What problem does it solve? Building autonomous AI agents involves hard architectural decisions around memory, tool access, reasoning patterns, and human oversight, and mistakes lead to unreliable or unsafe agent behavior. ## Core Features & Use Cases - Agent Architecture Guidance: Choose between single-agent and multi-agent designs, and apply ReAct or Chain-of-Thought reasoning patterns appropriately. - Memory & State Management: Persist agent state with LangGraph checkpointers, separate short-term from long-term memory, and prune context to fit token limits. - Safe Tooling & Human-in-the-Loop: Define validated tools with Pydantic or Zod schemas and insert interrupt points for approvals on critical actions like payments or deletions. - Use Case: When building a customer-support agent that queries a database and issues refunds, use this guide to structure the LangGraph state machine, add a human approval interrupt before refunds, and trace runs with LangSmith. ## Quick Start Ask the AI to design a multi-agent workflow with LangGraph that uses checkpointer persistence and a human-in-the-loop approval step before executing sensitive tool calls.
