Update agent.py
Browse files
agent.py
CHANGED
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@@ -11,6 +11,7 @@ from typing import TypedDict, Annotated, List
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from langchain_core.messages import trim_messages
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from langchain.chat_models import init_chat_model
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from langgraph.graph.message import add_messages
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import os
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from dotenv import load_dotenv
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load_dotenv()
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@@ -36,7 +37,7 @@ TOOLS = [
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# -------------------------------------------------
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-
# LLM INIT
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# -------------------------------------------------
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rate_limiter = InMemoryRateLimiter(
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requests_per_second=7 / 60,
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@@ -53,11 +54,10 @@ llm = init_chat_model(
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# -------------------------------------------------
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# SYSTEM PROMPT
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# -------------------------------------------------
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SYSTEM_PROMPT = f"""
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You are an autonomous quiz-solving agent.
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-
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Your job is to:
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1. Load each quiz page from the given URL.
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2. Extract instructions, parameters, and submit endpoint.
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@@ -66,40 +66,46 @@ Your job is to:
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5. Follow new URLs until none remain, then output END.
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Rules:
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-
- For base64 generation
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- Never hallucinate URLs or fields.
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- Never shorten endpoints.
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- Always inspect server response.
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- Never stop early.
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- Use tools for HTML, downloading, rendering, OCR,
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- Include:
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email = {EMAIL}
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secret = {SECRET}
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"""
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-
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# -------------------------------------------------
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# AGENT NODE
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# -------------------------------------------------
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def agent_node(state: AgentState):
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cur_time = time.time()
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cur_url = os.getenv("url")
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prev_time = url_time
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offset = os.getenv("offset")
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if prev_time is not None:
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prev_time = float(prev_time)
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diff = cur_time - prev_time
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-
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print("Timeout exceeded β instructing LLM to purposely submit wrong answer.", diff, "Offset=", offset)
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fail_instruction = """
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You
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Immediately call
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"""
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# LLM will figure out the right endpoint + JSON structure itself
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result = llm.invoke([
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{"role": "user", "content": fail_instruction}
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])
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@@ -111,15 +117,15 @@ def agent_node(state: AgentState):
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strategy="last",
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include_system=True,
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start_on="human",
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token_counter=llm,
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)
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result = llm.invoke(trimmed_messages)
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return {"messages": [result]}
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# -------------------------------------------------
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# ROUTE
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# -------------------------------------------------
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def route(state):
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last = state["messages"][-1]
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@@ -131,7 +137,13 @@ def route(state):
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content = getattr(last, "content", None)
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#
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if content is None:
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print("Content is None β END")
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return END
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@@ -147,8 +159,6 @@ def route(state):
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return "agent"
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-
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-
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# -------------------------------------------------
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# GRAPH
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# -------------------------------------------------
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@@ -156,17 +166,20 @@ graph = StateGraph(AgentState)
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graph.add_node("tools", ToolNode(TOOLS))
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graph.add_edge(START, "agent")
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graph.add_edge("tools", "agent")
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graph.add_conditional_edges("agent", route)
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-
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"initial_interval": 1,
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"backoff_factor": 2,
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"max_interval": 60,
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"max_attempts": 10
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}
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graph.add_node("agent", agent_node, retry=robust_retry)
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app = graph.compile()
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@@ -175,37 +188,31 @@ app = graph.compile()
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# RUNNER
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# -------------------------------------------------
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def run_agent(url: str):
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# system message is seeded ONCE here
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initial_messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": url}
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]
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# run agent and CAPTURE the output
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result = app.invoke(
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{"messages": initial_messages},
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config={"recursion_limit": RECURSION_LIMIT}
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)
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# Try to detect final server response if present
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try:
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last = result["messages"][-1]
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content = getattr(last, "content", "")
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# If LLM already output END β good
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if isinstance(content, str) and content.strip() == "END":
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print("Tasks completed successfully!")
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return
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# If the last content is JSON from server submission
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import json
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parsed = json.loads(content) if isinstance(content, str) else
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if parsed.get("url") is None:
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print("Tasks completed successfully!")
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return
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except Exception:
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pass
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# Default fallback
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print("Tasks completed successfully!")
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from langchain_core.messages import trim_messages
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from langchain.chat_models import init_chat_model
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from langgraph.graph.message import add_messages
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from langgraph.pregel.retry import RetryPolicy
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import os
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from dotenv import load_dotenv
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load_dotenv()
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# -------------------------------------------------
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# LLM INIT
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# -------------------------------------------------
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rate_limiter = InMemoryRateLimiter(
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requests_per_second=7 / 60,
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# -------------------------------------------------
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# SYSTEM PROMPT
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# -------------------------------------------------
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SYSTEM_PROMPT = f"""
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You are an autonomous quiz-solving agent.
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Your job is to:
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1. Load each quiz page from the given URL.
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2. Extract instructions, parameters, and submit endpoint.
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5. Follow new URLs until none remain, then output END.
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Rules:
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+
- For base64 generation NEVER use your own code β use "encode_image_to_base64"
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- Never hallucinate URLs or fields.
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- Never shorten endpoints.
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- Always inspect server response.
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- Never stop early.
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+
- Use tools for HTML, downloading, rendering, OCR, running code.
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- Include:
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email = {EMAIL}
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secret = {SECRET}
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"""
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# -------------------------------------------------
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# AGENT NODE
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# -------------------------------------------------
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def agent_node(state: AgentState):
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"""Fixes: KeyError, offset None, float(None)"""
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cur_time = time.time()
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cur_url = os.getenv("url") or "" # FIX 1: safe load
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prev_time = url_time.get(cur_url) # FIX 2: no KeyError
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offset = os.getenv("offset") or "0" # FIX 3: no None issues
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if prev_time is not None:
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prev_time = float(prev_time)
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diff = cur_time - prev_time
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# timeout logic unchanged, only made safe
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try:
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offset_f = float(offset)
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except:
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offset_f = 0.0
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if diff >= 180 or (offset_f != 0 and (cur_time - offset_f) > 90):
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print("Timeout exceeded β instructing LLM to purposely submit wrong answer.", diff, "Offset=", offset)
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fail_instruction = """
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You exceeded the time limit (130s).
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Immediately call `post_request` and submit a WRONG answer for the CURRENT quiz.
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"""
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result = llm.invoke([
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{"role": "user", "content": fail_instruction}
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])
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strategy="last",
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include_system=True,
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start_on="human",
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token_counter=llm,
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)
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result = llm.invoke(trimmed_messages)
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return {"messages": [result]}
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# -------------------------------------------------
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# ROUTE
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# -------------------------------------------------
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def route(state):
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last = state["messages"][-1]
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content = getattr(last, "content", None)
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# allow message dicts (post_request returns dicts)
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if isinstance(content, dict):
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if content.get("url") == "" or content.get("correct") is False:
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# not final, continue agent
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print("Route β agent (dict content)")
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return "agent"
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if content is None:
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print("Content is None β END")
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return END
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return "agent"
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# -------------------------------------------------
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# GRAPH
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# -------------------------------------------------
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graph.add_node("tools", ToolNode(TOOLS))
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# FIX 4 β LangGraph retry policy MUST be RetryPolicy(...)
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retry_policy = RetryPolicy(
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max_attempts=10,
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initial_interval=1,
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backoff_factor=2,
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max_interval=60
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)
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graph.add_node("agent", agent_node, retry=retry_policy)
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graph.add_edge(START, "agent")
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graph.add_edge("tools", "agent")
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graph.add_conditional_edges("agent", route)
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app = graph.compile()
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# RUNNER
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# -------------------------------------------------
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def run_agent(url: str):
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initial_messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": url}
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]
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result = app.invoke(
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{"messages": initial_messages},
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config={"recursion_limit": RECURSION_LIMIT}
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)
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try:
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last = result["messages"][-1]
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content = getattr(last, "content", "")
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if isinstance(content, str) and content.strip() == "END":
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print("Tasks completed successfully!")
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return
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import json
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parsed = json.loads(content) if isinstance(content, str) else content
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if parsed.get("url") is None:
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print("Tasks completed successfully!")
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return
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except Exception:
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pass
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print("Tasks completed successfully!")
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