Testing
Multi-agent systems (LangGraph workflows, CrewAI crews) are dramatically harder to test than single agents. State flows between agents, handoffs can fail silently, and emergent behaviors arise from agent interactions. This guide covers testing strategies for multi-agent orchestration: state validation, handoff testing, agent isolation, and failure injection. Single agents
Testing
LLM regressions are silent killers: the app still returns a response, but the quality degraded after a prompt change, model upgrade, or context window modification. Traditional regression tests that assert exact string equality fail immediately with LLMs. This guide covers behavioral regression testing, prompt versioning, LLM-as-judge evaluation, and
Testing
AI agents call external APIs — search engines, databases, Slack, GitHub, payment processors. Mocking these in tests is essential: real APIs are slow, rate-limited, non-deterministic, and sometimes irreversible. This guide covers four mocking strategies for agentic systems: HTTP interception, tool-level mocking, record/replay, and in-process fakes. An
Testing
AI agent workflows are harder to test than traditional software because the intermediate steps (which tools get called, in what order, with what arguments) are non-deterministic. This guide covers deterministic testing strategies: behavioral assertions over tool calls, golden dataset testing, state-based verification, and contract testing for agent-tool
Testing
MCP (Model Context Protocol) servers expose tools, resources, and prompts to AI agents. Testing them requires validating JSON-RPC protocol compliance, schema correctness, tool execution behavior, resource content, and error handling — none of which standard unit test frameworks handle out of the box. This guide covers how to test each
Testing
Off-the-shelf eval frameworks (DeepEval, Ragas, TruLens) cover 80% of use cases. For the remaining 20% — specialized domains, proprietary metrics, internal tooling constraints — you need to build your own. This guide walks you through designing metrics, curating datasets, implementing an LLM judge, and wiring it all into CI. When
Testing
TruLens is an open-source observability and evaluation framework for LLM applications. Its core contribution is the RAG Triad — three metrics (groundedness, context relevance, answer relevance) that together diagnose where a RAG pipeline breaks. Traces are stored locally or remotely, and a built-in dashboard makes debugging fast. What TruLens
Testing
LangSmith gives you production observability for LLM applications — full request traces, cost tracking, latency breakdowns, and human annotation queues. Combined with its evaluation layer, you can compare prompt versions, run automated evaluators, and catch regressions before they reach users. Why Tracing Matters for LLM Applications An LLM application isn'
Testing
Promptfoo is the standard tool for prompt regression testing and LLM red-teaming. Define test cases in YAML, run them against multiple models simultaneously, and catch prompt regressions before they reach production. The red-team mode automatically probes for jailbreaks, prompt injection, and safety failures. The Problem Promptfoo Solves Every
Testing
Ragas gives you a rigorous metric suite for RAG pipelines: faithfulness, answer relevancy, context precision, and context recall. Each metric isolates a different failure mode — bad retrieval vs. bad generation vs. incomplete recall. This guide shows you how to compute them, interpret scores, and integrate Ragas into CI. The RAG
Testing
DeepEval brings unit testing discipline to LLM applications. Write assertions on model outputs the same way you write pytest assertions on function return values — with G-Eval metrics, faithfulness checks, and hallucination detection. This tutorial walks you from installation to CI-integrated test suite. Why LLM Applications Need Unit Tests
Testing
AI is changing software testing at every level: unit test generation, selector healing, visual regression detection, and natural language test authoring. The changes shift the QA engineer's job from writing tests to reviewing, orchestrating, and improving AI-generated tests. The teams most ahead are using AI to expand