refactor: deprecate LLMConfig contextmanager, .current, .default

README.md

@@ -109,10 +109,10 @@ from autogen import AssistantAgent, UserProxyAgent, LLMConfig
 
 llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")
 
+assistant = AssistantAgent("assistant", llm_config=llm_config)
 
-with llm_config:
-    assistant = AssistantAgent("assistant")
 user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding", "use_docker": False})
+
 user_proxy.initiate_chat(assistant, message="Plot a chart of NVDA and TESLA stock price change YTD.")
 # This initiates an automated chat between the two agents to solve the task
 ```
@@ -148,22 +148,23 @@ from autogen import ConversableAgent, LLMConfig
 
 # 2. Define our LLM configuration for OpenAI's GPT-4o mini
 #    uses the OPENAI_API_KEY environment variable
-llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")
-
+llm_config = LLMConfig({
+    "api_type": "openai",
+    "model": "gpt-4o-mini",
+})
 
 # 3. Create our LLM agent
-with llm_config:
-  # Create an AI agent
-  assistant = ConversableAgent(
-      name="assistant",
-      system_message="You are an assistant that responds concisely.",
-  )
-
-  # Create another AI agent
-  fact_checker = ConversableAgent(
-      name="fact_checker",
-      system_message="You are a fact-checking assistant.",
-  )
+assistant = ConversableAgent(
+    name="assistant",
+    system_message="You are an assistant that responds concisely.",
+    llm_config=llm_config,
+)
+
+fact_checker = ConversableAgent(
+    name="fact_checker",
+    system_message="You are a fact-checking assistant.",
+    llm_config=llm_config,
+)
 
 # 4. Start the conversation
 assistant.initiate_chat(
@@ -192,23 +193,29 @@ from autogen import ConversableAgent, UserProxyAgent, LLMConfig
 
 # 2. Define our LLM configuration for OpenAI's GPT-4o mini
 #    uses the OPENAI_API_KEY environment variable
-llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")
-
+llm_config = LLMConfig({
+    "api_type": "openai",
+    "model": "gpt-4o-mini",
+})
 
 # 3. Create our LLM agent
-with llm_config:
-  assistant = ConversableAgent(
-      name="assistant",
-      system_message="You are a helpful assistant.",
-  )
+assistant = ConversableAgent(
+    name="assistant",
+    system_message="You are a helpful assistant.",
+    llm_config=llm_config,
+)
 
 # 4. Create a human agent with manual input mode
 human = ConversableAgent(
     name="human",
     human_input_mode="ALWAYS"
 )
+
 # or
-human = UserProxyAgent(name="human", code_execution_config={"work_dir": "coding", "use_docker": False})
+human = UserProxyAgent(
+    name="human",
+    code_execution_config={"work_dir": "coding", "use_docker": False},
+)
 
 # 5. Start the chat
 human.initiate_chat(
@@ -232,7 +239,10 @@ Note: Before running this code, make sure to set your `OPENAI_API_KEY` as an env
 from autogen import ConversableAgent, GroupChat, GroupChatManager, LLMConfig
 
 # Put your key in the OPENAI_API_KEY environment variable
-llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")
+llm_config = LLMConfig({
+    "api_type": "openai",
+    "model": "gpt-4o-mini",
+})
 
 planner_message = """You are a classroom lesson agent.
 Given a topic, write a lesson plan for a fourth grade class.
@@ -253,18 +263,19 @@ planner_description = "Creates or revises lesson plans."
 reviewer_description = """Provides one round of reviews to a lesson plan
 for the lesson_planner to revise."""
 
-with llm_config:
-    lesson_planner = ConversableAgent(
-        name="planner_agent",
-        system_message=planner_message,
-        description=planner_description,
-    )
+lesson_planner = ConversableAgent(
+    name="planner_agent",
+    system_message=planner_message,
+    description=planner_description,
+    llm_config=llm_config,
+)
 
-    lesson_reviewer = ConversableAgent(
-        name="reviewer_agent",
-        system_message=reviewer_message,
-        description=reviewer_description,
-    )
+lesson_reviewer = ConversableAgent(
+    name="reviewer_agent",
+    system_message=reviewer_message,
+    description=reviewer_description,
+    llm_config=llm_config,
+)
 
 # 2. The teacher's system message can also be used as a description, so we don't define it
 teacher_message = """You are a classroom teacher.
@@ -273,13 +284,13 @@ and reviewer to create and finalise lesson plans.
 When you are happy with a lesson plan, output "DONE!".
 """
 
-with llm_config:
-    teacher = ConversableAgent(
-        name="teacher_agent",
-        system_message=teacher_message,
-        # 3. Our teacher can end the conversation by saying DONE!
-        is_termination_msg=lambda x: "DONE!" in (x.get("content", "") or "").upper(),
-    )
+teacher = ConversableAgent(
+    name="teacher_agent",
+    system_message=teacher_message,
+    # 3. Our teacher can end the conversation by saying DONE!
+    is_termination_msg=lambda x: "DONE!" in (x.get("content", "") or "").upper(),
+    llm_config=llm_config,
+)
 
 # 4. Create the GroupChat with agents and selection method
 groupchat = GroupChat(
@@ -319,24 +330,28 @@ from typing import Annotated
 from autogen import ConversableAgent, register_function, LLMConfig
 
 # Put your key in the OPENAI_API_KEY environment variable
-llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")
+llm_config = LLMConfig({
+    "api_type": "openai",
+    "model": "gpt-4o-mini",
+})
 
 # 1. Our tool, returns the day of the week for a given date
 def get_weekday(date_string: Annotated[str, "Format: YYYY-MM-DD"]) -> str:
     date = datetime.strptime(date_string, "%Y-%m-%d")
     return date.strftime("%A")
 
 # 2. Agent for determining whether to run the tool
-with llm_config:
-    date_agent = ConversableAgent(
-        name="date_agent",
-        system_message="You get the day of the week for a given date.",
-    )
+date_agent = ConversableAgent(
+    name="date_agent",
+    system_message="You get the day of the week for a given date.",
+    llm_config=llm_config,
+)
 
 # 3. And an agent for executing the tool
 executor_agent = ConversableAgent(
     name="executor_agent",
     human_input_mode="NEVER",
+    llm_config=llm_config,
 )
 
 # 4. Registers the tool with the agents, the description will be used by the LLM

autogen/agentchat/conversable_agent.py

@@ -486,17 +486,12 @@ def _validate_llm_config(
         if llm_config is None:
             llm_config = LLMConfig.get_current_llm_config()
             if llm_config is None:
-                llm_config = cls.DEFAULT_CONFIG
-        elif isinstance(llm_config, dict):
-            llm_config = LLMConfig(**llm_config)
-        elif isinstance(llm_config, LLMConfig):
-            llm_config = llm_config.copy()
+                return cls.DEFAULT_CONFIG
+
         elif llm_config is False:
-            pass
-        else:
-            raise ValueError("llm_config must be a LLMConfig, dict or False or None.")
+            return False
 
-        return llm_config
+        return LLMConfig.ensure_config(llm_config)
 
     @classmethod
     def _create_client(cls, llm_config: LLMConfig | Literal[False]) -> OpenAIWrapper | None: