Agent Node
Overview
The Agent Node empowers your flow with decision-making capabilities by creating an AI agent that can use multiple tools to accomplish tasks. It combines a large language model with a set of tools, enabling complex reasoning and multi-step task completion based on the input and available tools.
Usage cost: 2 credits
Configuration
Settings
Model Selection
LLM Model*: Select the AI model for the agent (the selected model must support tools calling)
System Prompt: Initial instructions defining the agent's behavior and context
Agent Prompt*: The main instruction or task for the agent
Past Message History: Optional chat history for context
Tools Configuration
Tools*: Select one or more tools for the agent to use
Tools must be provided by previous nodes in the flow
Output format
Output as JSON (Toggle):
Off (Default): The node outputs the model's response as a plain string.
On: Instructs the model to format its response as JSON and attempts to parse the output string. If parsing succeeds, the
responseoutput port will contain a JSON object/array. If it fails,responsewill contain the original string.
Note: JSON structure and parsing success depend heavily on the model's ability to follow instructions. Clearly prompting for JSON format is recommended when this is enabled.
Output Ports
response(string): The agent's complete response after using tools and reasoning
Best Practices
Tool Selection
Only provide tools that are relevant to the task
Order tools from most to least commonly used
Ensure tools have clear, descriptive names
Limit the number of tools to prevent confusion
Prompt Engineering
Be specific about what tools should be used
Clearly define the expected outcome
Performance Optimization
Keep tool descriptions concise
Use appropriate model for complexity
Balance between instruction detail and flexibility
Common Issues
Tool execution timeouts
Incorrect tool selection by agent
Incomplete or unclear tool responses
Model context length limitations
Last updated