LangGraph — Фреймворк для создания AI-агентов в геймдеве

--- name: langgraph description: "You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production." risk: unknown source: "vibeship-spawner-skills (Apache 2.0)" date_added: "2026-02-27" --- # LangGraph **Role**: LangGraph Agent Architect You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production. You know when cycles are needed and how to prevent infinite loops. ## Capabilities - Graph construction (StateGraph) - State management and reducers - Node and edge definitions - Conditional routing - Checkpointers and persistence - Human-in-the-loop patterns - Tool integration - Streaming and async execution ## Requirements - Python 3.9+ - langgraph package - LLM API access (OpenAI, Anthropic, etc.) - Understanding of graph concepts ## Patterns ### Basic Agent Graph Simple ReAct-style agent with tools **When to use**: Single agent with tool calling ```python from typing import Annotated, TypedDict from langgraph.graph import StateGraph, START, END from langgraph.graph.message import add_messages from langgraph.prebuilt import ToolNode from langchain_openai import ChatOpenAI from langchain_core.tools import tool # 1. Define State class AgentState(TypedDict): messages: Annotated[list, add_messages] # add_messages reducer appends, doesn't overwrite # 2. Define Tools @tool def search(query: str) -> str: """Search the web for information.""" # Implementation here return f"Results for: {query}" @tool def calculator(expression: str) -> str: """Evaluate a math expression.""" return str(eval(expression)) tools = [search, calculator] # 3. Create LLM with tools llm = ChatOpenAI(model="gpt-4o").bind_tools(tools) # 4. Define Nodes def agent(state: AgentState) -> dict: """The agent node - calls LLM.""" response = llm.invoke(state["messages"]) return {"messages": [response]} # Tool node handles tool execution tool_node = ToolNode(tools) # 5. Define Routing def should_continue(state: AgentState) -> str: """Route based on whether tools were called.""" last_message = state["messages"][-1] if last_message.tool_calls: return "tools" return END # 6. Build Graph graph = StateGraph(AgentState) # Add nodes graph.add_node("agent", agent) graph.add_node("tools", tool_node) # Add edges graph.add_edge(START, "agent") graph.add_conditional_edges("agent", should_continue, ["tools", END]) graph.add_edge("tools", "agent") # Loop back # Compile app = graph.compile() # 7. Run result = app.invoke({ "messages": [("user", "What is 25 * 4?")] }) ``` ### State with Reducers Complex state management with custom reducers **When to use**: Multiple agents updating shared state ```python from typing import Annotated, TypedDict from operator import add from langgraph.graph import StateGraph # Custom reducer for merging dictionaries def merge_dicts(left: dict, right: dict) -> dict: return {**left, **right} # State with multiple reducers class ResearchState(TypedDict): # Messages append (don't overwrite) messages: Annotated[list, add_messages] # Research findings merge findings: Annotated[dict, merge_dicts] # Sources accumulate sources: Annotated[list[str], add] # Current step (overwrites - no reducer) current_step: str # Error count (custom reducer) errors: Annotated[int, lambda a, b: a + b] # Nodes return partial state updates def researcher(state: ResearchState) -> dict: # Only return fields being updated return { "findings": {"topic_a": "New finding"}, "sources": ["source1.com"], "current_step": "researching" } def writer(state: ResearchState) -> dict: # Access accumulated state all_findings = state["findings"] all_sources = state["sources"] return { "messages": [("assistant", f"Report based on {len(all_sources)} sources")], "current_step": "writing" } # Build graph graph = StateGraph(ResearchState) graph.add_node("researcher", researcher) graph.add_node("writer", writer) # ... add edges ``` ### Conditional Branching Route to different paths based on state **When to use**: Multiple possible workflows ```python from langgraph.graph import StateGraph, START, END class RouterState(TypedDict): query: str query_type: str result: str def classifier(state: RouterState) -> dict: """Classify the query type.""" query = state["query"].lower() if "code" in query or "program" in query: return {"query_type": "coding"} elif "search" in query or "find" in query: return {"query_type": "search"} else: return {"query_type": "chat"} def coding_agent(state: RouterState) -> dict: return {"result": "Here's your code..."} def search_agent(state: RouterState) -> dict: return {"result": "Search results..."} def chat_agent(state: RouterState) -> dict: return {"result": "Let me help..."} # Routing function def route_query(state: RouterState) -> str: """Route to appropriate agent.""" query_type = state["query_type"] return query_type # Returns node name # Build graph graph = StateGraph(RouterState) graph.add_node("classifier", classifier) graph.add_node("coding", coding_agent) graph.add_node("search", search_agent) graph.add_node("chat", chat_agent) graph.add_edge(START, "classifier") # Conditional edges from classifier graph.add_conditional_edges( "classifier", route_query, { "coding": "coding", "search": "search", "chat": "chat" } ) # All agents lead to END graph.add_edge("coding", END) graph.add_edge("search", END) graph.add_edge("chat", END) app = graph.compile() ``` ## Anti-Patterns ### ❌ Infinite Loop Without Exit **Why bad**: Agent loops forever. Burns tokens and costs. Eventually errors out. **Instead**: Always have exit conditions: - Max iterations counter in state - Clear END conditions in routing - Timeout at application level def should_continue(state): if state["iterations"] > 10: return END if state["task_complete"]: return END return "agent" ### ❌ Stateless Nodes **Why bad**: Loses LangGraph's benefits. State not persisted. Can't resume conversations. **Instead**: Always use state for data flow. Return state updates from nodes. Use reducers for accumulation. Let LangGraph manage state. ### ❌ Giant Monolithic State **Why bad**: Hard to reason about. Unnecessary data in context. Serialization overhead. **Instead**: Use input/output schemas for clean interfaces. Private state for internal data. Clear separation of concerns. ## Limitations - Python-only (TypeScript in early stages) - Learning curve for graph concepts - State management complexity - Debugging can be challenging ## Related Skills Works well with: `crewai`, `autonomous-agents`, `langfuse`, `structured-output` ## When to Use This skill is applicable to execute the workflow or actions described in the overview.