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Use Langflow Agents

Agents use LLMs as a brain to autonomously analyze problems and select tools to solve them.

Langflow's Agent component simplifies agent configuration so you can focus on application development.

The Agent component provides everything you need to create an agent, including multiple LLM providers and custom instructions.

Agent settings​

You can configure the Agent component to use your preferred provider and model, custom instructions, and tools.

Agent models and providers​

Use the Model Provider and Model Name settings to select the LLM that you want the Agent to use.

You must provide an authentication key for the selected provider, such as an OpenAI API key for OpenAI models.

Agent instructions and input​

In the Agent Instructions field, you can provide custom instructions that you want the Agent component to use for every conversation.

These instructions are applied in addition to the Input, which is provided at runtime.

Agent tools​

Agents are most useful when they have the appropriate tools available to complete requests.

An Agent component can use any Langflow component as a tool, as long as you attach it to the Agent component.

tip

To allow agents to use tools from MCP servers, use the MCP connection component.

When you attach a component as a tool, you must configure the component as a tool by enabling Tool Mode.

For more information, see Configure tools for agents

Use the Agent component in a flow​

tip

For a pre-built demonstration, open the Simple Agent template flow and follow along.

Create an agent in Langflow, starting with the Agent component and working outward.

  1. Click New Flow, and then click Blank Flow.
  2. Add an Agent component to your workspace.
  3. Use the default model or select another provider and model, and then provide credentials for your chosen provider. For example, to use the default model, you must provide an OpenAI API key.
  4. Add Chat input and Chat output components to your flow, and connect them to the tool calling agent.

Chat with agent component

This basic flow allows you to chat with the agent in the Playground, but you're only chatting with the OpenAI LLM. To unlock the power of the Agent component, connect some tools.

  1. Add the News Search, URL, and Calculator components to your flow.

  2. Enable Tool Mode in the News Search, URL, and Calculator components. In the component menu, enable Tool mode so you can use the component with an agent.

    Tool Mode makes a component into a tool by modifying the component's inputs to accept requests from the Agent component to use a tool's available actions. A component in tool mode has a Toolset port that you must connect to an Agent component's Tools port if you want to allow the agent to use the tool's actions.

  3. Connect the Toolset port on the three tool components to the Tools port on the Agent component.

Chat with agent component

  1. Open the Playground. Ask the agent, What tools are you using to answer my questions? The agent should respond with a list of the connected tools.

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I use a combination of my built-in knowledge (up to June 2024) and a set of external tools to answer your questions. Here are the main types of tools I can use:
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Web Search & Content Fetching: I can fetch and summarize content from web pages, including crawling links recursively.
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News Search: I can search for recent news articles using Google News via RSS feeds.
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Calculator: I can perform arithmetic calculations and evaluate mathematical expressions.
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Date & Time: I can provide the current date and time in various time zones.
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These tools help me provide up-to-date information, perform calculations, and retrieve specific data from the internet when needed. If you have a specific question, let me know, and I’ll use the most appropriate tool(s) to help!

  1. Ask the agent, Summarize today's tech news. The Playground displays the agent's tool calls, what input was provided, and the raw output the agent received before generating the summary. The agent should call the News Search component's search_news action.

You've successfully constructed a flow with the Langflow Agent. Connect more tools to solve more specialized problems.

See also​

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