langchain-rag-agents

Build a LangChain RAG agent using the create_agent API to coordinate iterative retrieval, reasoning, and tool use for complex research tasks. The skill provides step-by-step guidance and tooling examples for multi-source context gathering.

Agentic RAG enables iterative, autonomous retrieval and reasoning, whereas 2-step RAG follows a linear retrieve-then-generate process. This skill provides guidance on choosing the right pattern based on your workload complexity.

Create retrieval tools for a LangChain research assistant by configuring the create_agent API with custom tooling. The skill provides blueprints for building tools, managing multi-step retrieval, and handling context efficiently in production environments.

Use agentic RAG instead of 2-step RAG for knowledge-base search when your tasks require multi-source context, iterative retrieval, and complex reasoning. The skill outlines best practices to determine the appropriate pattern for specific workloads.

Yes, you can manage multi-step retrieval and context efficiently with LangChain agents using the create_agent API. The skill provides best practices and tooling examples for handling complex information gathering tasks in production.

The create_agent API for RAG-enabled LangChain agents requires coordinating retrieval, reasoning, and tool use, which can be complex. The skill helps navigate these limitations by providing blueprints and best practices for production scenarios.