Agents
Langflow's Agent component is critical for building agent flows. This component defines the behavior and capabilities of AI agents in your flows.
How do agents work?
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 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.
The Tool object's description tells the agent what the tool can do so that it can decide whether the tool is appropriate for a given request.
Examples of agent flows
For examples of flows using the Agent component, see the following:
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Langflow quickstart: Start with the Simple Agent template, modify its tools, and then learn how to use an agent flow in an application.
The Simple Agent template creates a basic agent flow with an Agent component that can use two other Langflow components as tools. The LLM specified in the Agent component's settings can use its own built-in functionality as well as the functionality provided by the connected tools when generating responses.
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Use an agent as a tool: Create a multi-agent flow.
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Use Langflow as an MCP client and Use Langflow as an MCP server: Use the Agent and MCP Tools component to implement the Model Context Protocol (MCP) in your flows.
Agent component
The Agent component is the primary agent actor in your agent flows. This component uses an LLM integration to respond to input, such as a chat message or file upload.
The agent can use the tools already available in the base LLM as well as additional tools that you connect to the Agent component's Tools port. You can connect any Langflow component as a tool, including other Agent components and MCP servers through the MCP Tools component.
For more information about using this component, see Use Langflow agents.
