Summary
In this chapter, we dived deep into advanced applications of LLMs and the architectural patterns that enable them, leveraging LangChain and LangGraph. The key takeaway is that effectively building complex AI systems goes beyond simply prompting an LLM; it requires careful architectural design of the workflow itself, tool usage, and giving an LLM partial control over the workflow. We also discussed different agentic AI design patterns and how to develop agents that leverage LLMs’ tool-calling abilities to solve complex tasks.
We explored how LangGraph streaming works and how to control what information is streamed back during execution. We discussed the difference between streaming state updates and partial streaming answer tokens, learned about the Command interface as a way to hand off execution to a specific node within or outside the current LangGraph workflow, looked at the LangGraph platform and its main capabilities, and discussed how to implement HIL with LangGraph...
