Artificial Intelligence

25 Open Source Tools For AI Developers

If you're building AI-powered apps, don't miss these amazing open-source projects.
Published on
November 14, 2023

The rapid pace of innovation in AI has led to an explosion of open-source tools and frameworks. From highly versatile frameworks to specialised libraries focused on a single capability, these tools can help you build faster, optimise performance, and craft sophisticated AI systems. If you're building AI-powered apps, don't miss these amazing open-source projects:


A TypeScript framework, Axflow consists of a suite of modules for building robust natural language applications, offering modular scalability.


  • Modular SDK for Al app construction.
  • Frameworks for connecting data and evaluating LLM output.
  • Libraries for efficient data processing and model fine-tuning.


Build customised Al flows with a user-friendly visual interface, facilitating the design of complex LLM applications.


  • Drag-and-drop interface for easy flow construction.
  • Integration with Langchain.
  • Node Typescript/JavaScript support for wider development compatibility.


MindsDB enables developers to utilise Al/ML models as virtual tables for SQL operations, streamlining Al applications' development.


  • Al models as virtual tables for familiar SOL interactions.
  • Seamless integration with wide array of data sources.
  • Instant provisioning of Al engines for immediate inference.


E2B Sandbox provides a secure cloud environment for Al agents and apps, allowing them to use a variety of development and multimedia tools.


  • Secure, sandboxed environments for Al safety.
  • Access to developer tools for enhanced Al capabilities.
  • Seamless connection to various LLMs and agents.


You're probably already familiar with LangChain. It provides a framework for chaining together multiple Al models into a cohesive workflow, enabling more complex and flexible Al applications.


  • Combination of models into flexible pipelines.
  • Support for vector storage, similarity search, and caching.
  • Streamlined support for major language models.

Haystack by Deepset

An end-to-end NLP framework, Haystack is suited for building Al apps like RAG and conversational agent chatbots with advanced retrieval methods.


  • Customisable pipelines for specific use cases.
  • Scalable to millions of documents.
  • State-of-the-art retrieval architecture.

Vercel AI SDK

A library to easily create Al-powered user interfaces, supporting various JavaScript frameworks and environments.


  • Full support for popular JavaScript frameworks.
  • Streamlined development for serverless and edge runtimes.
  • Intuitive tools for building conversational Uls.


A PostgreSQL extension for vector storage and retrieval, pgVector is considered essential for fast and accurate similarity searches.


  • Easy integration with existing Postgres setups.
  • Advanced KNN and ANN search capabilities.
  • Superior speed compared to standalone vector databases.


Developed by Microsoft, this language allows for complex dialog flows in Al conversations without complex state machines.


  • Simplifies complex dialogue scripting.
  • Maintains conversational context effortlessly.
  • Reduces development time with reusable modules.


A unified API that allows seamless switching between various language models like GPT-3 and Claude without code changes.


  • Model-agnostic API endpoints.
  • Cost-effective model management.
  • Built-in caching for improved performance.


A framework for autonomous Al agents, SuperAGI allows developers to build and manage agents with enhanced capabilities and performance telemetry.


  • Toolkit marketplace for extending agent capabilities.
  • Graphical interface for agent management.
  • Performance telemetry for optimisation insights.


Facilitates asynchronous chatbot conversation processing and maintains chat histories, crucial for scalable Al interactions.


  • Enhances user experience with faster response times.
  • Scalable architecture for growing user bases.
  • Simplifies backend complexity for chat systems.


Integrate accurate speech recognition into your applications, transforming spoken language into actionable data.


  • Multi-speaker diarisation for clear transcriptions
  • Advanced noise suppression for higher accuracy
  • Low-latency streaming for real-time applications


Simplifies the development and operation of generative Al applications with visual tools and a integrated API, enabling both backend efficiency and frontend creativity.


  • Visual tools for prompt engineering and operations.
  • Backend-as-a-Service API for streamlined integration.
  • Plug-and-play applications for rapid deployment.


An evaluation framework for language models. Ensures reliability of your Al system with targeted testing, helping to maintain factual accuracy and relevance.


  • Comprehensive testing suite for language models.
  • Identifies and addresses training data shortcomings.
  • Tracks LLM quality metrics over time.


Microsoft's AutoGen framework enables the development of Al applications through conversational agents that can collaborate on tasks.


  • Customisable agents for tailored interactions Human-like conversational abilities.
  • Simplifies the implementation of complex Al workflows.


A framework designed for prompt testing, helping developers benchmark and track the performance of Al models.


  • Quantitative analysis of prompt performance.
  • Automated regression detection.
  • Integration testing for reliable production deployment.


Langfuse provides detailed analytics and observability for LLM applications, focusing on production usage but also useful for development.


  • Granular production analytics for quality and cost management.
  • Nested view of app executions for in-depth monitoring.
  • Segment tracing for targeted debugging.


A monitoring toolkit tailored for Al applications, LLMonitor tracks performance metrics and aids in debugging through query replay.


  • Detailed cost, token, and latency analytics.
  • Debugging tools for tracing and prompt inspection.
  • User feedback collection and dataset labelling for model fine-tuning.


A serverless vector database that's developer-friendly and designed for Al applications, facilitating efficient long- term memory for LLMs.


  • Simplifies the management and retrieval of embeddings.
  • Supports a variety of data types for multi-modal search.
  • Offers native support for popular programming languages.


A developer platform that utilises event-driven architecture for building and running Al apps, ensuring scalability and resilience.


  • Integrates with major LLMs and vector databases.
  • Asynchronous, fault-tolerant architecture.
  • Tools for seamless deployment and app management.


An Al Automation Platform for efficient instruction management. Orchestrate instructions and task execution efficiently, enhancing Al's understanding and responsiveness for better task outcomes.


  • Adaptive memory management for improved performance.
  • Smart feature integration for task planning and execution.
  • Versatile plugin system for extended functionality.


A Python library that changing how machine learning models are demoed and shared. tI enables a web-Ul for various machine learning and data science projects.


  • Intuitive interfaces for model demos.
  • Drag-and-drop, text input, and voice recording capabilities.
  • Quick deployment with shareable links.


Provides a versatile environment to run, fine-tune, and manage LLMs in various applications. Run inference on any open- source LLM, deploy them on the cloud or on-premises, and build powerful Al applications.


  • Supports a broad range of open-source LLMs.
  • Capabilities for model fine-tuning and quantisation.
  • Streaming and batching support for enhanced performance.

With new open-source AI projects constantly emerging, there are likely other great tools we may have missed. If you know of any other helpful tools for building the next generation of AI applications, let us know!

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