docker-compose.yml

chatgpt_system_message_callback.py

"""
LiteLLM CustomLogger callback to strip system messages from requests
routed to ChatGPT subscription models.

System messages are merged into the first user message since the ChatGPT
Codex endpoint doesn't support the system role.

Registered via litellm_settings.callbacks in litellm-config.yaml.
"""

from typing import List, Dict, Any, Optional, Union
import logging

from litellm.integrations.custom_logger import CustomLogger

logger = logging.getLogger(__name__)


def _extract_text(content) -> str:
    """Extract plain text from message content (string or content-block list)."""
    if isinstance(content, str):
        return content
    if isinstance(content, list):
        parts = []
        for block in content:
            if isinstance(block, dict):
                parts.append(block.get("text", ""))
            elif isinstance(block, str):
                parts.append(block)
        return "\n\n".join(p for p in parts if p)
    return str(content) if content else ""


class ChatGPTSystemMessageCallback(CustomLogger):
    """
    Transforms system messages into user messages before the request
    is sent to the backend model.
    """

    def __init__(self):
        super().__init__()
        self.name = "chatgpt-system-message-handler"

    def transform_system_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """
        Merge all system messages into the first user message, preserving
        the relative order of non-system messages.
        """
        if not messages:
            return messages

        system_parts: list[str] = []
        non_system: list[Dict[str, Any]] = []

        for msg in messages:
            if msg.get("role") == "system":
                text = _extract_text(msg.get("content", ""))
                if text:
                    system_parts.append(text)
            else:
                non_system.append(msg)

        if not system_parts:
            return messages

        merged_system = "\n\n".join(system_parts)

        # Find first user message and prepend system content
        for i, msg in enumerate(non_system):
            if msg.get("role") == "user":
                original = _extract_text(msg.get("content", ""))
                non_system[i] = {
                    **msg,
                    "content": f"{merged_system}\n\n{original}".strip(),
                }
                break
        else:
            # No user message found — insert a synthetic one at the start
            non_system.insert(0, {"role": "user", "content": merged_system})

        logger.info("Merged %d system message(s) into first user message", len(system_parts))
        return non_system

    async def async_pre_call_hook(
        self,
        user_api_key_dict,
        cache,
        data: dict,
        call_type: str,
    ) -> Optional[Union[Exception, str, dict]]:
        """
        Called by LiteLLM before each LLM request.

        Handles two system-message formats:
        - OpenAI/chat format: system role entries in data["messages"]
        - Anthropic format: top-level data["system"] field
        """
        system_parts: list[str] = []
        modified = False

        # Anthropic format: top-level "system" field
        system_field = data.get("system")
        if system_field:
            text = _extract_text(system_field)
            if text:
                system_parts.append(text)
            data.pop("system", None)
            modified = True

        # OpenAI chat format: system-role entries in messages list
        messages = data.get("messages") or []
        non_system = []
        for msg in messages:
            if msg.get("role") == "system":
                text = _extract_text(msg.get("content", ""))
                if text:
                    system_parts.append(text)
                modified = True
            else:
                non_system.append(msg)

        if not modified or not system_parts:
            return None

        merged_system = "\n\n".join(system_parts)

        # Prepend merged system text into the first user message
        for i, msg in enumerate(non_system):
            if msg.get("role") == "user":
                original = _extract_text(msg.get("content", ""))
                non_system[i] = {
                    **msg,
                    "content": f"{merged_system}\n\n{original}".strip(),
                }
                break
        else:
            non_system.insert(0, {"role": "user", "content": merged_system})

        logger.info("Merged system prompt into first user message (model=%s call_type=%s)",
                     data.get("model"), call_type)
        data["messages"] = non_system
        return data


# Export an instance for LiteLLM's get_instance_fn
chatgpt_system_message_callback = ChatGPTSystemMessageCallback()

docker-compose.yml

  GNU nano 7.2                                                                                                         docker-compose.yml
name: litellm
services:
  litellm:
    image: ghcr.io/berriai/litellm:main-latest
    ports:
      - "4000:4000"
    volumes:
      - <host-absolute-path>/litellm-config.yaml:/app/config.yaml
      - <host-absolute-path>/chatgpt_system_message_callback.py:/app/chatgpt_system_message_callback.py
      - litellm-chatgpt-auth:/root/.config/litellm/chatgpt
    environment:
      - LITELLM_MASTER_KEY=sk-my-fake-litellm-key
      - DATABASE_URL=postgresql://litellm:litellm@db:5432/litellm
      - COOLDOWN_TTL=1800
      # - LITELLM_LOG=DEBUG
    command: ["--config", "/app/config.yaml", "--port", "4000"]
    depends_on:
      db:
        condition: service_healthy
    restart: on-failure
    extra_hosts:
      - "host.docker.internal:host-gateway"

  db:
    image: postgres:16
    environment:
      - POSTGRES_USER=litellm
      - POSTGRES_PASSWORD=litellm
      - POSTGRES_DB=litellm
    volumes:
      - litellm-db:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U litellm"]
      interval: 5s
      timeout: 5s
      retries: 5
    restart: unless-stopped

volumes:
  litellm-db:
  litellm-chatgpt-auth:

litellm-config.yaml

model_list:
  - model_name: anthropic/*
    litellm_params:
      model: chatgpt/gpt-5.3-codex
      mode: responses

litellm_settings:
  callbacks: ["chatgpt_system_message_callback.chatgpt_system_message_callback"]

router_settings:
  routing_strategy: usage-based-routing

general_settings:
  master_key: sk-my-fake-litellm-key
  database_url: postgresql://litellm:litellm@db:5432/litellm

chatgpt_system_message_callback.py is a workaround for BerriAI/litellm#22968

If you want to map each model type differently, you can do something like this:

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: chatgpt/gpt-5.4
      mode: responses
  - model_name: claude-sonnet-4-6
    litellm_params:
      model: chatgpt/gpt-5.3-codex
      mode: responses
  - model_name: claude-haiku-4-5-20251001
    litellm_params:
      model: chatgpt/gpt-5.2-codex
      mode: responses