Practical overview
When it fits
Use it as a learning walkthrough when you want to inspect the components of a Base and Uniswap V4 trading-agent pipeline, not as a production trading system.
Who it is for
- Developers learning how AI agents can interact directly with blockchain infrastructure.
- Readers exploring local model inference, fine-tuning and reinforcement learning in a Web3 trading example.
Documented uses
- Study a progression from manual swaps to stateless and memory-enabled trading agents.
- Explore model-training methods including LoRA fine-tuning, GAN-generated synthetic data, distillation and reinforcement learning.
- Prepare a local development setup with Python, Foundry and Ollama.
Requirements & limits
The repository is public and lists an Apache-2.0 license.
- The example targets Base and Uniswap V4, using a pool ID for token pairs rather than the separate V3 pool-contract pattern described in the README.
- The project combines Foundry for local Base-fork testing, Ollama for local inference and Python libraries for blockchain and machine-learning work.
- Hardware needs depend on the local model workflow: the README recommends at least 16 GB RAM and specifies 8 GB minimum VRAM for non-Apple local inference and training.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the local development environment
Set up the documented Python, Foundry and Ollama prerequisites for following the trading-agent walkthrough.
Before you start
- A supported development machine; the README recommends 16 GB RAM or more and discusses GPU requirements for local model work.
- Python and the repository's requirements.txt.
- Foundry and Ollama.
Steps
Clone the repository and enter its project directory using the README's repository setup instructions.
Install Python dependencies with pip install -r requirements.txt.
Install Foundry using the documented installer and run foundryup.
Install Ollama and use its help command to review the local installation.
Expected outcome
The documented development tools and Python packages are prepared for continuing through the repository's later chapters.
Guide limitations
- These steps cover setup only, not execution of the trading agent.
- The project states that its code is for learning and not for production.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- chainstacklabs/web3-ai-trading-agent · Official homepage title · retained in its source language