ai-engineer-expert

name	ai-engineer-expert
description	Expert in Agent Orchestration using LangChain, LangGraph, Tavily, and Gemini. Use this skill when the user wants to build, debug, or architect AI agents, specifically focusing on graph-based orchestration, web search integration, and cost-effective LLM usage with Gemini Flash Lite.

AI Engineer Expert

This skill provides expert guidance for building AI agents using LangChain and LangGraph, with Tavily for search and Gemini for intelligence.

Core Stack

• Orchestration: langgraph (State management, cyclicity, persistence)
• Framework: langchain, langchain-google-genai, langchain-community
• Tools: tavily-python (Web search)
• LLM: gemini-2.5-flash-lite (via Google Generative AI)

Best Practices

1. LLM Configuration (Critical)

Always prioritize cost-efficiency by using the gemini-2.5-flash-lite model unless the user explicitly requests otherwise.

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash-lite",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # other params...
)

2. Tavily Search Tool Integration

Use the standard TavilySearchResults tool from community packages.

from langchain_community.tools.tavily_search import TavilySearchResults

tool = TavilySearchResults(
    max_results=5,
    search_depth="advanced",
    include_answer=True,
    include_raw_content=True,
    include_images=False,
    # ...
)

3. LangGraph Architecture

Follow the standard StateGraph pattern. Always define a typed State using TypedDict or Pydantic (if needed for validation, though TypedDict is preferred for simple graphs).

Standard Graph Pattern

from typing import TypedDict, Annotated, List
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode

class AgentState(TypedDict):
    messages: Annotated[List, add_messages]

def agent_node(state: AgentState):
    # invokation logic
    pass

workflow = StateGraph(AgentState)
workflow.add_node("agent", agent_node)
workflow.add_node("tools", ToolNode([tool]))

workflow.set_entry_point("agent")
workflow.add_conditional_edges("agent", should_continue)
workflow.add_edge("tools", "agent")

app = workflow.compile()

4. General Engineering

• Type Hinting: Use strict type hints for all node functions.
• Async Support: Prefer async functions for IO-bound graph nodes.
• Environment Variables: Ensure GOOGLE_API_KEY and TAVILY_API_KEY are present in .env.

when to use

Use this skill for:

1. Setting up new agent projects.
2. Refactoring existing chains into graphs.
3. Optimizing agent costs by switching to Flash Lite.
4. Debugging LangGraph state or routing issues.