Universal tool calling ReAct agent

examples/_test_single_openai_tools_react.py

@@ -2,7 +2,7 @@
 from langchain_community.tools import DuckDuckGoSearchRun
 
 from motleycrew import MotleyCrew
-from motleycrew.agents.langchain.openai_tools_react import ReactOpenAIToolsAgent
+from motleycrew.agents.langchain.tool_calling_react import ReActToolCallingAgent
 from motleycrew.agents.langchain.react import ReactMotleyAgent
 from motleycrew.common import configure_logging
 from motleycrew.tasks import SimpleTask
@@ -14,7 +14,7 @@ def main():
 
     tools = [search_tool]
 
-    researcher = ReactOpenAIToolsAgent(tools=tools, verbose=True)
+    researcher = ReActToolCallingAgent(tools=tools, verbose=True)
     researcher2 = ReactMotleyAgent(tools=tools, verbose=True)
 
     outputs = []

examples/tool_calling_with_memory.py

@@ -0,0 +1,59 @@
+from dotenv import load_dotenv
+from langchain_community.tools import DuckDuckGoSearchRun
+
+from motleycrew import MotleyCrew
+from motleycrew.agents.langchain.tool_calling_react import ReActToolCallingAgent
+from motleycrew.common import configure_logging
+from motleycrew.tasks import SimpleTask
+
+from motleycrew.common.llms import init_llm
+from motleycrew.common import LLMFramework, LLMFamily
+
+
+def main():
+    search_tool = DuckDuckGoSearchRun()
+
+    tools = [search_tool]
+
+    researcher = ReActToolCallingAgent(
+        tools=tools,
+        verbose=True,
+        with_history=True,
+        # llm=init_llm(
+        #     llm_framework=LLMFramework.LANGCHAIN,
+        #     llm_family=LLMFamily.ANTHROPIC,
+        #     llm_name="claude-3-sonnet-20240229",
+        # ),
+    )
+
+    outputs = []
+
+    crew = MotleyCrew()
+    task = SimpleTask(
+        crew=crew,
+        name="produce comprehensive analysis report on AI advancements",
+        description="""Conduct a comprehensive analysis of the latest advancements in AI.
+      Identify key trends, breakthrough technologies, and potential industry impacts.
+      Your final answer MUST be a full analysis report""",
+        agent=researcher,
+    )
+    crew.run()
+    print(task.output)
+
+    # See whether the researcher's memory persists across tasks
+    tldr_task = SimpleTask(
+        crew=crew,
+        name="provide a TLDR summary of the report",
+        description="Write a short summary of the comprehensive analysis report on AI advancements that you just wrote.",
+        agent=researcher,
+    )
+
+    crew.run()
+    print(tldr_task.output)
+
+
+if __name__ == "__main__":
+    configure_logging(verbose=True)
+
+    load_dotenv()
+    main()

motleycrew/agents/langchain/__init__.py

@@ -1,4 +1,3 @@
 from .langchain import LangchainMotleyAgent
 
 from .react import ReactMotleyAgent
-from .openai_tools_react import OpenAIToolsAgentOutputParser

motleycrew/agents/langchain/langchain.py

@@ -1,11 +1,15 @@
 """ Module description """
+
 from typing import Any, Optional, Sequence, Callable
 
 from langchain.agents import AgentExecutor
 from langchain_core.runnables import RunnableConfig
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory, InMemoryChatMessageHistory
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.chat import ChatPromptTemplate
 
+
 from motleycrew.agents.parent import MotleyAgentParent
 from motleycrew.agents.abstract_parent import MotleyAgentAbstractParent
 
@@ -25,8 +29,10 @@ def __init__(
         agent_factory: MotleyAgentFactory | None = None,
         tools: Sequence[MotleySupportedTool] | None = None,
         verbose: bool = False,
+        with_history: bool = False,
+        chat_history: BaseChatMessageHistory | None = None,
     ):
-        """ Description
+        """Description
 
         Args:
             description (str):
@@ -43,13 +49,32 @@ def __init__(
             verbose=verbose,
         )
 
+        self.with_history = with_history
+        self.chat_history = chat_history or InMemoryChatMessageHistory()
+
+    def materialize(self):
+        """Materialize the agent and wrap it in RunnableWithMessageHistory if needed."""
+        if self.is_materialized:
+            return
+
+        super().materialize()
+        if self.with_history:
+            if isinstance(self._agent, RunnableWithMessageHistory):
+                return
+            self._agent = RunnableWithMessageHistory(
+                runnable=self._agent,
+                get_session_history=lambda _: self.chat_history,
+                input_messages_key="input",
+                history_messages_key="chat_history",
+            )
+
     def invoke(
         self,
         task_dict: dict,
         config: Optional[RunnableConfig] = None,
         **kwargs: Any,
     ) -> Any:
-        """ Description
+        """Description
 
         Args:
             task_dict (dict):
@@ -66,11 +91,17 @@ def invoke(
             raise ValueError("Task must have a prompt")
 
         config = add_default_callbacks_to_langchain_config(config)
+        if self.with_history:
+            config["configurable"] = config.get("configurable") or {}
+            config["configurable"]["session_id"] = (
+                config["configurable"].get("session_id") or "default"
+            )
 
         result = self.agent.invoke({"input": prompt}, config, **kwargs)
         output = result.get("output")
         if output is None:
             raise Exception("Agent {} result does not contain output: {}".format(self, result))
+
         return output
 
     @staticmethod
@@ -83,9 +114,10 @@ def from_function(
         tools: Sequence[MotleySupportedTool] | None = None,
         prompt: ChatPromptTemplate | Sequence[ChatPromptTemplate] | None = None,
         require_tools: bool = False,
+        with_history: bool = False,
         verbose: bool = False,
     ) -> "LangchainMotleyAgent":
-        """ Description
+        """Description
 
         Args:
             function (Callable):
@@ -123,6 +155,7 @@ def agent_factory(tools: dict[str, MotleyTool]):
             name=name,
             agent_factory=agent_factory,
             tools=tools,
+            with_history=with_history,
             verbose=verbose,
         )
 
@@ -133,7 +166,7 @@ def from_agent(
         tools: Sequence[MotleySupportedTool] | None = None,
         verbose: bool = False,
     ) -> "LangchainMotleyAgent":
-        """ Description
+        """Description
 
         Args:
             agent (AgentExecutor):