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# nodes/core.py
from states.state import AgentState
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI # Using OpenAI-compatible API for OpenRouter
from tools.langchain_tools import (
extract_text,
analyze_image_tool,
analyze_audio_tool,
add,
subtract,
multiply,
divide,
search_tool,
extract_youtube_transcript,
get_youtube_info,
calculate_expression,
factorial,
square_root,
percentage,
average
)
load_dotenv()
# Read your API key from the environment variable
openrouter_api_key = os.getenv("OPENROUTER_API_KEY")
if not openrouter_api_key:
raise ValueError("OPENROUTER_API_KEY not found in environment variables")
# Initialize OpenRouter ChatOpenAI with OpenRouter-specific configuration
chat = ChatOpenAI(
model="google/gemini-2.5-flash-preview-05-20", # Free multimodal model
# Alternative models you can use:
# model="mistralai/mistral-7b-instruct:free", # Fast, free text model
# model="google/gemma-2-9b-it:free", # Google's free model
# model="qwen/qwen-2.5-72b-instruct:free", # High-quality free model
temperature=0,
max_retries=2,
base_url="https://openrouter.ai/api/v1",
api_key=openrouter_api_key,
default_headers={
"HTTP-Referer": "https://your-app.com", # Optional: for analytics
"X-Title": "LangGraph Agent", # Optional: for analytics
}
)
# Core tools list (matching original structure)
tools = [
extract_text,
analyze_image_tool,
analyze_audio_tool,
extract_youtube_transcript,
add,
subtract,
multiply,
divide,
search_tool
]
# Extended tools list (if you want more capabilities)
extended_tools = tools + [
get_youtube_info,
calculate_expression,
factorial,
square_root,
percentage,
average
]
# Use core tools by default (matching original), but you can switch to extended_tools
chat_with_tools = chat.bind_tools(tools)
def assistant(state: AgentState):
"""
Assistant node - maintains the exact same system prompt for evaluation compatibility
"""
sys_msg = (
"You are a helpful assistant with access to tools. Understand user requests accurately. "
"Use your tools when needed to answer effectively. Strictly follow all user instructions and constraints. "
"Pay attention: your output needs to contain only the final answer without any reasoning since it will be "
"strictly evaluated against a dataset which contains only the specific response. "
"Your final output needs to be just the string or integer containing the answer, not an array or technical stuff."
)
return {
"messages": [chat_with_tools.invoke([sys_msg] + state["messages"])]
}
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