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# Introduction
If you want to learn agent engineering by doing instead of just reading about it, the best way is still to fork the original repos, run them locally, and modify them to use them. This is where the real learning happens. I've handpicked 10 of the best, most useful and widely recognized projects, so you can see how agent apps are being built today. So, let's begin.
# 1. OpenClaw
OpenClaw (~343k ⭐) is the one I'd target first if you want to see what the next wave of AI assistants might look like. It's designed as a personal assistant that works with your devices and connects to tools people already use, such as WhatsApp, Telegram, Slack, Discord, Signal, and iMessage. What makes it interesting is that it's not just a simple chat demo. It feels like a real assistant product, with multi-channel support, voice features, and a wide ecosystem of capabilities and controls. If you're looking for a repo that feels close to a real agent system, this is a solid place to start.
# 2. OpenHands
OpenHands (~70k ⭐) is a good repo to go if your interest is agent coding. It is built around AI-driven development and now has a wide ecosystem around it, including cloud, documents, CLI, SDKmeasurement, and integration. That's important because you're not just looking at one demo. You can learn the main agent, test the interface, and see how the team thinks about testing and implementation. If you want to build or customize a coding assistant, this is one of the easiest places to learn.
# 3. browser usage
browser usage (~85k ⭐) is one of the most useful projects if you are looking for agents who can do things on the web. The idea is simple: it makes websites easier for AI agents to use, so they can handle browser-based tasks with less friction. That makes it easier to test with, since most of the real agent's work ends up in the browser anyway – form filling, research, navigation, and repetitive internet tasks. It also has supporting repos and examples, making it easy to go from curiosity to something you can test in a real workflow.
# 4. DeerFlow
DeerFlow (~55k ⭐) is one of the most interesting projects if you want to understand agent systems for a long horizon. An open source open agent harness that brings together sub-agents, memory, sandboxes, capabilities, and tools for researching, coding, and creating long-running tasks. So, it's not just wrapping tool calls. It tries to handle a full frame about the behavior of a complex agent. If you want to see how modern agents are built around memory, communication, and extensibility, this is a very useful fork repo.
# 5. CrewAI
CrewAI (~48k ⭐) is still one of the easiest places to understand if you want multi-agent orchestration without being too complex. It is a fast, flexible framework for multi-agent automation, and is built independently rather than on top of LangChain. The conceptual model is simple, the setup is accessible, and the documentation and examples are friendly enough for beginners. If you're looking for a Python-first repo that you can fork and turn into something useful, CrewAI still deserves a place near the top.
# 6. LangGraph
LangGraph (~28k ⭐) is a repo to read when you want to understand the developer side of agents, not just the shiny demo side. LangChain describes it as a low-level orchestration framework for long-running, decentralized, controllable agents. It pushes you to think in terms of graphs, state, control flow, and robustness. It's especially useful if you want to go beyond simple prompt-plus-tool-call systems and understand how sensitive agent runtimes are compiled. It may not feel as quick to pick up as other repos, but it teaches a lot.
# 7. OpenAI Agents SDK
I OpenAI Agents SDK (~20k ⭐) is a good option if you want something lightweight but still modern. It is designed as a unified framework for multi-agent workflows, and documents it as a production-ready approach with a small set of useful building blocks. You get tools, handoffs, sessions, tracking, and real-time patterns without having to go into a big frame. If you like simple environments and direct control, this is one of the best launchers to check out.
# 8. AutoGen
AutoGen (~56k ⭐) is still one of the most important sites in the multi-agent space. Microsoft is pitching it as an agent AI programming framework, and the documentation extends to business workflows, research collaboration, and multi-agent distributed applications. It is on this list because there is so much to learn from it. Orchestration concepts, agent conversation patterns, and framework design are all worth studying. It may not be an easy start for everyone, but it is still one of the most influential projects in the category.
# 9. GPT Researcher (~26k ⭐)
GPT Researcher it is a good choice if you want to study an in-depth research agent instead of a general outline. It is an independent agent for intensive research using any language model (LLM) provider, and the surrounding material shows how it handles multi-agent research and report generation. This gives you one clear workflow to follow from start to finish. You can see editing, browsing, source collection, aggregation, and reporting all in one place. If you're looking for something tangible rather than intangible, this is one of the easiest places to find on the list.
# 10. Letta
Letta (~22k ⭐) stands out because it puts memory and state at the center of the agent's design. The repo describes it as a platform for building memory-enhanced agents that can learn and evolve over time. This is an important angle because most repos angles focus more on orchestration. Letta expands the image. It's a good repo to check out if you want agents that are persistent, memorable, and flexible instead of starting new all the time. With the agent's work focused on memory, it is one of the most interesting projects to forge today.
# Wrapping up
All ten are worth compiling, 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 AI and medicine. He co-authored the ebook “Increasing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, he strives for diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, having founded FEMCodes to empower women in STEM fields.
