mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-05-04 21:32:39 +02:00
feat(crypto): implement hybrid approach with real-time DexScreener tools
- Add crypto_realtime.py with get_live_token_price and get_live_token_data tools - Register real-time tools in registry.py (no dependencies required) - Export tool factories in __init__.py - Create LiveTokenPriceToolUI component for real-time price display - Create LiveTokenDataToolUI component for comprehensive market data - Register tool-ui components in new-chat page Hybrid Architecture: - RAG (search_knowledge_base): Historical context, trends from indexed data - Real-time tools: Current prices, live market data via direct API calls - AI agent decides which to use based on query intent
This commit is contained in:
API Test Bot
parent
f2e38c52a1
commit
d20cb8a538
9 changed files with 979 additions and 125 deletions
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@ -13,10 +13,16 @@ Available tools:
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- scrape_webpage: Extract content from webpages
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- save_memory: Store facts/preferences about the user
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- recall_memory: Retrieve relevant user memories
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- get_live_token_price: Get real-time crypto price from DexScreener
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- get_live_token_data: Get comprehensive real-time crypto market data
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"""
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# Registry exports
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# Tool factory exports (for direct use)
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from .crypto_realtime import (
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create_get_live_token_data_tool,
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create_get_live_token_price_tool,
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)
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from .display_image import create_display_image_tool
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from .knowledge_base import (
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CONNECTOR_DESCRIPTIONS,
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@ -48,6 +54,8 @@ __all__ = [
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# Tool factories
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"create_display_image_tool",
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"create_generate_podcast_tool",
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"create_get_live_token_data_tool",
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"create_get_live_token_price_tool",
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"create_link_preview_tool",
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"create_recall_memory_tool",
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"create_save_memory_tool",
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322
surfsense_backend/app/agents/new_chat/tools/crypto_realtime.py
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322
surfsense_backend/app/agents/new_chat/tools/crypto_realtime.py
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@ -0,0 +1,322 @@
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"""
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Real-time cryptocurrency data tools for the SurfSense agent.
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This module provides tools for fetching LIVE crypto data directly from DexScreener API.
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These tools complement the RAG-based search_knowledge_base tool:
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- RAG (search_knowledge_base): Historical context, trends, analysis from indexed data
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- Real-time tools: Current prices, live market data
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The AI agent decides which to use based on the query:
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- "What's the current price of BULLA?" → get_live_token_price (real-time)
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- "How has BULLA performed this week?" → search_knowledge_base (RAG)
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- "Analyze BULLA for me" → Both (RAG for context + real-time for current data)
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"""
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import hashlib
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import logging
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from typing import Any
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from langchain_core.tools import tool
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from app.connectors.dexscreener_connector import DexScreenerConnector
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logger = logging.getLogger(__name__)
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def generate_token_id(chain: str, address: str) -> str:
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"""Generate a unique ID for a token query."""
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hash_val = hashlib.md5(f"{chain}:{address}".encode()).hexdigest()[:12]
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return f"token-{hash_val}"
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def create_get_live_token_price_tool():
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"""
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Factory function to create the get_live_token_price tool.
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This tool fetches REAL-TIME price data directly from DexScreener API.
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Use this when users ask for current/live prices.
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Returns:
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A configured tool function for fetching live token prices.
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"""
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@tool
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async def get_live_token_price(
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chain: str,
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token_address: str,
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token_symbol: str | None = None,
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) -> dict[str, Any]:
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"""
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Get the LIVE/CURRENT price of a cryptocurrency token from DexScreener.
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Use this tool when the user asks for:
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- Current price: "What's the price of BULLA right now?"
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- Live data: "Show me live price for SOL"
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- Real-time info: "What's WETH trading at?"
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DO NOT use this for historical analysis - use search_knowledge_base instead.
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Args:
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chain: Blockchain network (e.g., 'solana', 'ethereum', 'base', 'bsc')
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token_address: The token's contract address
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token_symbol: Optional token symbol for display (e.g., 'BULLA', 'SOL')
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Returns:
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Dictionary with live price data including:
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- price_usd: Current price in USD
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- price_change_24h: 24-hour price change percentage
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- price_change_1h: 1-hour price change percentage
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- volume_24h: 24-hour trading volume
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- liquidity_usd: Total liquidity in USD
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- market_cap: Market capitalization
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- dex: DEX where the best liquidity is found
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- pair_url: Link to DexScreener chart
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"""
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token_id = generate_token_id(chain, token_address)
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try:
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# Initialize DexScreener connector
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connector = DexScreenerConnector()
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# Fetch live data from API
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pairs, error = await connector.get_token_pairs(chain, token_address)
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if error:
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logger.warning(f"[get_live_token_price] Error: {error}")
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return {
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"id": token_id,
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"kind": "live_token_price",
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"chain": chain,
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"token_address": token_address,
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"token_symbol": token_symbol,
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"error": error,
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}
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if not pairs:
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return {
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"id": token_id,
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"kind": "live_token_price",
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"chain": chain,
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"token_address": token_address,
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"token_symbol": token_symbol,
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"error": f"No trading pairs found for {token_symbol or token_address} on {chain}",
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}
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# Get the best pair (highest liquidity)
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best_pair = max(pairs, key=lambda p: float(p.get("liquidity", {}).get("usd", 0) or 0))
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# Extract data from best pair
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base_token = best_pair.get("baseToken", {})
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price_change = best_pair.get("priceChange", {})
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volume = best_pair.get("volume", {})
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liquidity = best_pair.get("liquidity", {})
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return {
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"id": token_id,
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"kind": "live_token_price",
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"chain": chain,
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"token_address": token_address,
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"token_symbol": token_symbol or base_token.get("symbol", "Unknown"),
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"token_name": base_token.get("name", "Unknown"),
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"price_usd": best_pair.get("priceUsd", "N/A"),
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"price_native": best_pair.get("priceNative", "N/A"),
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"price_change_5m": price_change.get("m5", 0),
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"price_change_1h": price_change.get("h1", 0),
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"price_change_6h": price_change.get("h6", 0),
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"price_change_24h": price_change.get("h24", 0),
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"volume_24h": volume.get("h24", 0),
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"volume_6h": volume.get("h6", 0),
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"volume_1h": volume.get("h1", 0),
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"liquidity_usd": liquidity.get("usd", 0),
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"market_cap": best_pair.get("marketCap", 0),
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"fdv": best_pair.get("fdv", 0),
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"dex": best_pair.get("dexId", "Unknown"),
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"pair_address": best_pair.get("pairAddress", ""),
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"pair_url": best_pair.get("url", ""),
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"total_pairs": len(pairs),
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"data_source": "DexScreener API (Real-time)",
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}
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except Exception as e:
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error_message = str(e)
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logger.error(f"[get_live_token_price] Error fetching {chain}/{token_address}: {error_message}")
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return {
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"id": token_id,
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"kind": "live_token_price",
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"chain": chain,
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"token_address": token_address,
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"token_symbol": token_symbol,
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"error": f"Failed to fetch live price: {error_message[:100]}",
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}
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return get_live_token_price
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def create_get_live_token_data_tool():
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"""
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Factory function to create the get_live_token_data tool.
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This tool fetches comprehensive REAL-TIME market data from DexScreener API.
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Use this when users want detailed current market information.
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Returns:
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A configured tool function for fetching live token market data.
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"""
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@tool
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async def get_live_token_data(
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chain: str,
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token_address: str,
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token_symbol: str | None = None,
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include_all_pairs: bool = False,
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) -> dict[str, Any]:
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"""
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Get comprehensive LIVE market data for a cryptocurrency token.
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Use this tool when the user asks for:
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- Detailed market info: "Show me full market data for BULLA"
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- Trading activity: "What's the trading volume for SOL?"
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- Liquidity info: "How much liquidity does WETH have?"
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- Transaction counts: "How many buys/sells for this token?"
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This returns more detailed data than get_live_token_price.
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For historical trends and analysis, use search_knowledge_base instead.
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Args:
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chain: Blockchain network (e.g., 'solana', 'ethereum', 'base', 'bsc')
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token_address: The token's contract address
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token_symbol: Optional token symbol for display
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include_all_pairs: If True, include data from all trading pairs
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Returns:
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Dictionary with comprehensive market data including:
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- All price data from get_live_token_price
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- Transaction counts (buys/sells in 24h, 6h, 1h)
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- All trading pairs (if include_all_pairs=True)
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- Aggregated volume across all pairs
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"""
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token_id = generate_token_id(chain, token_address)
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try:
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# Initialize DexScreener connector
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connector = DexScreenerConnector()
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# Fetch live data from API
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pairs, error = await connector.get_token_pairs(chain, token_address)
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if error:
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logger.warning(f"[get_live_token_data] Error: {error}")
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return {
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"id": token_id,
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"kind": "live_token_data",
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"chain": chain,
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"token_address": token_address,
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"token_symbol": token_symbol,
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"error": error,
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}
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if not pairs:
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return {
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"id": token_id,
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"kind": "live_token_data",
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"chain": chain,
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"token_address": token_address,
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"token_symbol": token_symbol,
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"error": f"No trading pairs found for {token_symbol or token_address} on {chain}",
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}
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# Get the best pair (highest liquidity)
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best_pair = max(pairs, key=lambda p: float(p.get("liquidity", {}).get("usd", 0) or 0))
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# Extract data from best pair
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base_token = best_pair.get("baseToken", {})
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price_change = best_pair.get("priceChange", {})
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volume = best_pair.get("volume", {})
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liquidity = best_pair.get("liquidity", {})
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txns = best_pair.get("txns", {})
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# Calculate aggregated stats across all pairs
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total_volume_24h = sum(float(p.get("volume", {}).get("h24", 0) or 0) for p in pairs)
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total_liquidity = sum(float(p.get("liquidity", {}).get("usd", 0) or 0) for p in pairs)
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total_buys_24h = sum(p.get("txns", {}).get("h24", {}).get("buys", 0) or 0 for p in pairs)
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total_sells_24h = sum(p.get("txns", {}).get("h24", {}).get("sells", 0) or 0 for p in pairs)
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result = {
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"id": token_id,
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"kind": "live_token_data",
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"chain": chain,
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"token_address": token_address,
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"token_symbol": token_symbol or base_token.get("symbol", "Unknown"),
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"token_name": base_token.get("name", "Unknown"),
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# Price data
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"price_usd": best_pair.get("priceUsd", "N/A"),
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"price_native": best_pair.get("priceNative", "N/A"),
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"price_change_5m": price_change.get("m5", 0),
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"price_change_1h": price_change.get("h1", 0),
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"price_change_6h": price_change.get("h6", 0),
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"price_change_24h": price_change.get("h24", 0),
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# Volume data (best pair)
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"volume_24h": volume.get("h24", 0),
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"volume_6h": volume.get("h6", 0),
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"volume_1h": volume.get("h1", 0),
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"volume_5m": volume.get("m5", 0),
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# Liquidity
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"liquidity_usd": liquidity.get("usd", 0),
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"liquidity_base": liquidity.get("base", 0),
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"liquidity_quote": liquidity.get("quote", 0),
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# Market metrics
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"market_cap": best_pair.get("marketCap", 0),
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"fdv": best_pair.get("fdv", 0),
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# Transaction counts (best pair)
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"txns_24h_buys": txns.get("h24", {}).get("buys", 0),
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"txns_24h_sells": txns.get("h24", {}).get("sells", 0),
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"txns_6h_buys": txns.get("h6", {}).get("buys", 0),
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"txns_6h_sells": txns.get("h6", {}).get("sells", 0),
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"txns_1h_buys": txns.get("h1", {}).get("buys", 0),
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"txns_1h_sells": txns.get("h1", {}).get("sells", 0),
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# Aggregated stats (all pairs)
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"total_volume_24h_all_pairs": total_volume_24h,
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"total_liquidity_all_pairs": total_liquidity,
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"total_buys_24h_all_pairs": total_buys_24h,
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"total_sells_24h_all_pairs": total_sells_24h,
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# DEX info
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"dex": best_pair.get("dexId", "Unknown"),
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"pair_address": best_pair.get("pairAddress", ""),
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"pair_url": best_pair.get("url", ""),
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"pair_created_at": best_pair.get("pairCreatedAt"),
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# Metadata
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"total_pairs": len(pairs),
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"data_source": "DexScreener API (Real-time)",
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}
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# Include all pairs if requested
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if include_all_pairs and len(pairs) > 1:
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result["all_pairs"] = [
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{
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"dex": p.get("dexId"),
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"pair_address": p.get("pairAddress"),
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"quote_symbol": p.get("quoteToken", {}).get("symbol"),
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"price_usd": p.get("priceUsd"),
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"liquidity_usd": p.get("liquidity", {}).get("usd", 0),
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"volume_24h": p.get("volume", {}).get("h24", 0),
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"url": p.get("url"),
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}
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for p in sorted(pairs, key=lambda x: float(x.get("liquidity", {}).get("usd", 0) or 0), reverse=True)[:10]
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]
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return result
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except Exception as e:
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error_message = str(e)
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logger.error(f"[get_live_token_data] Error fetching {chain}/{token_address}: {error_message}")
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return {
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"id": token_id,
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"kind": "live_token_data",
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"chain": chain,
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"token_address": token_address,
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"token_symbol": token_symbol,
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"error": f"Failed to fetch live data: {error_message[:100]}",
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}
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return get_live_token_data
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@ -43,6 +43,10 @@ from typing import Any
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from langchain_core.tools import BaseTool
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from .crypto_realtime import (
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create_get_live_token_data_tool,
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create_get_live_token_price_tool,
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)
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from .display_image import create_display_image_tool
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from .knowledge_base import create_search_knowledge_base_tool
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from .link_preview import create_link_preview_tool
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@ -179,6 +183,26 @@ BUILTIN_TOOLS: list[ToolDefinition] = [
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# factory=lambda deps: create_my_custom_tool(...),
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# requires=["search_space_id"],
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# ),
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# =========================================================================
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# CRYPTO REAL-TIME TOOLS - Hybrid approach (RAG + Real-time)
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# =========================================================================
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# These tools fetch LIVE data directly from DexScreener API.
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# Use alongside search_knowledge_base for comprehensive crypto analysis:
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# - search_knowledge_base: Historical context, trends (from indexed data)
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# - get_live_token_price: Current price (real-time API call)
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# - get_live_token_data: Full market data (real-time API call)
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ToolDefinition(
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name="get_live_token_price",
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description="Get LIVE/CURRENT cryptocurrency price from DexScreener API. Use for real-time price queries.",
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factory=lambda deps: create_get_live_token_price_tool(),
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requires=[],
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),
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ToolDefinition(
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name="get_live_token_data",
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description="Get comprehensive LIVE market data (price, volume, liquidity, transactions) from DexScreener API.",
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factory=lambda deps: create_get_live_token_data_tool(),
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requires=[],
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),
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]
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