying files between devices. It is about reducing the number of external services required just to access your own AI tools.
OpenMindAI is designed so that local workflows can process prompts, responses, project files, local documents, conversation history, and model data on the user's device.
This helps create a clearer boundary between local activity and intentional online activity.
When a user chooses Search, Research, a connected application, model download, or software update, network communication is required. When a user is simply chatting with a local model or working with local project files, there is no reason that workflow must automatically depend on a cloud AI provider.
This distinction is one of the core features of OpenMindAI, even though it is architectural rather than something represented by a single button.
OpenMindAI is built around the principle that powerful AI features should remain controlled.
File access, project agents, terminal capabilities, remote services, and other sensitive operations should have clear permission boundaries.
Retrieved web information is treated as external evidence rather than automatically trusted instructions. Remote services require authorization. Broader local execution should remain explicit rather than being silently granted.
This becomes increasingly important as OpenMindAI expands from conversational AI into project agents and the upcoming Code Agent.
The more capable the AI becomes, the more important it is that the user remains the final authority.
OpenMindAI does not claim that every feature can work offline.
Local AI chat, reasoning, local document work, project context, conversation history, supported vision, voice, and local model execution can work without constant internet access after the necessary components are installed.
Search, Deep Research, model downloads, runtime downloads, software updates, GitHub, Gmail, Microsoft 365, Slack, Notion, Dropbox, and other remote services naturally require an internet connection.
OpenMindAI is designed to make this distinction clear.
The platform is local-first because local work should remain local when possible, not because the internet is treated as something that should never be used.
OpenMindAI is designed for Windows, macOS, Linux, Android, and iOS.
The desktop applications are focused on deeper work: larger local models, project workspaces, coding, document generation, file workflows, connected applications, and local agents.
The mobile applications are focused on portability: local chat, thinking, voice, camera input, image understanding, document attachments, Search, Research, local model management, and everyday AI assistance.
The exact capabilities and performance can differ by platform because operating systems and hardware are different, but the overall philosophy remains consistent across the ecosystem.
OpenMindAI is designed for anyone who wants useful AI without making cloud dependency the default.
Students can use it to learn, summarize, explain, and study. Researchers can analyze documents, organize ideas, and perform public-source research. Developers can work with code, project files, Git context, and upcoming agent workflows. Engineers can use it for technical analysis and problem solving. Writers and professionals can generate and improve documents. Privacy-conscious users can keep more AI processing under their own control.
The platform is broad by design because the same local-first foundation can support many different workflows.
OpenMindAI is continuing to evolve from a local AI assistant into a broader personal AI workstation.
The current platform already combines local chat, reasoning, models, documents, research, vision, voice, projects, agents, connected services, and hardware-aware execution.
The upcoming OpenMindAI Code Agent represents the next major step in that direction.
It moves the platform closer to an environment where AI can not only answer questions but also understand projects, work with files, help perform real tasks, validate changes, and operate inside clearly defined permissions.
The long-term goal is to give users one AI environment that can support everyday conversation, development, research, documents, media, and controlled automation while remaining local whenever possible.
Local when possible. Connected when useful. Controlled by you.
OpenMindAI combines private local AI, optional online intelligence, desktop project workflows, mobile AI, multimodal capabilities, model control, and upcoming agent-based development tools in one growing ecosystem.
Windows · macOS · Linux · Android · iOS
Website: https://www.openmindai.org
Start with the latest published release, then choose where your models and local data should live.