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ai-building-chatbots

Build memory-enabled chatbots with DSPy and LangGraph for multi-turn conversations.

11|1|Updated Feb 8, 2026
Search Tags:#memory#langgraph#chatbot#state-management#multi-turn#conversational-ai#dspy
One-click install
npx skills add https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-building-chatbots
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-building-chatbots
Source: https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills/tree/main/skills/ai-building-chatbots
Command: npx skills add https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-building-chatbots

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build memory-enabled, multi-turn conversational AI chatbots that remember context across interactions and follow defined conversation flows using DSPy for response generation and LangGraph for memory and state management.

Core Features & Use Cases

  • Memory-enabled context tracking across turns with LangGraph
  • DSPy-based response generation and modular routing
  • Grounded responses using doc retrieval and guardrails for quality
  • Escalation and human-in-the-loop workflows for complex issues

Quick Start

Create a DSPy-powered chatbot that remembers prior messages and uses LangGraph to manage conversation state.

Frequently Asked Questions about ai-building-chatbots

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
Does this approach work for sales qualification and onboarding assistant bots?▼

Sales qualification and onboarding assistants benefit from memory-enabled conversational AI that follows defined conversation flows. Using LangGraph and DSPy, the chatbot maintains persistent dialogue state and grounds responses for reliable user interactions.

How does LangGraph handle state management for conversational AI?▼

LangGraph manages conversational state by structuring dialogue flows and retaining memory across interactions. It coordinates with DSPy for response generation, allowing modular routing and grounded replies for scalable chat experiences.

What's the best way to add guardrails and doc retrieval to a DSPy chatbot?▼

Grounded responses use document retrieval and guardrails to ensure quality and reliability in DSPy chatbots. This approach supports modular components, enabling compliant conversational AI for sales qualification and FAQ bots.

Can I implement human-in-the-loop escalation workflows for a customer support chatbot?▼

Human-in-the-loop workflows handle complex issues by escalating customer support chatbot conversations. The system supports escalation and modular routing to deliver reliable interactions when automated responses are insufficient.

Does this approach work for sales qualification and onboarding assistant bots?▼

Sales qualification and onboarding assistants benefit from memory-enabled conversational AI that follows defined conversation flows. Using LangGraph and DSPy, the chatbot maintains persistent dialogue state and grounds responses for reliable user interactions.

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