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139 lines
5.0 KiB
Markdown
139 lines
5.0 KiB
Markdown
---
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title: Agents
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slug: /components-agents
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---
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# Agent components in Langflow
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Agent components define the behavior and capabilities of AI agents in your flow.
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Agents use LLMs as a reasoning engine to decide which of the connected tool components to use to solve a problem.
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Tools in agentic functions are essentially functions that the agent can call to perform tasks or access external resources.
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A function is wrapped as a `Tool` object with a common interface the agent understands.
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Agents become aware of tools through tool registration where 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.
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The agent then uses a connected LLM to reason through the problem to decide which tool is best for the job.
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## Use an agent in a flow
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The [simple agent starter project](/simple-agent) uses an [agent component](#agent-component) connected to URL and Calculator tools to answer a user's questions. The OpenAI LLM acts as a brain for the agent to decide which tool to use. Tools are connected to agent components at the **Tools** port.
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For a multi-agent example see [Create a flow with an agent](/agents).
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## Agent component {#agent-component}
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This component creates an agent that can use tools to answer questions and perform tasks based on given instructions.
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The component includes an LLM model integration, a system message prompt, and a **Tools** port to connect tools to extend its capabilities.
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For more information on this component, see the [Agent documentation](/agents).
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<details>
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| agent_llm | Dropdown | The provider of the language model that the agent uses to generate responses. Options include OpenAI and other providers or Custom. |
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| system_prompt | String | The system prompt provides initial instructions and context to guide the agent's behavior. |
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| tools | List | The list of tools available for the agent to use. This field is optional and can be empty. |
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| input_value | String | The input task or question for the agent to process. |
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| add_current_date_tool | Boolean | When true this adds a tool to the agent that returns the current date. |
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| memory | Memory | An optional memory configuration for maintaining conversation history. |
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| max_iterations | Integer | The maximum number of iterations the agent can perform. |
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| handle_parsing_errors | Boolean | This determines whether to handle parsing errors during agent execution. |
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| verbose | Boolean | This enables verbose output for detailed logging. |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| response | Message | The agent's response to the given input task. |
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</details>
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## MCP tools {#mcp-connection}
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:::important
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Prior to Langflow 1.5, this component was named **MCP connection**.
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:::
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The **MCP tools** component connects to a [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) server and exposes the MCP server's tools as tools for Langflow agents.
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In addition to being an MCP client that can leverage MCP servers, the **MCP tools** component's [SSE mode](/mcp-client#mcp-sse-mode) allows you to connect your flow to the Langflow MCP server at the `/api/v1/mcp/sse` API endpoint, exposing all flows within your [project](/concepts-flows#projects) as tools within a flow.
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For more information, see [MCP client](/mcp-client).
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## Legacy components
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**Legacy** components are available for use but are no longer supported.
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### JSON Agent
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This component creates a JSON agent from a JSON or YAML file and an LLM.
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<details>
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| llm | LanguageModel | The language model to use for the agent. |
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| path | File | The path to the JSON or YAML file. |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| agent | AgentExecutor | The JSON agent instance. |
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</details>
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### Vector Store Agent
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This component creates a Vector Store Agent using LangChain.
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<details>
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| llm | LanguageModel | The language model to use for the agent. |
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| vectorstore | VectorStoreInfo | The vector store information for the agent to use. |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| agent | AgentExecutor | The Vector Store Agent instance. |
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</details>
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### Vector Store Router Agent
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This component creates a Vector Store Router Agent using LangChain.
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<details>
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| llm | LanguageModel | The language model to use for the agent. |
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| vectorstores | List[VectorStoreInfo] | The list of vector store information for the agent to route between. |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| agent | AgentExecutor | The Vector Store Router Agent instance. |
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</details> |