Originally published at https://www.nocobase.com/en/blog/top-12-ai-workflows-projects-with-the-most-github-stars
When discussing workflows and automation, tools like n8n, Dify, as well as well-known commercial products such as Zapier and Make, are likely familiar to you. However, in this GitHub AI project roundup, we’ll shift our focus back to GitHub workflows and highlight other notable projects. Among the tools with high star ratings, some may not be large in scale but are designed with a more specific focus, continuously refining the integration of workflows and AI. This article explores AI-integrated workflow projects, showcasing their unique features and use cases. It aims to provide a clearer understanding of how AI has enhanced workflow tools.
Based on project positioning and key features, this article categorizes AI workflow tools into three groups:
- Business System Platforms: NocoBase, Appsmith, OpenProject
- Automation Workflow Engines: Continue, Mastra, wshobson/agents, Activepieces, Trigger.dev
- Workflow Infrastructure & Scenario Tools: Temporal, Conductor, Dagger, UVDesk
💡 Read More: 5 Best Tools for Building Workflow Automation
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Business System Platforms
These platforms integrate AI, workflows, and business systems into one framework, enabling real-world business applications.
NocoBase
NocoBase is an open-source, self-hosted AI no-code/low-code platform designed for building business systems. It is built around data models and plugins, supporting the rapid creation and customization of complex business systems while embedding AI features for smarter collaboration.
GitHub Stars: 20.9k
GitHub: https://github.com/nocobase/nocobase
Official Website: https://www.nocobase.com
AI Features
- AI employees act as roles in business operations NocoBase’s AI functions as AI employees who can read data models, interface configurations, and business context. They assist in executing tasks when users interact or workflows are triggered. These AI employees are more than just conversational agents—they function as integral parts of the system, helping users complete tasks.
- Deep integration of AI employees into workflow nodes The platform’s workflow system includes nodes specifically for AI employees, such as text and multimodal conversations and structured output. These nodes allow AI to process workflow context, generate structured results, and contribute to decision-making, turning workflows from rule-based processes into ones that can incorporate intelligent inputs.
What can it be used for?
- Building internal business systems with AI collaboration NocoBase is ideal for creating internal systems like CRMs, approval systems, or asset management systems. In these systems, AI employees can understand data structures and context, assisting with tasks such as organizing information, completing fields, or generating content, which reduces repetitive manual work.
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- Enhancing workflow with AI at key decision points During workflow execution, AI employees can assist at critical points, such as verifying text content, generating structured outputs, or offering judgment before advancing the process. This adds intelligent processing without altering the underlying workflow structure.
- Context-aware workflow execution using knowledge bases With access to a knowledge base and vector databases, AI employees can retrieve and utilize historical documents or business data, helping to generate output based on relevant content. This is especially useful for workflows requiring historical or policy-related data, improving contextual understanding during automation.
Appsmith
Appsmith is an open-source low-code application platform designed to help developers and teams quickly create internal tools, business applications, and automation interfaces. In the AI space, Appsmith integrates various large model services and its own Appsmith AI features, allowing developers to embed AI capabilities into application logic and workflow execution, thereby enhancing the intelligence of internal processes.
💡 Read More: Top 5 Open-Source AI Internal Tools on GitHub
GitHub Stars: 38.7k
GitHub: https://github.com/appsmithorg/appsmith
Official Website: https://www.appsmith.com
AI Features
- Native integration of AI queries and model interactions Appsmith provides an official Appsmith AI feature, allowing users to perform text generation, classification, summarization, entity extraction, and image classification directly within their applications. Users can also upload files to provide context for the model, enabling the application to process content intelligently.
- Support for building intelligent assistants and programmable workflows Using Appsmith Agents, users can build intelligent assistants based on business data and backend logic. These assistants can retrieve backend data or trigger workflows automatically based on user queries, enabling AI-driven workflow actions.
What can it be used for?
- Build intelligent business automation dashboards Customer service or operations teams can use Appsmith to create automation dashboards. By combining Appsmith Workflows and AI capabilities, users can automatically send email notifications, update data statuses, and sync data between different systems in the background, improving overall business efficiency.
- Enhance the analytics capabilities of existing applications By integrating large language models (LLM) into custom applications, users can perform tasks such as text summarization, classification, and semantic search. For instance, feedback collected within Appsmith can be analyzed by the model to generate actionable business insights.
OpenProject
OpenProject is an open-source web project management software that supports full project lifecycle management, from planning and task management to progress tracking and collaboration. It supports both traditional project management methods and agile/hybrid approaches, helping teams organize workflows clearly with views like work packages, Kanban boards, and Gantt charts.
GitHub Stars: 13.4k
GitHub: https://github.com/opf/openproject
Official Website: https://www.openproject.org
AI Features
- AI-powered project management suggestions and analysis OpenProject uses large language models to provide project management suggestions. By analyzing project data, this feature offers insights that help teams improve project execution, identify risks early, and optimize processes. This feature is still under development but focuses on increasing workflow efficiency while automating routine tasks.
What can it be used for?
- Improve project management efficiency OpenProject visualizes complex work packages, task dependencies, and team assignments, making the project process more transparent. With AI suggestions, teams can better understand the project status and adjust plans to address potential risks.
- Intelligent document generation and improvement With AI capabilities like automated status reports, task summaries, and text analysis, users can save significant time on repetitive tasks when working on project documents, meeting notes, and planning summaries, allowing them to focus on improving content quality.
Automation Workflow Engines
These tools are centered around Agents or process execution, and are more aligned with frameworks, engines, or developer tools that need to integrate with existing systems.
Continue
Continue is an open-source AI programming assistant designed as an intelligent collaboration tool for developers' daily workflows. It focuses on the editor as the primary use case, deeply integrating code context, project structure, and historical modification data. This allows the AI to closely match real development workflows while coding, understanding code, and executing multi-step tasks.
GitHub Stars: 30.5k
GitHub: https://github.com/continuedev/continue
Official Website: https://continue.dev
AI Features
- Continuous collaboration based on code context Continue's core strength lies in its deep understanding of the current code repository. AI can understand file structures, function definitions, and dependencies, providing code suggestions or executing modifications based on this context, making it a seamless part of the development workflow.
- Multi-step task execution Continue is not limited to generating individual code snippets. It can execute a series of actions under user instructions, such as problem analysis, modifying multiple files, and providing explanations. This turns it into an intelligent workflow executor embedded within the development process.
What can it be used for?
- Improve daily development workflow efficiency Continue can assist with tasks like code completion, refactoring suggestions, and logic explanations, reducing time spent switching contexts or searching documentation, resulting in a smoother coding experience.
- Aid with complex changes and troubleshooting For projects requiring cross-file changes or debugging, Continue can provide suggestions based on the overall code structure, helping developers implement changes more efficiently. This makes AI an integral part of the development workflow, not just a standalone tool.
Mastra
Mastra is an open-source TypeScript framework designed for building intelligent applications and agents. It provides infrastructure for creating multi-step workflows, managing context and memory, integrating large language models, and building intelligent agents, allowing developers to define and orchestrate complex AI-driven processes in a unified manner.
GitHub Stars: 19k
GitHub: https://github.com/mastra-ai/mastra
Official Website: https://mastra.ai
AI Features
- Persistent context management and memory Mastra allows intelligent agents to retain context over time, enabling multi-step tasks to remember historical information, which supports more coherent execution and reusability of complex tasks. This memory function is crucial for long-term AI workflows.
What can it be used for?
- Build intelligent multi-step interactions with context retention For workflows that require continuous context understanding, Mastra enables intelligent agents to track previous states during multi-step tasks. For example, in knowledge retrieval workflows, it can first gather information from a data source and then use its memory to perform further tasks like content generation and summarization.
wshobson agents
wshobson agents is an open-source collection of AI Agent extensions and plugins designed to provide reusable tool capabilities and task components for AI Agents. Rather than creating a complete platform or execution engine, this project offers standardized Agent components that help developers quickly extend executable capabilities in existing AI Agent or workflow systems, enabling Agents to perform more specific, structured tasks.
GitHub Stars: 23.4k
GitHub: https://github.com/wshobson/agents
Official Website: https://sethhobson.com/
AI Features
- Plugin-based toolset for Agents The repository offers a variety of tool modules for Agents, allowing them to perform specific tasks like information processing, external service calls, and task assistance. This design allows Agents to expand their capabilities by combining plugins without needing to repeatedly implement underlying logic.
What can it be used for?
- Enhance AI workflows with executable capability modules In existing AI workflows or Agent orchestration systems, you can integrate tools from wshobson agents to let Agents execute specific tasks at defined steps, such as data processing or interacting with external systems, enhancing the overall process.
- Build composable Agent execution workflows By combining multiple Agent tools, developers can create structured execution workflows, ensuring more stable behavior patterns for AI in multi-step tasks. This approach is ideal for AI automation scenarios requiring predictability and control.
Activepieces
Activepieces is an open-source automation platform designed to help teams visually build and execute workflows, enabling seamless connection and collaboration between systems and services. As the platform evolves, Activepieces has integrated AI capabilities to add intelligent processing and Agent functionality, enabling more complex automation logic.
GitHub Stars: 20k
GitHub: https://github.com/activepieces/activepieces
Official Website: https://www.activepieces.com
AI Features
- Built-in AI Agent functionality for smarter workflows Activepieces offers built-in AI Agent capabilities that can be embedded directly into workflows to execute tasks based on triggers or context. This allows workflows to not only run based on fixed rules but also incorporate AI-driven language understanding, judgment, and decision-making, making the process more flexible when dealing with unstructured information.
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What can it be used for?
- Build automated workflows with intelligent decision-making Activepieces supports traditional trigger-action workflows and allows AI Agents to be integrated. By defining trigger events and step logic, users can have Agents analyze data, interpret text, and make decisions, minimizing manual intervention. This is ideal for customer service automation, email processing, and similar tasks.
- Extend cross-system automation capabilities Activepieces offers many pre-built integration components, allowing businesses to combine services like calendars, document services, messaging platforms, and AI capabilities to create workflows that not only perform rule-based tasks but also incorporate AI analysis or content generation, improving efficiency and reducing repetitive work.
Trigger.dev
Trigger.dev is an open-source platform for writing and running AI workflows and backend tasks, allowing developers to use standard asynchronous code to build reliable, scalable, and durable workflows. It not only supports regular workflow tasks but also provides AI-related capabilities, enabling long-running AI tasks, complex task queues, and intelligent agents to run smoothly.
GitHub Stars: 13.1k
GitHub: https://github.com/triggerdotdev/trigger.dev
Official Website: https://trigger.dev
AI Features
- Support for building persistent, production-grade AI workflows Trigger.dev is designed as a platform for building AI workflows and agents. It allows developers to define tasks using standard asynchronous code and supports features such as unlimited execution, queue management, automatic retries, and task observability. These features make long-running AI tasks feasible while providing the necessary infrastructure for building AI Agents.
What can it be used for?
- Execute long-running AI tasks In scenarios where AI tasks require extended processing time, such as image generation, video processing, or semantic analysis, Trigger.dev helps developers run these tasks in the background without risking timeout failures. Its task management, queue control, and automatic retry mechanisms ensure reliable completion of complex AI operations.
Workflow Infrastructure & Scenario-based Tools
Providing support for stable operations or specific business scenarios, primarily focusing on foundational capabilities or supplementary roles for specific cases.
Temporal
Temporal is an open-source distributed workflow orchestration platform designed to run persistent and reliable business process code. Developers can define workflow logic using familiar programming languages in its SDK, enabling processes to run stably across services and nodes while automatically handling failures and recovery.
GitHub Stars: 17.2k
GitHub: https://github.com/temporalio/temporal
Official Website: https://temporal.io
AI Features
- Persistent execution support for AI Agents Temporal records the execution state of workflows as event history, allowing processes to continue from a confirmed state even in case of node failure or service interruption. This execution model is well-suited for long-running AI Agent scenarios, where task progress and context can be continuously saved during multiple model calls or tool operations to support complex AI-powered workflows.
What can it be used for?
- Supporting complex, long-running AI workflows In intelligent workflows that require multiple model calls and several steps, Temporal is often used to manage task order and execution states. For example, in AI Agent scenarios, model inference and tool calls can be broken down into separate steps, with workflows scheduling and recovering them, ensuring the process continues even if disruptions occur.
- Underlying execution foundation for AI workflows When building reliable and scalable automation workflows, such as multi-step data analysis or model training pipelines, Temporal’s state persistence and retry mechanisms ensure continuous execution of each process. These features make Temporal a core part of production-grade backend workflow systems, supporting complex processes, including AI service calls.
Conductor
Conductor is an open-source microservices workflow orchestration engine originally released by Netflix, designed to coordinate and manage complex business processes in distributed systems. It defines workflows as executable tasks and orchestrates multiple tasks and service calls, helping teams maintain workflow controllability and recoverability in high concurrency and complex scenarios.
GitHub Stars: 31.7k
GitHub: https://github.com/conductor-oss/conductor
Official Website: https://conductor-oss.org/
AI Features
- Stable orchestration and control layer for AI-powered workflows Conductor is positioned as a general-purpose workflow orchestration engine, not a domain-specific tool. In AI scenarios, model calls, inference services, and data processing are typically encapsulated as independent tasks, with Conductor responsible for task scheduling, state management, failure retries, and compensation logic, ensuring reliable execution for AI-powered workflows.
What can it be used for?
- Middleware between AI services and business systems Conductor is often used to bridge business systems and backend services. After introducing AI capabilities, it serves as an intermediary layer, embedding AI inference steps into existing business workflows without requiring large-scale system reworks, gradually integrating AI into the existing automation framework.
Dagger
Dagger is an open-source workflow engine originally designed for continuous integration and delivery. The core idea is to define workflows as composable code modules. As use cases expanded, Dagger evolved to handle data processing and AI tasks, becoming a crucial tool for engineering workflows and AI pipelines.
GitHub Stars: 15.2k
GitHub: https://github.com/dagger/dagger
Official Website: https://dagger.io
AI Features
- Composing multi-step AI workflows as code Dagger allows complex processes to be broken down into modular, composable tasks. For workflows involving AI calls, tasks like data preparation, model inference, and result processing can be clearly orchestrated, making the entire AI workflow more maintainable, scalable, and extensible.
What can it be used for?
- Foundation for AI task automation In broader automation scenarios, Dagger can serve as the underlying execution tool, integrating AI inference or data processing tasks into existing engineering workflows, progressively automating and smartening them.
UVDesk
UVDesk is an open-source customer support and ticket management system designed to help teams build customer service centers and support workflows. Over time, UVDesk has integrated AI capabilities into the customer support scenario, enhancing ticket handling efficiency and response quality, adding intelligence to the support process based on automation.
GitHub Stars: 17k
GitHub: https://github.com/uvdesk
Official Website: https://www.uvdesk.com
AI Features
- Rule-based and intelligent suggestion-based process optimization UVDesk's core remains a rule-driven ticket process, with AI capabilities serving as a supplement. These AI features provide intelligent suggestions during ticket creation or handling, improving processing quality at critical steps without altering the core workflow structure.
What can it be used for?
- Building intelligent customer support workflows In customer support scenarios, UVDesk can organize ticket processes through ticket assignment, status transitions, and notification mechanisms, integrating AI auto-replies or content suggestions to reduce repetitive manual tasks and improve overall response efficiency.
- Automating processes for frequently occurring issues For common or standardized inquiries, UVDesk can quickly generate initial responses by combining automation rules with AI content generation capabilities, reducing customer waiting time when tickets enter the system.
Thank you for reading this. If this content was helpful, feel free to share it with teams exploring AI automation and workflow practices.
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