add main.py

main.py

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+import os
+import asyncio
+from typing import TypedDict, Annotated, List
+
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+
+# --- LLM configuration (OpenRouter) ---
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# --- Backend for deepagents ---
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# --- Tavily search tool ---
+@tool
+def tavily_search(query: str) -> str:
+    """Search the web using Tavily and return a short summary of the top result."""
+    from tavily import TavilyClient
+    client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
+    results = client.search(query, max_results=1)
+    if not results:
+        return "No relevant information found."
+    # Return the first result's content (title + snippet)
+    first = results[0]
+    return f"{first.title}\n{first.snippet}"
+
+# --- State definition for LangGraph (used inside a tool) ---
+class BriefState(TypedDict):
+    topic: str
+    outline: List[str] | None
+    step_index: int
+    notes: List[str]
+    final_brief: str | None
+
+# --- LangGraph graph implementation ---
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+# Helper to format outline as a numbered list
+def format_outline(outline: List[str]) -> str:
+    return "\n".join(f"{i+1}. {p}" for i, p in enumerate(outline))
+
+# Node: generate outline
+async def outline_node(state: BriefState) -> BriefState:
+    prompt = f"Create a concise 4–5 point outline for a research brief on the topic: {state['topic']}"
+    response = await llm.ainvoke([HumanMessage(content=prompt)])
+    outline_text = response.content.strip()
+    # Assume the LLM returns a numbered list; split into lines
+    outline = [line.strip() for line in outline_text.splitlines() if line.strip()]
+    state['outline'] = outline
+    state['step_index'] = 0
+    state['notes'] = []
+    return state
+
+# Node: research one step
+async def research_step_node(state: BriefState) -> BriefState:
+    current_point = state['outline'][state['step_index']]
+    # Use Tavily to get info
+    search_query = f"{current_point}"
+    search_result = tavily_search(search_query)
+    # Summarize the result with LLM
+    prompt = f"Summarize the following information in 5–8 sentences, focusing on the key points relevant to the research brief: {search_result}"
+    summary = await llm.ainvoke([HumanMessage(content=prompt)])
+    state['notes'].append(f"**{current_point}**\n{summary.content.strip()}")
+    state['step_index'] += 1
+    return state
+
+# Node: synthesize final brief
+async def synthesize_node(state: BriefState) -> BriefState:
+    notes_text = "\n\n".join(state['notes'])
+    prompt = f"Using the following notes, write a cohesive ½–1 page research brief. Include headings for each section.\n\n{notes_text}"
+    brief = await llm.ainvoke([HumanMessage(content=prompt)])
+    state['final_brief'] = brief.content.strip()
+    return state
+
+# Build the graph
+graph = StateGraph(BriefState)
+graph.add_node("outline", outline_node)
+graph.add_node("research_step", research_step_node)
+graph.add_node("synthesize", synthesize_node)
+
+# Define transitions
+graph.set_entry_point("outline")
+graph.add_conditional_edges(
+    "outline",
+    lambda _: "research_step",
+)
+
+def research_cond(state: BriefState):
+    return "research_step" if state['step_index'] < len(state['outline']) else "synthesize"
+
+graph.add_conditional_edges("research_step", research_cond)
+graph.add_edge("synthesize", END)
+
+# Compile the graph into a tool
+from langgraph.prebuilt import create_react_agent
+
+# The graph will be used as a tool inside deepagents
+@tool
+async def run_brief(topic: str) -> str:
+    """Generate a research brief for the given topic."""
+    # Initialize state
+    state: BriefState = {
+        "topic": topic,
+        "outline": None,
+        "step_index": 0,
+        "notes": [],
+        "final_brief": None,
+    }
+    # Run the graph
+    async for partial_state in graph.astream(state):
+        pass  # we just wait for completion
+    # After completion, return the brief
+    return state['final_brief']
+
+# --- DeepAgent setup ---
+agent = create_deep_agent(
+    model=llm,
+    tools=[tavily_search, run_brief],
+    backend=backend,
+    system_prompt="You are a research assistant that creates concise research briefs. Use the provided tools to gather information and synthesize a brief.",
+)
+
+# --- Demo execution ---
+async def main():
+    default_topic = "Как студенту безопасно подключать MCP к LangChain"
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=f"Create a research brief on: {default_topic}")]},
+        {"configurable": {"thread_id": "session-1"}},
+    )
+    print("\n--- Outline ---")
+    # The outline is part of the first tool call; extract it
+    for msg in result["messages"]:
+        if msg.type == "tool":
+            if "outline" in msg.content.lower():
+                print(msg.content)
+    print("\n--- Final Brief ---")
+    print(result["messages"][-1].content)
+
+if __name__ == "__main__":
+    asyncio.run(main())