Modernizer

Evaluate LangChain agent trajectories with AgentEvals trajectory matching or LLM-as-judge, optionally log to LangSmith. Use when scoring tool-call sequences vs a reference, catching prompt or tool regressions, pytest LangSmith runs, or the user mentions agentevals, trajectory match, or evaluators.

Organize and run LangChain agent integration tests against real LLM APIs using pytest markers, env-based secrets, structural assertions, cost controls, and optional HTTP record/replay. Use when tests need live models, CI secrets, flaky LLM output handling, or the user mentions integration tests, real API agents, or pytest -m integration.

Unit-test LangChain agents without API calls using GenericFakeChatModel and in-memory checkpointers. Use when writing pytest tests for create_agent flows, multi-turn state, or scripted tool-call sequences; when avoiding live LLM keys; or when the user mentions fake chat models, agent fixtures, or InMemorySaver for tests.

Implement LangChain handoffs — state-driven behavior changes between agents or configurations using tool-based transitions and Command. Use when building multi-stage conversational flows, customer support pipelines, sequential workflows where capabilities unlock after preconditions, or when an agent needs to transfer control to another agent.

Implement the LangChain agent skills pattern — packaging specialized prompts and domain knowledge as on-demand loadable skills via tool calling. Use when building agents that need many specializations without overwhelming context, distributing development across teams, or implementing progressive disclosure of prompts, schemas, and documentation. Covers basic load_skill pattern, dynamic tool registration, hierarchical skills, and reference-aware skills.

Engineer reliable LangChain agents by controlling model context, tool context, and life-cycle context. Use when building or debugging agents with dynamic prompts, message injection, tool filtering, model selection, structured output, or conversation summarization. Covers state (short-term), store (long-term), and runtime context (static config) patterns using middleware.

Add cross-session persistent memory to LangChain agents using LangGraph stores. Use when building agents that remember user preferences, past interactions, or any data that must persist across conversations. Covers store setup (InMemoryStore, PostgreSQL), namespace/key organization, reading and writing in tools, and vector search.

Design and implement LangChain multi-agent systems using subagents, handoffs, skills, and router patterns. Use when building supervisor/worker agents, state-driven handoffs between agents, on-demand skill loading, or routing pipelines. Covers pattern selection, context engineering across agents, sync/async execution, and tool-based coordination.

Stream LangGraph graph execution using stream modes (updates, values, messages, custom, checkpoints, tasks, debug). Use when implementing real-time output, token-by-token LLM streaming, custom progress events, subgraph streaming, or migrating to the v2 streaming format. Covers StreamPart format, get_stream_writer, filtering by node/tag, and async patterns.

Build, compose, and configure LangGraph subgraphs as nodes in parent graphs. Use when embedding one graph inside another, managing subgraph persistence (per-invocation, per-thread, stateless), streaming subgraph outputs, or inspecting subgraph state. Covers state schema patterns, checkpointer modes, namespace isolation, and interrupt support.

Write unit tests for LangGraph graphs using pytest. Use when testing LangGraph agents, individual graph nodes, partial execution paths, or state transitions. Covers full graph execution, single-node testing, partial execution with update_state, and interrupt_after patterns.