TLDR:
  • You’ll integrate OpenRouter’s Kimi K2 model with your trading agent for enhanced agentic intelligence in market analysis and decision-making.
  • You’ll run safe paper trading using Foundry Anvil fork to test strategies before deploying to live markets.
  • You’ll configure environment variables and API keys for OpenRouter access while maintaining all existing agent features.
  • By the end, you’ll have a trading agent powered by Kimi K2’s state-of-the-art mixture-of-experts capabilities.
Previous section: AI trading agent: Stateful agent
Project repository: Web3 AI trading agent
Remember that this is a NOT FOR PRODUCTION tutorial. In a production deployment, don’t store your private key in a config.py file.
This section demonstrates how to integrate OpenRouter’s Kimi K2 model with your trading agent. Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters, specifically designed for agentic intelligence and tool use scenarios like autonomous trading. And the best thing is we are going to use the free version of Kimi K2 from OpenRouter, so it will cost us zero to try out the Kimi K2 autonomous trading in our setup.

​
About Kimi K2

With the company name Moonshot AI—the company behind Kimi-K2—you know you just have to try their flagship model as the trading agent LLM. Kimi K2 at 1T parameters and an MoE architecture is theoretically decent fit for the trading agent, and of course is very fun to experiment with. Check out Kimi K2 at Hugging Face.

​
Quick highlights

  • 1 trillion total parameters with 32 billion activated parameters
  • Mixture-of-experts (MoE) design with 384 experts, selecting 8 per token
  • 128K context length for comprehensive market analysis (although note that our OpenRouter implementation has 65k context size)
  • Trained with the innovative Muon optimizer for exceptional stability
Trading-relevant strengths:
  • Superior performance in mathematical reasoning
  • Natural language understanding for market sentiment analysis

​
Prerequisites

Before starting, ensure you have:
  • Python 3.8+ installed
  • All dependencies from requirements.txt installed
  • Foundry installed (curl -L https://foundry.paradigm.xyz | bash && foundryup)
  • OpenRouter account with API key
  • Chainstack BASE RPC endpoint

​
OpenRouter setup

​
Create OpenRouter account

  1. Visit https://openrouter.ai/.
  2. Sign up for an account.
  3. Add credits to your account. Each trading decision will cost approximately $0.02-0.05 per decision.
  4. Navigate to https://openrouter.ai/keys.
  5. Create a new API key and copy it.

​
Configure model and API key

Edit config.py and make these changes: 
# Set the model key:
MODEL_KEY = "kimi-k2"

# Set your OpenRouter API key:
OPENROUTER_API_KEY = "YOUR_OPENROUTER_API_KEY"  # Replace with your real API key

# Set trading environment
USE_FORK = True   # True = Foundry fork (safe paper trading)
                  # False = Real BASE mainnet (uses real money!)
The Kimi K2 model is already pre-configured in your config.py file with the correct OpenRouter endpoint and context capacity settings.

​
Understanding trading environments

The agent can run in two environments: Foundry fork mode (USE_FORK = True)
  • Safe for testing and experimentation
  • Uses paper money (no real funds at risk)
  • Real market data from BASE mainnet
  • Real smart contract interactions
  • Connects to: http://localhost:8545 (Anvil fork)
BASE mainnet mode (USE_FORK = False)
  • Uses real money and real transactions
  • All trades are permanent and irreversible
  • Gas fees apply to every transaction
  • Connects to: Your Chainstack BASE RPC endpoint
Always start with fork mode to test your strategies before using real funds.

​
Set up RPC endpoints for mainnet mode

If you plan to use mainnet mode (USE_FORK = False), configure your BASE RPC endpoints in config.py: 
BASE_RPC_URLS = [
    "YOUR_CHAINSTACK_BASE_RPC_ENDPOINT",  # Replace with your Chainstack endpoint. For example, a Global Node
    "YOUR_CHAINSTACK_BASE_RPC_ENDPOINT",  # Fallback Chainstack endpoint. For example, a Trader Node
]
For fork mode (USE_FORK = True), the agent automatically uses http://localhost:8545 and these endpoints are not needed.

​
Licensing considerations

Important Licensing Information: Kimi K2 is released under a Modified MIT License with specific commercial usage requirements.

​
Modified MIT License Terms

The Kimi K2 model uses a Modified MIT License with the following key requirement: Commercial usage clause: If your trading application (or any derivative works) is used for commercial products or services that have:
  • More than 100 million monthly active users, OR
  • More than 20 million US dollars (or equivalent) in monthly revenue
You must prominently display “Kimi K2” on the user interface of such product or service. See the license in the Kimi K2 repository.

​
Configure trading parameters

Set your trading wallet private key in config.py: 
PRIVATE_KEY = "YOUR_PRIVATE_KEY"  # Use test key only, never production funds. You can use the Anvil fork console print.
Use a test wallet with minimal funds. This is for educational purposes only.

​
Start Foundry Anvil fork

​
Launch Anvil fork

Open a new terminal and start the Anvil fork of BASE mainnet: 
anvil --fork-url YOUR_CHAINSTACK_BASE_RPC_ENDPOINT --chain-id 8453
You should see output like: 
Available Accounts
==================
(0) 0xf39Fd6e51aad88F6F4ce6aB8827279cffFb92266 (10000.000000000000000000 ETH)
(1) 0x70997970C51812dc3A010C7d01b50e0d17dc79C8 (10000.000000000000000000 ETH)
...

Private Keys
==================
(0) 0xac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80
(1) 0x59c6995e998f97a5a0044966f0945389dc9e86dae88c7a8412f4603b6b78690d
...

Listening on 0.0.0.0:8545

​
Fund your trading account

If your trading account needs more ETH, use Anvil’s built-in accounts: 
# Send ETH from Anvil account to your trading account
cast send --from 0xf39Fd6e51aad88F6F4ce6aB8827279cffFb92266 \
  --private-key 0xac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80 \
  --rpc-url http://localhost:8545 \
  YOUR_TRADING_ADDRESS \
  --value 5ether

​
Verify configuration

​
Test OpenRouter connection

Create a test script to verify your OpenRouter setup: 
from openai import OpenAI
from config import OPENROUTER_API_KEY

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENROUTER_API_KEY
)

try:
    response = client.chat.completions.create(
        model="moonshotai/kimi-k2:free",
        messages=[{"role": "user", "content": "Hello, can you help with trading analysis?"}],
        max_tokens=512
    )
    print("✅ OpenRouter connection successful!")
    print(f"Response: {response.choices[0].message.content}")
except Exception as e:
    print(f"❌ OpenRouter connection failed: {e}")

​
Test Anvil connection

Verify the local fork is working: 
cast block-number --rpc-url http://localhost:8545
This should return the latest BASE block number.

​
Check trading account balance

Verify your trading account has sufficient funds: 
cast balance YOUR_TRADING_ADDRESS --rpc-url http://localhost:8545

​
Run the trading agent

​
Basic trading mode

Start the agent in normal trading mode: 
cd /path/to/web3-ai-trading-agent
python on-chain/uniswap_v4_stateful_trading_agent.py

​
Observation mode

Start with observation mode to see how Kimi K2 analyzes the market without executing trades: 
python on-chain/uniswap_v4_stateful_trading_agent.py --observe-cycles 10
This will:
  • Collect market data for 10 cycles
  • Have Kimi K2 analyze each market state
  • Generate an initial trading strategy
  • Switch to active trading

​
Test mode with reduced context

Test the context management system: 
python on-chain/uniswap_v4_stateful_trading_agent.py --test-mode --observe-cycles 5

​
Custom trading parameters

Modify trading behavior with command-line arguments: 
# Set custom ETH allocation target (60% ETH, 40% USDC)
python on-chain/uniswap_v4_stateful_trading_agent.py --target-allocation 0.6

# Run for specific number of iterations
python on-chain/uniswap_v4_stateful_trading_agent.py --iterations 100

# Extended observation period for better market analysis
python on-chain/uniswap_v4_stateful_trading_agent.py --observe-time 45  # 45 minutes

​
Configuration optimization

In config.py, you can adjust Kimi K2-specific settings: 
# Trading frequency (Kimi K2 can handle higher frequencies due to efficiency)
TRADE_INTERVAL = 8  # seconds between decisions

# Rebalancing sensitivity
REBALANCE_THRESHOLD = 0.4  # 40% deviation triggers rebalance

# Context management (Kimi K2 has 65,536 token context)
CONTEXT_WARNING_THRESHOLD = 0.9  # 90% context usage warning
Kimi K2’s agentic intelligence makes it particularly well-suited for autonomous trading scenarios, but always start with careful testing and small amounts.
This is for educational and testing purposes only. Use test wallets with minimal funds. Never use production private keys. Monitor OpenRouter costs regularly. Be aware of licensing requirements for commercial usage. The fork environment uses test funds, but configuration errors could affect real accounts.
Next section: AI trading agent: ERNIE 4.5 local integration with MLX-LM

​
About the author

8_Bi4fdM_400x400

Ake

Director of Developer Experience @ Chainstack Talk to me all things Web320 years in technology | 8+ years in Web3 full time years experienceTrusted advisor helping developers navigate the complexities of blockchain infrastructure