Qd Langchain Agents

Quality-Diversity Algorithm in LangChain

Developers can develop optimized architecture for AI agents or RAG systems but there’s a limit to how far this can go by hand. Exploring thousands of possible designs to find ones that are both diverse and high-performing is practically impossible.

**Quality-Diversity (QD)** based algorithmic approaches, such as **Enhanced MAP-Elites**, are now appearing as part of AI-based solutions to tackle this challenge and open up new possibilities for modern AI systems …


In this blog, we will create a complete pipeline for one of the famous Quality-Diversity (QD) algorithms, Enhanced MAP-Elites using LangChain and [pyribs](https://pyribs.org/).

You might be new to **Quality-Diversity** term, so we will start by …

Understanding the theoretical concepts behind QD algorithms from basics then implement them as a LangChain based RAG and agent system, and finally visualize and analyze their performance.

For AI models, we will use [Ollama](https://ollama.com/library) so that my work can be easily replicated.

qd-langchain-agents/
├── LICENSE
├── README.md
├── qd_algorithm_api.ipynb    # For API-based interactions
├── qd_algorithm_ollama.ipynb  # For local Ollama interactions
└── requirements.txt

Table of Contents

• What is Quality Diversity Algorithm?
• Pre-processing and Analyzing the TBMP Dataset
• Hierarchical Chunking Using Font-Based Heuristics
• Indexing the Knowledge Base
• Defining the LangGraph Agent Genotypes
• Genotype-to-Phenotype Compiler
• Implementing the Quality Function