Photo by the editor
# Entry
If you want to learn agent engineering rather than just reading about it, the best way is to fork the real repositories, run them locally, and change them for your own employ. This is where the real learning happens. I’ve hand-selected 10 of the best projects that are both useful and widely recognized, so you can see how agent applications are being developed today. So let’s get started.
# 1. OpenClaw
OpenClaw (~343k ⭐) is the one I would point to first if you want to see what the next wave of AI personal assistants might look like. It’s built as a personal assistant that works on your devices and connects to tools people already employ, like WhatsApp, Telegram, Slack, Discord, Signal, and iMessage. What’s compelling is that this isn’t just a uncomplicated chat demonstration. It looks like a true assistant product, with omnichannel support, voice capabilities, and a broader ecosystem of skills and control. If you want a repository that resembles a real agent system, this is a good place to start.
# 2. Open hands
Open your hands (~70k ⭐) is a great repository to fork if your main interest is coding agents. It is powered by AI-driven development and now has a broader ecosystem around it, including cloud, documents, CLI, SDKbenchmarking and integrations. This matters because you’re not just looking at one demo. You can study the main agent, check out the interface, and see what the team thinks about the evaluation and implementation. If you want to build or customize a coding assistant, this is one of the most practical repositories you can learn from.
# 3. using the browser
using the browser (~85k ⭐) is one of the most useful projects if you need agents that can actually do things online. The idea is uncomplicated: it makes websites easier for AI agents to employ so they can perform browser-based tasks with less friction. This makes experimenting easier because most of a real agent’s work ends up in the browser anyway – filling out forms, searching for information, navigating, and performing repetitive online tasks. It also includes supporting repositories and examples, making it simple to go from curiosity to something you can test in a real workflow.
# 4.DeerFlow
DeerFlow (~55k ⭐) is one of the more compelling projects if you want to understand agent systems with a long horizon. It is an open-source suite of super-agents that combines sub-agents, memory, sandboxes, skills, and tools for research, coding, and creation within longer tasks. So it’s not just about wrapping tool calls. It tries to manage the full structure around the agent’s more intricate behavior. If you want to see how current agent systems are built around memory, coordination, and extensibility, this is a very useful repository to fork.
# 5. CrewAI
CrewAI (~48k ⭐) is still one of the easiest repositories to understand if you want multi-agent orchestration without too much complexity. It is a swift and pliant multi-agent automation platform, built independently, not on LangChain. The mental model is uncomplicated, the setup is accessible, and the documentation and examples are genial enough for beginners. If you need a Python-based repository that you can fork and turn into something useful, CrewAI still deserves a place at the top.
# 6.LangGraph
LangGraf (~28k ⭐) is the repository to study if you want to understand the engineering side of agents, not just the flashy demo side. LangChain describes it as a low-level orchestration framework for long-running, stateful, and controlled agents. It forces you to think in terms of graphs, state, control flow and resilience. This is especially useful if you want to move beyond uncomplicated tooltip-and-call systems and understand how more stern agent runtimes are put together. It may not seem as quick to learn as other repositories, but it teaches you a lot.
# 7. OpenAI Agents SDK
The OpenAI Agents SDK (~20k ⭐) is a good option if you want something lithe but still current. It is built as a compact platform for multi-agent workflows, and the documentation presents it as a production-ready path with a miniature set of useful building blocks. You get tools, orders, sessions, tracking and patterns in real time without having to wade through a huge platform. If you like uncomplicated surfaces and direct control, this is one of the better starter repositories to explore.
# 8. AutoGen
AutoGen (~56k ⭐) is still one of the most essential repositories in the multi-agent space. Microsoft sees it as a development platform for agent-based AI, and the docs go further into business workflows, collaborative research, and distributed multi-agent applications. It belongs on this kind of list because there is a lot to learn from it. It’s worth exploring orchestration ideas, agent conversation patterns, and framework design. It may not be the easiest starting point for everyone, but it’s still one of the most influential projects in this category.
# 9. GPT Researcher (~26k ⭐)
GPT researcher it’s a great choice if you want to study a deep research agent rather than a general framework. It is an autonomous deep exploration agent using any huge language model (LLM) provider, and the surrounding materials show how it handles multi-agent exploration and report generation. This provides one clear learning workflow from start to finish. You can see planning, reviewing, sourcing, synthesizing and reporting – all in one place. If you want something specific rather than abstract, this is one of the most forked repositories on the list.
# 10. Read
To read (~22k ⭐) stands out because it puts memory and state at the center of the agent’s design. The repository describes it as a platform for building stateful agents with advanced memory that can learn and improve over time. This is an essential point of view because many agent repositories focus primarily on orchestration. Letta expands the picture. This is a good repository to check out if you want your agents to survive, remember, and evolve rather than starting over every time. When it comes to the work of a memory-centric agent, this is one of the most compelling contemporary projects.
# Summary
All ten are worth cloning, but they teach different things once you run them and start changing the code. This is where the real learning begins.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of artificial intelligence and medicine. She is co-author of the e-book “Maximizing Productivity with ChatGPT”. As a 2022 Google Generation Scholar for APAC, she promotes diversity and academic excellence. She is also recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a staunch advocate for change and founded FEMCodes to empower women in STEM fields.