Agent not returning all the records

[atulonweb]

I am using inbuilt agent which call api that returns 20 records but agent returns only 10 records. I could not find anywhere why it is behaving so. There is no configuration to control it.

[dosubot[bot]]

There is a configuration parameter in Langflow that limits the number of records returned by an agent. The parameter is number_of_results, which is an IntInput with the display name "Number of Results". It specifies the number of results to return and has a default value of 10. You can adjust this parameter to change the number of records returned by the agent ^[1].

Additionally, there is a similar open issue titled "Agent not returning all the records" that might be relevant to your situation. You can view it here ^[2].

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[atulonweb]

@dosu here is the code of inbuilt agent. it does not have any "Number of results" parameter. import logging
import os

Configure logging

logging.basicConfig(level=logging.DEBUG,
filename="c:/langflow/agent_debug.log", # Save logs to this file
filemode="w", # Overwrite the log file on each run
format="%(asctime)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(name)

Rest of the imports remain unchanged

class AgentComponent(ToolCallingAgentComponent):
display_name: str = "Agent"
description: str = "Define the agent's instructions, then enter a task to complete using tools."
icon = "bot"
beta = False
name = "Agent"

memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]

inputs = [
    DropdownInput(
        name="agent_llm",
        display_name="Model Provider",
        info="The provider of the language model that the agent will use to generate responses.",
        options=[*sorted(MODEL_PROVIDERS_DICT.keys()), "Custom"],
        value="OpenAI",
        real_time_refresh=True,
        input_types=[],
    ),
    *MODEL_PROVIDERS_DICT["OpenAI"]["inputs"],
    MultilineInput(
        name="system_prompt",
        display_name="Agent Instructions",
        info="System Prompt: 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.",
        advanced=False,
    ),
    *LCToolsAgentComponent._base_inputs,
    *memory_inputs,
    BoolInput(
        name="add_current_date_tool",
        display_name="Add tool Current Date",
        advanced=True,
        info="If true, will add a tool to the agent that returns the current date.",
        value=True,
    ),
]
outputs = [Output(name="response", display_name="Response", method="message_response")]

async def message_response(self) -> Message:
    logger.debug("Starting message_response method.")
    
    # Get the LLM model and validate
    llm_model, display_name = self.get_llm()
    self.model_name = get_model_name(llm_model, display_name=display_name)
    if llm_model is None:
        msg = "No language model selected"
        logger.error(msg)
        raise ValueError(msg)
    
    # Log retrieved chat history
    self.chat_history = self.get_memory_data()
    logger.debug(f"Chat history retrieved: {len(self.chat_history)} messages")

    # Add Current Date Tool if enabled
    if self.add_current_date_tool:
        if not isinstance(self.tools, list):  # type: ignore[has-type]
            self.tools = []
        # Convert CurrentDateComponent to a StructuredTool
        current_date_tool = CurrentDateComponent().to_toolkit()[0]
        if isinstance(current_date_tool, StructuredTool):
            self.tools.append(current_date_tool)
            logger.debug("Added CurrentDate tool to the agent.")
        else:
            msg = "CurrentDateComponent must be converted to a StructuredTool"
            logger.error(msg)
            raise ValueError(msg)

    # Validate tools
    if not self.tools:
        msg = "Tools are required to run the agent."
        logger.error(msg)
        raise ValueError(msg)

    # Set up the agent
    self.set(
        llm=llm_model,
        tools=self.tools,
        chat_history=self.chat_history,
        input_value=self.input_value,
        system_prompt=self.system_prompt,
    )
    logger.debug(f"Agent setup completed with input value: {self.input_value}")

    # Create the agent runnable
    agent = self.create_agent_runnable()

    # Run the agent
    logger.debug("Running the agent...")
    result = await self.run_agent(agent)
    
    # Log the result
    if isinstance(result, Message):
        logger.debug(f"Agent response generated with {result} characters.")
    else:
        logger.debug(f"Agent response: {result}")

    return result

def get_memory_data(self):
    logger.debug("Fetching memory data...")
    memory_kwargs = {
        component_input.name: getattr(self, f"{component_input.name}") for component_input in self.memory_inputs
    }

    memory_data = MemoryComponent().set(**memory_kwargs).retrieve_messages()
    logger.debug(f"Retrieved {memory_data} memory messages.")
    return memory_data

def get_llm(self):
    logger.debug(f"Getting LLM model for provider: {self.agent_llm}")
    if isinstance(self.agent_llm, str):
        try:
            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)
            if provider_info:
                component_class = provider_info.get("component_class")
                display_name = component_class.display_name
                inputs = provider_info.get("inputs")
                prefix = provider_info.get("prefix", "")
                model = self._build_llm_model(component_class, inputs, prefix)
                logger.debug(f"LLM model {display_name} built successfully.")
                return model, display_name
        except Exception as e:
            msg = f"Error building {self.agent_llm} language model"
            logger.error(msg)
            raise ValueError(msg) from e
    return self.agent_llm, None

def _build_llm_model(self, component, inputs, prefix=""):
    model_kwargs = {input_.name: getattr(self, f"{prefix}{input_.name}") for input_ in inputs}
    logger.debug(f"Building LLM model with kwargs: {model_kwargs}")
    return component.set(**model_kwargs).build_model()

[dosubot[bot]]

The AgentComponent class you provided does not explicitly include a "Number of Results" parameter. However, the number of results returned by an agent can be influenced by other components or configurations within Langflow. For instance, the ChromaVectorStoreComponent class has a limit attribute that can restrict the number of records returned ^[1]. Additionally, there are configuration settings like number_of_results and max_results in other parts of Langflow that can globally limit the number of records an agent can return ^[1][2].

If the agent is not returning the expected number of records, you might want to check if any of these settings are being applied elsewhere in your setup. Adjusting these parameters could help in controlling the number of records returned by the agent.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}