Code Generation

Generate production code from Aexol specifications using AI.

Pipeline

Specification
Your .aexol file
Parse to AST
Syntax tree
AI Generation
Model + KB context
Output Code
TS, Python, Rust, Go, JS

Target Languages

.tsTypeScript
.pyPython
.rsRust
.goGo
.jsJavaScript

Via Remote MCP

Remote MCP
curl -X POST "https://api.aexol.ai/mcp" \
  -H "Authorization: Bearer sk-aexol-team-..." \
  -d '{
    "jsonrpc": "2.0", "id": 1, "method": "tools/call",
    "params": {
      "name": "remote_start_inference",
      "arguments": {
  "spec": "type User { id: string, name: string }",
  "language": "typescript",
  "projectId": "proj_..."
}
    }
  }'

Check status:

Remote MCP
curl -X POST "https://api.aexol.ai/mcp" \
  -H "Authorization: Bearer sk-aexol-team-..." \
  -d '{
    "jsonrpc": "2.0", "id": 1, "method": "tools/call",
    "params": {
      "name": "remote_get_inference_task",
      "arguments": { "taskId": "task_..." }
    }
  }'

Wait for completion:

Remote MCP
curl -X POST "https://api.aexol.ai/mcp" \
  -H "Authorization: Bearer sk-aexol-team-..." \
  -d '{
    "jsonrpc": "2.0", "id": 1, "method": "tools/call",
    "params": {
      "name": "remote_wait_task",
      "arguments": { "taskId": "task_...", "taskType": "inference" }
    }
  }'

TypeScript Example

Input (Aexol):

type User { id: string, name: string, email: string, role: UserRole }
enum UserRole { admin, editor, viewer }

Output (TypeScript):

export interface User {
  id: string; name: string; email: string; role: UserRole;
}
export enum UserRole { Admin = "admin", Editor = "editor", Viewer = "viewer" }

Batch Generation

Generate multiple artifacts from one spec using commands:

Remote MCP
curl -X POST "https://api.aexol.ai/mcp" \
  -H "Authorization: Bearer sk-aexol-team-..." \
  -d '{
    "jsonrpc": "2.0", "id": 1, "method": "tools/call",
    "params": {
      "name": "remote_start_inference",
      "arguments": {
  "spec": "...",
  "commands": [
    { "language": "typescript", "target": "models" },
    { "language": "typescript", "target": "routes" },
    { "language": "python", "target": "models" }
  ]
}
    }
  }'

Knowledge Base Integration

When your project has Knowledge Base documents, the AI retrieves relevant chunks and includes them in the generation prompt — generated code follows your team's conventions automatically.

Task History

All generation tasks are logged at /studio/tasks and via remote_list_inference_tasks. Re-run any previous task with updated specs.

Next Steps