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# Introduction
If you wish to study agent engineering by doing as an alternative of simply studying about it, the easiest way continues to be to fork actual repos, run them domestically, and alter them to your personal use. That is the place the actual studying occurs. I’ve hand-picked one of the best 10 ones, the tasks which might be each helpful and well known, so you possibly can see how agent apps are being constructed right this moment. So, let’s get began.
# 1. OpenClaw
OpenClaw (~343k ⭐) is the one I’d level to first if you wish to see what the subsequent wave of private AI assistants would possibly seem like. It’s constructed as a private assistant that runs by yourself units and connects to the instruments folks already use, like WhatsApp, Telegram, Slack, Discord, Sign, and iMessage. What makes it attention-grabbing is that it isn’t only a easy chat demo. It looks like an actual assistant product, with multi-channel help, voice options, and a broader ecosystem round expertise and management. If you’d like a repo that feels near an actual agent system, this can be a sturdy place to start out.
# 2. OpenHands
OpenHands (~70k ⭐) is a good repo to fork in case your essential curiosity is coding brokers. It’s constructed round AI-driven growth and now has a wider ecosystem round it, together with cloud, docs, CLI, SDK, benchmarking, and integrations. That issues as a result of you aren’t simply one demo. You possibly can examine the core agent, verify the interface, and likewise see how the staff thinks about analysis and deployment. If you wish to construct or customise a coding assistant, this is among the most sensible repos to study from.
# 3. browser-use
browser-use (~85k ⭐) is among the most helpful tasks if you’d like brokers that may truly do issues on the internet. The concept is easy: it makes web sites simpler for AI brokers to make use of, to allow them to deal with browser-based duties with much less friction. That makes it straightforward to experiment with, since a whole lot of actual agent work results in the browser anyway — kind filling, analysis, navigation, and repetitive on-line duties. It additionally has supporting repos and examples, which makes it simpler to go from curiosity to one thing you possibly can check in an actual workflow.
# 4. DeerFlow
DeerFlow (~55k ⭐) is among the extra attention-grabbing tasks if you wish to perceive long-horizon agent methods. It’s an open-source super-agent harness that brings collectively sub-agents, reminiscence, sandboxes, expertise, and instruments to analysis, code, and create throughout longer duties. So, it isn’t simply wrapping device calls. It’s making an attempt to handle the total construction round extra complicated agent conduct. If you wish to see how trendy agent methods are being constructed round reminiscence, coordination, and extensibility, this can be a very helpful repo to fork.
# 5. CrewAI
CrewAI (~48k ⭐) continues to be one of many best repos to know if you’d like multi-agent orchestration with out an excessive amount of complexity. It’s a quick, versatile framework for multi-agent automation, and it’s constructed independently somewhat than on prime of LangChain. The psychological mannequin is easy, the setup is approachable, and the docs and examples are pleasant sufficient for newbies. If you’d like a Python-first repo that you would be able to fork and switch into one thing helpful, CrewAI nonetheless deserves a spot close to the highest.
# 6. LangGraph
LangGraph (~28k ⭐) is the repo to check once you need to perceive the engineering facet of brokers, not simply the flashy demo facet. LangChain describes it as a low-level orchestration framework for long-running, stateful, controllable brokers. It pushes you to assume when it comes to graphs, state, management circulation, and resilience. It’s particularly helpful if you wish to transfer past easy prompt-plus-tool-call methods and perceive how extra severe agent runtimes are put collectively. It might not really feel as fast to choose up as another repos, but it surely teaches loads.
# 7. OpenAI Brokers SDK
The OpenAI Brokers SDK (~20k ⭐) is an efficient choice if you’d like one thing light-weight however nonetheless trendy. It’s constructed as a compact framework for multi-agent workflows, and the docs current it as a production-ready path with a small set of helpful constructing blocks. You get instruments, handoffs, periods, tracing, and realtime patterns with out having to wade by means of an enormous framework. If you happen to like easy surfaces and direct management, this is among the higher starter repos to discover.
# 8. AutoGen
AutoGen (~56k ⭐) continues to be one of the vital repos within the multi-agent house. Microsoft frames it as a programming framework for agentic AI, and the docs go additional into enterprise workflows, analysis collaboration, and distributed multi-agent functions. It belongs in this sort of listing as a result of there’s a lot to study from it. The orchestration concepts, agent dialog patterns, and framework design are all value learning. It will not be the only start line for everybody, however it’s nonetheless one of the influential tasks within the class.
# 9. GPT Researcher (~26k ⭐)
GPT Researcher is a good alternative if you wish to examine a deep-research agent as an alternative of a basic framework. It’s an autonomous agent for deep analysis utilizing any massive language mannequin (LLM) supplier, and its surrounding materials exhibits the way it handles multi-agent analysis and report technology. This offers you one clear workflow to check from begin to end. You possibly can see planning, looking, supply gathering, synthesis, and reporting multi functional place. If you’d like one thing concrete somewhat than summary, this is among the most forkable repos on the listing.
# 10. Letta
Letta (~22k ⭐) stands out as a result of it places reminiscence and state on the heart of the agent design. The repo describes it as a platform for constructing stateful brokers with superior reminiscence that may study and enhance over time. This is a vital angle as a result of a whole lot of agent repos focus totally on orchestration. Letta widens the image. It’s a good repo to discover if you’d like brokers that persist, keep in mind, and evolve as an alternative of beginning recent each time. For memory-focused agent work, it is among the extra attention-grabbing tasks to fork right this moment.
# Wrapping Up
All ten are value cloning, however they educate various things when you truly run them and begin altering the code. That’s the place the actual studying begins.
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with medication. She co-authored the e book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Variety in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower girls in STEM fields.
