Designs Act and Cast architectures through dynamic questioning, outputting validated CLAUDE.md with mermaid diagrams. Covers resilience patterns (Saga/compensation, long-running threads with DeltaChannel, graceful drain checkpoints, node timeout boundaries) from langgraph v1.2+. Use when starting new Act project, adding cast, planning architecture, extracting sub-cast (10+ nodes), redesigning existing cast, or ask "design architecture", "plan cast", "redesign cast", "create CLAUDE.md".
Implements LangGraph cast components following systematic workflow (state, deps, nodes, conditions, graph). Use when implementing cast, building nodes/agents/tools, need LangGraph patterns (memory, retry, guardrails, vector stores, node timeouts, error handlers, DeltaChannel, graceful shutdown), or ask "implement cast", "build graph", "add node".
Implements DeepAgent components using LangChain's deepagents SDK. Use when building deep agents with create_deep_agent, configuring backends/subagents/skills/memory/interpreter, need DeepAgent patterns (sandbox, HITL interrupts, long-term memory, subagent spawning, subagent structured output, QuickJS code interpreter with programmatic tool calling), or ask "implement deep agent", "add subagent", "configure backend", "add interpreter".
Implements LangGraph v3 event streaming for graphs with subgraphs and agents. Use when adding streaming to runtime/API endpoint, need token streaming, custom stream projections, subagent streaming, or ask "add streaming", "stream tokens", "stream graph".
Guides pytest test writing for LangGraph casts with mocking patterns for LLM/API/Store calls. Use when writing tests, need mock strategies, setting up fixtures, testing nodes/graphs (v3 event streaming, timeouts, error handlers, graceful shutdown), or ask "write tests", "mock LLM", "test coverage".