If you’re building AI agents today, you have more framework options than ever. That’s good news and bad news - more choices, but harder to navigate.

I’ve spent the last two months working with the major agent frameworks, building test applications and talking to teams using them in production. Here’s what I’ve learned.

The Major Players

Let me start by mapping the landscape:

LangChain remains the most widely adopted framework. It’s comprehensive, well-documented, and has the largest ecosystem of integrations. The learning curve is steep, and the abstraction layers can be confusing, but the community and resources are unmatched.

LlamaIndex started as a data framework (connecting LLMs to data sources) but has expanded into agent capabilities. It excels at retrieval-augmented generation (RAG) and applications that need to work with large document collections.

AutoGPT / AgentGPT are the autonomous agent projects that captured attention in early 2024. They attempt fully autonomous task execution with minimal human oversight. Impressive demos, but reliability in production remains challenging.

Microsoft’s Semantic Kernel is gaining traction in enterprise environments, particularly for organizations already invested in Microsoft’s ecosystem. Good Azure integration, C# and Python support, enterprise-focused features.

Anthropic’s tool use and OpenAI’s function calling are first-party approaches that don’t require external frameworks. They’re simpler but less feature-rich.

CrewAI focuses on multi-agent systems - multiple AI agents collaborating on complex tasks. Good for applications that benefit from specialized agents working together.

Haystack from deepset is popular in Europe and for production search applications. Strong focus on retrieval and question-answering.

Framework Selection Criteria

How do you choose? Here’s the framework I use:

Use case complexity: For simple single-agent applications (chatbot with tools, document Q&A), you might not need a framework at all. Direct API calls with function calling might suffice. For complex multi-step reasoning, tool orchestration, or multi-agent systems, frameworks add value.

Model flexibility: Do you need to work with multiple LLM providers? LangChain and LlamaIndex have the broadest model support. Microsoft Semantic Kernel is optimized for Azure OpenAI.

Data integration needs: If RAG is central to your application, LlamaIndex is purpose-built for this. It has the most sophisticated chunking, retrieval, and indexing strategies.

Production requirements: Some frameworks are better for production use than others. Consider monitoring, observability, error handling, and deployment options. LangSmith (for LangChain) provides good observability tools.

Team expertise: What languages and patterns is your team comfortable with? The Python-native frameworks have different idioms than Semantic Kernel’s C# approach.

Enterprise considerations: If you’re in a large organization with procurement processes and compliance requirements, the backing organization matters. Microsoft and Anthropic may be easier to approve than open-source projects.

LangChain Deep Dive

Since LangChain is the most commonly encountered, let me go deeper:

Strengths:

• Comprehensive. Whatever you need to do, there’s probably a component for it.
• Excellent documentation and tutorials.
• LangSmith provides production-grade observability.
• LCEL (LangChain Expression Language) enables composable chains.
• Active development and community.

Weaknesses:

• Complex abstraction layers. You can spend a lot of time understanding the architecture.
• Breaking changes between versions have been an issue historically.
• Sometimes feels over-engineered for simple use cases.
• Performance overhead from abstraction layers.

Good fit when: You need a full-featured framework with maximum flexibility, and you have time to climb the learning curve.

Bad fit when: You need something simple and quick, or you’re building something highly specialized that the abstractions don’t fit.

LlamaIndex Deep Dive

Strengths:

• Best-in-class RAG capabilities. If your application involves querying document collections, this is the strongest choice.
• Sophisticated indexing and retrieval strategies.
• Good handling of complex documents (tables, hierarchical content).
• Strong agent capabilities built on RAG foundation.

Weaknesses:

• Less comprehensive than LangChain for non-RAG use cases.
• Smaller community and ecosystem.
• Documentation can be sparse for advanced features.

Good fit when: RAG is central to your application. You’re building document search, knowledge base querying, or similar applications.

Bad fit when: Your agent doesn’t primarily involve information retrieval.

The Autonomous Agent Question

AutoGPT-style fully autonomous agents deserve special discussion.

The promise is compelling: give the agent a goal, and it figures out how to achieve it with minimal human input. The demos are impressive.

The reality in production is harder. Autonomous agents make mistakes. They go down rabbit holes. They spend resources on irrelevant tasks. They occasionally take actions you didn’t expect or want.

The teams I’ve talked to who are having success with autonomous agents are all using heavy guardrails: sandboxed execution environments, budget limits, human approval checkpoints for significant actions, monitoring and alerting for anomalies.

My recommendation: don’t aim for full autonomy. Aim for useful autonomy within defined boundaries. A human-in-the-loop for consequential decisions isn’t a failure - it’s appropriate for the current state of the technology.

Multi-Agent Approaches

CrewAI and similar multi-agent frameworks enable patterns where specialized agents collaborate:

• A “researcher” agent gathers information
• An “analyst” agent synthesizes findings
• A “writer” agent produces output
• A “reviewer” agent checks quality

This can work well for complex tasks where different sub-tasks benefit from different prompting and tool configurations.

The overhead is real though. You’re managing more components, more prompts, more potential points of failure. I’d only reach for multi-agent approaches when the task complexity genuinely warrants it.

My Current Recommendation

For most teams starting agent development:

1. Start simple. Build your first agent with direct API calls and function calling. Understand the fundamentals before adding framework complexity.

2. Add framework when needed. When you hit limitations - need better RAG, need multi-step orchestration, need better observability - then adopt a framework that addresses your specific gaps.

3. LangChain for general-purpose complexity. If you need maximum flexibility and have the bandwidth for learning curve.

4. LlamaIndex if RAG is central. Best-in-class for document-based applications.

5. Semantic Kernel in Microsoft shops. The integration advantages are real if you’re already committed to Azure.

6. Build rather than buy when frameworks don’t fit. Sometimes the right answer is custom code that does exactly what you need.

The framework landscape will keep evolving. What matters most is understanding the underlying patterns - how agents reason, plan, use tools, and interact with data. Frameworks come and go, but the fundamentals persist.