Agents extend Large Language Models (LLMs) by integrating tools, which are functions that provide additional context and enable autonomous task execution.
These integrations make agents more specialized and powerful than standalone LLMs.
Whereas an LLM might generate acceptable, inert responses to general queries and tasks, an agent can leverage the integrated context and tools to provide more relevant responses and even take action.
-For example, you might create an agent that can access your company's knowledge base, repositories, and other resources to help your team with tasks that require knowledge of your specific products, customers, and code.
Agents use LLMs as a reasoning engine to process input, determine which actions to take to address the query, and then generate a response.
+For example, you might create an agent that can access your company's documentation, repositories, and other resources to help your team with tasks that require knowledge of your specific products, customers, and code.
Agents use LLMs as a reasoning engine to process input, determine which actions to take to address the query, and then generate a response.
The response could be a typical text-based LLM response, or it could involve an action, like editing a file, running a script, or calling an external API.
In an agentic context, tools are functions that the agent can run to perform tasks or access external resources.
A function is wrapped as a Tool object with a common interface that the agent understands.
Agents become aware of tools through tool registration, which is when the agent is provided a list of available tools typically at agent initialization.
@@ -115,8 +115,8 @@ With the given example, the agent should call the News Search c
For a multi-agent example, see Use an agent as a tool.
You can configure the Agent component to use your preferred provider and model, custom instructions, and tools.
-
Many optional Agent component input parameters are hidden by default in the visual editor.
-You can access all component parameters through the Controls in the component's header menu.
+
Some parameters are hidden by default in the visual editor.
+You can modify all parameters through the Controls in the component's header menu.
Use the Model Provider (agent_llm) and Model Name (llm_model) settings to select the model provider and LLM that you want the agent to use.
The Agent component includes many models from several popular model providers.
@@ -144,18 +144,19 @@ This memory allows them to retrieve and reference messages from previous convers
Chat memories are grouped by session ID (session_id).
It is recommended to use custom session IDs if you need to segregate chat memory for different users or applications that run the same flow.
By default, the Agent component uses your Langflow installation's storage, and it retrieves a limited number of chat messages, which you can configure with the Number of Chat History Messages parameter.
-
Although the Message History component isn't required for default chat memory, it provides more options for sorting, filtering, and limiting memories, and the Message History component is required to use external chat memory like Mem0.
+
The Message History component isn't required for default chat memory, but it is required if you want to use external chat memory like Mem0.
+Additionally, the Message History component provides more options for sorting, filtering, and limiting memories. Although, most of these options are built-in to the Agent component with default values.
For more information, see Store chat memory and Message History component.
-
With the Agent component, the available parameters can change depending on the selected provider and model.
-For example, some models support additional modes, arguments, or features like chat memory and temperature.
-
Some additional input parameters include the following:
+
With the Agent component, the available parameters can change depending on the selected provider and model, including support for additional modes, arguments, or features like chat memory and temperature.
+For example:
- Current Date (
add_current_date_tool): When enabled (true), this setting adds a tool to the agent that can retrieve the current date.
- Handle Parse Errors (
handle_parsing_errors): When enabled (true), this setting allows the agent to fix errors, like typos, when analyzing user input.
- Verbose (
verbose): When enabled (true), this setting records detailed logging output for debugging and analysis.
-
To view and configure all parameters, click Controls in the component's header menu.
+
Some parameters are hidden by default in the visual editor.
+You can modify all parameters through the Controls in the component's header menu.
The Agent component outputs a Response (response) that is Message data containing the agent's raw response to the query.
Typically, this is passed to a Chat Output component to return the response in a human-readable format.
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index 71b060c627..bfeda572fe 100644
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@@ -23,8 +23,8 @@
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