By R. Shivakumar and Chris A.
Remember when everyone couldn't stop talking about AI copilots? That was last year. Now the conversation has shifted to agents—software that can plan, reason, and act on its own. We've spent months testing these frameworks, building prototypes, and watching what works in the wild. What we found is a thriving open-source ecosystem that's already changing how software gets built.
What exactly is an agent kit?
An agent kit gives you the building blocks to connect AI models with real-world actions. Instead of just generating text, these tools let you build systems that can reason through problems, chain multiple tools together, pull data from different sources, and interact with APIs without constant hand-holding.
Developers—and increasingly, non-technical users—are using these kits to create agents that book flights, process documents, handle customer support tickets, and coordinate with other AI agents. While companies like OpenAI grab headlines with their proprietary tools, the real innovation is happening in open-source communities where anyone can experiment, contribute, and build.
How we tested and ranked these frameworks
We didn't just count GitHub stars. We actually used these tools, built things with them, and evaluated them on what matters:
- Community activity: Is anyone actually using this? Are maintainers responsive?
- Flexibility: Can you swap between different AI models easily?
- Tool integration: How well does it work with external APIs and functions?
- Stability: Will this break in production, or is it ready for real work?
- License: Can you actually use it commercially?
Every framework on this list is usable right now, not just theoretically promising.
The top 5 open-source agent frameworks
1. LangChain Agents – The industry standard
GitHub: langchain-ai/langchain | Stars: 80,000+
LangChain has been around long enough to work out most of its rough edges. It shines when you need agents that can plan multiple steps ahead and execute complex workflows.
Strengths: Handles multi-tool pipelines elegantly, includes memory management so agents remember context, and offers flexible agent executors that adapt to different tasks.
Best for: Production applications where reliability matters more than simplicity.
The catch: Heavy dependencies can feel like overkill for smaller projects. You're getting a Swiss Army knife when sometimes you just need a bottle opener.
We've used LangChain for everything from content research pipelines to customer service automation. It can do almost anything, which means there's a learning curve, but that flexibility pays off.
2. AutoGen – When one agent isn't enough
GitHub: microsoft/autogen | Stars: 35,000+
AutoGen takes a different approach. Instead of building one smart agent, you build a team. Specialized agents communicate with each other, divide tasks, and solve problems collectively.
Best for: Research automation, coding assistance, and any scenario where you need specialized expertise in different areas.
Key features: Multi-agent conversations that feel surprisingly natural, built-in planning and execution pipelines, and human-in-the-loop support for when you need to step in.
Trade-off: Resource-intensive for simple tasks, but watching agents collaborate in AutoGen feels like observing a miniature think tank at work.
We built a research assistant with AutoGen where one agent gathered sources, another analyzed them, and a third synthesized findings. The results were better than any single-agent system we'd tested.
3. CrewAI – Built for speed
GitHub: joaomdmoura/crewai
CrewAI won us over with its simplicity. You define agent roles in a YAML file, plug in your preferred language model, and suddenly you have multiple agents executing different tasks.
Why it's gaining traction: Fast setup, modular design, and minimal boilerplate code.
Best for: Startups testing ideas quickly or solo developers who want to experiment without wrestling with configuration files.
It doesn't have LangChain's massive ecosystem, but sometimes less is more. We spun up a research agent in under 30 minutes that would have taken half a day in other frameworks.
4. LlamaIndex – Your data problem solver
GitHub: run-llama/llama_index
Some frameworks focus on reasoning. LlamaIndex focuses on understanding. It connects agents to documents, databases, and APIs so your AI actually comprehends context before taking action.
Best for: Retrieval-Augmented Generation (RAG), enterprise knowledge bases, and anything involving large document collections.
Why it matters: It's the difference between an agent that hallucinates facts and one that cites actual sources. We've seen significantly fewer errors when pairing agents with LlamaIndex.
The reality: For serious applications that need to be right, not just sound right, LlamaIndex is non-negotiable.
5. Camel-AI – The experimental lab
GitHub: camel-ai
Camel-AI is where bold ideas get tested. It sets up two agents—typically a User and an Assistant—to communicate and solve problems autonomously, without constant human intervention.
Core concept: AI-to-AI communication that evolves toward solutions organically.
Use cases: Research simulations, negotiation modeling, and complex task chains where you want emergence rather than strict programming.
The caveat: Experimental by design. Most businesses shouldn't bet their production systems on it yet, but for researchers and innovators, it's fascinating.
Watching Camel-AI agents negotiate and problem-solve is messy but brilliant. It's a glimpse at what collaborative AI might look like in five years.
Worth keeping on your radar
- SmolAgents (Hugging Face): Minimalist and lightweight, perfect for quick prototypes
- Haystack Agents (deepset.ai): Strong choice for document-heavy workflows
- OpenDevin: Newer developer agent framework that's showing promise
Choosing the right framework for your project
| Your goal | Best framework |
|---|---|
| Quick prototyping and testing ideas | CrewAI |
| Production-ready complex agents | LangChain + LlamaIndex |
| Multi-agent team collaboration | AutoGen |
| Data-heavy or RAG applications | LlamaIndex |
| Research and experimentation | Camel-AI |
There's no universal best choice. Start small, experiment, and don't be afraid to combine frameworks. Many production systems use LangChain for orchestration, LlamaIndex for data grounding, and custom glue code to hold it together.
What's coming in 2026
The next 12 months look promising. We expect to see agent marketplaces where developers can share, sell, or license autonomous agents they've built. Multi-agent collaboration will move from research papers into real business applications, especially in finance, enterprise software, and research institutions.
Open standards will probably emerge to make different frameworks work together more easily. Security, monitoring, and auditability will mature—because autonomous agents are only useful if you can trust them.
Our bet is that open-source frameworks will remain where innovation happens first. Proprietary tools might dominate marketing budgets, but the real breakthroughs, the weird experiments that actually work, will come from open communities.
The bottom line
Open-source agent kits have graduated from hobby projects to legitimate tools for building real software. Each has its strengths:
- LangChain: The reliable veteran for serious builds
- AutoGen: The collaboration specialist
- CrewAI: The fast, accessible option
- LlamaIndex: The data expert
- Camel-AI: The experimental sandbox
Whether you're automating your own workflows, building a side project, or planning the next big SaaS product, these frameworks show that the future of AI agents is open, collaborative, and driven by people willing to experiment.
We're excited to see what gets built next. And we'll be right here, testing and experimenting alongside everyone else.
About the Authors
Shivakumar and Chris A. run Agent-Kits.com, where they test, review, and explain open-source AI frameworks. Their goal is to make agent development accessible to anyone curious enough to try.