The - Agentic Ai Bible Pdf Upd

def research_node(state: AgentState): query = state["query"] results = search.invoke(query) notes = [r["content"] for r in results] return "research_notes": notes, "iteration": state["iteration"]+1

# research_agent.py # Requires: pip install langgraph langchain-openai tavily-python from langgraph.graph import StateGraph, END from langchain_openai import ChatOpenAI from langchain_community.tools.tavily_search import TavilySearchResults from typing import TypedDict, List the agentic ai bible pdf upd

llm = ChatOpenAI(model="gpt-4o") search = TavilySearchResults(max_results=3) the agentic ai bible pdf upd

builder = StateGraph(AgentState) builder.add_node("research", research_node) builder.set_entry_point("research") builder.add_conditional_edges("research", should_continue) app = builder.compile() the agentic ai bible pdf upd

| Benchmark | What it measures | SOTA as of June 2026 | |-----------|----------------|----------------------| | | Real-world coding agents | 72% (OpenDevin) | | AgentBench | Multi-environment tasks | 68.5 (GPT-5-mini) | | WebArena | Web navigation | 52.3 (AutoWebAgent) | | ToolEmu | Tool use safety | Claude-4: 94% safe | | MetaTool | Tool selection accuracy | GPT-5: 91% | Updated PDF note : Download the latest leaderboard CSV from PapersWithCode or Hugging Face’s leaderboards space. Part 6: Practical Tutorial – Build a Research Agent (From Scratch) Here’s a minimal LangGraph agent (copy-paste into a .py file and run). This is the “Ur-text” of agentic AI.