Open Source TypeScript Agentic AI Tools - Page 2
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TypeScript Agentic AI Tools
View 102 business solutionsBrowse free open source TypeScript Agentic AI Tools and projects below. Use the toggles on the left to filter open source TypeScript Agentic AI Tools by OS, license, language, programming language, and project status.
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Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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LobeHub
Workspace to find, build, and collaborate with AI agents
LobeHub is an all-in-one workspace designed to help humans and AI agents collaborate, grow, and evolve together. It treats AI agents as true teammates rather than one-off tools, enabling deeper context, continuity, and productivity. Users can build personalized agent teams that understand their workflows, preferences, and goals over time. LobeHub brings multiple models, tools, and modalities into a single unified environment under the user’s control. With built-in collaboration features, agents can work in parallel, share context, and support complex projects seamlessly. The platform is built around the idea of co-evolution, where both humans and agents continuously learn and improve together. -
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GSD 2
A powerful meta-prompting, context engineering
GSD 2 is a project focused on automating and streamlining development workflows through structured build systems and tooling. It aims to simplify the process of configuring, building, and deploying applications by providing predefined templates and automation scripts. The system is designed to reduce manual setup and improve consistency across development environments. It supports modular configurations, allowing users to adapt the build process to different project requirements. The project also emphasizes efficiency, enabling faster iteration and deployment cycles. It is particularly useful for teams looking to standardize their workflows and reduce friction in development pipelines. Overall, gsd-2 functions as a productivity tool for managing complex build and deployment processes. -
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LLM Wiki
Open Source Implementation of Karpathy's LLM Wiki
LLM Wiki is a knowledge management and documentation system designed to organize, generate, and maintain structured information using large language models. It allows users to create interconnected knowledge bases that function similarly to a wiki but are enhanced with AI-driven content generation and summarization. The system emphasizes linking and context, enabling information to be connected across pages and topics for better navigation and understanding. It likely includes features for automatic content updates, ensuring that information remains relevant as new data becomes available. The architecture supports both manual editing and automated generation, providing flexibility in how knowledge is curated. It is particularly useful for teams or individuals managing large amounts of information across domains. Overall, llmwiki transforms static documentation into a dynamic, AI-assisted knowledge system. -
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5ire
5ire is a cross-platform desktop AI assistant, MCP client
5ire is a sleek, cross‑platform desktop AI assistant and MCP client that connects to major service providers, supports a local knowledge base and tool integration via MCP servers, enabling robust RAG and assistant features. These components are required as they constitute the runtime environment for the MCP Server. If you don't anticipate using the tools feature immediately, you may choose to skip this installation step and complete it later when the need arises. MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools. -
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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ClawRouter
Smart LLM router
ClawRouter is a flexible networking and routing framework designed to support AI-oriented distributed systems and agent ecosystems by managing how messages, requests, and responses are routed between components. It provides a programmable router abstraction that can handle complex traffic patterns, enabling dynamic message forwarding, load balancing, and custom routing logic based on content, context, or policy rules. Because distributed AI systems often involve many services, agents, and runtime components interacting with each other and with external APIs, ClawRouter helps ensure that communication paths remain clear, efficient, and adaptable as systems scale. The framework supports plugin-based extensions so developers can define custom protocols, transformation hooks, and monitoring handlers without modifying core routing logic. It also offers operational features like health checking, metrics reporting, and failure handling that make production deployments more reliable. -
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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. -
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OpenClaw Studio
A clean web dashboard for OpenClaw
OpenClaw Studio is a web-based dashboard designed to manage and interact with OpenClaw agents through a centralized interface. It allows users to connect to an OpenClaw Gateway, monitor agents, and control workflows from a single location. The platform provides real-time chat capabilities, approval management, and job configuration tools for agent operations. Built with a control-plane architecture, it handles communication between the browser and the gateway through server-managed connections. OpenClaw Studio supports both local and cloud deployments, making it flexible for different development and production environments. It is ideal for developers and teams looking to streamline agent management and improve visibility into autonomous AI workflows. -
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Agentic
AI agent stdlib that works with any LLM and TypeScript AI SDK
Agentic is an open source, TypeScript, AI agent standard library that works with any LLM and TS AI SDK. Agentic’s standard library of TypeScript AI tools are optimized for both TS-usage as well as LLM-based usage, which is really important for testing and debugging. -
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Compound Engineering
Official Compound Engineering plugin for Claude Code, Codex, Cursor
The Compound Engineering plugin project is an AI-driven workflow system designed to improve software development by turning each unit of work into a reusable and compounding asset. It provides a structured set of commands and agents that guide developers through stages such as brainstorming, planning, execution, review, and knowledge capture. The core philosophy is to reduce technical debt by emphasizing thorough planning and continuous learning, ensuring that each iteration improves future work rather than increasing complexity. The plugin integrates with multiple AI coding environments, including Claude Code and other tools, enabling consistent workflows across platforms. It also supports automated code review and ideation processes, leveraging multiple agents to enhance quality and decision-making. By codifying patterns and learnings, it creates a feedback loop that improves productivity over time. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Dify
One API for plugins and datasets, one interface for prompt engineering
Dify is an easy-to-use LLMOps platform designed to empower more people to create sustainable, AI-native applications. With visual orchestration for various application types, Dify offers out-of-the-box, ready-to-use applications that can also serve as Backend-as-a-Service APIs. Unify your development process with one API for plugins and datasets integration, and streamline your operations using a single interface for prompt engineering, visual analytics, and continuous improvement. Out-of-the-box web sites supporting form mode and chat conversation mode A single API encompassing plugin capabilities, context enhancement, and more, saving you backend coding effort Visual data analysis, log review, and annotation for applications -
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NemoClaw
NVIDIA plugin for secure installation of OpenClaw
NVIDIA NemoClaw is an open-source tool designed to simplify the deployment and management of always-on AI assistants using the OpenClaw ecosystem. It installs and configures the NVIDIA OpenShell runtime, which provides a secure environment for running autonomous AI agents. NemoClaw enables users to launch sandboxed agent environments that control network access, file permissions, and inference requests through policy-based security. The platform integrates with AI models such as NVIDIA Nemotron and supports multiple inference backends including cloud APIs, local NIM deployments, and vLLM. Through its command-line interface, developers can deploy, monitor, and manage AI assistants running inside isolated sandboxes. By combining sandbox orchestration, agent management, and AI model integration, NemoClaw provides a secure foundation for building and operating autonomous AI assistants. -
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OpenHands
Open-source autonomous AI software engineer
Welcome to OpenHands (formerly OpenDevin), an open-source autonomous AI software engineer who is capable of executing complex engineering tasks and collaborating actively with users on software development projects. Use AI to tackle the toil in your backlog, so you can focus on what matters: hard problems, creative challenges, and over-engineering your dotfiles We believe agentic technology is too important to be controlled by a few corporations. So we're building all our agents in the open on GitHub, under the MIT license. Our agents can do anything a human developer can: they write code, run commands, and use the web. We're partnering with AI safety experts like Invariant Labs to balance innovation with security. -
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DenchClaw
Fully Managed OpenClaw Framework for all knowledge work ever
DenchClaw is a local-first AI-powered CRM and productivity platform built on top of the OpenClaw framework, designed to transform a user’s entire computer into a programmable, agent-driven workspace. Unlike traditional cloud-based CRMs or AI tools, it runs entirely on the user’s machine and exposes a web interface locally, allowing full control over data, workflows, and automation without relying on external servers. The system combines database management, browser automation, and AI reasoning into a unified interface where users can interact with their data and tools using natural language commands. It can ingest data from sources such as Google Drive, Notion, Gmail, and CRM platforms, consolidating everything into a centralized workspace for analysis and action. One of its most distinctive capabilities is its ability to use the user’s existing browser session, enabling it to log into services, scrape data, and perform actions like outreach or research as if it were the user. -
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MCPJam
Postman for MCPs - A tool for testing and debugging MCPs
Inspector by MCPJam is a visual developer tool—akin to Postman—for testing and debugging MCP servers, with capabilities to simulate and trace tool execution via various transports and LLM integrations. -
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Nanobrowser
Open-Source Chrome extension for AI-powered web automation
Nanobrowser is an open-source AI web automation tool that runs in your browser. A free alternative to OpenAI Operator with flexible LLM options and a multi-agent system. Nanobrowser, as a chrome extension, delivers premium web automation capabilities while keeping you in complete control. No subscription fees or hidden costs. Just install and use your own API keys, and you only pay what you use with your own API keys. Everything runs in your local browser. Your credentials stay with you, never shared with any cloud service. Connect to your preferred LLM providers with the freedom to choose different models for different agents. -
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Actionbook
Browser action engine for AI agents. 10× faster, resilient by design
Actionbook is an AI-centric automation framework that equips intelligent agents with the ability to interact with real live web pages in a reliable and scalable way, eliminating the guesswork involved in navigating modern dynamic sites. Instead of having agents blindly scrape HTML or blindly try to click things, Actionbook supplies up-to-date action manuals and verified DOM structure, letting agents know exactly how to click, type, and navigate complex interfaces such as SPAs or streaming UIs. This design makes browsing up to 10× faster and far more resilient than ad-hoc approaches that break on minor page changes, because the action manuals codify expected flows and DOM targets. It provides multiple integration paths — a Rust-based CLI, MCP server support for AI IDEs, and a JavaScript SDK — so developers can plug it into a wide range of agent pipelines and toolchains. -
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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. -
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Botpress
Dev tools to reliably understand text and automate conversations
We make building chatbots much easier for developers. We have put together the boilerplate code and infrastructure you need to get a chatbot up and running. We propose you a complete dev-friendly platform that ships with all the tools you need to build, deploy and manage production-grade chatbots in record time. Built-in Natural Language Processing tasks such as intent recognition, spell checking, entity extraction, and slot tagging (and many others). A visual conversation studio to design multi-turn conversations and workflows. An emulator & a debugger to simulate conversations and debug your chatbot. Support for popular messaging channels like Slack, Telegram, MS Teams, Facebook Messenger, and an embeddable web chat. An SDK and code editor to extend the capabilities. Post-deployment tools like analytics dashboards, human handoff and more. -
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Eigent
The Open Source Cowork Desktop to Unlock Your Exceptional Productivity
Eigent is an open-source cowork desktop application designed to help you build, manage, and deploy a custom AI workforce. It enables multiple specialized AI agents to collaborate in parallel, turning complex workflows into automated, end-to-end tasks. Built on the CAMEL-AI multi-agent framework, Eigent emphasizes productivity, flexibility, and transparent system design. You can run Eigent fully locally for maximum privacy and data control, or choose a cloud-connected experience for quick access. The platform supports a wide range of AI models and integrates powerful tools through the Model Context Protocol (MCP). With human-in-the-loop controls and enterprise-ready features, Eigent balances automation with oversight and security. -
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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. -
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HybridClaw
The enterprise operating layer for open agents
HybridClaw is an emerging open-source framework focused on enabling hybrid AI agent systems that combine local execution, tool integration, and multi-agent orchestration into a cohesive development environment. It is designed to work alongside modern agent ecosystems such as OpenClaw, Claude Code, and similar agentic coding tools, providing a flexible infrastructure for managing agent behaviors, workflows, and capabilities. The project emphasizes modularity, allowing developers to define and compose “skills” or capabilities that agents can invoke dynamically, enabling more adaptive and context-aware automation. HybridClaw aims to bridge the gap between isolated AI tools and fully orchestrated agent systems by enabling communication, coordination, and shared context across multiple agents or processes. It is particularly relevant in scenarios where developers want to build complex autonomous systems that interact with codebases. -
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InsForge
InsForge is the backend built for AI-assisted development
InsForge is an open-source backend development platform designed specifically for AI-assisted or agent-driven application development, positioning itself as an agent-native alternative to tools like Supabase by exposing backend primitives (auth, database, storage, serverless functions, and AI integrations) in a way that intelligent agents can understand, reason about, and act upon directly. Rather than forcing developers to manually cobble together authentication flows, database schemas, storage buckets, and cloud functions, InsForge provides a semantic layer and toolchain that let agents configured with Model Context Protocol (MCP) understand the backend state, available operations, and how to manipulate these resources end to end. This enables AI coding assistants to complement human engineers by self-configuring backend components, connecting services, and evolving apps autonomously from prompts without switching contexts or manually provisioning infrastructure. -
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NanoClaw
A lightweight alternative to Clawdbot / OpenClaw
Nanoclaw is a lightweight, security-focused personal agent runtime designed as a slimmer alternative to larger “personal assistant” agent stacks, with an emphasis on being easy to audit and safe by default. It runs agent execution inside Apple containers to provide strong isolation boundaries, so individual chats and actions can be sandboxed with tighter filesystem and process separation than a typical single-process bot. The project connects directly to WhatsApp, letting you deploy an assistant that can chat in a familiar interface while still supporting real agent behaviors instead of simple call-and-response prompts. It includes memory so the assistant can retain important context across interactions, enabling more consistent follow-through on ongoing tasks. It also supports scheduled jobs, making it suitable for recurring reminders, periodic automations, and timed workflows without needing an external orchestrator. -
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Open Agents
An open source template for building cloud agents
The Open Agents project is an experimental platform developed to explore the design and deployment of open, composable AI agents. It focuses on enabling developers to create agents that can collaborate, execute tasks, and interact with tools in a structured environment. The framework provides abstractions for agent communication, task orchestration, and tool integration, allowing multiple agents to work together toward shared objectives. It emphasizes openness and interoperability, making it easier to integrate with different models, APIs, and external systems. The project also includes examples and templates that demonstrate how to build and deploy agents for real-world applications. By prioritizing composability, it allows developers to combine simple components into more complex agent systems. Overall, open-agents serves as a playground for building and experimenting with next-generation AI agent architectures. -
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Open Multi-Agent
One runTeam() call from goal to result
Open Multi-Agent is a flexible framework designed to enable the creation and coordination of multiple AI agents working together to solve complex tasks through collaboration. It focuses on distributing responsibilities across specialized agents, each handling a specific part of a problem, such as planning, execution, or validation. The system emphasizes modularity, allowing developers to define agent roles, communication protocols, and workflows. It supports iterative collaboration, where agents exchange information and refine outputs collectively. The architecture is designed to be extensible, enabling integration with external tools and APIs to expand agent capabilities. It is particularly useful for research, automation, and development workflows that require multiple perspectives or stages of processing. Overall, open-multi-agent provides a foundation for building scalable and cooperative AI systems.
