Python API Overview

from axon import Lexer, Parser, TypeChecker, IRGenerator

source = open("my_program.axon").read()

# Phase 1.1: Lexical analysis
lexer = Lexer(source, filename="my_program.axon")
tokens = lexer.tokenize()

# Phase 1.2: Parsing
parser = Parser(tokens)
ast = parser.parse()

# Phase 1.3: Type checking
checker = TypeChecker(ast)
errors = checker.check()

if errors:
    for error in errors:
        print(f"Error at line {error.line}: {error.message}")
    exit(1)

# Phase 1.4: IR generation
ir_gen = IRGenerator()
ir_program = ir_gen.generate(ast)

from axon import get_backend

# Select a backend
backend = get_backend("anthropic")

# Compile to provider-specific prompts
compiled = backend.compile_program(ir_program)

print(compiled.to_dict())

from axon.runtime import Executor, ModelClient
from anthropic import Anthropic

class AnthropicClient(ModelClient):
    def __init__(self, api_key: str):
        self.client = Anthropic(api_key=api_key)
    
    async def call(self, system_prompt: str, user_prompt: str, **kwargs):
        response = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            system=system_prompt,
            messages=[{"role": "user", "content": user_prompt}]
        )
        return ModelResponse(content=response.content[0].text)

# Execute the compiled program
client = AnthropicClient(api_key="...")
executor = Executor(client=client)

result = await executor.execute(compiled)

for unit in result.unit_results:
    print(f"Flow: {unit.flow_name}")
    for step in unit.step_results:
        print(f"  {step.step_name}: {step.response.content}")

pip install axon-lang

import axon
print(axon.__version__)  # '0.4.0a0'