Fix swarm context variable function injection

autogen/agentchat/contrib/swarm_agent.py

@@ -3,17 +3,23 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import copy
-import json
+import inspect
 import warnings
 from dataclasses import dataclass
 from enum import Enum
 from functools import partial
-from inspect import signature
 from types import MethodType
-from typing import Any, Callable, Literal, Optional, Union
+from typing import Annotated, Any, Callable, Literal, Optional, Union
 
 from pydantic import BaseModel, field_serializer
 
+from autogen.tools.dependency_injection import (
+    Depends,
+    inject_params,
+    on,
+)
+from autogen.tools.tool import Tool
+
 from ...doc_utils import export_module
 from ...oai import OpenAIWrapper
 from ..agent import Agent
@@ -298,8 +304,7 @@ def _link_agents_to_swarm_manager(agents: list[Agent], group_chat_manager: Agent
     Does not link the Tool Executor agent.
     """
     for agent in agents:
-        if agent.name not in [__TOOL_EXECUTOR_NAME__]:
-            agent._swarm_manager = group_chat_manager  # type: ignore[attr-defined]
+        agent._swarm_manager = group_chat_manager  # type: ignore[attr-defined]
 
 
 def _run_oncontextconditions(
@@ -339,16 +344,72 @@ def _run_oncontextconditions(
     return False, None
 
 
+def _modify_context_variables_param(f: Callable[..., Any], context_variables: dict[str, Any]) -> Callable[..., Any]:
+    """Modifies the context_variables parameter to use dependency injection and link it to the swarm context variables.
+
+    This essentially changes:
+    def some_function(some_variable: int, context_variables: dict[str, Any]) -> str:
+
+    to:
+
+    def some_function(some_variable: int, context_variables: Annotated[dict[str, Any], Depends(on(self._context_variables))]) -> str:
+    """
+    sig = inspect.signature(f)
+
+    # Check if context_variables parameter exists and update it if so
+    if __CONTEXT_VARIABLES_PARAM_NAME__ in sig.parameters:
+        new_params = []
+        for name, param in sig.parameters.items():
+            if name == __CONTEXT_VARIABLES_PARAM_NAME__:
+                # Replace with new annotation using Depends
+                new_param = param.replace(annotation=Annotated[dict[str, Any], Depends(on(context_variables))])
+                new_params.append(new_param)
+            else:
+                new_params.append(param)
+
+        # Update signature
+        new_sig = sig.replace(parameters=new_params)
+        f.__signature__ = new_sig  # type: ignore[attr-defined]
+
+    return f
+
+
+def _change_tool_context_variables_to_depends(
+    agent: ConversableAgent, current_tool: Tool, context_variables: dict[str, Any]
+) -> None:
+    """Checks for the context_variables parameter in the tool and updates it to use dependency injection."""
+
+    # If the tool has a context_variables parameter, remove the tool and reregister it without the parameter
+    if __CONTEXT_VARIABLES_PARAM_NAME__ in current_tool.tool_schema["function"]["parameters"]["properties"]:
+        # We'll replace the tool, so start with getting the underlying function
+        tool_func = current_tool._func
+
+        # Remove the Tool from the agent
+        name = current_tool._name
+        description = current_tool._description
+        agent.remove_tool_for_llm(current_tool)
+
+        # Recreate the tool without the context_variables parameter
+        tool_func = _modify_context_variables_param(current_tool._func, context_variables)
+        tool_func = inject_params(tool_func)
+        new_tool = ConversableAgent._create_tool_if_needed(func_or_tool=tool_func, name=name, description=description)
+
+        # Re-register with the agent
+        agent.register_for_llm()(new_tool)
+
+
 def _prepare_swarm_agents(
     initial_agent: ConversableAgent,
     agents: list[ConversableAgent],
+    context_variables: dict[str, Any],
     exclude_transit_message: bool = True,
 ) -> tuple[ConversableAgent, list[ConversableAgent]]:
     """Validates agents, create the tool executor, configure nested chats.
 
     Args:
         initial_agent (ConversableAgent): The first agent in the conversation.
         agents (list[ConversableAgent]): List of all agents in the conversation.
+        context_variables (dict[str, Any]): Context variables to assign to all agents.
         exclude_transit_message (bool): Whether to exclude transit messages from the agents.
 
     Returns:
@@ -382,16 +443,24 @@ def _prepare_swarm_agents(
     for agent in agents:
         _create_nested_chats(agent, nested_chat_agents)
 
+    # Update any agent's tools that have context_variables as a parameter
+    # To use Dependency Injection
+
     # Update tool execution agent with all the functions from all the agents
     for agent in agents + nested_chat_agents:
         tool_execution._function_map.update(agent._function_map)
+
         # Add conditional functions to the tool_execution agent
         for func_name, (func, _) in agent._swarm_conditional_functions.items():  # type: ignore[attr-defined]
             tool_execution._function_map[func_name] = func
 
-        # Register tools from the tools property
+        # Update any agent tools that have context_variables parameters to use Dependency Injection
+        for tool in agent.tools:
+            _change_tool_context_variables_to_depends(agent, tool, context_variables)
+
+        # Add all tools to the Tool Executor agent
         for tool in agent.tools:
-            tool_execution.register_for_execution()(tool)
+            tool_execution.register_for_execution(serialize=False)(tool)
 
     if exclude_transit_message:
         # get all transit functions names
@@ -876,8 +945,11 @@ def custom_afterwork_func(last_speaker: ConversableAgent, messages: list[dict[st
         dict[str, Any]: Updated Context variables.
         ConversableAgent:     Last speaker.
     """
+    context_variables = context_variables or {}
 
-    tool_execution, nested_chat_agents = _prepare_swarm_agents(initial_agent, agents, exclude_transit_message)
+    tool_execution, nested_chat_agents = _prepare_swarm_agents(
+        initial_agent, agents, context_variables, exclude_transit_message
+    )
 
     processed_messages, last_agent, swarm_agent_names, temp_user_list = _process_initial_messages(
         messages, user_agent, agents, nested_chat_agents
@@ -902,7 +974,7 @@ def custom_afterwork_func(last_speaker: ConversableAgent, messages: list[dict[st
     manager = _create_swarm_manager(groupchat, swarm_manager_args, agents)
 
     # Point all ConversableAgent's context variables to this function's context_variables
-    _setup_context_variables(tool_execution, agents, manager, context_variables or {})
+    _setup_context_variables(tool_execution, agents, manager, context_variables)
 
     # Link all agents with the GroupChatManager to allow access to the group chat
     # and other agents, particularly the tool executor for setting _swarm_next_agent
@@ -926,7 +998,7 @@ def custom_afterwork_func(last_speaker: ConversableAgent, messages: list[dict[st
 
     _cleanup_temp_user_messages(chat_result)
 
-    return chat_result, context_variables, manager.last_speaker  # type: ignore[return-value]
+    return chat_result, context_variables if context_variables != {} else None, manager.last_speaker  # type: ignore[return-value]
 
 
 @export_module("autogen")
@@ -976,7 +1048,10 @@ def custom_afterwork_func(last_speaker: ConversableAgent, messages: list[dict[st
         dict[str, Any]: Updated Context variables.
         ConversableAgent:     Last speaker.
     """
-    tool_execution, nested_chat_agents = _prepare_swarm_agents(initial_agent, agents, exclude_transit_message)
+    context_variables = context_variables or {}
+    tool_execution, nested_chat_agents = _prepare_swarm_agents(
+        initial_agent, agents, context_variables, exclude_transit_message
+    )
 
     processed_messages, last_agent, swarm_agent_names, temp_user_list = _process_initial_messages(
         messages, user_agent, agents, nested_chat_agents
@@ -1001,7 +1076,7 @@ def custom_afterwork_func(last_speaker: ConversableAgent, messages: list[dict[st
     manager = _create_swarm_manager(groupchat, swarm_manager_args, agents)
 
     # Point all ConversableAgent's context variables to this function's context_variables
-    _setup_context_variables(tool_execution, agents, manager, context_variables or {})
+    _setup_context_variables(tool_execution, agents, manager, context_variables)
 
     if len(processed_messages) > 1:
         last_agent, last_message = await manager.a_resume(messages=processed_messages)
@@ -1021,7 +1096,7 @@ def custom_afterwork_func(last_speaker: ConversableAgent, messages: list[dict[st
 
     _cleanup_temp_user_messages(chat_result)
 
-    return chat_result, context_variables, manager.last_speaker  # type: ignore[return-value]
+    return chat_result, context_variables if context_variables != {} else None, manager.last_speaker  # type: ignore[return-value]
 
 
 class SwarmResult(BaseModel):
@@ -1155,6 +1230,8 @@ def _update_conditional_functions(agent: ConversableAgent, messages: Optional[li
                 condition = condition.format(context_variables=agent._context_variables)
             elif callable(condition):
                 condition = condition(agent, messages)
+
+            # TODO: Don't add it if it's already there
             agent._add_single_function(func, func_name, condition)
 
 
@@ -1185,26 +1262,11 @@ def _generate_swarm_tool_reply(
         tool_responses_inner = []
         contents = []
         for index in range(tool_call_count):
-            # Deep copy to ensure no changes to messages when we insert the context variables
             message_copy = copy.deepcopy(message)
 
             # 1. add context_variables to the tool call arguments
             tool_call = message_copy["tool_calls"][index]
 
-            if tool_call["type"] == "function":
-                function_name = tool_call["function"]["name"]
-
-                # Check if this function exists in our function map
-                if function_name in agent._function_map:
-                    func = agent._function_map[function_name]  # Get the original function
-
-                    # Inject the context variables into the tool call if it has the parameter
-                    sig = signature(func)
-                    if __CONTEXT_VARIABLES_PARAM_NAME__ in sig.parameters:
-                        current_args = json.loads(tool_call["function"]["arguments"])
-                        current_args[__CONTEXT_VARIABLES_PARAM_NAME__] = agent._context_variables
-                        tool_call["function"]["arguments"] = json.dumps(current_args, default=BaseModel.model_dump_json)
-
             # Ensure we are only executing the one tool at a time
             message_copy["tool_calls"] = [tool_call]
 
@@ -1217,6 +1279,7 @@ def _generate_swarm_tool_reply(
             # 3. update context_variables and next_agent, convert content to string
             for tool_response in tool_message["tool_responses"]:
                 content = tool_response.get("content")
+
                 if isinstance(content, SwarmResult):
                     if content.context_variables != {}:
                         agent._context_variables.update(content.context_variables)
@@ -1225,6 +1288,10 @@ def _generate_swarm_tool_reply(
                 elif isinstance(content, Agent):
                     next_agent = content
 
+                # Serialize the content to a string
+                if content is not None:
+                    tool_response["content"] = str(content)
+
                 tool_responses_inner.append(tool_response)
                 contents.append(str(tool_response["content"]))
 

autogen/agentchat/conversable_agent.py

@@ -58,7 +58,7 @@
 )
 from ..oai.client import ModelClient, OpenAIWrapper
 from ..runtime_logging import log_event, log_function_use, log_new_agent, logging_enabled
-from ..tools import ChatContext, Tool, get_function_schema, load_basemodels_if_needed, serialize_to_str
+from ..tools import ChatContext, Tool, load_basemodels_if_needed, serialize_to_str
 from .agent import Agent, LLMAgent
 from .chat import ChatResult, _post_process_carryover_item, a_initiate_chats, initiate_chats
 from .utils import consolidate_chat_info, gather_usage_summary
@@ -400,7 +400,7 @@ def _add_functions(self, func_list: list[Callable[..., Any]]):
             self._add_single_function(func)
 
     def _add_single_function(self, func: Callable, name: Optional[str] = None, description: Optional[str] = ""):
-        """Add a single function to the agent, removing context variables for LLM use.
+        """Add a single function to the agent
 
         Args:
             func (Callable): The function to register.
@@ -418,23 +418,8 @@ def _add_single_function(self, func: Callable, name: Optional[str] = None, descr
             # Use function's docstring, strip whitespace, fall back to empty string
             func._description = (func.__doc__ or "").strip()
 
-        f = get_function_schema(func, name=func._name, description=func._description)
-
-        # Remove context_variables parameter from function schema
-        f_no_context = f.copy()
-        if __CONTEXT_VARIABLES_PARAM_NAME__ in f_no_context["function"]["parameters"]["properties"]:
-            del f_no_context["function"]["parameters"]["properties"][__CONTEXT_VARIABLES_PARAM_NAME__]
-        if "required" in f_no_context["function"]["parameters"]:
-            required = f_no_context["function"]["parameters"]["required"]
-            f_no_context["function"]["parameters"]["required"] = [
-                param for param in required if param != __CONTEXT_VARIABLES_PARAM_NAME__
-            ]
-            # If required list is empty, remove it
-            if not f_no_context["function"]["parameters"]["required"]:
-                del f_no_context["function"]["parameters"]["required"]
-
-        self.update_tool_signature(f_no_context, is_remove=False)
-        self.register_function({func._name: func})
+        # Register the function
+        self.register_for_llm(name=name, description=description)(func)
 
     def _register_update_agent_state_before_reply(
         self, functions: Optional[Union[list[Callable[..., Any]], Callable[..., Any]]]
@@ -3038,7 +3023,7 @@ def function_map(self) -> dict[str, Callable[..., Any]]:
         """Return the function map."""
         return self._function_map
 
-    def _wrap_function(self, func: F, inject_params: dict[str, Any] = {}) -> F:
+    def _wrap_function(self, func: F, inject_params: dict[str, Any] = {}, *, serialize: bool = True) -> F:
         """Wrap the function inject chat context parameters and to dump the return value to json.
 
         Handles both sync and async functions.
@@ -3057,15 +3042,15 @@ def _wrapped_func(*args, **kwargs):
             retval = func(*args, **kwargs, **inject_params)
             if logging_enabled():
                 log_function_use(self, func, kwargs, retval)
-            return serialize_to_str(retval)
+            return serialize_to_str(retval) if serialize else retval
 
         @load_basemodels_if_needed
         @functools.wraps(func)
         async def _a_wrapped_func(*args, **kwargs):
             retval = await func(*args, **kwargs, **inject_params)
             if logging_enabled():
                 log_function_use(self, func, kwargs, retval)
-            return serialize_to_str(retval)
+            return serialize_to_str(retval) if serialize else retval
 
         wrapped_func = _a_wrapped_func if inspect.iscoroutinefunction(func) else _wrapped_func
 
@@ -3181,6 +3166,8 @@ def register_for_execution(
         self,
         name: Optional[str] = None,
         description: Optional[str] = None,
+        *,
+        serialize: bool = True,
     ) -> Callable[[Union[Tool, F]], Tool]:
         """Decorator factory for registering a function to be executed by an agent.
 
@@ -3223,7 +3210,9 @@ def _decorator(
             chat_context = ChatContext(self)
             chat_context_params = {param: chat_context for param in tool._chat_context_param_names}
 
-            self.register_function({tool.name: self._wrap_function(tool.func, chat_context_params)})
+            self.register_function({
+                tool.name: self._wrap_function(tool.func, chat_context_params, serialize=serialize)
+            })
 
             return tool
 

autogen/tools/dependency_injection.py

@@ -27,6 +27,7 @@
     "get_context_params",
     "inject_params",
     "on",
+    "remove_params",
 ]
 
 
@@ -135,7 +136,7 @@ def _is_depends_param(param: inspect.Parameter) -> bool:
     )
 
 
-def _remove_params(func: Callable[..., Any], sig: inspect.Signature, params: Iterable[str]) -> None:
+def remove_params(func: Callable[..., Any], sig: inspect.Signature, params: Iterable[str]) -> None:
     new_signature = sig.replace(parameters=[p for p in sig.parameters.values() if p.name not in params])
     func.__signature__ = new_signature  # type: ignore[attr-defined]
 
@@ -147,7 +148,7 @@ def _remove_injected_params_from_signature(func: Callable[..., Any]) -> Callable
 
     sig = inspect.signature(func)
     params_to_remove = [p.name for p in sig.parameters.values() if _is_context_param(p) or _is_depends_param(p)]
-    _remove_params(func, sig, params_to_remove)
+    remove_params(func, sig, params_to_remove)
     return func