diff --git a/src/backend/base/langflow/base/agents/agent.py b/src/backend/base/langflow/base/agents/agent.py index 7219c6385265..ca2e52d275f9 100644 --- a/src/backend/base/langflow/base/agents/agent.py +++ b/src/backend/base/langflow/base/agents/agent.py @@ -1,6 +1,8 @@ from abc import abstractmethod -from typing import TYPE_CHECKING, cast +from collections.abc import AsyncIterator +from typing import TYPE_CHECKING, Any, cast +from fastapi.encoders import jsonable_encoder from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent from langchain.agents.agent import RunnableAgent from langchain_core.runnables import Runnable @@ -12,6 +14,7 @@ from langflow.inputs.inputs import InputTypes from langflow.io import BoolInput, HandleInput, IntInput, MessageTextInput from langflow.schema import Data +from langflow.schema.log import LogFunctionType from langflow.schema.message import Message from langflow.template import Output from langflow.utils.constants import MESSAGE_SENDER_AI @@ -109,7 +112,26 @@ async def run_agent(self, agent: AgentExecutor) -> Text: msg = "Output key not found in result. Tried 'output'." raise ValueError(msg) - return cast(str, result.get("output")) + return cast(str, result) + + async def handle_chain_start(self, event: dict[str, Any]) -> None: + if event["name"] == "Agent": + self.log(f"Starting agent: {event['name']} with input: {event['data'].get('input')}") + + async def handle_chain_end(self, event: dict[str, Any]) -> None: + if event["name"] == "Agent": + self.log(f"Done agent: {event['name']} with output: {event['data'].get('output', {}).get('output', '')}") + + async def handle_tool_start(self, event: dict[str, Any]) -> None: + self.log(f"Starting tool: {event['name']} with inputs: {event['data'].get('input')}") + + async def handle_tool_end(self, event: dict[str, Any]) -> None: + self.log(f"Done tool: {event['name']}") + self.log(f"Tool output was: {event['data'].get('output')}") + + @abstractmethod + def create_agent_runnable(self) -> Runnable: + """Create the agent.""" class LCToolsAgentComponent(LCAgentComponent): @@ -146,16 +168,76 @@ async def run_agent( if self.chat_history: input_dict["chat_history"] = data_to_messages(self.chat_history) - result = runnable.invoke( - input_dict, config={"callbacks": [AgentAsyncHandler(self.log), *self.get_langchain_callbacks()]} + result = await process_agent_events( + runnable.astream_events( + input_dict, + config={"callbacks": [AgentAsyncHandler(self.log), *self.get_langchain_callbacks()]}, + version="v2", + ), + self.log, ) - self.status = result - if "output" not in result: - msg = "Output key not found in result. Tried 'output'." - raise ValueError(msg) - return cast(str, result.get("output")) + self.status = result + return cast(str, result) @abstractmethod def create_agent_runnable(self) -> Runnable: """Create the agent.""" + + +# Add this function near the top of the file, after the imports + + +async def process_agent_events(agent_executor: AsyncIterator[dict[str, Any]], log_callback: LogFunctionType) -> str: + """Process agent events and return the final output. + + Args: + agent_executor: An async iterator of agent events + log_callback: A callable function for logging messages + + Returns: + str: The final output from the agent + """ + final_output = "" + async for event in agent_executor: + match event["event"]: + case "on_chain_start": + if event["data"].get("input"): + log_callback(f"Agent initiated with input: {event['data'].get('input')}", name="🚀 Agent Start") + + case "on_chain_end": + data_output = event["data"].get("output", {}) + if data_output and "output" in data_output: + final_output = data_output["output"] + log_callback(f"{final_output}", name="✅ Agent End") + elif data_output and "agent_scratchpad" in data_output and data_output["agent_scratchpad"]: + agent_scratchpad_messages = data_output["agent_scratchpad"] + json_encoded_messages = jsonable_encoder(agent_scratchpad_messages) + log_callback(json_encoded_messages, name="🔍 Agent Scratchpad") + + case "on_tool_start": + log_callback( + f"Initiating tool: '{event['name']}' with inputs: {event['data'].get('input')}", + name="🔧 Tool Start", + ) + + case "on_tool_end": + log_callback(f"Tool '{event['name']}' execution completed", name="🏁 Tool End") + log_callback(f"{event['data'].get('output')}", name="📊 Tool Output") + + case "on_tool_error": + tool_name = event.get("name", "Unknown tool") + error_message = event["data"].get("error", "Unknown error") + log_callback(f"Tool '{tool_name}' failed with error: {error_message}", name="❌ Tool Error") + + if "stack_trace" in event["data"]: + log_callback(f"{event['data']['stack_trace']}", name="🔍 Tool Error") + + if "recovery_attempt" in event["data"]: + log_callback(f"{event['data']['recovery_attempt']}", name="🔄 Tool Error") + + case _: + # Handle any other event types or ignore them + pass + + return final_output diff --git a/src/backend/base/langflow/components/agents/tool_calling.py b/src/backend/base/langflow/components/agents/tool_calling.py index ec4603192d79..f02a35358868 100644 --- a/src/backend/base/langflow/components/agents/tool_calling.py +++ b/src/backend/base/langflow/components/agents/tool_calling.py @@ -1,15 +1,15 @@ from langchain.agents import create_tool_calling_agent -from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, PromptTemplate +from langchain_core.prompts import ChatPromptTemplate from langflow.base.agents.agent import LCToolsAgentComponent -from langflow.inputs import MultilineInput +from langflow.inputs import MessageTextInput from langflow.inputs.inputs import DataInput, HandleInput from langflow.schema import Data class ToolCallingAgentComponent(LCToolsAgentComponent): display_name: str = "Tool Calling Agent" - description: str = "Agent that uses tools" + description: str = "An agent designed to utilize various tools seamlessly within workflows." icon = "LangChain" beta = True name = "ToolCallingAgent" @@ -17,34 +17,33 @@ class ToolCallingAgentComponent(LCToolsAgentComponent): inputs = [ *LCToolsAgentComponent._base_inputs, HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True), - MultilineInput( + MessageTextInput( name="system_prompt", display_name="System Prompt", - info="System prompt for the agent.", - value="You are a helpful assistant", + info="Initial instructions and context provided to guide the agent's behavior.", + value="You are a helpful assistant that can use tools to answer questions and perform tasks.", ), - MultilineInput( - name="user_prompt", display_name="Prompt", info="This prompt must contain 'input' key.", value="{input}" + MessageTextInput( + name="input_value", + display_name="Input", + info="The input provided by the user for the agent to process.", ), - DataInput(name="chat_history", display_name="Chat History", is_list=True, advanced=True), + DataInput(name="chat_history", display_name="Chat Memory", is_list=True, advanced=True), ] def get_chat_history_data(self) -> list[Data] | None: return self.chat_history def create_agent_runnable(self): - if "input" not in self.user_prompt: - msg = "Prompt must contain 'input' key." - raise ValueError(msg) messages = [ ("system", self.system_prompt), ("placeholder", "{chat_history}"), - HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=["input"], template=self.user_prompt)), + ("human", self.input_value), ("placeholder", "{agent_scratchpad}"), ] prompt = ChatPromptTemplate.from_messages(messages) try: - return create_tool_calling_agent(self.llm, self.tools, prompt) + return create_tool_calling_agent(self.llm, self.tools or [], prompt) except NotImplementedError as e: - message = f"{self.display_name} does not support tool calling." "Please try using a compatible model." + message = f"{self.display_name} does not support tool calling. Please try using a compatible model." raise NotImplementedError(message) from e