OpenClaw and OpenHands Lead: 10 AI Repositories to Urgently Fork

OpenClaw: The AI Personal Assistant of Tomorrow

OpenClaw is a project that has garnered attention with its 343,000 stars on GitHub, positioning itself as a model for the next generation of AI personal assistants. This project is designed to run directly on users' devices while seamlessly connecting with popular communication tools such as WhatsApp, Telegram, Slack, Discord, Signal, and iMessage. Unlike simple chat demonstrations, OpenClaw presents itself as a true assistant product, integrating multi-channel support and voice features. The ecosystem surrounding it is vast, with skills and controls that make it a comprehensive agent system. For those looking to explore a repository close to a real agent system, OpenClaw is a wise choice.

OpenHands: An Ecosystem for Agent Coding

OpenHands, with its 70,000 stars, is an essential repository for developers interested in coding AI agents. This project focuses on AI-driven development and offers a complete ecosystem that includes cloud services, documentation, a command-line interface (CLI), a software development kit (SDK), as well as benchmarking tools and various integrations. The appeal of OpenHands lies in its ability to go beyond a simple demonstration. Users can study the main agent, explore the interface, and understand how the team approaches evaluation and deployment. For those looking to build or customize a coding assistant, OpenHands provides a practical and informative foundation.

browser-use: Facilitating Web Tasks for Agents

browser-use is a project that has stood out with its 85,000 stars, focusing on improving AI agents' interaction with websites. The idea behind this project is simple yet powerful: to facilitate the use of websites by AI agents so they can manage browser-based tasks with less friction. This includes tasks such as form filling, searching, browsing, and other repetitive online activities. The project also offers repositories and supporting examples, making experimentation more accessible and allowing users to transition from curiosity to a real workflow.

DeerFlow: Understanding Long-Term Agent Systems

DeerFlow is a project that has accumulated 55,000 stars and stands out for its approach to long-term agent systems. This open-source framework for super-agents encompasses sub-agents, memory, sandboxes, skills, and tools for research, coding, and creation within the context of longer tasks. DeerFlow does not merely encapsulate tool calls; it manages the complete structure around more complex agent behavior. For those wanting to see how modern agent systems are built around memory, coordination, and scalability, DeerFlow is a very useful repository to explore and fork.

CrewAI: Simplified Orchestration of Multiple Agents

CrewAI, with its 48,000 stars, is a repository that offers a fast and flexible framework for multi-agent automation. Unlike other projects, CrewAI is built independently of LangChain, simplifying the mental model and making setup accessible. The documentation and examples provided are user-friendly enough for beginners, making it an excellent starting point for those looking to fork a Python-focused project and transform it into something useful.

LangGraph: Engineering Agents Beyond Demos

LangGraph is a repository that has attracted attention with its 28,000 stars, focusing on agent engineering rather than flashy demonstrations. LangChain describes this project as a low-level orchestration framework for controllable, stateful, and long-duration agents. LangGraph encourages users to think in terms of graphs, state, control flow, and resilience. It is particularly useful for those who want to go beyond simple prompt-based systems and tool calls, and understand how more serious agent environments are assembled.

OpenAI Agents SDK: A Compact and Modern Framework

The OpenAI Agents SDK, with its 20,000 stars, is a project that offers a compact framework for multi-agent workflows. The documentation presents it as a production-ready path with a small set of useful building blocks. Users benefit from tools, transfers, sessions, tracing, and real-time models without having to navigate through a massive framework. For those who appreciate simple surfaces and direct control, the OpenAI Agents SDK is one of the best starting repositories to explore.

AutoGen: An Influential Framework for Agentic AI

AutoGen is a major project in the multi-agent space, boasting 56,000 stars. Presented by Microsoft, AutoGen positions itself as a programming framework for agentic AI, and its documentation delves into commercial workflows, collaborative research, and distributed multi-agent applications. This project earns its place on this list because it offers a wealth of information on orchestration ideas, agent conversation models, and framework design. Although it may not be the simplest starting point for everyone, AutoGen remains one of the most influential projects in this category.

GPT Researcher: An In-Depth Research Agent

GPT Researcher is a project that has distinguished itself with its 26,000 stars, focusing on in-depth research rather than a general framework. This autonomous agent utilizes any large language model (LLM) provider, and its surrounding material demonstrates how it handles multi-agent research and report generation. GPT Researcher offers a clear workflow to study from end to end, allowing users to see planning, navigation, source collection, synthesis, and reporting all in one place.

Letta: Memory at the Core of Agents

Letta, with its 22,000 stars, stands out for its focus on memory and state in agent design. This repository is described as a platform for building stateful agents with advanced memory that can learn and improve over time. This is an important angle as many agent repositories primarily focus on orchestration. Letta broadens the picture by enabling the creation of agents that persist, remember, and evolve rather than starting from scratch each time. For work on memory-focused agents, Letta is one of the most interesting projects to fork today.

All these projects deserve to be cloned, as they teach different aspects of agent engineering once you actually run them and start modifying the code. It is in this practice that true learning begins.