CUGA

import Icon from "@site/src/components/icon"; import PartialParams from '@site/docs/_partial-hidden-params.mdx';

<Icon name="Blocks" aria-hidden="true" /> Bundles contain custom components that support specific third-party integrations with Langflow.

:::warning Model Provider Limitations The CUGA component only supports OpenAI and watsonx models. To use other model providers, use the core Agent component instead. :::

The CUGA (ConfigUrable Generalist Agent) component is an advanced AI agent that executes complex tasks using tools, optional browser automation, and structured output generation.

The CUGA component can be used in flows in place of an Agent component. Like the core Agent component, the CUGA component can use tools connected to its Tools port, and can be used as a tool itself. It also includes some additional features:

• Browser automation for web scraping with Playwright. To enable web scraping, set the component's browser_enabled parameter to true.
• Load custom instructions for the agent to execute. To use this feature, use the component's Instructions input to attach markdown files containing agent instructions.

For more information, see the CUGA project repository.

Use the CUGA component in a flow

For demonstration purposes, the following example modifies a template flow to use the CUGA component.

1. Create a flow based on the Simple Agent template, and then replace the Agent component with the CUGA component.

2. Connect an MCP Tools component and a Calculator component to the CUGA component's Tools port, and then connect the MCP Tools component to any MCP server. This example connects to a server containing sales data for a business organization.

3. Add a Read File component, and then connect it to the CUGA component's Instructions port. Alternatively, click <Icon name="Scan" aria-hidden="true" /> Edit text to open the Edit text content pane, and enter your instructions directly into the CUGA component.

4. Create a Markdown file on your computer called instructions.md, and then insert the following content. It's important to clearly format the document with ## Plan and ## Answer for the agent to understand your instructions.

   markdownCopy

   ## Plan
   
   - Break down complex queries into subtasks
   - Prioritize information gathering before execution
   - Consider dependencies between actions
   - Validate intermediate results before proceeding
   
   ## Answer
   
   - Provide concise summaries with key findings
   - Include relevant data points and metrics
   - Cite sources when using MCP tool results
   - Use clear structure and formatting for readability
   

5. In the Read File component, click <Icon name="Plus" aria-hidden="true"/> Add File, and then attach your instructions.md file.

6. Open the Playground, and then ask the agent a question that could use your connected MCP server. This example asked about the sales data provided by the MCP Server, such as Which accounts are available?. The agent describes the tool calls it makes and then returns an answer according to the instructions. For example, the list of available accounts is very large, but the CUGA component returns a concise summary as requested in the policy.

   Copy

   Based on the available data, here are the accounts:
   
       Summit Solutions (NY) - Revenue: $1,200,000
       Pacific Ventures (CA) - Revenue: $9,500,000
       Lone Star Corp (TX) - Revenue: $4,500,000
       Mountain Peak Systems (CO) - Revenue: $2,100,000
       Digital Dynamics (CA) - Revenue: $5,500,000
       Cascade Computing (WA) - Revenue: $4,300,000
       Data Flow Systems (CA) - Revenue: $8,900,000
       Rocky Mountain Enterprises (CO) - Revenue: $3,200,000
       Blue Sky Partners (TX) - Revenue: $500,000
       Liberty Manufacturing (PA) - Revenue: $3,400,000
   
   This is a partial list; there are more accounts available. The total revenue across all accounts is $210,200,000.
   

CUGA parameters

<PartialParams />

Name	Type	Description
agent_llm	Dropdown	Model provider for the agent.
instructions	Multiline String	Custom instructions that define the agent's planning and answers. Can be provided directly or through Markdown files. Formatting is important in order for the agent to understand the instructions. See Use the CUGA component in a flow.
n_messages	Integer	Number of chat history messages to retrieve. Useful for maintaining context in ongoing conversations identified by session_id. Default: 100.
format_instructions	Multiline String	Template for structured output.
output_schema	Table	When output_schema is provided, structured responses are validated against a dynamically built schema. Invalid items are returned alongside validation errors. Fields: name, description, type (str, int, float, bool, dict), multiple (as list).
add_current_date_tool	Boolean	If true, adds a tool that returns the current date. Default: true.
lite_mode	Boolean	Set to true to enable CugaLite mode for faster execution when using a smaller number of tools. Default: true.
lite_mode_tool_threshold	Integer	The threshold to automatically enable CugaLite. If the CUGA component has fewer tools connected than this threshold, CugaLite is activated. Default: 25.
shortlisting_tool_threshold	Integer	The threshold for tool shortlisting. When the total number of tools exceeds this threshold, the CUGA component enables its find_tools feature to filter tools down to a smaller subset before making tool selection decisions. This helps reduce token usage and improve performance when working with large numbers of tools. Default: 35.
decomposition_strategy	Dropdown	Strategy for task decomposition. flexible allows multiple subtasks per app. exact enforces one subtask per app. Default: flexible.
browser_enabled	Boolean	Enable a built-in browser for web scraping and search. Allows the agent to use general web search in its responses. Disable (false) to restrict the agent to the context provided in the flow. Default: false.