9 Interesting Open Source Projects You Should Explore Right Now🥷🏻 🎉

Introduction

Open-source software and tools are used almost everywhere today. A recent study found that around 97% of audited codebases contain open-source software.

For individual developers, contributing to open-source projects is a great way to learn and improve their skills.

Whether you're exploring new technologies like AI, looking for useful tools, or collaborating with a global community, open-source projects offer countless opportunities.

In this article, I’ve gathered 9 interesting open-source projects you should explore right now.

Let's dive in!🚀

Feel free to contribute to these projects and tools to help improve them, or use these tools inside your own projects.

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OPAL - Administration layer for Policy Engine

OPAL is an open-source administration layer designed to work with policy engines like Open Policy Agent (OPA) and AWS Cedar.

If you’re a developer working on large projects with complex access controls, OPAL simplifies the process of managing permissions across teams or user groups.

It detects changes to policies and policy data in real time, ensuring your app’s authorization layer stays up-to-date.

Whether your data changes via APIs, Git, or third-party services, OPAL automatically syncs the necessary policies and authorization data to your services with proper access flow.

Now, let’s have a look at some of the features and use cases of OPAL:

✅ Real-Time Permission Updates: OPAL automatically grants or revokes permissions in real-time, eliminating the need for manual access updates across applications.

✅ Policy as Code: Developers can write policies as code, making them easier to version, review, and manage for consistent policy handling.

✅ Cloud-Native Integration: OPAL easily integrates with cloud-based microservices like AWS or Kubernetes, streamlining secure and automated policy updates.

✅ Fine-Grained Authorization: OPAL supports precise, end-to-end authorization and is compatible with multiple policy languages and decentralized data sources, making it ideal for complex, distributed systems.

This tool is very helpful for adding real-time permission updates to your project, and it also allows you to easily review and manage user roles at different levels in large applications.

With 4.5k stars on GitHub, it has strong community, join their Slack or star the project to show your support:

Star OPAL on GitHub ⭐️

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The team behind OPAL, Permit.io, is hosting an exciting Launch Week from October 28th to November 1st, 2024!

If you’re a developer working with authorizations and permissions and want to see what the Permit team has been building, this event is for you! 🔥

✅ Sign up to join the event.

✅ Win limited edition Stickers.

✅ Share your event ticket on social media to score some swag package.

✅ Join the live streams to discover new features and win a custom keyboard!

Use your GitHub account to register and get ready for some exciting launches! 🎁

🔥 Register Here 🔥

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Hexabot AI - Chatbot Builder

Building AI chatbots usually means complex coding and utilizing multiple tools. If you're looking for a simpler way, this is for you.

Hexabot AI is an open-source platform that lets you create intelligent chatbots without complex coding. Using its visual editor, you can build, manage, and deploy chatbots across multiple channels and languages.

You can build zero-code customer support bots using a visual editor and create multi-channel shopping assistants for seamless product discovery with Hexabot.

You can also deploy AI-powered appointment schedulers with multi-language support for small businesses. Lots of things you can do by creating different kinds of bots for different purposes.

Star Hexabot on GitHub ⭐️

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KitOps- Standards based Packaging and Versioning System

If you work in AI, ML, or SRE teams, you might be using different artifacts like models, source codes, datasets, etc that are stored and versioned separately.

KitOps is the market’s only open-source tool that uses OCI standards to bring it all together and helps in smooth collaboration among data scientists, application developers, and SREs working on integrating or managing self-hosted AI/ML models.

Now, let’s have a look at some of the features and use cases of KitOps:

✅ Unified Packaging and Versioning: KitOps allows organizations to package models, datasets, configurations, and code into ModelKits, which are OCI-compliant, reproducible, and versioned. This ensures compatibility across pipelines and simplifies handoffs between teams.

✅ Automation and Flexibility: KitOps integrates with CI/CD workflows, enabling automatic packaging, testing, and deployment. It also supports fine-tuning large language models and creating RAG pipelines, with flexible syntax using YAML.

✅ Security and Auditing: Each ModelKit is secured with an SHA digest and artifact signing, ensuring tamper-proofing and reliable provenance tracking for all model assets.

✅ Integration and Portability: ModelKits are standards-based, allowing them to be stored in any OCI-compliant registry and used across various AI/ML projects.

This tool is ideal for DevOps/MLOps teams looking for a library of versioned packages for AI projects, stored in an enterprise registry they are already using.

KitOps is a new and exciting project that addresses a critical problem for developers - managing and versioning AI/ML models efficiently, join their Discord or star the project to show your support:

Star KitOps on GitHub ⭐️

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Langflow - Low code AI Apps

If you’re a developer looking to build complex AI applications without too much coding. Langflow can be a great tool for this. Its intuitive interface allows users to focus on application logic and functionality rather than the underlying infrastructure.

Langflow is an open-source low-code application builder designed for creating Retrieval-Augmented Generation (RAG) and multi-agent AI applications.

With its Python-based framework, Langflow enables developers to seamlessly integrate various models, APIs, and databases without being tied to a specific technology stack.

Now, let’s have a look at some of the features and use cases of Langflow:

✅ Visual Development Interface: A drag-and-drop interface that simplifies workflow creation and testing, reducing the need for extensive coding. This design accelerates the development of complex applications, making it accessible for both experienced developers and those just starting.

✅ Flexible Integrations: Langflow supports a wide range of models, APIs, and databases, including well-known options like OpenAI, Pinecone, and MongoDB. This enables developers to integrate their existing tech stack while building intelligent agents and customized systems for a variety of applications.

✅ Real-Time Prototyping and Deployment: With integrated monitoring and debugging tools, Langflow enables immediate testing of workflows, allowing developers to optimize performance on the go. This capability is crucial for developing responsive applications, enabling teams to fine-tune their solutions based on user feedback and interactions.

This tool is very helpful for AI developers working with popular and emerging AI platforms and frameworks like Hugging Face, LangChain, and others.

With 31k+ stars on GitHub, it has a strong and growing community, star the project to show your support:

Star Langflow on GitHub ⭐️

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Readyset - Database Caching Layer

If you're a developer working with Postgres or MySQL, you're probably familiar with the performance issues that arise when handling complex SQL queries.

You might also find yourself making multiple changes to your application while implementing new caching solutions. ReadySet can be a helpful tool in this situation!

Readyset is a transparent database caching layer that boosts application performance and scalability.

It integrates easily with your existing setup, turning complex SQL queries into fast lookups while keeping cached results in sync with your database.

It acts as a layer between your application and the database, maintaining consistency between cached results and the underlying data. It can also be used with your current ORM or database client.

This tool is extremely useful if you are working on read-heavy applications, like content platforms or analytics tools.

Star Readyset on GitHub ⭐️

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Instant DB - Realtime DB for Frontend

Most developers today want to build at least one cool real-time application without dealing with complex infrastructure. Managing multiple tools for authentication, servers, and backend can be both costly and challenging.

If you’re a developer looking for a solution, InstantDB might be the tool for you. It is very helpful for creating smooth, real-time user experiences while simplifying backend logic management and scaling.

You can easily implement collaborative features, such as displaying who’s typing, supporting multiple cursor positions, and enabling multi-device sync in your applications using Instant.

Star InstantDB on GitHub ⭐️

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LanceDB - DB for Multimodal AI

Developers often struggle with managing and querying large amounts of diverse data.

Traditional vector databases usually require separate storage for embeddings and their metadata, which complicates workflows and adds extra costs. This separation can make it harder to keep everything in sync and create efficient applications.

LanceDB addresses this issue by allowing you to store and manage both embeddings and the actual data, such as images, videos, text, and more in the Lance format.

Now, let’s have a look at some of the features and use cases of LanceDB:

✅ Scalable and Efficient Search Capabilities: With production-scale vector search capabilities and support for vector similarity, full-text search, and SQL queries, LanceDB improves the efficiency of data retrieval and management, making it ideal for applications that require fast access to complex data sets.

✅ Flexible Deployment Options: Available in both an open-source embedded version and a cloud-based serverless option, LanceDB offers flexibility to developers. This allows you to choose the setup that best fits your project needs, whether you prefer self-hosting or leveraging cloud infrastructure without the hassle of server management.

If you’re into AI and working with RAG, AI models, and datasets, and you’re a fan of local-first AI applications, it can be super helpful.

LanceDB OSS allows you to run an embedded vector database on your own infrastructure.

Star LanceDB on GitHub ⭐️

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Phidata - Framework for AI Agents

AI agents are widely used by developers or organizations to automate specific tasks with the help of AI. Developers often face challenges in building applications that can manage complex tasks and adapt to user needs.

Phidata solves this by providing a framework for creating agentic systems that effectively manage state and memory.

With Phidata, developers can easily run agents locally or in the cloud and utilize built-in monitoring tools to track performance.

It simplifies the development process by managing agents' states and offering a user-friendly interface for interaction, helping developers build and optimize their systems efficiently.

If you want to build domain-specific agents that require memory, knowledge, and interactions with external tools, Phidata allows you to use different providers, knowledge bases, and storage options, such as OpenAI, Ollama, Mistral, Pinecone, Qdrant, Postgres, Langchain, DynamoDB and many more.

Star Phidata on GitHub ⭐️

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Stack Auth - Managed user Authentication

The final tool on our list is Stack Auth, an open-source alternative to Auth0 and Clerk. If you're looking to integrate a self-hosted, customizable user authentication system, Stack Auth is an excellent choice, as its self-hosted version is completely free.

Stack Auth’s extensive support for features like single sign-on (SSO), OAuth, and multi-factor authentication (MFA).

It’s particularly suited for projects where you need to avoid vendor lock-in or reduce costs associated with third-party auth services.

The ability to self-host ensures that sensitive user data stays within your infrastructure, offering enhanced security for applications with strict compliance needs.

One interesting point to note is that Stack Auth provides a customizable, self-hosted authentication system, making it easy for developers to handle user access.

On the other hand, OPAL focuses on real-time policy management, ensuring that authorization rules remain aligned with changing application requirements.

Star StackAuth on GitHub ⭐️

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Conclusion

In this article, I’ve highlighted 9 exciting open-source tools that can assist you with various tasks, whether you're working with AI, managing permissions, or handling databases.

These tools can help address challenges developers face in both small and large-scale applications.

If you know of any other cool projects I missed, please share them in the comments.

Team Permit Supported me for writing this article, but they did not influence the content of this write-up. Join Permit Launch Week.

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Thank you for Reading till the end.