LangGraph - HelpMeTest

AI Testing

Unit Testing LangGraph StateGraph Nodes with pytest: Mocks, State Reducers, and Checkpointers

Most LangGraph developers test their graphs end-to-end: compile the graph, call .invoke(), assert on the final output. This works until something goes wrong. When a multi-hop workflow produces a wrong answer, you have no idea which node introduced the error, whether it was a routing decision or

AI Agents

Testing LangGraph State Machine Agents: Nodes, Edges, State Transitions, and Checkpointer Mocking

LangGraph compiles agent behavior into a directed graph. Nodes are functions. Edges are transitions. State flows through the graph, getting modified at each node. It's a clean mental model — and it creates a natural seam for testing. The problem is that most LangGraph developers test the graph as

AI Agents

Testing CrewAI Agent Crews: Task Delegation, Role Validation, Tool Assertions, and Output Validation

CrewAI orchestrates multiple agents working together toward a shared goal. A researcher agent gathers information. A writer agent drafts content. An editor agent refines it. Each has a role, a set of tools, and a defined responsibility. The complexity that makes CrewAI powerful — multi-agent coordination, role-based delegation, sequential

AI Agents

Testing AI Safety Guardrails: Input/Output Filter Validation, PII Detection, and Adversarial Jailbreak Testing

AI guardrails are the safety layer between user input and your language model. They block prompt injections, filter inappropriate content, redact PII from outputs, and resist jailbreak attempts. They're also the part of your AI system that's most likely to be skipped in testing. The reasoning

AI Agents

Testing AI Agent Observability: LangSmith Integration Tests, Trace Validation, and Cost Regression Testing

Observability for AI agents is different from observability for traditional software. A web server trace shows function calls and database queries. An AI agent trace shows LLM calls, tool invocations, token counts, and reasoning chains. The failure modes are different too: not just errors and timeouts, but hallucinations, reasoning loops,

AI Agents

End-to-End Testing of Multi-Agent Pipelines: Deterministic Replay, Agent Mocks, and Pipeline Assertions

A multi-agent pipeline is a chain of AI agents where each agent's output becomes the next agent's input. Agent A researches. Agent B synthesizes. Agent C writes. Agent D edits. When the final output is wrong, you have a debugging problem: which agent introduced the

Testing

Testing Multi-Agent Orchestration with LangGraph and CrewAI

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

AI Testing

How to Test LangGraph Agents Before They Hit Production

You built a LangGraph agent. The nodes look right. The graph compiles. You run it manually and it works. Then you ship it, and a user hits an edge case in your routing logic, your agent ends up in an infinite loop, and your production system starts burning tokens until