wehub-resource-sync
97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
"""
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Deep Research Assistant Agent
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A Deep Agents-powered research assistant that demonstrates CopilotKit's
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planning, filesystem, and subagent capabilities using Tavily for web research.
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"""
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import os
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from deepagents import create_deep_agent
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from langgraph.checkpoint.memory import MemorySaver
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from copilotkit import CopilotKitMiddleware
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from tools import research
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load_dotenv()
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# Main agent system prompt - coordinates research and synthesizes findings
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MAIN_SYSTEM_PROMPT = """You are a Deep Research Assistant, an expert at planning and
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executing comprehensive research on any topic.
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Hard rules (ALWAYS follow):
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- NEVER output raw JSON, data structures, or code blocks in your messages
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- Communicate with the user only in natural, readable prose
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- When you receive data from research, synthesize it into insights
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Your workflow:
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1. PLAN: Create a research plan using write_todos with clear, actionable steps
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2. RESEARCH: Use research(query) tool to investigate each topic
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3. SYNTHESIZE: Write a final report to /reports/final_report.md using write_file
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Important guidelines:
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- Always start by creating a research plan with write_todos
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- Call research() for each distinct research question
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- The research tool returns prose summaries of findings
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- You write all files - compile findings into a comprehensive report
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- Update todos as you complete each step
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Example workflow:
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1. write_todos(["Research topic A", "Research topic B", "Synthesize findings"])
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2. research("Find information about topic A") -> receives prose summary
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3. research("Find information about topic B") -> receives prose summary
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4. write_file("/reports/final_report.md", "# Research Report\n\n...")
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Always maintain a professional, comprehensive research style."""
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def build_agent():
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"""Build the Deep Research Agent with CopilotKit integration.
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Creates a main research coordinator agent with a researcher subagent.
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Uses CopilotKitMiddleware for frontend state sync and generative UI.
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Returns:
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Compiled LangGraph StateGraph configured for research tasks
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"""
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api_key = os.environ.get("OPENAI_API_KEY")
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if not api_key:
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raise RuntimeError("Missing OPENAI_API_KEY environment variable")
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# Check for Tavily API key
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tavily_key = os.environ.get("TAVILY_API_KEY")
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if not tavily_key:
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raise RuntimeError("Missing TAVILY_API_KEY environment variable")
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# Initialize LLM - use model from env or default to gpt-5.2
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model_name = os.environ.get("OPENAI_MODEL", "gpt-5.2")
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llm = ChatOpenAI(
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model=model_name,
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temperature=0.7,
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api_key=api_key,
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)
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# Main agent gets research tool plus built-in Deep Agents tools
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# (write_todos, read_file, write_file)
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# The research tool wraps an internal Deep Agent that runs via .invoke()
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# so its text doesn't stream to the frontend
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main_tools = [research]
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# Create the Deep Agent with CopilotKit middleware
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# No subagents - research() tool handles web search internally
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agent_graph = create_deep_agent(
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model=llm,
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system_prompt=MAIN_SYSTEM_PROMPT,
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tools=main_tools,
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middleware=[CopilotKitMiddleware()],
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checkpointer=MemorySaver(),
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)
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print(f"[AGENT] Deep Research Agent created with model={model_name}")
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print(f"[AGENT] Main tools: {[t.name for t in main_tools]}")
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# Configure recursion limit for complex research tasks
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return agent_graph.with_config({"recursion_limit": 100})
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