Open Source TypeScript Agentic AI Tools - Page 3
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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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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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OpenWork AI
An open-source alternative to Claude Cowork, powered by opencode
OpenWork is a framework for building decentralized collaborative work environments powered by AI and human contributions. At its core, the project enables contributors to define tasks, workflows, and goals that can be split, shared, and recombined across distributed nodes while agents and humans cooperate to advance progress. It offers structured templates for work items, decision logic for task allocation, and consensus mechanisms that let groups verify and validate results toward shared objectives. This project also includes moderation and reputation layers so that contributor trust and quality can be assessed and integrated into future task assignments. Rather than a single monolithic workflow engine, it emphasizes openness — providing APIs and interfaces so communities can build custom dashboards, integrate specialized agents, or add bespoke evaluation criteria. -
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kimaki
Like openclaw but on top of opencode. all opencode features
Kimaki is an AI-powered developer tool that integrates coding workflows directly into Discord, allowing users to control and automate code editing sessions through natural language messages. Acting as a bridge between Discord and an AI coding agent (via OpenCode), it enables developers to interact with their codebase conversationally, effectively turning Discord into a collaborative development interface. Each Discord channel is mapped to a specific project directory, and messages sent within that channel trigger AI-driven actions such as editing files, running commands, or searching the codebase. The system is designed to streamline development workflows by eliminating context switching between communication tools and coding environments. Kimaki supports both quick setup through a shared bot and more advanced self-hosted configurations, offering flexibility for different user needs. -
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A2UI
A Protocol for Agent-Driven Interfaces
A2UI (Agent-to-User Interface) is an open-source protocol and set of libraries developed by Google to enable AI agents to generate rich, interactive user interfaces instead of relying solely on text-based responses. The project introduces a declarative JSON format that allows agents to describe the structure, components, and behavior of a user interface, which is then rendered by the client using its own native components. This approach separates UI intent from UI implementation, making it possible for the same agent-generated interface to be rendered across different platforms such as web, mobile, and desktop applications. A key design principle of A2UI is security, as it avoids executing arbitrary code generated by models and instead restricts output to structured data that maps to a predefined catalog of trusted UI components. The system also supports incremental updates, allowing agents to progressively modify the interface as a conversation evolves. -
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Ax
Build LLM powered Agents and "Agentic workflows"
Build intelligent agents quickly — inspired by the power of "Agentic workflows" and the Stanford DSPy paper. Seamlessly integrates with multiple LLMs and VectorDBs to build RAG pipelines or collaborative agents that can solve complex problems. Advanced features streaming validation, multi-modal DSPy, etc. We've renamed from "llmclient" to "ax" to highlight our focus on powering agentic workflows. We agree with many experts like "Andrew Ng" that agentic workflows are the key to unlocking the true power of large language models and what can be achieved with in-context learning. Also, we are big fans of the Stanford DSPy paper, and this library is the result of all of this coming together to build a powerful framework for you to build with. -
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Cloudflare Agents
Build and deploy AI Agents on Cloudflare
Cloudflare Agents is an open-source framework designed to help developers build, deploy, and manage AI agents that run at the network edge. It provides infrastructure for creating stateful, event-driven agents capable of real-time interaction while maintaining low latency through Cloudflare’s distributed platform. The project includes SDKs, templates, and deployment tooling that simplify the process of connecting agents to external APIs, storage systems, and workflows. Its architecture emphasizes persistent memory, enabling agents to maintain context across sessions and interactions. Developers can orchestrate complex behaviors using workflows and durable objects, making it suitable for production-grade autonomous systems. Overall, Cloudflare Agents aims to streamline the development of scalable AI automation that operates close to users for improved performance. -
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Dive
Dive is an open-source MCP Host Desktop Application
Dive is an open‑source MCP host desktop application that serves as a bridge between MCP servers and any large language models supporting function calling, designed to deliver a seamless AI agent experience across environments. Compatible with ChatGPT, Anthropic, Ollama and OpenAI-compatible models. Enabling seamless MCP AI agent integration on both stdio and SSE mode. One-click access to managed MCP servers via OAPHub.ai - eliminates complex local deployments. Modern Tauri version alongside traditional Electron version for optimal performance. -
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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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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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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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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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OpenClaw Opik Observability Plugin
Official plugin for OpenClaw that exports agent traces to Opik
OpenClaw Opik Observability Plugin is an open-source plugin designed to add observability and monitoring capabilities to OpenClaw autonomous AI agents by exporting operational traces to the Opik observability platform. The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability. -
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oh-my-agent
Portable multi-agent harness for .agents-based skills, workflows
oh-my-agent is a flexible and extensible framework designed to simplify the creation, management, and orchestration of AI agents across various tasks and environments. It builds on the idea of modular agent systems, allowing developers to define specialized roles and capabilities that can be combined into larger workflows. The framework emphasizes usability, making it easier to configure agents, assign tasks, and manage interactions without requiring deep expertise in AI system design. It likely includes support for plugins or skills, enabling agents to extend their functionality through integrations with external tools. The system also focuses on coordination, allowing multiple agents to collaborate on complex tasks in a structured manner. Its architecture supports experimentation, making it suitable for both prototyping and iterative development. Overall, oh-my-agent provides a practical foundation for building and managing multi-agent systems. -
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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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Better Chatbot
Just a Better Chatbot. Powered by MCP Client & Workflows
Better‑chatbot is an AI chatbot framework powered by MCP protocols and workflows, allowing developers to deploy and integrate AI-powered chat systems with ease. Integrates all major LLMs: OpenAI, Anthropic, Google, xAI, Ollama, and more. MCP protocol, web search, JS/Python code execution, data visualization. Custom agents, visual workflows, artifact generation. Custom agents, visual workflows, artifact generation. Realtime voice chat with full MCP tool integration. -
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Claude Octopus
Put up to 8 AI models on every coding task
Claude Octopus is a multi-model orchestration plugin that enables developers to run multiple AI models simultaneously on a single coding task to reduce blind spots and improve output quality. It allows up to eight different models to analyze the same problem in parallel, surfacing diverse perspectives before finalizing results. The system is particularly useful for detecting inconsistencies, edge cases, and errors that a single model might miss. It integrates directly into coding workflows, making it easy to compare outputs without leaving the development environment. The plugin supports multiple providers and model types, enabling flexible combinations of local and cloud-based models. It emphasizes collaborative intelligence, where models effectively act as reviewers of each other’s work. Overall, Claude Octopus enhances reliability and decision-making by introducing redundancy and diversity into AI-assisted coding. -
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Claw3D
Claw3D is an open source 3D engine built on OpenClaw
Claw3D is an experimental open-source platform that combines elements of 3D simulation, developer tooling, and AI orchestration by creating an interactive virtual workspace where AI agents can be visualized as active participants in a shared environment. It is designed as a 3D “virtual office” where users can observe, manage, and interact with multiple AI agents performing tasks such as coding, reviewing pull requests, and coordinating workflows in real time. Instead of relying on traditional dashboards or logs, Claw3D introduces a spatial interface that allows users to navigate through a simulated office and watch agents collaborate, effectively turning abstract processes into tangible visual interactions. The system supports task assignment, progress tracking, and communication between agents, creating a representation of autonomous or semi-autonomous workflows. It can be self-hosted, giving users full control over deployment, customization, and scaling of their AI workspace. -
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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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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
Lossless Claw is an advanced context management plugin for the OpenClaw agent ecosystem that redefines how conversational memory is handled in large language model systems. Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. The system stores every interaction in a persistent database and incrementally summarizes older content into a hierarchical directed acyclic graph, allowing efficient compression without discarding information. This structure enables agents to dynamically reconstruct detailed context by expanding summaries when needed, effectively simulating perfect long-term memory. -
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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. -
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Openwork
Open source Al coworker that lives on your desktop
Openwork™ is an open-source AI coworker that runs locally on your Mac and lives right on your desktop. It reads your files, writes and rewrites documents, and automates repetitive knowledge work while keeping everything on your machine. You choose which folders it can access, and nothing leaves your computer unless you explicitly allow it. Openwork works with your own AI models and API keys, with no subscriptions, upsells, or hidden services. Every action it takes is visible, logged, and requires your approval before it runs. -
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TinyClaw
The original Tiny Claw as your personal autonomous AI companion
TinyClaw is an open-source autonomous AI companion framework designed to make personal AI agents simpler, cheaper to run, and more accessible to individual users. The project is built from scratch with a deliberately small native core and a modular plugin architecture that allows capabilities to expand without turning the system into a heavy monolith. Its philosophy centers on creating a persistent AI companion that behaves more like a helpful digital partner than a purely configurable assistant. TinyClaw incorporates self-improving memory and smart routing mechanisms intended to reduce large language model costs by tiering queries intelligently. The framework is designed to be self-configuring and easy to set up compared to more complex agent stacks, with a Bun-native runtime and built-in web interface. Overall, TinyClaw aims to democratize autonomous AI agents by delivering a lightweight, extensible, and personality-driven companion platform that evolves with the user over time. -
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Maestro
Agent Orchestration Command Center
Maestro is a cross-platform desktop app for orchestrating your fleet of AI agents and projects. It's a high-velocity solution for hackers who are juggling multiple projects in parallel. Designed for power users who live on the keyboard and rarely touch the mouse. Collaborate with AI to create detailed specification documents, then let Auto Run execute them automatically, each task in a fresh session with clean context. Allowing for long-running unattended sessions, my current record is nearly 24 hours of continuous runtime. Run multiple agents in parallel with a Linear/Superhuman-level responsive interface. Currently supporting Claude Code, OpenAI Codex, and OpenCode with plans for additional agentic coding tools (Aider, Gemini CLI, Qwen3 Coder) based on user demand. -
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Agent Orchestrator
Agentic orchestrator for parallel coding agents
Agent Orchestrator from Composio is an open-source orchestration layer designed to manage fleets of parallel AI coding agents working on a shared codebase. It enables each agent to operate independently in isolated git worktrees, handling tasks like fixing CI failures, addressing code review comments, and creating pull requests. The platform automates the coordination of multiple agents, reducing the need for manual oversight in complex development workflows. It supports a wide range of agents, runtimes, and tools, making it flexible and framework-agnostic. Through a centralized dashboard, developers can monitor progress, review outputs, and intervene only when human judgment is required. Agent Orchestrator transforms AI-assisted development into a scalable, autonomous system for continuous code improvement. -
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Agentic Coding Flywheel Setup
System tool for beginners wanting agentic engineering capabilities
Agentic Coding Flywheel Setup (ACFS) is a comprehensive environment bootstrap project that configures a full stack of tools for autonomous AI-assisted coding workflows. With a single shell installer, ACFS transforms a fresh compute environment into a ready-to-use development setup that includes modern shells, language runtimes, AI coding agents (like Claude Code, Codex CLI, and Gemini CLI), and a coordinated toolchain for orchestration and safety. The system is designed for developers who want to run multi-agent coding assistants on personal or VPS hosts with minimal manual configuration. It comes with a battle-tested suite of utilities for agent coordination, orchestration, and developer productivity enhancements, such as named tmux panes, agent mail coordination layers, and cloud CLI integrations.
