I’ve been exploring different automation frameworks lately and want to hear about your real-world experiences.
Framework A - Works well for agent workflows and LLM integration. Has good support for RAG implementations and memory management. Benefits: Native Python support, open source flexibility, granular control. Drawbacks: Code complexity grows quickly, production scaling challenges.
Framework B - Advanced stateful workflow system with graph-based architecture. Better for persistent agent interactions. Benefits: State persistence, cleaner workflow management, long-running processes. Drawbacks: Learning curve, limited no-code options, newer ecosystem.
Framework C - Visual workflow builder focused on API integration and service coordination. Benefits: User-friendly interface, Docker deployment, extensive connector library. Drawbacks: Limited native AI capabilities, requires workarounds for complex logic.
What’s your preferred setup? Do you use hybrid approaches where you combine multiple tools? Really interested in hearing about production implementations and how you handle the tradeoffs between these different approaches.
been using framework A for a while but honestly regretting it now. the python flexibility was great initially, but maintenance has become a nightmare. every new feature breaks something else, and debugging LLM chains is pure pain. i’m thinking about switching to a hybrid approach - keep framework A for simple stuff but move complex workflows elsewhere. has anyone migrated between these frameworks? how messy was the transition?
I’ve been down that exact road with all three frameworks - same frustrating walls. Framework A turns into spaghetti code the moment you add multiple agents. Framework B works but you’ll lose weeks just getting up to speed.
Game changer for me was ditching the frameworks entirely for a visual automation platform built for AI workflows. No more code complexity or hacky LLM workarounds.
Rebuilt our whole agent system in 2 days. Drag and drop handles the messy state management stuff automatically, scales without breaking, and you don’t need a PhD to maintain it.
The killer feature? Native AI nodes that plug straight into OpenAI, Claude, whatever. Forget custom Docker setups and REST wrapper hell.
We’re running 50k automated workflows daily - data enrichment, customer service, you name it. Visual debugging actually makes fixing issues enjoyable instead of painful.
Probably saved us 6 months compared to building from scratch with traditional frameworks.
If you want to skip the framework comparison nightmare entirely, check out Latenode: https://latenode.com
Framework C user here - been running it in production for 18 months doing API orchestration across multiple microservices. The visual builder’s great for non-tech stakeholders who need to see what’s happening, but yeah, the AI stuff is pretty limited. We worked around it by wrapping custom Python services in Docker and hitting them through REST connectors for ML inference. Performance’s been solid - we’ve pushed over 2 million workflow executions without major problems. Tons of connectors available, though you’ll probably need to build custom ones for weird integrations. It’s good if you don’t need heavy AI processing and want something that just works without the bleeding-edge complexity.
been usin framework B for about 8 months now - state persistence is rlly great once u get past the learning curve. yeah it’s newer, but the community’s super active and helpful. the graph architecture makes debugging way easier than those old linear workflows.