from trl import Environment

class MyCustomEnv(Environment):
    def __init__(self, ...):
        # Initialize anything your agent needs, like code execution,
        # tool calls, or external APIs.
        pass

    def tool_call(self, ...):
        # Define any environment-specific methods you want to use.
        pass

    def generate(self, vllm_client, generation_config: VLLMClientGenerationConfig, prompts: list[str]) -> list:
        # This is the method GRPO training uses to generate responses.
        # It must return a list of completion IDs (tokenized responses).
        return completion_ids

from trl import CodeAgentEnvironment, E2BExecutor

code_executor = E2BExecutor(api_key="YOUR_E2B_TOKEN")
my_env = CodeAgentEnvironment(
    code_executor=code_executor,
    tokenizer=tokenizer,
    parsing_string="<code>",
    stop_string="</code>",
)

from trl import VLLMClientGenerationConfig, VLLMClient

client = VLLMClient()
gen_config = VLLMClientGenerationConfig(
    n=8,
    repetition_penalty=1.0,
    temperature=0.8,
    top_p=0.9,
    top_k=10,
    min_p=0.0,
    max_tokens=256,
)

responses = my_env.run_agent(  # Main method in CodeAgentEnvironment to generate responses
    vllm_client=client,
    generation_config=gen_config,
    prompts=prompts
)

from trl import GRPOConfig, GRPOTrainer

training_args = GRPOConfig(
    output_dir="code_agent",
    use_vllm=True,
    # ... other config options ...
)

trainer = GRPOTrainer(
    model=...,                # Your model or model name
    reward_funcs=...,         # Your reward function(s)
    args=training_args,
    environment=my_env,       # Your custom or built-in environment
    # ... other trainer args ...
)