Bridge Layer: Communication between backend and clients
WebSocket server/client
Command handling
Event propagation
Client Interfaces:
TypeScript/JavaScript framework
Python wrapper
Interactive CLI
Extensions:
Bridge implementations
Wallet integrations
DEX connectors
Coding Conventions
Julia Conventions
Use module-level organization for related functionality
Implement comprehensive error handling
Write docstrings for all public functions
Use type annotations where appropriate
Write unit tests for all functionality
Example:
"""
create_agent(name::String, type::String, config::Dict) -> Dict
Create a new agent with the specified name, type, and configuration.
# Arguments
- `name::String`: The name of the agent
- `type::String`: The type of agent (e.g., "trading", "research")
- `config::Dict`: Configuration parameters for the agent
# Returns
- `Dict`: The created agent object with an assigned ID
# Examples
```julia
agent = create_agent("MyAgent", "trading", Dict("risk_level" => "medium"))
"""
function create_agent(name::String, type::String, config::Dict)
# Implementation
end
### TypeScript/JavaScript Conventions
- Use TypeScript for type safety
- Follow ESLint and Prettier configurations
- Use async/await for asynchronous code
- Document functions with JSDoc comments
- Write unit tests for all functionality
- Use named exports for better tree-shaking
Example:
```typescript
/**
* Creates a new agent with the specified parameters
* @param {string} name - The name of the agent
* @param {string} type - The type of agent (e.g., "trading", "research")
* @param {object} config - Configuration parameters for the agent
* @returns {Promise<Agent>} The created agent object
*/
export async function createAgent(
name: string,
type: string,
config: Record<string, any>
): Promise<Agent> {
// Implementation
}
Python Conventions
Follow PEP 8 style guidelines
Use type hints (PEP 484)
Document functions with docstrings
Use async/await for asynchronous code
Write unit tests for all functionality
Example:
async def create_agent(
name: str,
agent_type: AgentType,
config: Dict[str, Any]
) -> Agent:
"""Create a new agent with the specified parameters.
Args:
name: The name of the agent
agent_type: The type of agent (e.g., TRADING, RESEARCH)
config: Configuration parameters for the agent
Returns:
The created agent object
Raises:
ConnectionError: If connection to the backend fails
AgentError: If agent creation fails
"""
# Implementation
