Pydantic’s Post

The AI agent space is evolving fast - and honestly, we're all still figuring out the best approaches. I've been exploring different frameworks for agentic workflows, and each comes with fascinating trade-offs. Here's what I've learned building production-grade solutions: LangChain: ✅ Rich feature set - RAG, tool calling, everything you need ✅ Fast prototyping ❌ Documentation feels disconnected from reality ❌ Can get messy as complexity grows CrewAI ✅ Excellent multi-agent orchestration, you can easily setup a multi-agent task ✅ Many tools out-of-the-box ❌ Black-box approach to memory modules AND task/agent delegation ❌ Non-OpenAI configurations (Azure endpoints) were challenging ❌ Self-hosted observability was harder than expected Pydantic AI ✅ Simplicity is its superpower ✅ Leverages Pydantic's type safety (addressing Python's biggest weakness) ✅ Seamless integration of data models into agentic workflows ✅ Easy custom tool implementation ✅ Built-in observability via Logfire (OpenTelemetry) ✅ Agents → FastAPI objects (drop into existing backends) ❌ Fewer out-of-box tools = you write more code ✅ But this forces you to understand your domain better (I see this as a feature) My take: If you value understanding how things work under the hood, Pydantic AI is worth exploring. The "write more code" trade-off actually makes you a better engineer because you're forced to understand the problem you want to solve. I'd also give CrewAI a chance - it's remarkably easy to build agents and tasks, especially if you leverage Claude for prompt engineering. Currently exploring task decomposition and retrieval strategies for real-world applications. Pydantic AI is my top choice for now, but who knows what's next? What frameworks are you using? What trade-offs are you navigating? #AIEngineering #AIAgents #Python #PydanticAI #LangChain #CrewAI #GenerativeAI #SoftwareEngineering #TechLearning