Top 8 Open Source AI Assistant Tools by GitHub Stars

Originally published at https://www.nocobase.com/en/blog/top-open-source-ai-assistant-tools-github-stars

Over the past few weeks, we have organized several open source tools that work well with popular AI Agents:

💡After using Claude Code, you need these 6 open source tools

💡Do not let Codex generate apps from scratch: 6 composable open source projects

Recently, while browsing Reddit, we noticed that besides AI Agents, AI Assistants are still a topic many users actively discuss. Compared with Agents, which emphasize autonomous planning and task execution, Assistants are more like everyday work entry points. They can serve individuals, small teams, and enterprises.

In a discussion on r/AI_Agents, one user was looking for a personal AI Assistant that is actually worth using long term. They hoped it could help manage daily information such as notes, tasks, calendars, emails, reminders, and contacts.

Another discussion was more focused on enterprise and team scenarios. The user runs a service agency and needs to handle website content, emails, and social media work for small business clients, so they wanted to know which AI Assistants or AI tools would fit their needs.

From these discussions, we can see that AI Assistant needs roughly fall into two categories: personal use and enterprise or team use.

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Personal AI Assistants: mainly help individual users manage daily information and improve productivity, such as organizing notes, following up on tasks, managing calendars, processing emails, setting reminders, calling local tools, or providing support in desktop and development environments.

Enterprise AI Assistants: better suited for teams and enterprises. They can connect to business systems, be embedded into application interfaces, handle customer communication, support content operations, automate workflows, and provide support around data, permissions, and task collaboration.

Based on this idea, we collected high-star projects ranked near the top under GitHub’s ai-assistant topic and selected 8 open source projects that are worth introducing in more detail.

Below, we will review them in two categories: enterprise AI Assistants and personal AI Assistants. Since personal AI Assistants usually have more direct use cases around desktops, development tools, and daily productivity, while enterprise AI Assistants often involve business systems, permission workflows, data management, application embedding, and team collaboration, this article will focus more on enterprise AI Assistants.

Category	Project	GitHub Stars	Positioning
Enterprise AI Assistant	NocoBase	22.7k	AI-powered no-code platform for building and maintaining business systems
Enterprise AI Assistant	NanoClaw	29.8k	AI Assistant with chat-based entry points and containerized runtime
Enterprise AI Assistant	CopilotKit	34.3k	Frontend framework for in-app AI Copilots and generative UI
Enterprise AI Assistant	Ruflo	58.6k	Multi-Agent collaboration and automation workflow assistant
Personal AI Assistant	DeepChat	6k	Desktop assistant that connects models, tools, and personal context
Personal AI Assistant	Everywhere	6k	Desktop context-aware AI Assistant
Personal AI Assistant	CopilotForXcode	6.1k	AI coding assistant for Xcode
Personal AI Assistant	Leon	17.3k	Open source personal AI Assistant

Enterprise AI Assistant

NocoBase

Website: https://www.nocobase.com/

Online Demo: https://demo.nocobase.com/new

Documentation: https://docs.nocobase.com/

GitHub: https://github.com/nocobase/nocobase

Stars: 22.7k

Introduction

NocoBase is an open source AI-powered no-code platform for quickly building enterprise internal business systems. In NocoBase, users can describe business requirements in natural language and let AI help with data model design, page configuration, workflow orchestration, permission setup, plugin management, and more. NocoBase also supports AI Employees, AI Skills, CLI, and other capabilities, allowing AI to continue working around existing business systems instead of stopping at one-off app generation.

This is especially important for enterprise internal systems. Many business applications are not finished after being generated once. They often need continuous adjustments to fields, permissions, workflows, pages, and data relationships. The value of NocoBase is that it provides AI with a structured system environment, allowing AI to work within existing data models, permissions, workflows, and plugin systems.

Core features

• AI participates in business system building

NocoBase supports describing business requirements in natural language and letting AI help with the initial creation of data tables, fields, relationships, pages, and workflows. For example, users can describe a customer management, ticket management, contract approval, or project dashboard scenario. AI can first generate the system foundation, and the team can then review and adjust it.

This approach is better suited for enterprise internal systems, because business systems usually require clear data structures, role permissions, and workflow rules. AI improves building efficiency, while humans confirm business logic and control system boundaries.

🔎Related resources:

CLI: https://docs.nocobase.com/en/api/cli/

Skills: https://docs.nocobase.com/en/ai-builder#nocobase-skills

MCP: https://docs.nocobase.com/en/ai/mcp/

• AI Employees can continue participating in system operations

NocoBase’s AI Employees can automatically use page data and structure as context, understand business scenarios, and directly execute tasks. They can summarize emails, analyze data, organize unstructured content and automatically fill forms, design data models, and even write JavaScript code. Each AI Employee has independent Skills, tools, and knowledge bases. Enterprises can create any number of AI Employees to build their own AI team.

• No-code capabilities reduce later maintenance costs

Many AI generation tools can quickly generate code, but later maintenance often depends on developers. NocoBase is different. After AI generates the system, users can still enter the no-code interface to continue modifying fields, pages, menus, action buttons, permissions, and workflows.

For business teams, this means the system does not remain a one-off demo. Whenever the business changes, teams can continue adjusting it inside the platform, without needing to return to the code development process every time.

• Suitable for enterprise-level data, permissions, and workflows

When enterprise AI Assistants are actually implemented, data, permissions, and workflows are unavoidable. NocoBase has capabilities such as data models, role permissions, workflows, plugin extensions, and multiple data sources, making it a strong foundation for AI Assistants to work inside business systems.

For example, AI can work around business objects such as customers, orders, tickets, projects, inventory, and contracts. At the same time, different roles can have different data access scopes and operation permissions, preventing everyone from seeing the same information or performing the same actions.

• Suitable for starting from specific scenarios

When using NocoBase, it is not recommended to ask AI to build a complete ERP or complex enterprise system from the beginning. A better approach is to start with a clear small scenario, such as customer management, purchase requests, equipment ledgers, after-sales tickets, contract approval, project dashboards, or inventory records.

After one specific scenario works well, you can gradually expand fields, pages, workflows, and permissions. This makes it easier for AI-generated results to match the business and makes later review and maintenance easier for the team.

Suitable scenarios

• CRM customer management system
• Approval systems
• Ticketing systems
• Project management systems
• Lightweight ERP modules such as procurement, inventory, and orders
• Operations backends and admin backends
• Equipment management, asset management, and archive management
• Customer follow-up and service management
• Enterprise internal systems that require private deployment
• Business applications that require permissions, workflows, data models, and continuous adjustment
• Teams that want to combine AI and no-code capabilities to build internal systems

NanoClaw

GitHub: https://github.com/nanocoai/nanoclaw

Official website: https://nanoclaw.dev

Stars: 29.8k

Introduction

NanoClaw is a lightweight AI Assistant project focused on running Agents in containers and connecting to messaging channels such as WhatsApp, Telegram, Slack, Discord, and Gmail. It is more like an enterprise-focused AI Assistant with chat-based entry points and container isolation, making it suitable for users who want to call Agents, handle tasks, and arrange automation workflows through chat tools.

Compared with ordinary chat assistants, NanoClaw places more emphasis on security isolation and controllable runtime environments. Each Agent can run in an independent container, reducing its impact on the host system and other tasks.

Core features

• Containerized runtime: Agents run in independent containers, making it easier to isolate the file system and runtime environment.
• Rich messaging channels: Supports Telegram, Discord, WhatsApp, Slack, Gmail, and other entry points, making it suitable for connecting AI Assistants to daily collaboration tools.
• Lightweight and customizable: Compared with large Agent systems, NanoClaw emphasizes clear code structure, easy understanding, and convenient customization.

Suitable scenarios

• Calling AI Assistants through messaging tools such as Telegram, Discord, and Slack.
• Building a self-hosted task processing entry point for individuals or teams.
• Running Agent tasks in a safer and isolated environment.

CopilotKit

GitHub: https://github.com/CopilotKit/CopilotKit

Official website: https://docs.copilotkit.ai

Stars: 34.4k

Introduction

CopilotKit is a frontend framework for building AI Copilots, generative UI, and Agent applications. It is better understood as a development framework for in-app AI Assistants. It can embed Agent capabilities into interfaces users already use, such as Web, mobile apps, Slack, and Teams.

Its focus is not only providing a chat window. It also supports generative UI, shared state, and human confirmation workflows, making it suitable for teams that need to deeply integrate AI Assistants into product interactions.

Core features

• In-app Copilot: Embed AI Assistants directly into existing applications, instead of asking users to switch to a separate chat tool.
• Generative UI: Agents can generate or update UI components based on task status and user intent.
• Human confirmation workflows: Supports human-in-the-loop, allowing users to confirm, modify, or take over tasks at key steps.

Suitable scenarios

• Embedding an AI Copilot into SaaS products.
• Building AI Assistants that can read application state, call tools, and generate interfaces.
• Teams that need to integrate Agent workflows into Slack, Teams, or Web applications.

Ruflo

GitHub: https://github.com/ruvnet/ruflo

Official website: https://flo.ruv.io

Stars: 58.6k

Introduction

Ruflo is a multi-Agent collaboration platform for Claude Code and Codex, used to coordinate multiple specialized Agents, automated workflows, and team-level tasks. It is more like the Agent collaboration foundation behind enterprise AI Assistants, and is suitable for complex development, testing, security, documentation, architecture, and DevOps scenarios.

Its core value is expanding the capability of a single Agent into multi-Agent collaboration, allowing tasks to be split, routed, executed, and remembered.

Core features

• Multi-Agent collaboration: Supports multiple specialized Agents working together on development, testing, security, documentation, and other tasks.
• Designed for complex workflows: Can work with Claude Code / Codex to handle coding, testing, auditing, documentation generation, and other workflows.
• Plugin and memory capabilities: Provides a plugin system, vector memory, and task coordination capabilities, making it suitable for more complex Agent working environments.

Suitable scenarios

• Multiple Agents need to collaborate on development tasks.
• Teams want to automate code review, test generation, documentation maintenance, security scanning, and similar workflows.
• Teams need to extend more complex Agent collaboration capabilities on top of Claude Code or Codex.

Personal AI Assistant

DeepChat

GitHub: https://github.com/ThinkInAIXYZ/deepchat

Official website: https://deepchat.thinkinai.xyz

Stars: 6k

Introduction

DeepChat is an open source desktop AI Assistant platform that supports multiple cloud and local large language models, and integrates MCP tool calling, Skills, ACP Agent integration, and remote control capabilities. It is more like a desktop assistant that connects models, tools, and personal context.

For individual users, DeepChat’s advantage is that it brings multi-model chat, local models, tool calling, search enhancement, and skill workflows into one desktop application.

Core features

• Unified multi-model management: Supports multiple model providers, including OpenAI, Gemini, Anthropic, DeepSeek, and Ollama.
• Tool and skill support: Built-in MCP, Skills, and ACP Agent integrations can extend task capabilities for code review, document processing, PDFs, PPTs, spreadsheets, and more.
• Remote control: Supports controlling DeepChat sessions through Telegram, Feishu, QQBot, Discord, WeChat iLink, and other channels.

Everywhere

GitHub: https://github.com/Sylinko/Everywhere

Official website: https://everywhere.sylinko.com

Stars: 6k

Introduction

Everywhere is a desktop AI Assistant focused on screen context awareness and contextual understanding. It can perceive the current application and screen content, allowing users to call AI for help in the current work environment without frequently taking screenshots, copying content, or switching windows.

It is more like a desktop context-aware assistant, suitable for individual users when reading, translating, troubleshooting, summarizing, and working across applications.

Core features

• Screen context awareness: Understands the current screen and application environment, reducing copy-paste and window switching.
• Quick invocation: Uses shortcuts to call the AI Assistant in the current scenario, making it closer to desktop workflows.
• Supports multiple models and MCP tools: Can combine different LLMs and MCP tools to complete more operations in the desktop environment.

GitHub Copilot for Xcode

GitHub: https://github.com/github/CopilotForXcode

Official website: https://github.com/github/CopilotForXcode

Stars: 6.1k

Introduction

GitHub Copilot for Xcode is an AI coding assistant for Xcode, mainly serving Swift, Objective-C, iOS, and macOS development scenarios. It is more like a developer assistant embedded directly into Xcode, providing code completion, chat, code review, Agent Mode, and other capabilities.

For developers in the Apple ecosystem, its value is bringing AI programming assistance into the native development environment and reducing the need to switch back and forth between Xcode and other AI tools.

Core features

• Native Xcode integration: Built for Swift, Objective-C, iOS, and macOS development, and used directly in Xcode.
• Code completion and chat: Supports code suggestions, Copilot Chat, code review, and other features.
• Agent Mode: Can understand and modify codebases, search related files, create files, and run MCP tools.

Leon

GitHub: https://github.com/leon-ai/leon

Official website: https://getleon.ai

Stars: 17.3k

Introduction

Leon is an open source personal AI Assistant built around tools, context, memory, and Agent-style execution. It is more like a personal assistant that can run locally or on a server, suitable for users who value privacy, control, and extensibility.

Compared with ordinary chat assistants, Leon emphasizes completing tasks through real tools and combining memory with context, making its answers and actions closer to the user’s real environment.

Core features

• Clear personal assistant positioning: Designed for personal tasks, voice, automation, memory, and local environments.
• Supports tools, skills, and memory: Can use local tools, skills, and hierarchical memory to complete more specific tasks.
• Balances local and remote models: Supports local and remote AI providers, making it easier to choose between privacy and capability.

FAQ

1. What should you consider when choosing an open source AI Assistant?

When choosing an open source AI Assistant, you can focus on five aspects: whether the project is actively maintained, whether it supports your use case, whether it can connect to existing tools or systems, whether it supports self-hosting or private deployment, and whether it has clear documentation and an active community.

Individual users can prioritize ease of use, model support, local tool calling, and desktop experience. Enterprise users should focus more on permissions, data structures, workflow automation, system integration, and long-term maintenance capabilities.

2. If I want to build internal business systems for an enterprise, which AI Assistant project should I choose?

If your goal is to build enterprise internal business systems such as CRM, approval systems, ticketing systems, project management, inventory management, and operations backends, you can look at NocoBase first.

NocoBase is better suited for supporting enterprise business data, permissions, workflows, and page configuration. It can not only help build systems with AI, but also continue adjusting fields, pages, permissions, and workflows through a no-code interface after generation. For enterprise internal systems that require long-term maintenance, this type of structured platform is more reliable.

3. If I only need a desktop AI Assistant for personal use, which one should I choose?

If you mainly use it personally and want to use multiple models, process files, call tools, and organize information from one desktop app, you can first look at DeepChat or Everywhere.

DeepChat is better suited for bringing multiple models, MCP, Skills, and remote control capabilities into one desktop application. Everywhere emphasizes screen context awareness and contextual understanding, making it suitable for reading, translating, troubleshooting, and summarizing webpages or documents.

4. If I am a developer and want to use an AI Assistant inside development tools, which one should I choose?

If you are a developer in the Apple ecosystem and mainly use Xcode to build iOS, macOS, Swift, or Objective-C projects, you can look at GitHub Copilot for Xcode.

It is better suited as a developer assistant embedded directly into Xcode, providing code completion, chat, code review, Agent Mode, and other capabilities. If your needs lean more toward multi-Agent collaboration, code review, test generation, documentation maintenance, and DevOps workflows, you can also look at Ruflo.

5. If I want to embed an AI Assistant into my own product interface, which one should I choose?

If you are building a SaaS product, Web application, or internal product and want to embed an AI Assistant directly into the application interface, you can first look at CopilotKit.

CopilotKit is better suited for building in-app Copilots. It allows AI Assistants to read application state, call tools, generate interfaces, and add human confirmation workflows at key steps. For teams that want to add AI Copilots, generative UI, or Agent workflows to products, it is a better fit.

6. How should I choose if I want to build an open source personal AI Assistant?

Leon leans more toward the personal assistant direction and is suitable for personal tasks, voice interaction, tool calling, memory, and automation scenarios. It is suitable for users who value privacy, self-hosting, long-term memory, and extensibility. If you want an assistant that can gradually connect to your local environment and personal workflows, Leon is closer to that direction than a simple chat tool.

7. Is NocoBase only for enterprises? Can individual users use it?

NocoBase is not only for enterprises. Individual users can also use it to build their own management systems or knowledge bases. Its open source edition is free and friendly to individual users.

For example, individuals can use NocoBase to build knowledge bases, reading logs, task management systems, resource libraries, project dashboards, customer follow-up tables, content topic libraries, and other systems. As long as your needs involve data tables, fields, relationships, pages, and long-term organization, NocoBase can serve as a lightweight personal management platform.

⭐ If this article inspired you, feel free to share it with friends who are also interested in open source AI tools and productivity improvement.

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