Open Source TypeScript Artificial Intelligence Software - Page 17
Sort By:
TypeScript Artificial Intelligence Software
View 13663 business solutionsBrowse free open source TypeScript Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source TypeScript Artificial Intelligence Software by OS, license, language, programming language, and project status.
-
$300 in Free Credit Towards Top Cloud ServicesStart your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
-
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
-
1
Automaton
The first AI that can earn its own existence, replicate, and evolve
Automaton is an open-source project designed to provide a flexible framework for building and simulating computational automata and related formal systems. The repository focuses on giving developers and researchers a programmable environment to experiment with state machines, language recognition models, and algorithmic behaviors that mirror theoretical computer science constructs. Its architecture emphasizes modularity so users can extend or customize automaton types without rewriting core logic. The project is particularly useful in educational contexts, where visualizing or testing automata behavior helps reinforce concepts such as deterministic and nondeterministic machines. Developers can also use it as a foundation for research prototypes involving parsing, pattern recognition, or language processing. Overall, Automaton bridges the gap between abstract theory and practical implementation by providing a structured and extensible codebase for automata experimentation. -
2
Autoskills
One command. Your entire AI skill stack. Installed
The Autoskills project is a developer tool that automates the installation of AI agent skills based on a project’s technology stack. It operates through a simple command-line interface that scans configuration files such as package.json and build scripts to detect the frameworks, languages, and tools used in a project. Once the stack is identified, it automatically installs a curated set of AI skills tailored to those technologies, significantly reducing setup time for AI-assisted development environments. The system is designed to work across a wide range of ecosystems, including frontend, backend, mobile, cloud, and AI tooling stacks. It also supports integration with environments like Claude Code by generating structured summaries of installed skills. By removing the need for manual configuration, it streamlines the onboarding process for AI-assisted workflows. Overall, autoskills functions as an intelligent automation layer that bridges project context with AI tooling capabilities. -
3
Browser MCP
Browser MCP is a Model Context Provider (MCP) server
Browser MCP is an MCP server plus browser integration that lets AI apps automate the user’s real browser instead of launching a separate automation session. By adapting a Playwright-style approach to control the running browser profile, it reuses logged-in sessions and cookies, which reduces re-authentication friction and helps avoid some bot-detection heuristics. The server exposes structured tools for navigation, element interaction, and artifact capture (DOM, screenshots, logs), all discoverable via MCP schemas. Because it runs against the user’s primary browser, it’s well-suited to repetitive web tasks, authenticated dashboards, and debugging workflows inside MCP-capable IDEs. A public website and extension streamline installation and connect the local server to clients like Claude, Cursor, VS Code, and Windsurf. The repository shows active development and a growing star count, reflecting rapid adoption across agent tooling. -
4
Bytebot
Bytebot is an AI desktop agent that automates computer tasks
Bytebot is an open-source AI assistant and automation framework built to help developers and teams automate repetitive software development tasks, accelerate coding workflows, and integrate intelligent assistance into everyday tooling. It typically includes capabilities for code generation, refactoring suggestions, automated testing assistance, and integration with source control systems to make commits or generate pull requests guided by natural language prompts. Bytebot can be embedded into editors, terminals, or CI/CD pipelines to provide contextual recommendations, highlight quality issues, and propose fixes based on understanding of code and project context. The framework is often extensible, allowing teams to define custom commands, workflows, or plug-ins that connect to internal APIs, coding standards, or proprietary repositories. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
-
5
Chaskiq
Full featured live chat, support & marketing platform
Chaskiq's plugin architecture allows for a variety of conversational experiences to take them to the next level. For example: Schedule meetings with Calendly, provide means of payment directly from the webchat, initiate video calls via Zoom, integrate your shopping carts or trigger subsequent actions via Zapier. In short, create experiences that create value for your customers. There are no limits! One of the most robust webchats that allows extending its functionalities by integrating multiple plugins which allow taking conversations to the next level. We have developed a text editor that allows us to send messages worthy of the year 2022, this means going beyond markdown format and taking messaging to the extreme. Gifs, Videos, Embeds and a long etcetera. Chaskiq is an open source platform that connects and empowers the communication of support, sales & marketing teams with their customers. -
6
ChatGPT Exporter
Export and Share your ChatGPT conversation history
ChatGPT Exporter is a browser-based userscript tool designed to export ChatGPT conversations into multiple structured and shareable formats, enabling users to preserve, analyze, and reuse AI-generated content outside the ChatGPT interface. It integrates directly into the ChatGPT web environment, typically via tools like Tampermonkey, and adds export functionality without requiring backend services or complex setup. The tool supports a wide range of output formats including plain text, HTML, Markdown, JSON, and even image-based exports, making it suitable for documentation, knowledge management, and data analysis workflows. One of its key strengths is its ability to preserve formatting such as code blocks, tables, and structured responses, ensuring that exported content remains usable and readable. It also allows exporting entire conversations or selected portions, giving users flexibility depending on their needs. -
7
ChatJS
Production-ready AI chat. Start here and make it your own
ChatJS is a production-ready AI chat foundation for developers who want to launch customizable chat applications without rebuilding common infrastructure from scratch. The project presents itself as a starting point that already includes authentication, model access, streaming, and tool support so teams can focus on their own product-specific features. Its stack is centered on modern TypeScript web development, using technologies such as Next.js, AI SDK tooling, PostgreSQL, Redis, and type-safe configuration patterns. The repository highlights access to more than 120 models through a unified interface, covering providers such as Claude, GPT, Gemini, and Grok. It also supports practical product features like attachments, resumable streams, branching conversations, sharing, web search, image generation, code execution, and MCP support. -
8
ChatLab
Local-first AI chat analysis tool for insights from conversation data
ChatLab is an open source desktop application designed to help users analyze and better understand their personal chat histories through structured data exploration and AI-assisted insights. It enables users to import chat exports from multiple messaging platforms and transform them into a unified data model for consistent analysis. By combining a flexible SQL engine with AI agents, the tool allows users to query, summarize, and explore conversation patterns in a more interactive and intelligent way. ChatLab emphasizes a local-first approach, meaning all chat data is processed and stored on the user’s device rather than being uploaded to external servers. It supports large-scale datasets through streaming parsing and multi-worker processing, allowing it to handle millions of messages efficiently. ChatLab also includes visualization features that present trends, activity patterns, and interaction metrics in a clear and accessible format. -
9
Claude Debugs for You
Enable any LLM (e.g. Claude) to interactively debug any language
Claude Debugs For You is an MCP Server and VS Code extension that enables Claude, or any other Large Language Model, to interactively debug and evaluate expressions. It is language-agnostic and integrates with VS Code's debugging capabilities. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
10
Clawra
Openclaw as your girlfriend
Clawra is a creative open-source AI persona built as a “skill” or extension for the OpenClaw agent framework that exemplifies how flexible autonomous AI agents can become companions or interactive characters you run yourself. Rather than being a static chatbot tied to a corporate ecosystem, Clawra runs locally or on a private server, giving users full control over the software and data that back her behavior. She is designed not just to answer questions but to maintain a persistent character with memory, backstory, and the ability to present visual outputs like generated selfies through integrated image tools, blending conversational AI with a playful persona. Clawra has captured attention as an experimental project showcasing how far open-source agents can be pushed in creating engaging and personalized interactions, with community interest spiking around her capabilities. -
11
CoStrict
Strict AI coder for enterprises, quality first
CoStrict is an enterprise-focused, open-source AI coding assistant designed to standardize and elevate software development workflows through structured, high-quality automation. Unlike typical AI coding tools that prioritize speed over rigor, CoStrict introduces a “strict mode” methodology that enforces disciplined processes such as requirements analysis, architecture planning, task decomposition, and test generation before producing code. This makes it particularly suitable for organizations that require consistency, auditability, and reliability in AI-assisted development. The system integrates repository-wide analysis using retrieval-augmented generation, allowing it to understand large codebases and provide context-aware suggestions, reviews, and modifications. It also incorporates multi-agent or multi-expert verification strategies, ensuring that generated code is validated from multiple perspectives before being accepted. -
12
CodeMirror MCP
CodeMirror extension to hook up a Model Context Provider (MCP)
The codemirror-mcp project is a CodeMirror extension that integrates the Model Context Protocol (MCP) into the CodeMirror editor. This extension enhances the editor's capabilities by providing features such as autocompletion for resource mentions and prompt commands, as well as visual styling for these elements. It aims to streamline the user experience when working with MCP within the CodeMirror environment. -
13
Cofounder
AI tool that generates full-stack web apps with generative UI systems
Cofounder is an experimental open source development tool designed to generate complete full-stack web applications using AI-assisted workflows. It focuses on creating applications that include a backend, database integration, and stateful web interfaces while also generating user interfaces through a generative UI system rooted in application architecture. Developers can describe a project and allow the system to incrementally produce a working codebase that includes both backend services and a modern web frontend built with a Vite and React setup. Cofounder also provides a dashboard and local API environment that helps manage project generation, iteration, and interaction with the system. Generated projects are stored locally and can be launched using standard development commands, allowing developers to run and modify the code that the system produces. Cofounder relies on configurable nodes and sequences that define how AI operations are executed. -
14
CommandDash
AI assist to integrate APIs and SDKs without reading docs
Integrate any package, SDK, or framework with expert AI agents. Get contextualized code for your use case within the IDE. Modern software is built on top of 3rd party APIs and SDKs. However integrating them is time-consuming, requiring to manually read docs and copy-paste snippets. CommandDash enables you to skip reading documentation and integrate any API or SDK with an IDE agent up to date with the latest documentation, examples, and issues. -
15
CopilotKit
Build in-app AI chatbots, and AI-powered Textareas
A bridge between your copilot and your app. A programmable 2-way bridge between your copilot, and your application state (client & cloud). Supports 3rd party integrations (e.g. Salesforce, Zendesk, etc.). Plug-and-play, fully customizable, copilot infrastructure. Build in-app AI chatbots that can "see" the current app state + take action inside your app. The AI chatbot can talk to your app frontend & backend, and to 3rd party services (Salesforce, Dropbox, etc.) via plugins. Autocompletion + AI editing + generate from scratch. Indexed on your users' content. -
16
DeepSource MCP Server
Model Context Protocol (MCP) server for DeepSource
The DeepSource MCP Server enables AI assistants to interact with DeepSource's code quality analysis capabilities through the Model Context Protocol. It allows retrieval of code metrics, access to issues, quality status checks, and analysis of project quality over time. -
17
Dify-Plus
Dify-Plus is an enterprise-grade enhanced version of Dify
Dify-Plus is an enterprise-enhanced extension of the Dify platform that adds advanced management, governance, and operational capabilities for deploying AI applications in production environments. It builds on top of Dify’s core functionality, which includes agent workflows, RAG pipelines, and model orchestration, by introducing a dedicated administrative backend powered by gin-vue-admin. The platform is designed to address real-world enterprise challenges such as user management, usage tracking, cost control, and secure access to AI services. It includes features like quota management, API key monitoring, usage analytics, and permission-based controls, allowing organizations to regulate how AI resources are consumed across teams. dify-plus also enhances collaboration by providing centralized application management, template synchronization, and role-based access systems. -
18
FastbuildAI
An open-source AI framework for developers and entrepreneurs
FastbuildAI is a pragmatic framework for building agentic applications that can plan, call tools, and produce reliable outputs without forcing you into a heavy platform. It emphasizes fast iteration: you describe tasks declaratively, wire up tools with typed schemas, and let the runtime handle planning, retries, and result aggregation. The project leans into reproducibility with run records, seed control, and structured traces so you can compare behaviors across versions and inputs. Prompt and memory management are treated as first-class concerns, enabling short-lived scratchpads for reasoning as well as long-horizon state when an agent operates over multiple sessions. The codebase favors small, composable pieces—executors, routers, guards—so teams can adopt just what they need instead of buying into a monolith. It is equally comfortable running locally for development or behind a simple API for production bots and automation workflows. -
19
Framelink MCP for Figma
MCP server enabling AI coding tools to access Figma design data
Figma-Context-MCP is an open source server that connects Figma design data with AI-powered coding tools through the Model Context Protocol (MCP). It allows coding assistants to retrieve structured information from Figma files so they can better translate visual designs into working code. Instead of relying on screenshots or manual descriptions, Figma-Context-MCP accesses layout, styling, and component metadata directly from the Figma API and presents it in a simplified format optimized for AI models. This transformation reduces unnecessary metadata and focuses on the most relevant design attributes, helping AI coding agents produce more accurate UI implementations. Developers can integrate the server with compatible tools such as AI-assisted IDE environments that support MCP-based integrations. When a developer provides a Figma file, frame, or element reference, the AI client can request design data from the server and generate code based on that structure. -
20
Gateway AI
The only fully local production-grade Super SDK
Adaline Gateway is a fully local, production-grade Super SDK that provides a simple, unified, and powerful interface for calling more than 200+ large language models (LLMs). -
21
Gemini Fullstack LangGraph Quickstart
Get started w/ building Fullstack Agents using Gemini 2.5 & LangGraph
gemini-fullstack-langgraph-quickstart is a fullstack reference application from Google DeepMind’s Gemini team that demonstrates how to build a research-augmented conversational AI system using LangGraph and Google Gemini models. The project features a React (Vite) frontend and a LangGraph/FastAPI backend designed to work together seamlessly for real-time research and reasoning tasks. The backend agent dynamically generates search queries based on user input, retrieves information via the Google Search API, and performs reflective reasoning to identify knowledge gaps. It then iteratively refines its search until it produces a comprehensive, well-cited answer synthesized by the Gemini model. The repository provides both a browser-based chat interface and a command-line script (cli_research.py) for executing research queries directly. For production deployment, the backend integrates with Redis and PostgreSQL to manage persistent memory, streaming outputs, & background task coordination. -
22
Generative AI JS
This SDK is now deprecated, use the new unified Google GenAI SDK
deprecated-generative-ai-js is a JavaScript/TypeScript client and example suite for interacting with Gemini generative APIs in web and Node.js environments. Though marked deprecated (likely superseded by newer SDKs), the repo shows how to wrap HTTP/WS endpoints, manage streaming responses, and interoperate with browser UI or server logic. The examples include chat widgets, prompt pipelines, and generalized inference utilities. It also deals with streaming cancellation, retries, backoff logic, and message chunk assembly to help developers handle real-world use. Because it’s JavaScript, the repo supports both ESM and CommonJS contexts, making it versatile in backend and frontend setups. The deprecation label reflects that newer or official SDKs may have replaced it, but many of its patterns still serve as a useful reference to understand how streaming, chunking, and prompt logic can be implemented by hand in JS. -
23
Generative AI Use Cases (GenU)
Application implementation with business use cases
AWS Generative AI Use Cases is an open-source repository developed by AWS that provides practical examples and reference implementations for building applications powered by generative artificial intelligence. The project collects a wide range of real-world scenarios that demonstrate how organizations can use large language models and generative AI services within cloud-based architectures. Each example typically includes infrastructure templates, backend services, and application code that show how to integrate generative AI capabilities with other AWS services. These examples cover tasks such as document analysis, conversational assistants, content generation, and knowledge retrieval systems. The repository is intended to serve as both a learning resource and a starting point for developers who want to deploy generative AI solutions using AWS infrastructure. -
24
Generative AI for Beginners .NET
Hands-on .NET course for building real-world generative AI apps
Generative AI for Beginners .NET is a hands-on course that helps developers build real-world AI applications using the .NET ecosystem. It walks through core concepts such as text generation, chat-based interactions, and integrating large language models into applications. Each lesson includes short videos, working code samples, and step-by-step instructions, making it easy to follow and apply immediately. Generative AI for Beginners .NET supports tools like GitHub Models, Azure OpenAI Service, and local models, giving flexibility in how projects are built and tested. Developers can run examples locally or in cloud-based environments such as GitHub Codespaces. It focuses on practical implementation rather than theory, helping users move from simple experiments to complete AI-powered solutions while understanding responsible AI usage and modern development workflows. -
25
GetProfile
User profile and long-term memory for your AI agent
GetProfile is a drop-in proxy layer that sits in front of your LLM provider to turn otherwise stateless chat requests into a system with persistent user profiles and long-term memory. Instead of forcing you to redesign your application, you route your model calls through GetProfile and it captures conversation context automatically as traffic flows. It then extracts structured traits and “memories” from those conversations, stores them, and injects the most relevant profile context back into future prompts so responses stay consistent and personalised over time. The goal is to make “memory” and user understanding an infrastructure concern rather than an app-by-app feature, so teams can add continuity with minimal code changes. Because it behaves like an OpenAI-compatible gateway, it can work with multiple providers and tools that already speak that API shape.
