Projects sharing features with Gptme

gh-aw is a GitHub automation platform and orchestration framework that uses an agentic workflow engine to automate repository management and code reviews. It translates natural language markdown and configuration files into secure, automated task sequences driven by large language models. The system integrates a Model Context Protocol gateway to route calls between AI agents and external tools. It distinguishes itself through a comprehensive security guardrail system that provides sandboxed execution for protocol servers, network egress controls via domain allowlists, and human-in-the-loop ap

EvoAgentX is an agent platform that combines human-in-the-loop checkpoints, MCP tool integration, multi-agent workflow orchestration, and self-improvement capabilities. It functions as a self-improving agent framework that connects to MCP-compatible servers and orchestrates multi-agent workflows using natural-language goals, while also serving as a platform that discovers, configures, and manages tools from MCP servers for use in automated agent workflows. The platform distinguishes itself through a dual-memory agent architecture that maintains short-term and persistent memory stores, enablin

OpenSquilla is an LLM agent orchestration framework designed to coordinate multi-step AI workflows and tool execution using directed acyclic graphs. It functions as a centralized system for managing specialized skill packages and executing complex reasoning sequences. The project distinguishes itself through a routing gateway that directs tasks to different AI providers based on complexity, cost, and performance. It utilizes a multi-tier AI memory system that organizes working, episodic, and semantic knowledge using local embeddings and SQLite, alongside a secure execution sandbox that isolat

This project is an AI-powered IDE extension and LLM coding assistant that provides a conversational interface for generating, refactoring, and debugging code. It functions as an AI agent framework and a Model Context Protocol client, connecting AI models to external data sources and tools to automate complex development tasks. The system is distinguished by its use of autonomous AI agents capable of multi-step task execution, including the ability to read files, modify code, and run terminal commands iteratively. It supports recursive agent orchestration through subagent delegation and employ

Osaurus is a local AI workflow engine and LLM agent orchestration framework designed for private execution on local hardware. It functions as a desktop application automator and a voice-controlled AI interface, enabling the development of autonomous agents that can write code, execute tools, and operate a computer without keyboard or mouse input. The system is distinguished by its ability to control native desktop applications via accessibility APIs and manage web interactions through a headless browser automation tool. It supports a local-first execution model and on-premises deployment with

This is an open-source Python SDK for building and orchestrating production-grade AI agents. It provides a unified framework for creating conversational agents that can use tools, maintain state, and coordinate across multiple language model providers including OpenAI, Anthropic, Google, Amazon Bedrock, and locally-hosted models. The SDK supports multi-agent orchestration through graphs, teams, and swarms, allowing several specialized agents to collaborate on complex tasks. Agents can be composed as callable tools that other agents invoke, and the framework includes policy handlers that inspe

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

AdalFlow is an autonomous AI agent framework and LLM application library designed for building modular workflows. It serves as a model-agnostic interface and RAG pipeline orchestrator, allowing users to develop ReAct agents that utilize iterative reasoning and external tool execution to solve complex tasks. The project distinguishes itself through a prompt optimization system that uses textual gradient descent to automatically refine prompt templates and few-shot examples. It treats model feedback as a differentiable signal, enabling a form of LLM backpropagation to iteratively improve output

GenericAgent is an LLM agent framework and autonomous system controller designed to manage local systems, web browsers, and hardware interfaces through action and observation loops. It functions as a tool orchestrator that routes model calls to local executors, enabling the automation of complex tasks on a host machine. The project is distinguished by its self-evolving AI agent capabilities, which convert successful execution paths into reusable procedural scripts and skill trees to reduce future reasoning overhead. It employs a context optimization engine that utilizes layered memory hierarc

Agent Zero is an LLM agent framework and multi-agent orchestrator that provides an AI-powered interface for operating system tasks. It functions as a containerized AI workspace, allowing large language models to interact with a filesystem and terminal within an isolated Linux environment. The system distinguishes itself through a hierarchical orchestration model that decomposes complex goals by spawning specialized sub-agents to collaborate and consolidate results. It features a plugin-based architecture for extending capabilities via a community plugin hub, a custom skills system, and extern

Hermes-webui is a self-hosted AI orchestrator and web interface for managing autonomous agents. It serves as a multi-provider gateway that connects cloud and local large language models, providing a central hub to execute scheduled background jobs, run shell commands, and manage agent memory on private hardware. The system distinguishes itself through a persistent memory manager that utilizes knowledge graphs and markdown files for long-term context across sessions. It features a model context protocol host for extending agent capabilities with standardized tools and supports the orchestratio

Claude Code is a command-line interface and multi-agent orchestration framework designed for autonomous software engineering. It enables AI agents to perform codebase modifications, debugging, and Git workflow management while coordinating multiple specialized agents to decompose and execute complex engineering tasks in parallel. The system distinguishes itself through a high degree of isolation and safety, utilizing Git worktrees to create independent working directories for concurrent agents and implementing a tiered permission system that combines user rules, project policies, and OS-level

This project is a framework for building AI coding agents that automate software development tasks using large language models. It includes a task lifecycle manager that tracks complex development goals through a persistent graph of dependent tasks and a system for multi-agent orchestration to delegate tasks to specialized sub-agents. The framework implements a Model Context Protocol client to discover and execute tools from external servers and provides a remote development bridge to synchronize local command line interfaces with remote containers or desktop environments. The system covers

The inspector is a diagnostic and validation tool for the Model Context Protocol. It provides an interactive interface and a transport proxy to discover, inspect, and execute the tools, prompts, and resources provided by an MCP server. The project serves as a debugger and compliance tester to verify that server implementations adhere to the protocol specification and JSON-RPC standards. It allows for real-time monitoring of message exchanges and logs between clients and servers across various transport layers, such as standard input/output and Server-Sent Events. The tool covers a broad rang

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

This project is a toolkit for interacting with large language models through a command line interface, an integration library, and a workflow orchestrator. It provides a framework for embedding language model logic directly into scripts and managing automated sequences of AI tasks. The system utilizes a plugin framework and a provider-agnostic interface to route requests across different model providers. This architecture allows for the addition of custom capabilities and the ability to switch providers without altering the core logic. The project covers several functional areas, including A

ECC is an LLM agent orchestration framework and cross-platform AI tooling suite designed to coordinate multi-model workflows. It provides a system for managing specialized agent roles, reusable skills, and structured planning to execute complex software development tasks across different AI-powered code editors. The project distinguishes itself as a Model Context Protocol manager, providing a configuration layer to integrate external servers and audit tool execution. It further implements an agentic security sandbox that restricts sensitive file access and scans for secret leakage to secure a

Mods is a terminal-based AI client that sends prompts to large language models and streams responses back to the command line. It functions as a multi-provider AI gateway, routing queries to OpenAI, Cohere, Groq, Gemini, and local endpoints, and includes a conversation history manager that saves, caches, branches, and resumes text-based interactions. The tool also operates as a Model Context Protocol client, connecting to external MCP servers via stdio, SSE, or HTTP to extend model capabilities with specialized tools and data. The project distinguishes itself through a config-driven provider

jcode is a framework for developing autonomous AI coding agents that automate software development tasks. It functions as an agent orchestrator, tool runtime, and semantic memory engine, enabling the creation of agents that can modify code, run tests, and iterate on their own functionality. The project is distinguished by its use of recursive agent swarming, where a hierarchy of collaborating agents can spawn child agents to decompose complex tasks. It implements a semantic memory system that combines vector-based retrieval with graph-based relationship mapping to maintain context across sess

WeKnora is a multi-tenant retrieval-augmented generation (RAG) knowledge platform and autonomous AI agent framework. It transforms raw documents into queryable knowledge bases and integrates large language models with vector databases to provide grounded AI responses. The system also functions as a Model Context Protocol (MCP) tool server, exposing knowledge search and agentic capabilities to external AI clients. The platform distinguishes itself through an autonomous agent framework that utilizes iterative reasoning, tool calling, and web search to solve multi-step tasks. It implements a sta

Free-Auto-GPT is an autonomous agent framework and local AI environment designed to execute multi-step goals using large language models. It functions as a web-enabled AI researcher capable of planning and performing actions independently within a containerized workspace. The system is distinguished by its use of a free language model API wrapper, which connects agents to models via session cookies or open interfaces instead of paid API subscriptions. This allows for local AI task execution and autonomous goal completion without requiring paid external service keys. The project covers a rang

Bytebot is an LLM desktop automation framework and virtual Linux desktop environment. It enables AI agents to plan and execute mouse and keyboard actions on a virtual computer using natural language, allowing for autonomous desktop automation and the integration of legacy systems that lack native APIs. The system operates as an LLM API gateway and a Model Context Protocol server, routing requests across multiple language model providers with integrated load balancing and rate limiting. It provides isolated, containerized environments where agents use visual reasoning to interpret screenshots

This project is a multi-channel AI agent and chatbot framework that allows a single AI intelligence to be deployed across various messaging platforms, web interfaces, and email accounts. It functions as a cross-model AI gateway, providing a unified interface to route requests between different large language model providers. The system is distinguished by its autonomous task planning and knowledge management capabilities. It can decompose complex goals into sequential execution steps using external tools and a headless browser, while simultaneously extracting information from conversations to

OpenHarness is a framework for building and orchestrating AI agents that utilize tools and plugins to execute complex tasks. It provides an orchestration system for managing language model lifecycles and a multi-agent coordination system for delegating workloads across teams of specialized subagents. The project features an agent gateway that bridges language model agents to external chat platforms and communication channels. It includes a tool integration engine for executing shell, file, and web operations, supported by a memory and skill manager that handles persistent user preferences and

This project is an autonomous software engineering platform and orchestration framework designed to manage specialized artificial intelligence agents. It provides a suite of tools for coordinating autonomous entities to execute complex development tasks, ranging from architectural planning and code reviews to performance optimization. The platform distinguishes itself through its multi-agent orchestration layer, which dynamically assigns roles based on an analysis of a project's technology stack. By utilizing a modular agent registry, the system scales capabilities across different software m

This project provides a comprehensive framework for building, deploying, and orchestrating autonomous agents within a decentralized network. It serves as a collection of patterns and examples for developing intelligent software entities capable of performing complex tasks, making decisions, and interacting with other agents to achieve shared goals. The framework distinguishes itself through its focus on multi-agent orchestration and decentralized communication. It enables the coordination of specialized agent teams that collaborate on workflows through structured messaging protocols, allowing