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from dataclasses import dataclass
from typing import List, Optional, Dict, Any
from datetime import datetime
import asyncio
from loguru import logger
import json
import base58
from decimal import Decimal
# Swarms imports
from swarms import Agent
# Solana imports
from solders.rpc.responses import GetTransactionResp
from solders.transaction import Transaction
from anchorpy import Provider, Wallet
from solders.keypair import Keypair
import aiohttp
# Specialized Solana Analysis System Prompt
SOLANA_ANALYSIS_PROMPT = """You are a specialized Solana blockchain analyst agent. Your role is to:
1. Analyze real-time Solana transactions for patterns and anomalies
2. Identify potential market-moving transactions and whale movements
3. Detect important DeFi interactions across major protocols
4. Monitor program interactions for suspicious or notable activity
5. Track token movements across significant protocols like:
- Serum DEX
- Raydium
- Orca
- Marinade
- Jupiter
- Other major Solana protocols
When analyzing transactions, consider:
- Transaction size relative to protocol norms
- Historical patterns for involved addresses
- Impact on protocol liquidity
- Relationship to known market events
- Potential wash trading or suspicious patterns
- MEV opportunities and arbitrage patterns
- Program interaction sequences
Provide analysis in the following format:
{
"analysis_type": "[whale_movement|program_interaction|defi_trade|suspicious_activity]",
"severity": "[high|medium|low]",
"details": {
"transaction_context": "...",
"market_impact": "...",
"recommended_actions": "...",
"related_patterns": "..."
}
}
Focus on actionable insights that could affect:
1. Market movements
2. Protocol stability
3. Trading opportunities
4. Risk management
"""
@dataclass
class TransactionData:
"""Data structure for parsed Solana transaction information"""
signature: str
block_time: datetime
slot: int
fee: int
lamports: int
from_address: str
to_address: str
program_id: str
instruction_data: Optional[str] = None
program_logs: List[str] = None
@property
def sol_amount(self) -> Decimal:
"""Convert lamports to SOL"""
return Decimal(self.lamports) / Decimal(1e9)
def to_dict(self) -> Dict[str, Any]:
"""Convert transaction data to dictionary for agent analysis"""
return {
"signature": self.signature,
"timestamp": self.block_time.isoformat(),
"slot": self.slot,
"fee": self.fee,
"amount_sol": str(self.sol_amount),
"from_address": self.from_address,
"to_address": self.to_address,
"program_id": self.program_id,
"instruction_data": self.instruction_data,
"program_logs": self.program_logs,
}
class SolanaSwarmAgent:
"""Intelligent agent for analyzing Solana transactions using swarms"""
def __init__(
self,
agent_name: str = "Solana-Analysis-Agent",
model_name: str = "gpt-4",
):
self.agent = Agent(
agent_name=agent_name,
system_prompt=SOLANA_ANALYSIS_PROMPT,
model_name=model_name,
max_loops=1,
autosave=True,
dashboard=False,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path="solana_agent.json",
user_name="solana_analyzer",
retry_attempts=3,
context_length=4000,
)
# Initialize known patterns database
self.known_patterns = {
"whale_addresses": set(),
"program_interactions": {},
"recent_transactions": [],
}
logger.info(
f"Initialized {agent_name} with specialized Solana analysis capabilities"
)
async def analyze_transaction(
self, tx_data: TransactionData
) -> Dict[str, Any]:
"""Analyze a transaction using the specialized agent"""
try:
# Update recent transactions for pattern analysis
self.known_patterns["recent_transactions"].append(
tx_data.signature
)
if len(self.known_patterns["recent_transactions"]) > 1000:
self.known_patterns["recent_transactions"].pop(0)
# Prepare context for agent
context = {
"transaction": tx_data.to_dict(),
"known_patterns": {
"recent_similar_transactions": [
tx
for tx in self.known_patterns[
"recent_transactions"
][-5:]
if abs(
TransactionData(tx).sol_amount
- tx_data.sol_amount
)
< 1
],
"program_statistics": self.known_patterns[
"program_interactions"
].get(tx_data.program_id, {}),
},
}
# Get analysis from agent
analysis = await self.agent.run_async(
f"Analyze the following Solana transaction and provide insights: {json.dumps(context, indent=2)}"
)
# Update pattern database
if tx_data.sol_amount > 1000: # Track whale addresses
self.known_patterns["whale_addresses"].add(
tx_data.from_address
)
# Update program interaction statistics
if (
tx_data.program_id
not in self.known_patterns["program_interactions"]
):
self.known_patterns["program_interactions"][
tx_data.program_id
] = {"total_interactions": 0, "total_volume": 0}
self.known_patterns["program_interactions"][
tx_data.program_id
]["total_interactions"] += 1
self.known_patterns["program_interactions"][
tx_data.program_id
]["total_volume"] += float(tx_data.sol_amount)
return json.loads(analysis)
except Exception as e:
logger.error(f"Error in agent analysis: {str(e)}")
return {
"analysis_type": "error",
"severity": "low",
"details": {
"error": str(e),
"transaction": tx_data.signature,
},
}
class SolanaTransactionMonitor:
"""Main class for monitoring and analyzing Solana transactions"""
def __init__(
self,
rpc_url: str,
swarm_agent: SolanaSwarmAgent,
min_sol_threshold: Decimal = Decimal("100"),
):
self.rpc_url = rpc_url
self.swarm_agent = swarm_agent
self.min_sol_threshold = min_sol_threshold
self.wallet = Wallet(Keypair())
self.provider = Provider(rpc_url, self.wallet)
logger.info("Initialized Solana transaction monitor")
async def parse_transaction(
self, tx_resp: GetTransactionResp
) -> Optional[TransactionData]:
"""Parse transaction response into TransactionData object"""
try:
if not tx_resp.value:
return None
tx_value = tx_resp.value
meta = tx_value.transaction.meta
if not meta:
return None
tx: Transaction = tx_value.transaction.transaction
# Extract transaction details
from_pubkey = str(tx.message.account_keys[0])
to_pubkey = str(tx.message.account_keys[1])
program_id = str(tx.message.account_keys[-1])
# Calculate amount from balance changes
amount = abs(meta.post_balances[0] - meta.pre_balances[0])
return TransactionData(
signature=str(tx_value.transaction.signatures[0]),
block_time=datetime.fromtimestamp(
tx_value.block_time or 0
),
slot=tx_value.slot,
fee=meta.fee,
lamports=amount,
from_address=from_pubkey,
to_address=to_pubkey,
program_id=program_id,
program_logs=(
meta.log_messages if meta.log_messages else []
),
)
except Exception as e:
logger.error(f"Failed to parse transaction: {str(e)}")
return None
async def start_monitoring(self):
"""Start monitoring for new transactions"""
logger.info(
"Starting transaction monitoring with swarm agent analysis"
)
async with aiohttp.ClientSession() as session:
async with session.ws_connect(self.rpc_url) as ws:
await ws.send_json(
{
"jsonrpc": "2.0",
"id": 1,
"method": "transactionSubscribe",
"params": [
{"commitment": "finalized"},
{
"encoding": "jsonParsed",
"commitment": "finalized",
},
],
}
)
async for msg in ws:
if msg.type == aiohttp.WSMsgType.TEXT:
try:
data = json.loads(msg.data)
if "params" in data:
signature = data["params"]["result"][
"value"
]["signature"]
# Fetch full transaction data
tx_response = await self.provider.connection.get_transaction(
base58.b58decode(signature)
)
if tx_response:
tx_data = (
await self.parse_transaction(
tx_response
)
)
if (
tx_data
and tx_data.sol_amount
>= self.min_sol_threshold
):
# Get agent analysis
analysis = await self.swarm_agent.analyze_transaction(
tx_data
)
logger.info(
f"Transaction Analysis:\n"
f"Signature: {tx_data.signature}\n"
f"Amount: {tx_data.sol_amount} SOL\n"
f"Analysis: {json.dumps(analysis, indent=2)}"
)
except Exception as e:
logger.error(
f"Error processing message: {str(e)}"
)
continue
async def main():
"""Example usage"""
# Start monitoring
try:
# Initialize swarm agent
swarm_agent = SolanaSwarmAgent(
agent_name="Solana-Whale-Detector", model_name="gpt-4"
)
# Initialize monitor
monitor = SolanaTransactionMonitor(
rpc_url="wss://api.mainnet-beta.solana.com",
swarm_agent=swarm_agent,
min_sol_threshold=Decimal("100"),
)
await monitor.start_monitoring()
except KeyboardInterrupt:
logger.info("Shutting down gracefully...")
if __name__ == "__main__":
asyncio.run(main())
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