Nachiket Mehendale·Oct 19Protecting User Data in LangChainPassing the Logged-In User’s ID to a Tool Through Agent State, Without Exposing It to the LLM
raees zakir·Sep 14Why do we need Langchain ?When building AI applications, we can interact with an LLM directly through its API and implement everything ourselves.That approach has an…
Nachiket Mehendale·Aug 26Dynamic Prompt Engineering in LangChain@dynamic_prompt is a Python decorator provided by LangChain. It lets an agent build its system prompt using runtime context.
InLevel Up CodingbyAbdullah @writer·Aug 24From LLM to Agent, Part 1: Why Tool Calling Changes EverythingPart 1 of a 3-part series on building AI agents with LangChain. This part goes deep on the foundation everything else stands on: what…
InCodeToDeploybyNachiket Mehendale·Aug 21Tool Calls and Runtime Context in LangChainTools can access application data without exposing it to the model.
Nachiket Mehendale·Aug 18Wrap-Style Middleware in LangChainDynamically control agent tools at runtime based on user context
Nachiket Mehendale·Aug 8Understanding content_blocks in LangChainA simple OpenAI and Gemini tool-call example
Talha Khalid·Aug 74 Steps to Create a Simple RAG ChatbotThis article assumes you’re already comfortable with Python and FastAPI, and have at least a working familiarity with LangChain and vector…
Jason Gomes·Jul 22How Google Workspace Enterprise Customers Should Build Zero-Trust AI with MCP, ADK and LangGraphHow Google Workspace Enterprise Customers Should Build Zero-Trust AI with MCP and LangGraph
