Open-source alternatives to Browser Tools Mcp

LaVague is an LLM web agent framework and large action model designed to translate natural language instructions into executable browser automation scripts. It functions as a multi-modal orchestrator that reasons over web page states and HTML content to automate multi-step tasks via a Selenium-based automation engine. The framework features a modular model provider layer, allowing users to swap between different language and vision models from providers such as Anthropic, Gemini, and Azure OpenAI. It employs a multi-modal world model to process screenshots and HTML structures, utilizing retri

gptme is a multi-agent orchestration platform designed for autonomous software engineering, terminal-based AI integration, and RAG-enhanced code navigation. It enables the deployment of persistent agents and specialized subagents to decompose complex tasks and execute parallel technical workflows. The system distinguishes itself through a combination of vision-based GUI automation for controlling desktop applications and surgical patching mechanisms for targeted source code modifications. It utilizes git-based memory management to maintain a versioned history of agent identities, lessons, and

This project provides an agentic web interaction engine designed to facilitate autonomous browser automation and large-scale data extraction. It serves as a framework for building and deploying agents that can navigate complex, JavaScript-rendered websites, interact with page elements, and execute multi-step workflows. By providing a structured environment for browser control, the system enables the creation of reusable automation scripts that can be deployed across diverse web platforms. The platform distinguishes itself through a comprehensive suite of security and traffic management tools,

Poem is a comprehensive toolkit for building type-safe web applications, APIs, and servers using the Rust programming language. It provides a foundation for developing web servers that handle HTTP requests with strong type safety. The framework distinguishes itself by supporting multiple communication protocols through a protocol-agnostic handler mapping. This allows a single internal logic to be exposed across HTTP, gRPC services using protobuf definitions, and the Model Context Protocol for AI model integration. Additionally, it includes built-in tooling for generating OpenAPI v3 specificat

This project is a Model Context Protocol server and automation framework designed to control and automate iOS and Android devices. It provides a unified API that abstracts interactions between physical hardware and simulators across different mobile operating systems, functioning as a cross-platform device bridge. The system is distinguished by a visual UI automation toolkit that uses screenshots and coordinate-based gestures—such as tapping, swiping, and long-pressing—rather than relying on element selectors. It supports remote connectivity via an HTTP server using Server-Sent Events, which

mcp-go is a Go implementation of the Model Context Protocol (MCP) providing an SDK and framework for building servers that connect large language model applications to external tools and data sources. It serves as a developer kit for implementing bidirectional communication and structured data exchange between AI clients and servers. The framework enables the creation of executable tools with structured output schemas, reusable prompt templates, and data resource exposure via URI templates. It supports multiple transport layers, including stdio, HTTP, and Server-Sent Events, using a transport

Model Context Protocol is a standardized framework for connecting large language models to external data sources and executable tools. It enables the creation of a universal interface where servers expose tools, resources, and prompts that can be discovered and utilized by various AI clients. The protocol utilizes a JSON-RPC message system that is transport-agnostic, supporting both standard input/output for local processes and HTTP with server-sent events for remote connections. It emphasizes security and control by delegating model sampling to the client to keep API keys secure from servers

fastmcp is a Python library and framework for building servers and clients that implement the Model Context Protocol. It serves as a tool integration library designed to connect large language models to external tools and data sources. The framework features an interactive tool user interface renderer, which allows for the display of visual interfaces for tools directly within a conversational flow. It also provides a library for automatically generating schemas and validation for tools used by language models. The project covers server and client development, including tool and resource exp

This project is a Model Context Protocol server designed to bridge the gap between local frontend component libraries and language models. It functions as a development assistant that provides AI tools with the structural context, dependency requirements, and installation patterns necessary to generate accurate, framework-specific UI code. The server distinguishes itself by utilizing schema-driven metadata extraction and static file system analysis to interpret component structures without requiring runtime execution. By decoupling component definitions from specific UI libraries, it supports

This is a software development kit and framework for implementing the Model Context Protocol in Go. It provides a standardized system for building servers and clients that exchange external resources, proprietary data, and executable tools to provide context for large language models. The SDK includes a JSON-RPC communication library and an integration framework to expose local data, prompt templates, and typed functions to AI models. It enables the development of both protocol servers that provide external context and clients that consume these remote tools and resources. The project covers

The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec

This project provides a translation layer and set of adapters designed to bridge AI agents with the Model Context Protocol. It functions as an integration layer that allows agents to operate as protocol-compliant servers and enables the conversion of protocol-based tools into formats compatible with agent frameworks and logic graphs. The adapters facilitate tool interoperability by wrapping external protocol tools for use within agent workflows and exposing internal agent capabilities to any client implementing the Model Context Protocol. This creates a communication bridge that supports inte

mcp-feedback-enhanced is a human-in-the-loop orchestrator and feedback tool for the Model Context Protocol. It provides a graphical interface for reviewing and approving AI-driven operations to prevent automated execution errors, using a socket-based system to synchronize state between backend AI processes and a user-facing interface. The project distinguishes itself by supporting a cross-platform deployment model available via native desktop wrappers and web browsers. It includes environment-aware context detection to identify if the system is running locally, via SSH, or within WSL, allowin

Magic MCP is a Model Context Protocol server and AI component generator that translates natural language descriptions into functional user interface code. It acts as an LLM design orchestrator, producing responsive web elements and layouts anchored on utility-first CSS styling patterns. The system features a side-by-side variation engine that generates multiple stylistic interpretations of a single prompt for comparative selection. It incorporates SVG-based asset integration for branding and iconography and utilizes template-based assembly to combine pre-defined style patterns with user-speci

MemMachine is a centralized memory management server and model-agnostic memory layer for large language models. It functions as a persistence layer that stores user profiles and conversational context, providing a decoupled data store that prevents vendor lock-in by serving different AI models through a consistent API. The system implements the Model Context Protocol to share persistent agent memories and session data with compatible AI clients. It utilizes a multi-tiered memory hierarchy, combining a graph-based conversation store for episodic interactions with a vector knowledge base for se

This project is an autonomous workflow engine and orchestration platform designed to coordinate specialized AI agents. It functions as a development framework that manages the end-to-end lifecycle of complex, multi-step tasks, including persona definition, persistent memory management, and the execution of automated coding workflows. By acting as a Model Context Protocol server, it enables standardized communication between development tools and external AI models. The platform distinguishes itself through an event-driven architecture that routes typed messages between agent personas, allowin

This project is a Model Context Protocol server and AI agent database connector. It provides a standardized communication layer that allows language models to interact with relational data stores, read database schemas, and manage PostgreSQL database resources. The implementation acts as a serverless host for the Model Context Protocol, deploying on distributed edge functions to connect AI assistants to a project. This enables AI agents to perform database administration, execute SQL queries, and handle schema migrations through an AI-compatible interface. The system covers broader capabilit

This project is a Model Context Protocol server that acts as a programmatic bridge between large language models and private messaging accounts. It provides an automation interface for interacting with WhatsApp by exposing messaging and data retrieval capabilities as tools for AI assistants. The system utilizes browser automation to control the web application interface, allowing for stateful session management to maintain authentication. It enables the transmission of various content types, including plain text, documents, and audio files formatted as voice messages. The server covers conve

This project is a specialized browser debugging interface designed to monitor DOM elements, network traffic, and JavaScript execution. It provides a client-side user interface for inspecting and debugging web applications, allowing for the real-time modification of CSS styles and the investigation of the JavaScript runtime. The toolkit includes dedicated analysis tools for WebAssembly, featuring disassembly highlighting, scope inspection, and binary execution profiling. It also provides a network traffic inspector for analyzing HTTP requests and a CSS style editor for testing properties and a

CodeGraphContext is a code graph indexer and visualization tool that analyzes source code to build graphs of functions, classes, and inheritance relationships. It functions as a Model Context Protocol server, providing a structured codebase index to AI assistants for context retrieval and natural language querying. The project features an interactive web interface that uses force-directed layouts to visualize code dependencies and symbols. To accelerate the setup of large projects, it supports the import of pre-calculated knowledge bundles for popular repositories. The system provides capabi

Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project

This platform serves as a centralized management system for organizing, refining, and versioning AI instructions and agent skills. It functions as a repository that enables users to store, categorize, and retrieve structured prompts, ensuring consistent performance across various artificial intelligence models. By integrating with the Model Context Protocol, the system allows external AI assistants and development environments to discover and access these instruction libraries directly. The platform distinguishes itself through its focus on prompt engineering and automated refinement, utilizi

Playwright MCP is a browser automation server that provides a standardized interface for connecting large language models to web navigation and interaction capabilities. By operating as a Model Context Protocol server, it enables external AI agents to execute browser-based tasks, extract data, and perform complex web sequences through a unified communication protocol. The project distinguishes itself by acting as a remote controller that manages headless browser lifecycles and isolated automation contexts. It maintains session-based state isolation, allowing for distinct user profiles and per

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

OpenReplay is a session replay platform and frontend debugging suite designed to record and play back user browser sessions. It functions as a user behavior monitoring system that captures interaction patterns and technical metadata to identify conversion issues and revenue loss. The platform is distinguished by its self-hosted infrastructure model, allowing the recording and analytics pipeline to be deployed on private servers for full control over data residency. It also includes a browser co-browsing tool for real-time screen sharing and direct communication to provide immediate technical

container-use is a containerized AI execution environment and code sandbox designed to provide a secure space for AI coding agents to execute commands and build applications. It functions as a workspace orchestrator that provisions isolated containers mapped to git branches, allowing multiple agents to operate in parallel without state conflicts or affecting the host system. The project serves as a Model Context Protocol server, bridging AI agents to containerized environments for standardized tool access. It enables a workflow for reviewing and merging changes made by agents within these iso

TablePro is a cross-platform database management client designed for browsing, querying, and administering both SQL and NoSQL databases. It functions as a unified workspace that integrates a code-centric SQL editor with schema visualization tools, allowing developers to manage complex data models and execute queries across diverse database engines. The application distinguishes itself through an agentic AI integration layer that connects language models directly to database tools, enabling automated query generation, optimization, and error fixing with configurable approval gates. It features