Drop-in TruLens evaluation harness for tool-calling LangGraph agents. Swap LLM providers (OpenAI, Anthropic via LiteLLM, Bedrock, Cortex, Gemini, Ollama) with a single env var. Ships with the RAG Triad plus Plan Quality, Plan Adherence, Execution Efficiency, and Logical Consistency metrics.

Pluggable DeepEval scaffold for RAG, agents, and LLM apps across Anthropic, Bedrock, Azure OpenAI, and Vertex. Ships traceability, test synthesis, safety/PII gating, multi-turn conversation eval, agentic tool-use scoring, JSON validation, judge benchmarks, hyperparameter sweeps, and pytest CI — one Makefile target per feature.

📚 Stateful, self-correcting RAG research assistant built with LangGraph & Streamlit. Features hybrid search (dense + BM25), Cohere reranking, Tavily web/arXiv claim verification, session-isolated Qdrant storage, Guardrails AI, DeepEval testing, LangSmith tracing, /btw off-topic channel and AWS Fargate deployment configured with Terraform & CI/CD.

A production-ready LLM evaluation framework built with DeepEval and a custom Groq-powered judge (LLaMA 3.3 70B). Evaluates LLM outputs across 5 key metrics — Answer Relevancy, Faithfulness, Hallucination, Toxicity & G-Eval — without requiring an OpenAI API key. Fast, free-tier compatible.

This is a portable lifecycle benchmark harness for comparing AI coding agents on real QA engineering tasks. It rigorously evaluates agent competencies across test authoring, test repair, root-cause debugging, token efficiency, framework adaptation, and complex browser or mobile automation workflows.

RAG Evaluation Suite on Finance Documents

RAG pipeline with LLM evaluation using DeepEval, Ollama/Groq judge models, and Langfuse observability.