LlamaIndex plugins

Build and evaluate production-grade AI agents using LangGraph, RAG systems, MCP servers, and prompt engineering patterns—with behavioral testing and reliability monitoring.

Add DeepEval evaluation loops to AI applications: instrument LLM calls, agents, and RAG pipelines with OpenTelemetry or native tracing, generate datasets, run pytest eval suites, and iterate on failures with Confident AI reporting.

Explain machine learning model predictions using SHAP, LIME, and feature importance to identify influential features and debug behavior. Generate production-ready AI/ML code from context, including validation, error handling, performance metrics, insights, artifacts, and documentation.

Instrument Python and TypeScript code in LLM apps and agents with MLflow tracing for observability, analyze traces and multi-turn sessions to debug issues, evaluate outputs using datasets and judges to optimize accuracy and reduce costs, query aggregated metrics, and iterate improvements.

Add persistent memory and personalization to AI applications with semantic search, guided memory recall, and automatic context management for Claude workflows.

Build production-grade LLM apps in Python: implement RAG pipelines with embeddings and hybrid search, design LangChain/LangGraph agents, optimize prompts, tune vector indexes, and evaluate performance using AI agents, skills, and commands for architecture, code gen, and benchmarking.

Build production-ready LLM applications by delegating to expert AI agents that engineer prompts, manage dynamic contexts with vector DBs and knowledge graphs, optimize single and multi-agent performance, and orchestrate RAG, multimodal, and enterprise AI workflows.

Convert docs, repos, PDFs, videos, and more into AI-ready skill packages for LLM platforms like Claude, OpenAI, and Gemini, with auto-detection of source types and configurable preset levels.