'I want to build a DeFi Agent' — easy to say, but when you actually sit down to start, many people don't know where to begin: which framework? How to set up the wallet? Where to store private keys? What's the first tool to write?
This article gives you a directly executable roadmap: the minimum viable architecture for building your first Onchain Agent from scratch, including technology stack selection, architecture design, and a pre-deployment security checklist. The goal is to have an Agent running on testnet within two days — not reading all the documentation before starting.
Before writing the first line of code, there are three questions you should be able to clearly answer — their answers determine your technical choices:
Question 1: Which chain will your Agent run on?
The most important first decision. Ethereum mainnet Gas fees are too high for beginners — a single test rebalancing operation might cost $5-20, making debugging very expensive. Strong recommendation: build your first Onchain Agent on Base or Sepolia (Ethereum testnet). Base is Ethereum's L2 with Gas fees at 1/100 of mainnet; DeFi ecosystem is rich enough (Aave, Compound both have Base versions) — ideal for a beginner's first Agent. Sepolia is Ethereum's public testnet where you can get free test ETH from faucets — completely free but with a smaller DeFi ecosystem.
Question 2: What specifically will your Agent do?
'DeFi strategy Agent' is too vague. Your first Agent should have a very specific, narrow-scope task. Recommended first task: 'Query Aave and Morpho USDC rates every 30 minutes; if the spread exceeds 0.5%, send a Telegram notification (do NOT auto-execute rebalancing).' This task: read-only operations (no on-chain risk); verifiable output (Telegram notification); lets you practice tool function design, Context management, and Agent framework use — but won't lose funds due to bugs. Recommendation: get the 'read + notify' version working well, then gradually add 'auto-execute' functionality.
Question 3: What are your safety boundaries?
Before adding auto-execute functionality to your Agent, decide: maximum single operation amount (recommend starting at $50-100 USDC, increasing only after confirming Agent behavior matches expectations); which protocols are on the whitelist (recommend only 1-2 protocols you're familiar with and that have been audited); what conditions trigger auto-execution (what spread size executes); whether human confirmation is needed (strongly recommend including Telegram human confirmation for the first version). Designing your Agent with clear answers to these three questions avoids most beginner mistakes.
For a first Onchain Agent, the following Python-centric technology stack is recommended:
Agent framework: LangChain + LangGraph. LangChain provides tool wrapping, Memory management, and LLM integration infrastructure; LangGraph provides directed graph Agent execution flow control, letting you precisely define the steps 'read → analyze → confirm → execute.' LangGraph has a slightly higher learning curve than other frameworks, but it's the most friendly for Onchain Agent security design (can prevent direct read-to-execute jumps at the graph design level). Install: pip install langchain langgraph.
LLM: Claude Sonnet or GPT-4o mini. For a first Agent's task (rate comparison + decision), Claude Sonnet is a balanced performance-cost choice ($3/million input tokens); if budget is tighter, GPT-4o mini also works ($0.15/million input tokens). Both Anthropic and OpenAI API Keys can be applied for in their respective developer consoles with some initial free credit.
Blockchain interaction: Web3.py. Python's standard Ethereum library, providing complete functionality for reading on-chain data, building transactions, signing, and broadcasting. Install: pip install web3. For Base chain, RPC nodes can use Alchemy (free tier: 300 requests/day) or Base's official RPC (https://mainnet.base.org, completely free but rate-limited).
Deployment environment: Railway. Railway provides the simplest Python application deployment experience (GitHub repo direct deployment, done in 5 minutes), has a free Starter plan ($5 free credit), supports environment variables (for storing API Keys and private keys without hardcoding), and has a built-in logging system. For a first Agent, Railway is the lowest-friction choice.
Notification system: Telegram Bot. Build a Telegram Bot with the python-telegram-bot library; a few lines of code let the Agent push rate comparison results and decisions to your Telegram. Compared to email notifications, Telegram has lower latency and easier two-way interaction setup (let the Bot wait for your 'confirm execute' reply).
Using 'USDC rate monitoring + Telegram notification Agent' as an example, describing the minimum viable architecture:
Directory structure:
defi-rate-agent/ ├── main.py (main program, starts Agent loop) ├── tools/ │ ├── get_rates.py (tool function querying Aave and Morpho APY) │ └── notify.py (tool function sending Telegram notifications) ├── agent/ │ └── graph.py (LangGraph graph definition) ├── .env (API Keys, not committed to Git) └── railway.toml (Railway deployment config)
Core flow design (LangGraph graph):
The graph has three nodes: fetch_rates (calls get_rates tool, gets current USDC APY from Aave and Morpho) → analyze (LLM reasoning: calculates spread, judges whether worth notifying) → notify (if spread exceeds threshold, calls notify tool to send Telegram message).
In the analyze node's System Prompt, add grounding rules: 'All referenced rate numbers must come from the fetch_rates tool return; do not use any numbers from training memory. If fetch_rates returns failure, output ABORT and terminate this cycle.'
Tool function design (get_rates.py):
Tool input: none (rate queries need no parameters). What the tool does: calls DeFiLlama's API (free, no API Key required) to get Aave and Morpho USDC supply APY on Base; does numerical reasonability validation (APY >30% treated as anomalous, uses last cached value); returns formatted result string: 'Aave USDC APY: 4.2%, Morpho USDC APY: 5.1%, spread: 0.9% (query time: 2026-06-29 03:12:44 UTC).' Note: return a natural language string rather than JSON to reduce LLM parsing error risk.
Main program design (main.py):
Use Python's schedule library to run the Agent graph every 30 minutes; add maximum loop count limit (maximum 5 loops per run to prevent infinite loops); log each run's result (including LLM's complete Thought output) to a log file (using Python logging module).
Before deploying the Agent to any environment, confirm the following list item by item:
Code security:
.env file added to .gitignore (confirm private keys and API Keys won't be uploaded to GitHub); all API Keys and private keys read via environment variables, none hardcoded in code; search code for '0x' — confirm no addresses or private keys hardcoded.
Tool function security:
Read tool return values contain no functions that could trigger on-chain operations; tool return values have reasonability validation (APY outside 0-50% range logged as anomalous, not entering LLM Context); all tool calls have timeout settings (preventing API non-response from causing Agent to freeze).
Agent behavior:
System Prompt has 'data grounding rules' (requiring LLM to cite tool data, abort on tool failure); maximum loop count limit set (preventing infinite loops); logging present, able to answer 'what did the Agent do in its last run.'
Pre-first-deployment testing:
Run locally once first, confirm Agent can normally query APIs and send Telegram notifications; print the LLM's complete Thought output, confirm it cites tool-returned numbers rather than other numbers; test the 'API timeout' scenario (change the API address to a nonexistent address), confirm the Agent's behavior is 'log failure + stop' rather than 'continue reasoning and fill with memory.'
Your first Onchain Agent doesn't need to be complex, but it needs the right architecture — an Agent correctly designed from day one for read/write isolation, data grounding, and logging has far lower costs to safely extend when adding auto-execute functionality later, compared to one built 'fast first, figure it out later.'
The 'read + notify first, then gradually add auto-execute' path isn't just a safety consideration — it's also the fastest route to 'a useful Agent.' An Agent that accurately judges 'what is the current spread' and immediately pushes to your Telegram has real use value from day one, rather than waiting for all features to be complete before it's usable. Starting from this minimum viable version, add one feature per week — in 3-4 weeks you'll have a complete DeFi strategy Agent.