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# Introduction
If you wish to be taught agent engineering by doing as an alternative of simply studying about it, one of the simplest ways continues to be to fork actual repos, run them regionally, and alter them on your personal use. That is the place the true studying occurs. I’ve hand-picked the most effective 10 ones, the initiatives which might be each helpful and well known, so you may see how agent apps are being constructed in the present day. So, let’s get began.
# 1. OpenClaw
OpenClaw (~343k ⭐) is the one I might level to first if you wish to see what the subsequent wave of private AI assistants may appear like. It’s constructed as a private assistant that runs by yourself units and connects to the instruments individuals already use, like WhatsApp, Telegram, Slack, Discord, Sign, and iMessage. What makes it attention-grabbing is that it’s not 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 robust place to begin.
# 2. OpenHands
OpenHands (~70k ⭐) is a good repo to fork in case your predominant 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 may research the core agent, examine the interface, and in addition see how the staff thinks about analysis and deployment. If you wish to construct or customise a coding assistant, this is likely one of the most sensible repos to be taught from.
# 3. browser-use
browser-use (~85k ⭐) is likely one of the most helpful initiatives in order for you brokers that may really do issues on the net. The thought is straightforward: 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 simple to experiment with, since quite a 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 may take a look at in an actual workflow.
# 4. DeerFlow
DeerFlow (~55k ⭐) is likely one of the extra attention-grabbing initiatives if you wish to perceive long-horizon agent techniques. 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’s not simply wrapping software calls. It’s making an attempt to handle the total construction round extra complicated agent habits. If you wish to see how fashionable agent techniques 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 in order for you 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 quite than on high of LangChain. The psychological mannequin is straightforward, the setup is approachable, and the docs and examples are pleasant sufficient for freshmen. 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 whenever 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 suppose by way of graphs, state, management circulate, and resilience. It’s particularly helpful if you wish to transfer past easy prompt-plus-tool-call techniques and perceive how extra severe agent runtimes are put collectively. It could not really feel as fast to select up as another repos, but it surely teaches lots.
# 7. OpenAI Brokers SDK
The OpenAI Brokers SDK (~20k ⭐) is an efficient choice in order for you one thing light-weight however nonetheless fashionable. 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 way of an enormous framework. If you happen to like easy surfaces and direct management, this is likely one of the higher starter repos to discover.
# 8. AutoGen
AutoGen (~56k ⭐) continues to be one of the crucial necessary repos within the multi-agent area. 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 checklist as a result of there’s a lot to be taught from it. The orchestration concepts, agent dialog patterns, and framework design are all value finding out. It is probably not the only place to begin for everybody, however it’s nonetheless one of the crucial influential initiatives within the class.
# 9. GPT Researcher (~26k ⭐)
GPT Researcher is a good alternative if you wish to research a deep-research agent as an alternative of a normal framework. It’s an autonomous agent for deep analysis utilizing any giant language mannequin (LLM) supplier, and its surrounding materials exhibits the way it handles multi-agent analysis and report era. This provides you one clear workflow to check from begin to end. You may see planning, shopping, supply gathering, synthesis, and reporting multi function place. If you’d like one thing concrete quite than summary, this is likely one of the most forkable repos on the checklist.
# 10. Letta
Letta (~22k ⭐) stands out as a result of it places reminiscence and state on the middle of the agent design. The repo describes it as a platform for constructing stateful brokers with superior reminiscence that may be taught and enhance over time. This is a crucial angle as a result of quite a lot of agent repos focus totally on orchestration. Letta widens the image. It’s a good repo to discover in order for you brokers that persist, bear in mind, and evolve as an alternative of beginning contemporary each time. For memory-focused agent work, it is likely one of the extra attention-grabbing initiatives to fork in the present day.
# Wrapping Up
All ten are value cloning, however they educate various things when you really run them and begin altering the code. That’s the place the true studying begins.
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for information science and the intersection of AI with medication. She co-authored the e book “Maximizing Productiveness with ChatGPT”. As a Google Era Scholar 2022 for APAC, she champions variety and tutorial excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.
Picture by Editor
# Introduction
If you wish to be taught agent engineering by doing as an alternative of simply studying about it, one of the simplest ways continues to be to fork actual repos, run them regionally, and alter them on your personal use. That is the place the true studying occurs. I’ve hand-picked the most effective 10 ones, the initiatives which might be each helpful and well known, so you may see how agent apps are being constructed in the present day. So, let’s get began.
# 1. OpenClaw
OpenClaw (~343k ⭐) is the one I might level to first if you wish to see what the subsequent wave of private AI assistants may appear like. It’s constructed as a private assistant that runs by yourself units and connects to the instruments individuals already use, like WhatsApp, Telegram, Slack, Discord, Sign, and iMessage. What makes it attention-grabbing is that it’s not 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 robust place to begin.
# 2. OpenHands
OpenHands (~70k ⭐) is a good repo to fork in case your predominant 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 may research the core agent, examine the interface, and in addition see how the staff thinks about analysis and deployment. If you wish to construct or customise a coding assistant, this is likely one of the most sensible repos to be taught from.
# 3. browser-use
browser-use (~85k ⭐) is likely one of the most helpful initiatives in order for you brokers that may really do issues on the net. The thought is straightforward: 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 simple to experiment with, since quite a 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 may take a look at in an actual workflow.
# 4. DeerFlow
DeerFlow (~55k ⭐) is likely one of the extra attention-grabbing initiatives if you wish to perceive long-horizon agent techniques. 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’s not simply wrapping software calls. It’s making an attempt to handle the total construction round extra complicated agent habits. If you wish to see how fashionable agent techniques 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 in order for you 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 quite than on high of LangChain. The psychological mannequin is straightforward, the setup is approachable, and the docs and examples are pleasant sufficient for freshmen. 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 whenever 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 suppose by way of graphs, state, management circulate, and resilience. It’s particularly helpful if you wish to transfer past easy prompt-plus-tool-call techniques and perceive how extra severe agent runtimes are put collectively. It could not really feel as fast to select up as another repos, but it surely teaches lots.
# 7. OpenAI Brokers SDK
The OpenAI Brokers SDK (~20k ⭐) is an efficient choice in order for you one thing light-weight however nonetheless fashionable. 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 way of an enormous framework. If you happen to like easy surfaces and direct management, this is likely one of the higher starter repos to discover.
# 8. AutoGen
AutoGen (~56k ⭐) continues to be one of the crucial necessary repos within the multi-agent area. 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 checklist as a result of there’s a lot to be taught from it. The orchestration concepts, agent dialog patterns, and framework design are all value finding out. It is probably not the only place to begin for everybody, however it’s nonetheless one of the crucial influential initiatives within the class.
# 9. GPT Researcher (~26k ⭐)
GPT Researcher is a good alternative if you wish to research a deep-research agent as an alternative of a normal framework. It’s an autonomous agent for deep analysis utilizing any giant language mannequin (LLM) supplier, and its surrounding materials exhibits the way it handles multi-agent analysis and report era. This provides you one clear workflow to check from begin to end. You may see planning, shopping, supply gathering, synthesis, and reporting multi function place. If you’d like one thing concrete quite than summary, this is likely one of the most forkable repos on the checklist.
# 10. Letta
Letta (~22k ⭐) stands out as a result of it places reminiscence and state on the middle of the agent design. The repo describes it as a platform for constructing stateful brokers with superior reminiscence that may be taught and enhance over time. This is a crucial angle as a result of quite a lot of agent repos focus totally on orchestration. Letta widens the image. It’s a good repo to discover in order for you brokers that persist, bear in mind, and evolve as an alternative of beginning contemporary each time. For memory-focused agent work, it is likely one of the extra attention-grabbing initiatives to fork in the present day.
# Wrapping Up
All ten are value cloning, however they educate various things when you really run them and begin altering the code. That’s the place the true studying begins.
Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for information science and the intersection of AI with medication. She co-authored the e book “Maximizing Productiveness with ChatGPT”. As a Google Era Scholar 2022 for APAC, she champions variety and tutorial excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.
