I’m trying to understand what the workflow looks like in research labs (especially PhD labs and university groups) that work on LLMs, AI agents, or applied AI. I’m planning to study in graduate school in Computer Science. There are now a lot of agent frameworks and tools available, such as: OpenAI Agents SDK LangChain / LangGraph CrewAI Google ADK AutoGen Semantic Kernel MCP PydanticAI Agno Mastra Do research groups actually use these frameworks in their projects, or do they mostly implement their own orchestration, tool calling, memory, and agent loops directly in Python? Or maybe it’s a mixture of both ? I can understand why companies might use frameworks to ship products faster, but I’m curious about academia, where reproducibility and experimental control matter more. Some specific questions: If you’re a PhD student or researcher, what does your codebase typically look like? Are frameworks common, or are they considered too opinionated? Which parts do you usually implement yourself (agent loop, planning, memory, RAG, evaluation, tracing, etc.)? Are there any frameworks that have become standard in research labs? If you’re publishing papers, do reviewers or collaborators prefer minimal dependencies? I’d especially love to hear from PhD students, professors, or research engineers working on LLMs or AI agents. Thanks! submitted by /u/One_Fix5763
Originally posted by u/One_Fix5763 on r/ArtificialInteligence