feat: +runtime multi-llm support

config/config2.example.yaml

@@ -59,3 +59,21 @@ iflytek_api_key: "YOUR_API_KEY"
 iflytek_api_secret: "YOUR_API_SECRET"
 
 metagpt_tti_url: "YOUR_MODEL_URL"
+
+models:
+#  "YOUR_MODEL_NAME_1": # model: "gpt-4-turbo"  # or gpt-3.5-turbo
+#    api_type: "openai"  # or azure / ollama / groq etc.
+#    base_url: "YOUR_BASE_URL"
+#    api_key: "YOUR_API_KEY"
+#    proxy: "YOUR_PROXY"  # for LLM API requests
+#    # timeout: 600 # Optional. If set to 0, default value is 300.
+#    # Details: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
+#    pricing_plan: "" # Optional. Use for Azure LLM when its model name is not the same as OpenAI's
+#  "YOUR_API_TYPE": # api_type: "openai"  # or azure / ollama / groq etc.
+#    api_type: "openai"  # or azure / ollama / groq etc.
+#    base_url: "YOUR_BASE_URL"
+#    api_key: "YOUR_API_KEY"
+#    proxy: "YOUR_PROXY"  # for LLM API requests
+#    # timeout: 600 # Optional. If set to 0, default value is 300.
+#    # Details: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
+#    pricing_plan: "" # Optional. Use for Azure LLM when its model name is not the same as OpenAI's

metagpt/actions/action.py

@@ -8,11 +8,12 @@
 
 from __future__ import annotations
 
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 from metagpt.actions.action_node import ActionNode
+from metagpt.configs.models_config import ModelsConfig
 from metagpt.context_mixin import ContextMixin
 from metagpt.schema import (
     CodePlanAndChangeContext,
@@ -35,6 +36,17 @@ class Action(SerializationMixin, ContextMixin, BaseModel):
     prefix: str = ""  # aask*时会加上prefix，作为system_message
     desc: str = ""  # for skill manager
     node: ActionNode = Field(default=None, exclude=True)
+    # The model name or API type of LLM of the `models` in the `config2.yaml`;
+    #   Using `None` to use the `llm` configuration in the `config2.yaml`.
+    llm_name_or_type: Optional[str] = None
+
+    @model_validator(mode="after")
+    @classmethod
+    def _update_private_llm(cls, data: Any) -> Any:
+        config = ModelsConfig.default().get(data.llm_name_or_type)
+        if config:
+            data.llm.config = config
+        return data
 
     @property
     def repo(self) -> ProjectRepo:

metagpt/configs/llm_config.py

@@ -10,9 +10,9 @@ from typing import Optional
 
 from pydantic import field_validator
 
-from metagpt.const import LLM_API_TIMEOUT
+from metagpt.const import CONFIG_ROOT, LLM_API_TIMEOUT, METAGPT_ROOT
 from metagpt.utils.yaml_model import YamlModel
-from metagpt.const import METAGPT_ROOT, CONFIG_ROOT
+
 
 class LLMType(Enum):
     OPENAI = "openai"
@@ -97,12 +97,13 @@ class LLMConfig(YamlModel):
             repo_config_path = METAGPT_ROOT / "config/config2.yaml"
             root_config_path = CONFIG_ROOT / "config2.yaml"
             if root_config_path.exists():
-                 raise ValueError(
-                    f"Please set your API key in {root_config_path}. If you also set your config in {repo_config_path}, \nthe former will overwrite the latter. This may cause unexpected result.\n")
+                raise ValueError(
+                    f"Please set your API key in {root_config_path}. If you also set your config in {repo_config_path}, \nthe former will overwrite the latter. This may cause unexpected result.\n"
+                )
             elif repo_config_path.exists():
                 raise ValueError(f"Please set your API key in {repo_config_path}")
             else:
-                raise ValueError(f"Please set your API key in config2.yaml")
+                raise ValueError("Please set your API key in config2.yaml")
         return v
 
     @field_validator("timeout")

metagpt/configs/models_config.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+models_config.py
+
+This module defines the ModelsConfig class for handling configuration of LLM models.
+
+Attributes:
+    CONFIG_ROOT (Path): Root path for configuration files.
+    METAGPT_ROOT (Path): Root path for MetaGPT files.
+
+Classes:
+    ModelsConfig (YamlModel): Configuration class for LLM models.
+"""
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from pydantic import Field, field_validator
+
+from metagpt.config2 import merge_dict
+from metagpt.configs.llm_config import LLMConfig
+from metagpt.const import CONFIG_ROOT, METAGPT_ROOT
+from metagpt.utils.yaml_model import YamlModel
+
+
+class ModelsConfig(YamlModel):
+    """
+    Configuration class for LLM models.
+
+    Attributes:
+        models (Dict[str, LLMConfig]): Dictionary mapping model names to LLMConfig objects.
+
+    Methods:
+        update_llm_model(cls, value): Validates and updates LLM model configurations.
+        from_home(cls, path): Loads configuration from ~/.metagpt/config2.yaml.
+        default(cls): Loads default configuration from predefined paths.
+        get(self, name_or_type: str) -> Optional[LLMConfig]: Retrieves LLMConfig by name or API type.
+    """
+
+    models: Dict[str, LLMConfig] = Field(default_factory=dict)
+
+    @field_validator("models", mode="before")
+    @classmethod
+    def update_llm_model(cls, value):
+        """
+        Validates and updates LLM model configurations.
+
+        Args:
+            value (Dict[str, Union[LLMConfig, dict]]): Dictionary of LLM configurations.
+
+        Returns:
+            Dict[str, Union[LLMConfig, dict]]: Updated dictionary of LLM configurations.
+        """
+        for key, config in value.items():
+            if isinstance(config, LLMConfig):
+                config.model = config.model or key
+            elif isinstance(config, dict):
+                config["model"] = config.get("model") or key
+        return value
+
+    @classmethod
+    def from_home(cls, path):
+        """
+        Loads configuration from ~/.metagpt/config2.yaml.
+
+        Args:
+            path (str): Relative path to configuration file.
+
+        Returns:
+            Optional[ModelsConfig]: Loaded ModelsConfig object or None if file doesn't exist.
+        """
+        pathname = CONFIG_ROOT / path
+        if not pathname.exists():
+            return None
+        return ModelsConfig.from_yaml_file(pathname)
+
+    @classmethod
+    def default(cls):
+        """
+        Loads default configuration from predefined paths.
+
+        Returns:
+            ModelsConfig: Default ModelsConfig object.
+        """
+        default_config_paths: List[Path] = [
+            METAGPT_ROOT / "config/config2.yaml",
+            CONFIG_ROOT / "config2.yaml",
+        ]
+
+        dicts = [ModelsConfig.read_yaml(path) for path in default_config_paths]
+        final = merge_dict(dicts)
+        return ModelsConfig(**final)
+
+    def get(self, name_or_type: str) -> Optional[LLMConfig]:
+        """
+        Retrieves LLMConfig object by name or API type.
+
+        Args:
+            name_or_type (str): Name or API type of the LLM model.
+
+        Returns:
+            Optional[LLMConfig]: LLMConfig object if found, otherwise None.
+        """
+        if not name_or_type:
+            return None
+        model = self.models.get(name_or_type)
+        if model:
+            return model
+        for m in self.models.values():
+            if m.api_type == name_or_type:
+                return m
+        return None

metagpt/utils/token_counter.py

@@ -259,7 +259,6 @@ TOKEN_MAX = {
     "qwen-7b-chat": 32000,
     "qwen-1.8b-longcontext-chat": 32000,
     "qwen-1.8b-chat": 8000,
-    
 }
 
 # For Amazon Bedrock US region

tests/data/config/config2.yaml

@@ -0,0 +1,27 @@
+llm:
+  api_type: "openai"  # or azure / ollama / groq etc.
+  base_url: "YOUR_gpt-3.5-turbo_BASE_URL"
+  api_key: "YOUR_gpt-3.5-turbo_API_KEY"
+  model: "gpt-3.5-turbo"  # or gpt-3.5-turbo
+  proxy: "YOUR_gpt-3.5-turbo_PROXY"  # for LLM API requests
+  # timeout: 600 # Optional. If set to 0, default value is 300.
+  # Details: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
+  pricing_plan: "" # Optional. Use for Azure LLM when its model name is not the same as OpenAI's
+
+models:
+  "YOUR_MODEL_NAME_1": # model: "gpt-4-turbo"  # or gpt-3.5-turbo
+    api_type: "openai"  # or azure / ollama / groq etc.
+    base_url: "YOUR_MODEL_1_BASE_URL"
+    api_key: "YOUR_MODEL_1_API_KEY"
+    proxy: "YOUR_MODEL_1_PROXY"  # for LLM API requests
+    # timeout: 600 # Optional. If set to 0, default value is 300.
+    # Details: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
+    pricing_plan: "" # Optional. Use for Azure LLM when its model name is not the same as OpenAI's
+  "YOUR_MODEL_NAME_2": # model: "gpt-4-turbo"  # or gpt-3.5-turbo
+    api_type: "openai"  # or azure / ollama / groq etc.
+    base_url: "YOUR_MODEL_2_BASE_URL"
+    api_key: "YOUR_MODEL_2_API_KEY"
+    proxy: "YOUR_MODEL_2_PROXY"  # for LLM API requests
+    # timeout: 600 # Optional. If set to 0, default value is 300.
+    # Details: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
+    pricing_plan: "" # Optional. Use for Azure LLM when its model name is not the same as OpenAI's

tests/metagpt/configs/test_models_config.py

@@ -0,0 +1,34 @@
+import pytest
+
+from metagpt.actions.talk_action import TalkAction
+from metagpt.configs.models_config import ModelsConfig
+from metagpt.const import METAGPT_ROOT, TEST_DATA_PATH
+from metagpt.utils.common import aread, awrite
+
+
+@pytest.mark.asyncio
+async def test_models_configs(context):
+    default_model = ModelsConfig.default()
+    assert default_model is not None
+
+    models = ModelsConfig.from_yaml_file(TEST_DATA_PATH / "config/config2.yaml")
+    assert models
+
+    default_models = ModelsConfig.default()
+    backup = ""
+    if not default_models.models:
+        backup = await aread(filename=METAGPT_ROOT / "config/config2.yaml")
+        test_data = await aread(filename=TEST_DATA_PATH / "config/config2.yaml")
+        await awrite(filename=METAGPT_ROOT / "config/config2.yaml", data=test_data)
+
+    try:
+        action = TalkAction(context=context, i_context="who are you?", llm_name_or_type="YOUR_MODEL_NAME_1")
+        assert action.private_llm.config.model == "YOUR_MODEL_NAME_1"
+        assert context.config.llm.model != "YOUR_MODEL_NAME_1"
+    finally:
+        if backup:
+            await awrite(filename=METAGPT_ROOT / "config/config2.yaml", data=backup)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-s"])